Long EMA Strategy with Advanced Exit OptionsThis strategy is designed for traders seeking a trend-following system with a focus on precision and adaptability.
**Core Strategy Concept**
The essence of this strategy lies in use of Exponential Moving Averages (EMAs) to identify potential long (buy) positions based on the relative positions of short-term, medium-term, and long-term EMAs. The use of EMAs is a classic yet powerful approach to trend detection, as these indicators smooth out price data over time, emphasizing the direction of recent price movements and potentially signaling the beginning of new trends.
**Customizable Parameters**
- **EMA Periods**: Users can define the periods for three EMAs - long-term, medium-term, and short-term - allowing for a tailored approach to capture trends based on individual trading styles and market conditions.
- **Volatility Filter**: An optional Average True Range (ATR)-based volatility filter can be toggled on or off. When activated, it ensures that trades are only entered when market volatility exceeds a user-defined threshold, aiming to filter out entries during low-volatility periods which are often characterized by indecisive market movements.
- **Trailing Stop Loss**: A trailing stop loss mechanism, expressed as a percentage of the highest price achieved since entry, provides a dynamic way to manage risk by allowing profits to run while cutting losses.
- **EMA Exit Condition**: This advanced exit option enables closing positions when the short-term EMA crosses below the medium-term EMA, serving as a signal that the immediate trend may be reversing.
- **Close Below EMA Exit**: An additional exit condition, which is disabled by default, allows positions to be closed if the price closes below a user-selected EMA. This provides an extra layer of flexibility and risk management, catering to traders who prefer to exit positions based on specific EMA thresholds.
**Operational Mechanics**
Upon activation, the strategy evaluates the current price in relation to the set EMAs. A long position is considered when the current price is above the long-term EMA, and the short-term EMA is above the medium-term EMA. This setup aims to identify moments where the price momentum is strong and likely to continue.
The strategy's versatility is further enhanced by its optional settings:
- The **Volatility Filter** adjusts the sensitivity of the strategy to market movements, potentially improving the quality of the entries during volatile market conditions.
The Average True Range (ATR) is a key component of this filter, providing a measure of market volatility by calculating the average range between the high and low prices over a specified number of periods. Here's how you can adjust the volatility filter settings for various market conditions, focusing on filtering out low-volatility markets:
Setting Examples for Volatility Filter
1. High Volatility Markets (e.g., Cryptocurrencies, Certain Forex Pairs):
ATR Periods: 14 (default)
ATR Multiplier: Setting the multiplier to a lower value, such as 1.0 or 1.2, can be beneficial in high-volatility markets. This sensitivity allows the strategy to react to volatility changes more quickly, ensuring that you're entering trades during periods of significant movement.
2. Medium Volatility Markets (e.g., Major Equity Indices, Medium-Volatility Forex Pairs):
ATR Periods: 14 (default)
ATR Multiplier: A multiplier of 1.5 (default) is often suitable for medium volatility markets. It provides a balanced approach, ensuring that the strategy filters out low-volatility conditions without being overly restrictive.
3. Low Volatility Markets (e.g., Some Commodities, Low-Volatility Forex Pairs):
ATR Periods: Increasing the ATR period to 20 or 25 can smooth out the volatility measure, making it less sensitive to short-term fluctuations. This adjustment helps in focusing on more significant trends in inherently stable markets.
ATR Multiplier: Raising the multiplier to 2.0 or even 2.5 increases the threshold for volatility, effectively filtering out low-volatility conditions. This setting ensures that the strategy only triggers trades during periods of relatively higher volatility, which are more likely to result in significant price movements.
How to Use the Volatility Filter for Low-Volatility Markets
For traders specifically interested in filtering out low-volatility markets, the key is to adjust the ATR Multiplier to a higher level. This adjustment increases the threshold required for the market to be considered sufficiently volatile for trade entries. Here's a step-by-step guide:
Adjust the ATR Multiplier: Increase the ATR Multiplier to create a higher volatility threshold. A multiplier of 2.0 to 2.5 is a good starting point for very low-volatility markets.
Fine-Tune the ATR Periods: Consider lengthening the ATR calculation period if you find that the strategy is still entering trades in undesirable low-volatility conditions. A longer period provides a more averaged-out measure of volatility, which might better suit your needs.
Monitor and Adjust: Volatility is not static, and market conditions can change. Regularly review the performance of your strategy in the context of current market volatility and adjust the settings as necessary.
Backtest in Different Conditions: Before applying the strategy live, backtest it across different market conditions with your adjusted settings. This process helps ensure that your approach to filtering low-volatility conditions aligns with your trading objectives and risk tolerance.
By fine-tuning the volatility filter settings according to the specific characteristics of the market you're trading in, you can enhance the performance of this strategy
- The **Trailing Stop Loss** and **EMA Exit Conditions** provide two layers of exit strategies, focusing on capital preservation and profit maximization.
**Visualizations**
For clarity and ease of use, the strategy plots the three EMAs and, if enabled, the ATR threshold on the chart. These visual cues not only aid in decision-making but also help in understanding the market's current trend and volatility state.
**How to Use**
Traders can customize the EMA periods to fit their trading horizon, be it short, medium, or long-term trading. The volatility filter and exit options allow for further customization, making the strategy adaptable to different market conditions and personal risk tolerance levels.
By offering a blend of trend-following principles with advanced risk management features, this strategy aims to cater to a wide range of trading styles, from cautious to aggressive. Its strength lies in its flexibility, allowing traders to fine-tune settings to their specific needs, making it a potentially valuable tool in the arsenal of any trader looking for a disciplined approach to navigating the markets.
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Octopus Nest Strategy Hello Fellas,
Hereby, I come up with a popular strategy from YouTube called Octopus Nest Strategy. It is a no repaint, lower timeframe scalping strategy utilizing PSAR, EMA and TTM Squeeze.
The strategy considers these market factors:
PSAR -> Trend
EMA -> Trend
TTM Squeeze -> Momentum and Volatility by incorporating Bollinger Bands and Keltner Channels
Note: As you can see there is a potential improvement by incorporating volume.
What's Different Compared To The Original Strategy?
I added an option which allows users to use the Adaptive PSAR of @loxx, which will hopefully improve results sometimes.
Signals
Enter Long -> source above EMA 100, source crosses above PSAR and TTM Squeeze crosses above 0
Enter Short -> source below EMA 100, source crosses below PSAR and TTM Squeeze crosses below 0
Exit Long and Exit Short are triggered from the risk management. Thus, it will just exit on SL or TP.
Risk Management
"High Low Stop Loss" and "Automatic High Low Take Profit" are used here.
High Low Stop Loss: Utilizes the last high for short and the last low for long to calculate the stop loss level. The last high or low gets multiplied by the user-defined multiplicator and if no recent high or low was found it uses the backup multiplier.
Automatic High Low Take Profit: Utilizes the current stop loss level of "High Low Stop Loss" and gets calculated by the user-defined risk ratio.
Now, follows the bunch of knowledge for the more inexperienced readers.
PSAR: Parabolic Stop And Reverse; Developed by J. Welles Wilders and a classic trend reversal indicator.
The indicator works most effectively in trending markets where large price moves allow traders to capture significant gains. When a security’s price is range-bound, the indicator will constantly be reversing, resulting in multiple low-profit or losing trades.
TTM Squeeze: TTM Squeeze is a volatility and momentum indicator introduced by John Carter of Trade the Markets (now Simpler Trading), which capitalizes on the tendency for price to break out strongly after consolidating in a tight trading range.
The volatility component of the TTM Squeeze indicator measures price compression using Bollinger Bands and Keltner Channels. If the Bollinger Bands are completely enclosed within the Keltner Channels, that indicates a period of very low volatility. This state is known as the squeeze. When the Bollinger Bands expand and move back outside of the Keltner Channel, the squeeze is said to have “fired”: volatility increases and prices are likely to break out of that tight trading range in one direction or the other. The on/off state of the squeeze is shown with small dots on the zero line of the indicator: red dots indicate the squeeze is on, and green dots indicate the squeeze is off.
EMA: Exponential Moving Average; Like a simple moving average, but with exponential weighting of the input data.
Don't forget to check out the settings and keep it up.
Best regards,
simwai
---
Credits to:
@loxx
@Bjorgum
@Greeny
Four WMA Strategy with TP and SLBasically I read a research paper on how they used different moving averages for long entries and short entries, and it kind of dawned on me that I always used the same one for long entry or exit, or even swing trading. So I smashed this together to see what would happen.
The strategy combines the use of four different WMAs for identifying trade entry points, along with a predefined take profit (TP) and stop loss (SL) for risk management. Here's a detailed description of its features and how it operates:
Main Features
1. **WMAs as the Core Indicator**:
- The strategy uses four WMAs with different lengths. Two WMAs (`longM1` and `longM2`) are used for long entry signals, and the other two (`shortM1` and `shortM2`) for short entry signals.
- The lengths of these WMAs are adjustable through input parameters.
2. **Trade Entry Conditions**:
- A long entry is signaled when the shorter WMA crosses under the longer WMA .
- Conversely, a short entry is signaled when the shorter WMA crosses under the longer WMA.
3. **Take Profit and Stop Loss**:
- The strategy includes a take profit and stop loss mechanism.
- The TP and SL levels are set as a percentage of the entry price, with the percentage values being adjustable through input parameters.
4. **Visual Representation**:
- The WMAs are plotted on the chart for visual aid, each with a distinct color for easy identification.
How It Works
- The strategy continuously monitors the crossing of WMAs to detect potential entry points for long and short positions.
- Upon detecting a long or short condition, it automatically enters a trade and sets the corresponding TP and SL levels based on the current price and the specified percentages.
- The strategy then actively manages the trade, exiting the position when either the TP or SL level is reached.
Drawbacks
- **Overreliance on WMAs**: The strategy heavily relies on WMAs for trade signals. While WMAs are useful for identifying trends, they might not always provide timely entry and exit signals.
- **Market Conditions**: It may not perform well in highly volatile or sideways markets where WMA crossovers could lead to false signals.
- **Risk Management**: The fixed percentage for TP and SL might not be suitable for all market conditions. Traders might need to adjust these values frequently based on market volatility and their risk tolerance.
Apparently I need to emphasize to use brains when using indicators and setting them up to achieve the results you can or want. Also risk of 12% is considered very high so I lowered the numbers to 5%, which tanked the profits, try adjusting them on your own. Check the properties settings for more info on comission and slippage.
Conclusion
The "Four WMA Strategy with TP and SL" is suitable for traders who prefer a moving average-based approach to trading, combined with a straightforward mechanism for risk management through take profit and stop loss. However, like all strategies, it should be used with an understanding of its limitations and ideally tested thoroughly in various market conditions before applying it to live trading.
Targets For Overlay Indicators [LuxAlgo]The Targets For Overlay Indicators is a useful utility tool able to display targets during crossings made between the price and external indicators on the user chart. Users can display a series of two targets, one for crossover events and another one for crossunder event.
Alerts are included for the occurrence of a new target as well as for reached targets.
🔶 USAGE
In order for targets to be displayed users need to select an appropriate input source from the "Source" drop-down input setting. In the example above we apply the indicator to a volatility stop.
This can also easily be done by adding the "Targets For Overlay Indicators" script on the VStop indicator directly.
Targets can help users determine the price limit where the price might start deviating from an indication given by one or multiple indicators. In the context of trading, targets can help secure profits/reduce losses of a trade, as such this tool can be useful to evaluate/determine user take profits/stop losses.
Due to these essentially being horizontal levels, they can also serve as potential support/resistances, with breakouts potentially confirming new trends.
Users might be interested in obtaining new targets once one is reached, this can be done by enabling "New Target When Reached" in the target logic setting section, resulting in more frequent targets.
Lastly, users can restrict new target creation until current ones are reached. This can result in fewer and longer-term targets, with a higher reach rate.
🔹 Examples
The indicator can be applied to many overlay indicators that naturally produce crosses with the price, such as moving average, trailing stops, bands...etc.
Users can use trailing stops such as the SuperTrend or VStop to more easily create clean targets. Do note that certain SuperTrend scripts separate the upper and lower extremities of the SuperTrend into two different plot, which cannot be used with this tool, you may use the provided SuperTrend script below to have a compatible version with our tool:
//@version=5
indicator("SuperTrend", overlay = true)
factor = input.float(3, 'Factor', minval = 0)
atrLen = input.int(10, 'ATR Length', minval = 1)
= ta.supertrend(factor, atrLen)
plot(spt, 'SuperTrend', dir != dir ? na : dir < 0 ? #089981 : #f23645, 2)
plot(spt, 'Circles', dir > dir ? #f23645 : dir < dir ? #089981 : na, 3, plot.style_circles)
Using moving averages can produce more targets than other overlay indicators.
Users can apply the tool twice when using bands or any overlay indicator returning two outputs, using crossover targets for obtaining targets using the upper band as source and crossunder targets for targets using the lower band. We can also use the Trendlines with breaks indicator as example:
🔹 Dashboard
A dashboard is displayed on the top right of the chart, displaying the amount, reach rate of targets 1/2, and total amount.
This dashboard can be useful to evaluate the selected target distances relative to the selected conditions, with a higher reach rate suggesting the distance of the targets from the price allows them to be reached.
🔶 SETTINGS
Source: Indicator source used to create targets. Targets are created when the closing price crosses the specified source.
Show Target Labels: Display target labels on the chart.
Candle Coloring: Apply candle coloring based on the most recent active target.
🔹 Target
Crossover and Crossunder targets use the same settings below:
Show Target: Determines if the target is displayed or not.
Above Price Target: If selected, will create targets above the closing price.
Wait Until Reached: When enabled will not create a new target until an existing one is reached.
New Target When Reached: Will create a new target when an existing one is reached.
Evaluate Wicks: Will use high/low prices to determine if a target is reached. Unselecting this setting will use the closing price.
Target Distance From Price: Controls the distance of a target from the price. Can be determined in currencies/points, percentages, ATR multiples, or ticks.
buy/sell signals with Support/Resistance (InvestYourAsset) 📣The present indicator is a MACD based buy/sell signals indicator with support and resistance, that can be used to identify potential buy and sell signals in a security's price.
📣It is based on the MACD (Moving Average Convergence Divergence) indicator, which is a momentum indicator that shows the relationship between two moving averages of a security's price.
📣 The indicator also plots support and resistance levels, which can be used to confirm buy and sell signals. The support and resistance can also be used as a stoploss for existing position.
👉 To use the indicator, simply add it to your trading chart. The indicator will plot three sections:
📈 Price and Signals: This section plots the security's price and the MACD buy and sell signals.
📈 MACD Oscillator: This section plots the MACD oscillator, which is a histogram that shows the difference between the two moving averages.
📈 Moving Averages: This section plots the two moving averages that the MACD oscillator is based on.
📈 Support and Resistance: This section plots support and resistance levels, which are calculated based on the security's recent price action.
👉 To identify buy and sell signals, you can look for the following:
📈 Buy signal: When shorter Moving Average crosses over longer Moving Average.
📈 Sell signal: When shorter moving average crosses under longer moving average.
📈 You can also look for divergences between the MACD oscillator and the security's price. A divergence occurs when the MACD oscillator is moving in one direction, but the security's price is moving in the opposite direction. Divergences can be a sign of a potential trend reversal.
👉 To confirm buy and sell signals, you can look for support and resistance levels take a look at below snapshot. If a buy signal occurs at a support level, it is a stronger signal than if it occurs at a random price level. Similarly, if a sell signal occurs at a resistance level, it is a stronger signal than if it occurs at a random price level.
⚡ Here is a example of how to use the indicator to identify buy signal:
☑ Add the indicator to your trading chart.
☑Look for a buy signal when short MA crosses over Long MA.
☑Look for the buy signal to occur at a support level.
☑Enter a long position at the next candle.
☑Place a stop loss order below the support level.
☑Take profit when the MACD line crosses below the signal line, or when the security reaches a resistance level.
⚡ Here is an example of how to use the indicator to identify a sell signal:
☑Add the indicator to your trading chart.
☑Look for a sell signal, when shorter moving average crosses under longer moving average.
☑Look for the sell signal to occur at a resistance level.
☑Enter a short position at the next candle.
☑Place a stop loss order above the resistance level.
☑Take profit when the MACD line crosses above the signal line, or when the security reaches a support level.
✅Things to consider while using the indicator:
📈Look for buy signals in an uptrend and sell signals in a downtrend. This will increase the likelihood of your trades being successful.
📈Place your stop losses below the previous swing low or support for buy signals and above the previous swing high or resistance for sell signals. This will help to limit your losses if the trade goes against you.
📈Consider taking profits at key resistance and support levels. This will help you to lock in your profits and avoid giving them back to the market.
Follow us for timely updates regarding indicators that we may publish in future and give it a like if you appreciate the indicator.
Entry Assistant & News AlertIntention Of This Indicator
This indicator is intended to be used as an assistant in combination with a technical strategy.
This indicator has several functions intended to assist you at entering positions.
This indicator is intended to be used with strategies that place Stop Losses above / below candles, and entries at the BOC ( Break Of The Previous Candle , For Longs it is when price goes above the previous candles high, For Shorts it is when price goes below the previous candles low)
This indicator allows you to enter daily news release times, and it will warn you before and after that news release time ( to help you stay out of trading news )
This indicator Draw / Displays the following
A line below ( for Longs ) / above ( for Shorts ) the current candle, with an additional pip value for extra space ( this displays where to place your Stop Loss )
A label displaying the price of the Stop Loss line, to assist in placing the Stop Loss
A line displaying where the BOC is ( based off of going Long or going Short )
A box that appears when the BOC has occurred ( entry signal )
A line displaying where the news release is going to happen ( only according to your time input settings )
A box that surrounds the news release ( only according to your time input settings )
A table in the bottom right corner that shows you when there is Active News ( only according to your time input settings )
Inputs
Inputs to change the aesthetics ( colours etc. )
Numeric inputs to modify the placement / spacing of the Stop Loss / Entry signal / News
Toggles to activate or deactivate features
Disclaimer
This indicator does not guaranteed to work for every instrument ( always test before use! )
It is not at all intended to be a signal indicator on its own, but rather only to give a signal when used with specific technical strategies that us BOC entries.
This indicator is not guaranteed to be accurate, or error free.
This indicator is not signalling winning entries or high probability entries.
You must manually enter the news time inputs, this indicator does not automatically show you when there is a news release
This is a combination indicator of my Entry Assistant and my News Alert indicator, both can be found and used separately.
Entry Assistant by IvanIntention Of This Indicator
This indicator is intended to be used as an assistant in combination with a technical strategy.
This indicator has several functions intended to assist you at entering positions.
This indicator is intended to be used with strategies that place Stop Losses above / below candles, and entries at the BOC ( Break Of The Previous Candle , For Longs it is when price goes above the previous candles high, For Shorts it is when price goes below the previous candles low)
This indicator Draw / Displays the following
A line below ( for Longs ) / above ( for Shorts ) the current candle, with an additional pip value for extra space ( this displays where to place your Stop Loss )
A label displaying the price of the Stop Loss line, to assist in placing the Stop Loss
A line displaying where the BOC is ( based off of going Long or going Short )
A box that appears when the BOC has occurred ( entry signal )
Inputs
Inputs to change the aesthetics ( colours etc. )
Numeric inputs to modify the placement / spacing of the Stop Loss / Entry signal
Toggles to activate or deactivate features
Disclaimer
This indicator does not currently work for every instrument ( it only works for most Forex pairs and some Indices )
It is not at all intended to be a signal indicator on its own, but rather only to give a signal when used with specific technical strategies that us BOC entries.
This indicator is not guaranteed to be accurate, or error free.
This indicator is not signalling winning entries or high probability entries.
Risk to Reward - FIXED SL BacktesterDon't know how to code? No problem! TradingView is an excellent platform for you. ✅ ✅
If you have an indicator that you want to backtest using a risk-to-reward ratio or fixed take profit/stop loss levels, then the Risk to Reward - FIXED SL Backtester script is the perfect solution for you.
introducing Risk to Reward - FIXED SL Backtester Script which will allow you to test any indicator / Signal with RR or Fixed SL system
How does it work ?!
Once you connect the script to your indicator, it will analyze your entry points and perform calculations based on them. It will then open trades for you according to the specified inputs in the script settings.
HOW TO CONNECT IT to your indicator?
simply open your indicator code and add the below line of code to it
plot(Signal ? 100 : 0,"Signal",display = display.data_window)
Replace Signal with the long condition from your own indicator. You can also modify the value 100 to any number you prefer. After that, open the settings.
Once the script is connected to your indicator, you can choose from two options:
Risk To Reward Ratio System
Fixed TP/ SL System
🔸if you select the Risk to Reward System ⤵️
The Risk-to-Reward System requires the calculation of a stop loss. That's why I have included three different types of stop-loss calculations for you to choose from:
ATR Based SL
Pivot Low SL
VWAP Based SL
Your stop loss and take profit levels will be automatically calculated based on the selected stop loss method and your risk-to-reward ratio.
You can also adjust their values to match your desired risk level. The trades will be displayed on the chart.
with the ability to change their values to match your risk.
once this is done, trades will be displayed on the chart
🔸if you select the Fixed system ⤵️
You have 2 inputs, which are FIXED TP & Fixed SL
input the values you want, and trades will be on your chart...
I have also added a Breakeven feature for you.
with this Breakeven feature the trade will not just move SL to Entry ?! NO NO, it will place it above entry by a % you input yourself, so you always win! 🚀
Here is an example
Enjoy, and have fun, if you have any questions do not hesitate to ask
FalconRed 5 EMA Indicator (Powerofstocks)Improved version:
This indicator is based on Subhashish Pani's "Power of Stocks" 5 EMA Strategy, which aims to identify potential buying and selling opportunities in the market. The indicator plots the 5 EMA (Exponential Moving Average) and generates Buy/Sell signals with corresponding Target and Stoploss levels.
Subhashish Pani's 5 EMA Strategy is a straightforward approach. For intraday trading, a 5-minute timeframe is recommended for selling. In this strategy, you can choose to sell futures, sell calls, or buy puts as part of your selling strategy. The goal is to capture market tops by selling at the peak, anticipating a reversal for profitable trades. Although this strategy may result in frequent stop losses, they are typically small, while the minimum target should be at least three times the risk taken. By staying aligned with the trend, significant profits can be achieved. Subhashish Pani claims that this strategy has a 60% success rate.
Strategy for Selling (Short Future/Call/Stock or Buy Put):
1. When a candle completely closes above the 5 EMA (with no part of the candle touching the 5 EMA), it is considered an Alert Candle.
2. If the next candle is also entirely above the 5 EMA and does not break the low of the previous Alert Candle, ignore the previous Alert Candle and consider the new candle as the new Alert Candle.
3. Continue shifting the Alert Candle in this manner. However, when the next candle breaks the low of the Alert Candle, take a short trade (e.g., short futures, calls, stocks, or buy puts).
4. Set the stop loss above the high of the Alert Candle, and the minimum target should be 1:3 (at least three times the stop loss).
Strategy for Buying (Buy Future/Call/Stock or Sell Put):
1. When a candle completely closes below the 5 EMA (with no part of the candle touching the 5 EMA), it is considered an Alert Candle.
2. If the next candle is also entirely below the 5 EMA and does not break the high of the previous Alert Candle, ignore the previous Alert Candle and consider the new candle as the new Alert Candle.
3. Continue shifting the Alert Candle in this manner. However, when the next candle breaks the high of the Alert Candle, take a long trade (e.g., buy futures, calls, stocks, or sell puts).
4. Set the stop loss below the low of the Alert Candle, and the minimum target should be 1:3 (at least three times the stop loss).
Buy/Sell with Additional Conditions:
An additional condition is added to the buying/selling strategy:
1. Check if the closing price of the current candle is lower than the closing price of the Alert Candle for selling, or higher than the closing price of the Alert Candle for buying.
- This condition aims to filter out false moves, potentially preventing entering trades based on temporary fluctuations. However, it may cause you to miss out on significant moves, as you will enter trades after the candle closes, rather than at the breakout point.
Note: According to Subhashish Pani, the recommended timeframe for intraday buying is 15 minutes. However, this strategy can also be applied to positional/swing trading. If used on a monthly timeframe, it can be beneficial for long-term investing as well. The rules remain the same for all types of trades and timeframes.
If you need a deeper understanding of this strategy, you can search for "Subhashish Pani's (Power of Stocks) 5 EMA Strategy" on YouTube for further explanations.
Note: This strategy is not limited to intraday trading and can be applied to positional/swing
Alpha Fractal BandsWilliams fractals are remarkable support and resistance levels used by many traders. However, it can sometimes be challenging to use them frequently and get confirmation from other oscillators and indicators. With the new "Alpha Fractal Bands", a unique blend of Williams Fractals and Bollinger Bands emerges, offering a fresh perspective. Extremes can be utilized as price reversals or for taking profits. I look forward to hearing your thoughts. Best regards... Happy trading!
An easy solution for long positions is to:
Identify a bullish trend or a potential entry point for a long position.
Set a stop-loss order to limit potential losses if the trade goes against you.
Determine a target price or take-profit level to lock in profits.
Consider using technical indicators or analysis tools to confirm the strength of the bullish trend.
Regularly monitor the trade and make necessary adjustments based on market conditions.
An easy solution for short positions could be to follow these steps:
Identify a bearish trend or a potential entry point for a short position.
Set a stop-loss order to limit potential losses if the trade goes against you.
Determine a target price or take-profit level to lock in profits.
Consider using technical indicators or analysis tools to confirm the strength of the bearish trend.
Regularly monitor the trade and make necessary adjustments based on market conditions.
Remember, it's important to conduct thorough research and analysis before entering any trade and to manage your risk effectively.
To stay updated with the content, don't forget to follow and engage with it on TV, my friends. Remember to leave comments as well :)
Wyckoff Range StrategyThe Wyckoff Range Strategy is a trading strategy that aims to identify potential accumulation and distribution phases in the market using the principles of Wyckoff analysis. It also incorporates the detection of spring and upthrust patterns.
Here's a step-by-step explanation of how to use this strategy:
Understanding Accumulation and Distribution Phases:
Accumulation Phase: This is a period where smart money (large institutional traders) accumulates a particular asset at lower prices. It is characterized by a sideways or consolidating price action.
Distribution Phase: This is a period where smart money distributes or sells a particular asset at higher prices. It is also characterized by a sideways or consolidating price action.
Input Variables:
crossOverLength: This variable determines the length of the moving average crossover used to identify accumulation and distribution phases. You can adjust this value based on the market you are trading and the time frame you are analyzing.
stopPercentage: This variable determines the percentage used to calculate the stop loss level. It helps you define a predefined level at which you would exit a trade if the price moves against your position.
Strategy Conditions:
Enter Long: The strategy looks for a crossover of the close price above the SMA of the close price with a length of crossOverLength and a crossover of the low price above the SMA of the low price with a length of 20. This combination suggests the start of an accumulation phase and a potential buying opportunity.
Exit Long: The strategy looks for a crossunder of the close price below the SMA of the close price with a length of crossOverLength or a crossunder of the high price below the SMA of the high price with a length of 20. This combination suggests the end of an accumulation phase and a potential exit signal for long positions.
Enter Short: The strategy looks for a crossunder of the close price below the SMA of the close price with a length of crossOverLength and a crossunder of the high price below the SMA of the high price with a length of 20. This combination suggests the start of a distribution phase and a potential selling opportunity.
Exit Short: The strategy looks for a crossover of the close price above the SMA of the close price with a length of crossOverLength or a crossover of the low price above the SMA of the low price with a length of 20. This combination suggests the end of a distribution phase and a potential exit signal for short positions.
Stop Loss:
The strategy sets a stop loss level for both long and short positions. The stop loss level is calculated based on the stopPercentage variable, which represents the percentage of the current close price. If the price reaches the stop loss level, the strategy will automatically exit the position.
Plotting Wyckoff Schematics:
The strategy plots different shapes on the chart to indicate the identified phases and patterns. Green and red labels indicate the accumulation and distribution phases, respectively. Blue triangles indicate spring patterns, and orange triangles indicate upthrust patterns.
To use this strategy, you can follow these steps:
Jim Forte — Anatomy of a Trading Range
robertbrain.com/Bull...+a+Trading+Range.pdf
Inside candle (Inside Bar) Strategy- by smartanuThe Inside Candle strategy is a popular price action trading strategy that can be used to trade in a variety of markets. Here's how you can trade the Inside Candle strategy using the Pine script code provided:
1. Identify an Inside Candle: Look for a candlestick pattern where the current candle is completely engulfed within the previous candle's high and low. This is known as an Inside Candle.
2. Enter a Long Position: If an Inside Candle is identified, enter a long position at the open of the next candle using the Pine script code provided.
3. Set Stop Loss and Take Profit: Set a stop loss at a reasonable level to limit your potential losses if the trade goes against you. Set a take profit at a reasonable level to take profit when the price reaches the desired level.
4. Manage the Trade: Monitor the trade closely and adjust the stop loss and take profit levels if necessary. You can use the Pine script code to automatically exit the trade when the stop loss or take profit level is hit.
5. Exit the Trade: Exit the trade when the price reaches the take profit level or the stop loss level is hit.
It's important to note that the Inside Candle strategy is just one of many strategies that traders use to trade the markets. It's important to perform your own analysis and use additional indicators before making any trades. Additionally, it's important to practice proper risk management techniques and never risk more than you can afford to lose.
Goertzel Cycle Composite Wave [Loxx]As the financial markets become increasingly complex and data-driven, traders and analysts must leverage powerful tools to gain insights and make informed decisions. One such tool is the Goertzel Cycle Composite Wave indicator, a sophisticated technical analysis indicator that helps identify cyclical patterns in financial data. This powerful tool is capable of detecting cyclical patterns in financial data, helping traders to make better predictions and optimize their trading strategies. With its unique combination of mathematical algorithms and advanced charting capabilities, this indicator has the potential to revolutionize the way we approach financial modeling and trading.
*** To decrease the load time of this indicator, only XX many bars back will render to the chart. You can control this value with the setting "Number of Bars to Render". This doesn't have anything to do with repainting or the indicator being endpointed***
█ Brief Overview of the Goertzel Cycle Composite Wave
The Goertzel Cycle Composite Wave is a sophisticated technical analysis tool that utilizes the Goertzel algorithm to analyze and visualize cyclical components within a financial time series. By identifying these cycles and their characteristics, the indicator aims to provide valuable insights into the market's underlying price movements, which could potentially be used for making informed trading decisions.
The Goertzel Cycle Composite Wave is considered a non-repainting and endpointed indicator. This means that once a value has been calculated for a specific bar, that value will not change in subsequent bars, and the indicator is designed to have a clear start and end point. This is an important characteristic for indicators used in technical analysis, as it allows traders to make informed decisions based on historical data without the risk of hindsight bias or future changes in the indicator's values. This means traders can use this indicator trading purposes.
The repainting version of this indicator with forecasting, cycle selection/elimination options, and data output table can be found here:
Goertzel Browser
The primary purpose of this indicator is to:
1. Detect and analyze the dominant cycles present in the price data.
2. Reconstruct and visualize the composite wave based on the detected cycles.
To achieve this, the indicator performs several tasks:
1. Detrending the price data: The indicator preprocesses the price data using various detrending techniques, such as Hodrick-Prescott filters, zero-lag moving averages, and linear regression, to remove the underlying trend and focus on the cyclical components.
2. Applying the Goertzel algorithm: The indicator applies the Goertzel algorithm to the detrended price data, identifying the dominant cycles and their characteristics, such as amplitude, phase, and cycle strength.
3. Constructing the composite wave: The indicator reconstructs the composite wave by combining the detected cycles, either by using a user-defined list of cycles or by selecting the top N cycles based on their amplitude or cycle strength.
4. Visualizing the composite wave: The indicator plots the composite wave, using solid lines for the cycles. The color of the lines indicates whether the wave is increasing or decreasing.
This indicator is a powerful tool that employs the Goertzel algorithm to analyze and visualize the cyclical components within a financial time series. By providing insights into the underlying price movements, the indicator aims to assist traders in making more informed decisions.
█ What is the Goertzel Algorithm?
The Goertzel algorithm, named after Gerald Goertzel, is a digital signal processing technique that is used to efficiently compute individual terms of the Discrete Fourier Transform (DFT). It was first introduced in 1958, and since then, it has found various applications in the fields of engineering, mathematics, and physics.
The Goertzel algorithm is primarily used to detect specific frequency components within a digital signal, making it particularly useful in applications where only a few frequency components are of interest. The algorithm is computationally efficient, as it requires fewer calculations than the Fast Fourier Transform (FFT) when detecting a small number of frequency components. This efficiency makes the Goertzel algorithm a popular choice in applications such as:
1. Telecommunications: The Goertzel algorithm is used for decoding Dual-Tone Multi-Frequency (DTMF) signals, which are the tones generated when pressing buttons on a telephone keypad. By identifying specific frequency components, the algorithm can accurately determine which button has been pressed.
2. Audio processing: The algorithm can be used to detect specific pitches or harmonics in an audio signal, making it useful in applications like pitch detection and tuning musical instruments.
3. Vibration analysis: In the field of mechanical engineering, the Goertzel algorithm can be applied to analyze vibrations in rotating machinery, helping to identify faulty components or signs of wear.
4. Power system analysis: The algorithm can be used to measure harmonic content in power systems, allowing engineers to assess power quality and detect potential issues.
The Goertzel algorithm is used in these applications because it offers several advantages over other methods, such as the FFT:
1. Computational efficiency: The Goertzel algorithm requires fewer calculations when detecting a small number of frequency components, making it more computationally efficient than the FFT in these cases.
2. Real-time analysis: The algorithm can be implemented in a streaming fashion, allowing for real-time analysis of signals, which is crucial in applications like telecommunications and audio processing.
3. Memory efficiency: The Goertzel algorithm requires less memory than the FFT, as it only computes the frequency components of interest.
4. Precision: The algorithm is less susceptible to numerical errors compared to the FFT, ensuring more accurate results in applications where precision is essential.
The Goertzel algorithm is an efficient digital signal processing technique that is primarily used to detect specific frequency components within a signal. Its computational efficiency, real-time capabilities, and precision make it an attractive choice for various applications, including telecommunications, audio processing, vibration analysis, and power system analysis. The algorithm has been widely adopted since its introduction in 1958 and continues to be an essential tool in the fields of engineering, mathematics, and physics.
█ Goertzel Algorithm in Quantitative Finance: In-Depth Analysis and Applications
The Goertzel algorithm, initially designed for signal processing in telecommunications, has gained significant traction in the financial industry due to its efficient frequency detection capabilities. In quantitative finance, the Goertzel algorithm has been utilized for uncovering hidden market cycles, developing data-driven trading strategies, and optimizing risk management. This section delves deeper into the applications of the Goertzel algorithm in finance, particularly within the context of quantitative trading and analysis.
Unveiling Hidden Market Cycles:
Market cycles are prevalent in financial markets and arise from various factors, such as economic conditions, investor psychology, and market participant behavior. The Goertzel algorithm's ability to detect and isolate specific frequencies in price data helps trader analysts identify hidden market cycles that may otherwise go unnoticed. By examining the amplitude, phase, and periodicity of each cycle, traders can better understand the underlying market structure and dynamics, enabling them to develop more informed and effective trading strategies.
Developing Quantitative Trading Strategies:
The Goertzel algorithm's versatility allows traders to incorporate its insights into a wide range of trading strategies. By identifying the dominant market cycles in a financial instrument's price data, traders can create data-driven strategies that capitalize on the cyclical nature of markets.
For instance, a trader may develop a mean-reversion strategy that takes advantage of the identified cycles. By establishing positions when the price deviates from the predicted cycle, the trader can profit from the subsequent reversion to the cycle's mean. Similarly, a momentum-based strategy could be designed to exploit the persistence of a dominant cycle by entering positions that align with the cycle's direction.
Enhancing Risk Management:
The Goertzel algorithm plays a vital role in risk management for quantitative strategies. By analyzing the cyclical components of a financial instrument's price data, traders can gain insights into the potential risks associated with their trading strategies.
By monitoring the amplitude and phase of dominant cycles, a trader can detect changes in market dynamics that may pose risks to their positions. For example, a sudden increase in amplitude may indicate heightened volatility, prompting the trader to adjust position sizing or employ hedging techniques to protect their portfolio. Additionally, changes in phase alignment could signal a potential shift in market sentiment, necessitating adjustments to the trading strategy.
Expanding Quantitative Toolkits:
Traders can augment the Goertzel algorithm's insights by combining it with other quantitative techniques, creating a more comprehensive and sophisticated analysis framework. For example, machine learning algorithms, such as neural networks or support vector machines, could be trained on features extracted from the Goertzel algorithm to predict future price movements more accurately.
Furthermore, the Goertzel algorithm can be integrated with other technical analysis tools, such as moving averages or oscillators, to enhance their effectiveness. By applying these tools to the identified cycles, traders can generate more robust and reliable trading signals.
The Goertzel algorithm offers invaluable benefits to quantitative finance practitioners by uncovering hidden market cycles, aiding in the development of data-driven trading strategies, and improving risk management. By leveraging the insights provided by the Goertzel algorithm and integrating it with other quantitative techniques, traders can gain a deeper understanding of market dynamics and devise more effective trading strategies.
█ Indicator Inputs
src: This is the source data for the analysis, typically the closing price of the financial instrument.
detrendornot: This input determines the method used for detrending the source data. Detrending is the process of removing the underlying trend from the data to focus on the cyclical components.
The available options are:
hpsmthdt: Detrend using Hodrick-Prescott filter centered moving average.
zlagsmthdt: Detrend using zero-lag moving average centered moving average.
logZlagRegression: Detrend using logarithmic zero-lag linear regression.
hpsmth: Detrend using Hodrick-Prescott filter.
zlagsmth: Detrend using zero-lag moving average.
DT_HPper1 and DT_HPper2: These inputs define the period range for the Hodrick-Prescott filter centered moving average when detrendornot is set to hpsmthdt.
DT_ZLper1 and DT_ZLper2: These inputs define the period range for the zero-lag moving average centered moving average when detrendornot is set to zlagsmthdt.
DT_RegZLsmoothPer: This input defines the period for the zero-lag moving average used in logarithmic zero-lag linear regression when detrendornot is set to logZlagRegression.
HPsmoothPer: This input defines the period for the Hodrick-Prescott filter when detrendornot is set to hpsmth.
ZLMAsmoothPer: This input defines the period for the zero-lag moving average when detrendornot is set to zlagsmth.
MaxPer: This input sets the maximum period for the Goertzel algorithm to search for cycles.
squaredAmp: This boolean input determines whether the amplitude should be squared in the Goertzel algorithm.
useAddition: This boolean input determines whether the Goertzel algorithm should use addition for combining the cycles.
useCosine: This boolean input determines whether the Goertzel algorithm should use cosine waves instead of sine waves.
UseCycleStrength: This boolean input determines whether the Goertzel algorithm should compute the cycle strength, which is a normalized measure of the cycle's amplitude.
WindowSizePast: These inputs define the window size for the composite wave.
FilterBartels: This boolean input determines whether Bartel's test should be applied to filter out non-significant cycles.
BartNoCycles: This input sets the number of cycles to be used in Bartel's test.
BartSmoothPer: This input sets the period for the moving average used in Bartel's test.
BartSigLimit: This input sets the significance limit for Bartel's test, below which cycles are considered insignificant.
SortBartels: This boolean input determines whether the cycles should be sorted by their Bartel's test results.
StartAtCycle: This input determines the starting index for selecting the top N cycles when UseCycleList is set to false. This allows you to skip a certain number of cycles from the top before selecting the desired number of cycles.
UseTopCycles: This input sets the number of top cycles to use for constructing the composite wave when UseCycleList is set to false. The cycles are ranked based on their amplitudes or cycle strengths, depending on the UseCycleStrength input.
SubtractNoise: This boolean input determines whether to subtract the noise (remaining cycles) from the composite wave. If set to true, the composite wave will only include the top N cycles specified by UseTopCycles.
█ Exploring Auxiliary Functions
The following functions demonstrate advanced techniques for analyzing financial markets, including zero-lag moving averages, Bartels probability, detrending, and Hodrick-Prescott filtering. This section examines each function in detail, explaining their purpose, methodology, and applications in finance. We will examine how each function contributes to the overall performance and effectiveness of the indicator and how they work together to create a powerful analytical tool.
Zero-Lag Moving Average:
The zero-lag moving average function is designed to minimize the lag typically associated with moving averages. This is achieved through a two-step weighted linear regression process that emphasizes more recent data points. The function calculates a linearly weighted moving average (LWMA) on the input data and then applies another LWMA on the result. By doing this, the function creates a moving average that closely follows the price action, reducing the lag and improving the responsiveness of the indicator.
The zero-lag moving average function is used in the indicator to provide a responsive, low-lag smoothing of the input data. This function helps reduce the noise and fluctuations in the data, making it easier to identify and analyze underlying trends and patterns. By minimizing the lag associated with traditional moving averages, this function allows the indicator to react more quickly to changes in market conditions, providing timely signals and improving the overall effectiveness of the indicator.
Bartels Probability:
The Bartels probability function calculates the probability of a given cycle being significant in a time series. It uses a mathematical test called the Bartels test to assess the significance of cycles detected in the data. The function calculates coefficients for each detected cycle and computes an average amplitude and an expected amplitude. By comparing these values, the Bartels probability is derived, indicating the likelihood of a cycle's significance. This information can help in identifying and analyzing dominant cycles in financial markets.
The Bartels probability function is incorporated into the indicator to assess the significance of detected cycles in the input data. By calculating the Bartels probability for each cycle, the indicator can prioritize the most significant cycles and focus on the market dynamics that are most relevant to the current trading environment. This function enhances the indicator's ability to identify dominant market cycles, improving its predictive power and aiding in the development of effective trading strategies.
Detrend Logarithmic Zero-Lag Regression:
The detrend logarithmic zero-lag regression function is used for detrending data while minimizing lag. It combines a zero-lag moving average with a linear regression detrending method. The function first calculates the zero-lag moving average of the logarithm of input data and then applies a linear regression to remove the trend. By detrending the data, the function isolates the cyclical components, making it easier to analyze and interpret the underlying market dynamics.
The detrend logarithmic zero-lag regression function is used in the indicator to isolate the cyclical components of the input data. By detrending the data, the function enables the indicator to focus on the cyclical movements in the market, making it easier to analyze and interpret market dynamics. This function is essential for identifying cyclical patterns and understanding the interactions between different market cycles, which can inform trading decisions and enhance overall market understanding.
Bartels Cycle Significance Test:
The Bartels cycle significance test is a function that combines the Bartels probability function and the detrend logarithmic zero-lag regression function to assess the significance of detected cycles. The function calculates the Bartels probability for each cycle and stores the results in an array. By analyzing the probability values, traders and analysts can identify the most significant cycles in the data, which can be used to develop trading strategies and improve market understanding.
The Bartels cycle significance test function is integrated into the indicator to provide a comprehensive analysis of the significance of detected cycles. By combining the Bartels probability function and the detrend logarithmic zero-lag regression function, this test evaluates the significance of each cycle and stores the results in an array. The indicator can then use this information to prioritize the most significant cycles and focus on the most relevant market dynamics. This function enhances the indicator's ability to identify and analyze dominant market cycles, providing valuable insights for trading and market analysis.
Hodrick-Prescott Filter:
The Hodrick-Prescott filter is a popular technique used to separate the trend and cyclical components of a time series. The function applies a smoothing parameter to the input data and calculates a smoothed series using a two-sided filter. This smoothed series represents the trend component, which can be subtracted from the original data to obtain the cyclical component. The Hodrick-Prescott filter is commonly used in economics and finance to analyze economic data and financial market trends.
The Hodrick-Prescott filter is incorporated into the indicator to separate the trend and cyclical components of the input data. By applying the filter to the data, the indicator can isolate the trend component, which can be used to analyze long-term market trends and inform trading decisions. Additionally, the cyclical component can be used to identify shorter-term market dynamics and provide insights into potential trading opportunities. The inclusion of the Hodrick-Prescott filter adds another layer of analysis to the indicator, making it more versatile and comprehensive.
Detrending Options: Detrend Centered Moving Average:
The detrend centered moving average function provides different detrending methods, including the Hodrick-Prescott filter and the zero-lag moving average, based on the selected detrending method. The function calculates two sets of smoothed values using the chosen method and subtracts one set from the other to obtain a detrended series. By offering multiple detrending options, this function allows traders and analysts to select the most appropriate method for their specific needs and preferences.
The detrend centered moving average function is integrated into the indicator to provide users with multiple detrending options, including the Hodrick-Prescott filter and the zero-lag moving average. By offering multiple detrending methods, the indicator allows users to customize the analysis to their specific needs and preferences, enhancing the indicator's overall utility and adaptability. This function ensures that the indicator can cater to a wide range of trading styles and objectives, making it a valuable tool for a diverse group of market participants.
The auxiliary functions functions discussed in this section demonstrate the power and versatility of mathematical techniques in analyzing financial markets. By understanding and implementing these functions, traders and analysts can gain valuable insights into market dynamics, improve their trading strategies, and make more informed decisions. The combination of zero-lag moving averages, Bartels probability, detrending methods, and the Hodrick-Prescott filter provides a comprehensive toolkit for analyzing and interpreting financial data. The integration of advanced functions in a financial indicator creates a powerful and versatile analytical tool that can provide valuable insights into financial markets. By combining the zero-lag moving average,
█ In-Depth Analysis of the Goertzel Cycle Composite Wave Code
The Goertzel Cycle Composite Wave code is an implementation of the Goertzel Algorithm, an efficient technique to perform spectral analysis on a signal. The code is designed to detect and analyze dominant cycles within a given financial market data set. This section will provide an extremely detailed explanation of the code, its structure, functions, and intended purpose.
Function signature and input parameters:
The Goertzel Cycle Composite Wave function accepts numerous input parameters for customization, including source data (src), the current bar (forBar), sample size (samplesize), period (per), squared amplitude flag (squaredAmp), addition flag (useAddition), cosine flag (useCosine), cycle strength flag (UseCycleStrength), past sizes (WindowSizePast), Bartels filter flag (FilterBartels), Bartels-related parameters (BartNoCycles, BartSmoothPer, BartSigLimit), sorting flag (SortBartels), and output buffers (goeWorkPast, cyclebuffer, amplitudebuffer, phasebuffer, cycleBartelsBuffer).
Initializing variables and arrays:
The code initializes several float arrays (goeWork1, goeWork2, goeWork3, goeWork4) with the same length as twice the period (2 * per). These arrays store intermediate results during the execution of the algorithm.
Preprocessing input data:
The input data (src) undergoes preprocessing to remove linear trends. This step enhances the algorithm's ability to focus on cyclical components in the data. The linear trend is calculated by finding the slope between the first and last values of the input data within the sample.
Iterative calculation of Goertzel coefficients:
The core of the Goertzel Cycle Composite Wave algorithm lies in the iterative calculation of Goertzel coefficients for each frequency bin. These coefficients represent the spectral content of the input data at different frequencies. The code iterates through the range of frequencies, calculating the Goertzel coefficients using a nested loop structure.
Cycle strength computation:
The code calculates the cycle strength based on the Goertzel coefficients. This is an optional step, controlled by the UseCycleStrength flag. The cycle strength provides information on the relative influence of each cycle on the data per bar, considering both amplitude and cycle length. The algorithm computes the cycle strength either by squaring the amplitude (controlled by squaredAmp flag) or using the actual amplitude values.
Phase calculation:
The Goertzel Cycle Composite Wave code computes the phase of each cycle, which represents the position of the cycle within the input data. The phase is calculated using the arctangent function (math.atan) based on the ratio of the imaginary and real components of the Goertzel coefficients.
Peak detection and cycle extraction:
The algorithm performs peak detection on the computed amplitudes or cycle strengths to identify dominant cycles. It stores the detected cycles in the cyclebuffer array, along with their corresponding amplitudes and phases in the amplitudebuffer and phasebuffer arrays, respectively.
Sorting cycles by amplitude or cycle strength:
The code sorts the detected cycles based on their amplitude or cycle strength in descending order. This allows the algorithm to prioritize cycles with the most significant impact on the input data.
Bartels cycle significance test:
If the FilterBartels flag is set, the code performs a Bartels cycle significance test on the detected cycles. This test determines the statistical significance of each cycle and filters out the insignificant cycles. The significant cycles are stored in the cycleBartelsBuffer array. If the SortBartels flag is set, the code sorts the significant cycles based on their Bartels significance values.
Waveform calculation:
The Goertzel Cycle Composite Wave code calculates the waveform of the significant cycles for specified time windows. The windows are defined by the WindowSizePast parameters, respectively. The algorithm uses either cosine or sine functions (controlled by the useCosine flag) to calculate the waveforms for each cycle. The useAddition flag determines whether the waveforms should be added or subtracted.
Storing waveforms in a matrix:
The calculated waveforms for the cycle is stored in the matrix - goeWorkPast. This matrix holds the waveforms for the specified time windows. Each row in the matrix represents a time window position, and each column corresponds to a cycle.
Returning the number of cycles:
The Goertzel Cycle Composite Wave function returns the total number of detected cycles (number_of_cycles) after processing the input data. This information can be used to further analyze the results or to visualize the detected cycles.
The Goertzel Cycle Composite Wave code is a comprehensive implementation of the Goertzel Algorithm, specifically designed for detecting and analyzing dominant cycles within financial market data. The code offers a high level of customization, allowing users to fine-tune the algorithm based on their specific needs. The Goertzel Cycle Composite Wave's combination of preprocessing, iterative calculations, cycle extraction, sorting, significance testing, and waveform calculation makes it a powerful tool for understanding cyclical components in financial data.
█ Generating and Visualizing Composite Waveform
The indicator calculates and visualizes the composite waveform for specified time windows based on the detected cycles. Here's a detailed explanation of this process:
Updating WindowSizePast:
The WindowSizePast is updated to ensure they are at least twice the MaxPer (maximum period).
Initializing matrices and arrays:
The matrix goeWorkPast is initialized to store the Goertzel results for specified time windows. Multiple arrays are also initialized to store cycle, amplitude, phase, and Bartels information.
Preparing the source data (srcVal) array:
The source data is copied into an array, srcVal, and detrended using one of the selected methods (hpsmthdt, zlagsmthdt, logZlagRegression, hpsmth, or zlagsmth).
Goertzel function call:
The Goertzel function is called to analyze the detrended source data and extract cycle information. The output, number_of_cycles, contains the number of detected cycles.
Initializing arrays for waveforms:
The goertzel array is initialized to store the endpoint Goertzel.
Calculating composite waveform (goertzel array):
The composite waveform is calculated by summing the selected cycles (either from the user-defined cycle list or the top cycles) and optionally subtracting the noise component.
Drawing composite waveform (pvlines):
The composite waveform is drawn on the chart using solid lines. The color of the lines is determined by the direction of the waveform (green for upward, red for downward).
To summarize, this indicator generates a composite waveform based on the detected cycles in the financial data. It calculates the composite waveforms and visualizes them on the chart using colored lines.
█ Enhancing the Goertzel Algorithm-Based Script for Financial Modeling and Trading
The Goertzel algorithm-based script for detecting dominant cycles in financial data is a powerful tool for financial modeling and trading. It provides valuable insights into the past behavior of these cycles. However, as with any algorithm, there is always room for improvement. This section discusses potential enhancements to the existing script to make it even more robust and versatile for financial modeling, general trading, advanced trading, and high-frequency finance trading.
Enhancements for Financial Modeling
Data preprocessing: One way to improve the script's performance for financial modeling is to introduce more advanced data preprocessing techniques. This could include removing outliers, handling missing data, and normalizing the data to ensure consistent and accurate results.
Additional detrending and smoothing methods: Incorporating more sophisticated detrending and smoothing techniques, such as wavelet transform or empirical mode decomposition, can help improve the script's ability to accurately identify cycles and trends in the data.
Machine learning integration: Integrating machine learning techniques, such as artificial neural networks or support vector machines, can help enhance the script's predictive capabilities, leading to more accurate financial models.
Enhancements for General and Advanced Trading
Customizable indicator integration: Allowing users to integrate their own technical indicators can help improve the script's effectiveness for both general and advanced trading. By enabling the combination of the dominant cycle information with other technical analysis tools, traders can develop more comprehensive trading strategies.
Risk management and position sizing: Incorporating risk management and position sizing functionality into the script can help traders better manage their trades and control potential losses. This can be achieved by calculating the optimal position size based on the user's risk tolerance and account size.
Multi-timeframe analysis: Enhancing the script to perform multi-timeframe analysis can provide traders with a more holistic view of market trends and cycles. By identifying dominant cycles on different timeframes, traders can gain insights into the potential confluence of cycles and make better-informed trading decisions.
Enhancements for High-Frequency Finance Trading
Algorithm optimization: To ensure the script's suitability for high-frequency finance trading, optimizing the algorithm for faster execution is crucial. This can be achieved by employing efficient data structures and refining the calculation methods to minimize computational complexity.
Real-time data streaming: Integrating real-time data streaming capabilities into the script can help high-frequency traders react to market changes more quickly. By continuously updating the cycle information based on real-time market data, traders can adapt their strategies accordingly and capitalize on short-term market fluctuations.
Order execution and trade management: To fully leverage the script's capabilities for high-frequency trading, implementing functionality for automated order execution and trade management is essential. This can include features such as stop-loss and take-profit orders, trailing stops, and automated trade exit strategies.
While the existing Goertzel algorithm-based script is a valuable tool for detecting dominant cycles in financial data, there are several potential enhancements that can make it even more powerful for financial modeling, general trading, advanced trading, and high-frequency finance trading. By incorporating these improvements, the script can become a more versatile and effective tool for traders and financial analysts alike.
█ Understanding the Limitations of the Goertzel Algorithm
While the Goertzel algorithm-based script for detecting dominant cycles in financial data provides valuable insights, it is important to be aware of its limitations and drawbacks. Some of the key drawbacks of this indicator are:
Lagging nature:
As with many other technical indicators, the Goertzel algorithm-based script can suffer from lagging effects, meaning that it may not immediately react to real-time market changes. This lag can lead to late entries and exits, potentially resulting in reduced profitability or increased losses.
Parameter sensitivity:
The performance of the script can be sensitive to the chosen parameters, such as the detrending methods, smoothing techniques, and cycle detection settings. Improper parameter selection may lead to inaccurate cycle detection or increased false signals, which can negatively impact trading performance.
Complexity:
The Goertzel algorithm itself is relatively complex, making it difficult for novice traders or those unfamiliar with the concept of cycle analysis to fully understand and effectively utilize the script. This complexity can also make it challenging to optimize the script for specific trading styles or market conditions.
Overfitting risk:
As with any data-driven approach, there is a risk of overfitting when using the Goertzel algorithm-based script. Overfitting occurs when a model becomes too specific to the historical data it was trained on, leading to poor performance on new, unseen data. This can result in misleading signals and reduced trading performance.
Limited applicability:
The Goertzel algorithm-based script may not be suitable for all markets, trading styles, or timeframes. Its effectiveness in detecting cycles may be limited in certain market conditions, such as during periods of extreme volatility or low liquidity.
While the Goertzel algorithm-based script offers valuable insights into dominant cycles in financial data, it is essential to consider its drawbacks and limitations when incorporating it into a trading strategy. Traders should always use the script in conjunction with other technical and fundamental analysis tools, as well as proper risk management, to make well-informed trading decisions.
█ Interpreting Results
The Goertzel Cycle Composite Wave indicator can be interpreted by analyzing the plotted lines. The indicator plots two lines: composite waves. The composite wave represents the composite wave of the price data.
The composite wave line displays a solid line, with green indicating a bullish trend and red indicating a bearish trend.
Interpreting the Goertzel Cycle Composite Wave indicator involves identifying the trend of the composite wave lines and matching them with the corresponding bullish or bearish color.
█ Conclusion
The Goertzel Cycle Composite Wave indicator is a powerful tool for identifying and analyzing cyclical patterns in financial markets. Its ability to detect multiple cycles of varying frequencies and strengths make it a valuable addition to any trader's technical analysis toolkit. However, it is important to keep in mind that the Goertzel Cycle Composite Wave indicator should be used in conjunction with other technical analysis tools and fundamental analysis to achieve the best results. With continued refinement and development, the Goertzel Cycle Composite Wave indicator has the potential to become a highly effective tool for financial modeling, general trading, advanced trading, and high-frequency finance trading. Its accuracy and versatility make it a promising candidate for further research and development.
█ Footnotes
What is the Bartels Test for Cycle Significance?
The Bartels Cycle Significance Test is a statistical method that determines whether the peaks and troughs of a time series are statistically significant. The test is named after its inventor, George Bartels, who developed it in the mid-20th century.
The Bartels test is designed to analyze the cyclical components of a time series, which can help traders and analysts identify trends and cycles in financial markets. The test calculates a Bartels statistic, which measures the degree of non-randomness or autocorrelation in the time series.
The Bartels statistic is calculated by first splitting the time series into two halves and calculating the range of the peaks and troughs in each half. The test then compares these ranges using a t-test, which measures the significance of the difference between the two ranges.
If the Bartels statistic is greater than a critical value, it indicates that the peaks and troughs in the time series are non-random and that there is a significant cyclical component to the data. Conversely, if the Bartels statistic is less than the critical value, it suggests that the peaks and troughs are random and that there is no significant cyclical component.
The Bartels Cycle Significance Test is particularly useful in financial analysis because it can help traders and analysts identify significant cycles in asset prices, which can in turn inform investment decisions. However, it is important to note that the test is not perfect and can produce false signals in certain situations, particularly in noisy or volatile markets. Therefore, it is always recommended to use the test in conjunction with other technical and fundamental indicators to confirm trends and cycles.
Deep-dive into the Hodrick-Prescott Fitler
The Hodrick-Prescott (HP) filter is a statistical tool used in economics and finance to separate a time series into two components: a trend component and a cyclical component. It is a powerful tool for identifying long-term trends in economic and financial data and is widely used by economists, central banks, and financial institutions around the world.
The HP filter was first introduced in the 1990s by economists Robert Hodrick and Edward Prescott. It is a simple, two-parameter filter that separates a time series into a trend component and a cyclical component. The trend component represents the long-term behavior of the data, while the cyclical component captures the shorter-term fluctuations around the trend.
The HP filter works by minimizing the following objective function:
Minimize: (Sum of Squared Deviations) + λ (Sum of Squared Second Differences)
Where:
1. The first term represents the deviation of the data from the trend.
2. The second term represents the smoothness of the trend.
3. λ is a smoothing parameter that determines the degree of smoothness of the trend.
The smoothing parameter λ is typically set to a value between 100 and 1600, depending on the frequency of the data. Higher values of λ lead to a smoother trend, while lower values lead to a more volatile trend.
The HP filter has several advantages over other smoothing techniques. It is a non-parametric method, meaning that it does not make any assumptions about the underlying distribution of the data. It also allows for easy comparison of trends across different time series and can be used with data of any frequency.
However, the HP filter also has some limitations. It assumes that the trend is a smooth function, which may not be the case in some situations. It can also be sensitive to changes in the smoothing parameter λ, which may result in different trends for the same data. Additionally, the filter may produce unrealistic trends for very short time series.
Despite these limitations, the HP filter remains a valuable tool for analyzing economic and financial data. It is widely used by central banks and financial institutions to monitor long-term trends in the economy, and it can be used to identify turning points in the business cycle. The filter can also be used to analyze asset prices, exchange rates, and other financial variables.
The Hodrick-Prescott filter is a powerful tool for analyzing economic and financial data. It separates a time series into a trend component and a cyclical component, allowing for easy identification of long-term trends and turning points in the business cycle. While it has some limitations, it remains a valuable tool for economists, central banks, and financial institutions around the world.
Goertzel Browser [Loxx]As the financial markets become increasingly complex and data-driven, traders and analysts must leverage powerful tools to gain insights and make informed decisions. One such tool is the Goertzel Browser indicator, a sophisticated technical analysis indicator that helps identify cyclical patterns in financial data. This powerful tool is capable of detecting cyclical patterns in financial data, helping traders to make better predictions and optimize their trading strategies. With its unique combination of mathematical algorithms and advanced charting capabilities, this indicator has the potential to revolutionize the way we approach financial modeling and trading.
█ Brief Overview of the Goertzel Browser
The Goertzel Browser is a sophisticated technical analysis tool that utilizes the Goertzel algorithm to analyze and visualize cyclical components within a financial time series. By identifying these cycles and their characteristics, the indicator aims to provide valuable insights into the market's underlying price movements, which could potentially be used for making informed trading decisions.
The primary purpose of this indicator is to:
1. Detect and analyze the dominant cycles present in the price data.
2. Reconstruct and visualize the composite wave based on the detected cycles.
3. Project the composite wave into the future, providing a potential roadmap for upcoming price movements.
To achieve this, the indicator performs several tasks:
1. Detrending the price data: The indicator preprocesses the price data using various detrending techniques, such as Hodrick-Prescott filters, zero-lag moving averages, and linear regression, to remove the underlying trend and focus on the cyclical components.
2. Applying the Goertzel algorithm: The indicator applies the Goertzel algorithm to the detrended price data, identifying the dominant cycles and their characteristics, such as amplitude, phase, and cycle strength.
3. Constructing the composite wave: The indicator reconstructs the composite wave by combining the detected cycles, either by using a user-defined list of cycles or by selecting the top N cycles based on their amplitude or cycle strength.
4. Visualizing the composite wave: The indicator plots the composite wave, using solid lines for the past and dotted lines for the future projections. The color of the lines indicates whether the wave is increasing or decreasing.
5. Displaying cycle information: The indicator provides a table that displays detailed information about the detected cycles, including their rank, period, Bartel's test results, amplitude, and phase.
This indicator is a powerful tool that employs the Goertzel algorithm to analyze and visualize the cyclical components within a financial time series. By providing insights into the underlying price movements and their potential future trajectory, the indicator aims to assist traders in making more informed decisions.
█ What is the Goertzel Algorithm?
The Goertzel algorithm, named after Gerald Goertzel, is a digital signal processing technique that is used to efficiently compute individual terms of the Discrete Fourier Transform (DFT). It was first introduced in 1958, and since then, it has found various applications in the fields of engineering, mathematics, and physics.
The Goertzel algorithm is primarily used to detect specific frequency components within a digital signal, making it particularly useful in applications where only a few frequency components are of interest. The algorithm is computationally efficient, as it requires fewer calculations than the Fast Fourier Transform (FFT) when detecting a small number of frequency components. This efficiency makes the Goertzel algorithm a popular choice in applications such as:
1. Telecommunications: The Goertzel algorithm is used for decoding Dual-Tone Multi-Frequency (DTMF) signals, which are the tones generated when pressing buttons on a telephone keypad. By identifying specific frequency components, the algorithm can accurately determine which button has been pressed.
2. Audio processing: The algorithm can be used to detect specific pitches or harmonics in an audio signal, making it useful in applications like pitch detection and tuning musical instruments.
3. Vibration analysis: In the field of mechanical engineering, the Goertzel algorithm can be applied to analyze vibrations in rotating machinery, helping to identify faulty components or signs of wear.
4. Power system analysis: The algorithm can be used to measure harmonic content in power systems, allowing engineers to assess power quality and detect potential issues.
The Goertzel algorithm is used in these applications because it offers several advantages over other methods, such as the FFT:
1. Computational efficiency: The Goertzel algorithm requires fewer calculations when detecting a small number of frequency components, making it more computationally efficient than the FFT in these cases.
2. Real-time analysis: The algorithm can be implemented in a streaming fashion, allowing for real-time analysis of signals, which is crucial in applications like telecommunications and audio processing.
3. Memory efficiency: The Goertzel algorithm requires less memory than the FFT, as it only computes the frequency components of interest.
4. Precision: The algorithm is less susceptible to numerical errors compared to the FFT, ensuring more accurate results in applications where precision is essential.
The Goertzel algorithm is an efficient digital signal processing technique that is primarily used to detect specific frequency components within a signal. Its computational efficiency, real-time capabilities, and precision make it an attractive choice for various applications, including telecommunications, audio processing, vibration analysis, and power system analysis. The algorithm has been widely adopted since its introduction in 1958 and continues to be an essential tool in the fields of engineering, mathematics, and physics.
█ Goertzel Algorithm in Quantitative Finance: In-Depth Analysis and Applications
The Goertzel algorithm, initially designed for signal processing in telecommunications, has gained significant traction in the financial industry due to its efficient frequency detection capabilities. In quantitative finance, the Goertzel algorithm has been utilized for uncovering hidden market cycles, developing data-driven trading strategies, and optimizing risk management. This section delves deeper into the applications of the Goertzel algorithm in finance, particularly within the context of quantitative trading and analysis.
Unveiling Hidden Market Cycles:
Market cycles are prevalent in financial markets and arise from various factors, such as economic conditions, investor psychology, and market participant behavior. The Goertzel algorithm's ability to detect and isolate specific frequencies in price data helps trader analysts identify hidden market cycles that may otherwise go unnoticed. By examining the amplitude, phase, and periodicity of each cycle, traders can better understand the underlying market structure and dynamics, enabling them to develop more informed and effective trading strategies.
Developing Quantitative Trading Strategies:
The Goertzel algorithm's versatility allows traders to incorporate its insights into a wide range of trading strategies. By identifying the dominant market cycles in a financial instrument's price data, traders can create data-driven strategies that capitalize on the cyclical nature of markets.
For instance, a trader may develop a mean-reversion strategy that takes advantage of the identified cycles. By establishing positions when the price deviates from the predicted cycle, the trader can profit from the subsequent reversion to the cycle's mean. Similarly, a momentum-based strategy could be designed to exploit the persistence of a dominant cycle by entering positions that align with the cycle's direction.
Enhancing Risk Management:
The Goertzel algorithm plays a vital role in risk management for quantitative strategies. By analyzing the cyclical components of a financial instrument's price data, traders can gain insights into the potential risks associated with their trading strategies.
By monitoring the amplitude and phase of dominant cycles, a trader can detect changes in market dynamics that may pose risks to their positions. For example, a sudden increase in amplitude may indicate heightened volatility, prompting the trader to adjust position sizing or employ hedging techniques to protect their portfolio. Additionally, changes in phase alignment could signal a potential shift in market sentiment, necessitating adjustments to the trading strategy.
Expanding Quantitative Toolkits:
Traders can augment the Goertzel algorithm's insights by combining it with other quantitative techniques, creating a more comprehensive and sophisticated analysis framework. For example, machine learning algorithms, such as neural networks or support vector machines, could be trained on features extracted from the Goertzel algorithm to predict future price movements more accurately.
Furthermore, the Goertzel algorithm can be integrated with other technical analysis tools, such as moving averages or oscillators, to enhance their effectiveness. By applying these tools to the identified cycles, traders can generate more robust and reliable trading signals.
The Goertzel algorithm offers invaluable benefits to quantitative finance practitioners by uncovering hidden market cycles, aiding in the development of data-driven trading strategies, and improving risk management. By leveraging the insights provided by the Goertzel algorithm and integrating it with other quantitative techniques, traders can gain a deeper understanding of market dynamics and devise more effective trading strategies.
█ Indicator Inputs
src: This is the source data for the analysis, typically the closing price of the financial instrument.
detrendornot: This input determines the method used for detrending the source data. Detrending is the process of removing the underlying trend from the data to focus on the cyclical components.
The available options are:
hpsmthdt: Detrend using Hodrick-Prescott filter centered moving average.
zlagsmthdt: Detrend using zero-lag moving average centered moving average.
logZlagRegression: Detrend using logarithmic zero-lag linear regression.
hpsmth: Detrend using Hodrick-Prescott filter.
zlagsmth: Detrend using zero-lag moving average.
DT_HPper1 and DT_HPper2: These inputs define the period range for the Hodrick-Prescott filter centered moving average when detrendornot is set to hpsmthdt.
DT_ZLper1 and DT_ZLper2: These inputs define the period range for the zero-lag moving average centered moving average when detrendornot is set to zlagsmthdt.
DT_RegZLsmoothPer: This input defines the period for the zero-lag moving average used in logarithmic zero-lag linear regression when detrendornot is set to logZlagRegression.
HPsmoothPer: This input defines the period for the Hodrick-Prescott filter when detrendornot is set to hpsmth.
ZLMAsmoothPer: This input defines the period for the zero-lag moving average when detrendornot is set to zlagsmth.
MaxPer: This input sets the maximum period for the Goertzel algorithm to search for cycles.
squaredAmp: This boolean input determines whether the amplitude should be squared in the Goertzel algorithm.
useAddition: This boolean input determines whether the Goertzel algorithm should use addition for combining the cycles.
useCosine: This boolean input determines whether the Goertzel algorithm should use cosine waves instead of sine waves.
UseCycleStrength: This boolean input determines whether the Goertzel algorithm should compute the cycle strength, which is a normalized measure of the cycle's amplitude.
WindowSizePast and WindowSizeFuture: These inputs define the window size for past and future projections of the composite wave.
FilterBartels: This boolean input determines whether Bartel's test should be applied to filter out non-significant cycles.
BartNoCycles: This input sets the number of cycles to be used in Bartel's test.
BartSmoothPer: This input sets the period for the moving average used in Bartel's test.
BartSigLimit: This input sets the significance limit for Bartel's test, below which cycles are considered insignificant.
SortBartels: This boolean input determines whether the cycles should be sorted by their Bartel's test results.
UseCycleList: This boolean input determines whether a user-defined list of cycles should be used for constructing the composite wave. If set to false, the top N cycles will be used.
Cycle1, Cycle2, Cycle3, Cycle4, and Cycle5: These inputs define the user-defined list of cycles when 'UseCycleList' is set to true. If using a user-defined list, each of these inputs represents the period of a specific cycle to include in the composite wave.
StartAtCycle: This input determines the starting index for selecting the top N cycles when UseCycleList is set to false. This allows you to skip a certain number of cycles from the top before selecting the desired number of cycles.
UseTopCycles: This input sets the number of top cycles to use for constructing the composite wave when UseCycleList is set to false. The cycles are ranked based on their amplitudes or cycle strengths, depending on the UseCycleStrength input.
SubtractNoise: This boolean input determines whether to subtract the noise (remaining cycles) from the composite wave. If set to true, the composite wave will only include the top N cycles specified by UseTopCycles.
█ Exploring Auxiliary Functions
The following functions demonstrate advanced techniques for analyzing financial markets, including zero-lag moving averages, Bartels probability, detrending, and Hodrick-Prescott filtering. This section examines each function in detail, explaining their purpose, methodology, and applications in finance. We will examine how each function contributes to the overall performance and effectiveness of the indicator and how they work together to create a powerful analytical tool.
Zero-Lag Moving Average:
The zero-lag moving average function is designed to minimize the lag typically associated with moving averages. This is achieved through a two-step weighted linear regression process that emphasizes more recent data points. The function calculates a linearly weighted moving average (LWMA) on the input data and then applies another LWMA on the result. By doing this, the function creates a moving average that closely follows the price action, reducing the lag and improving the responsiveness of the indicator.
The zero-lag moving average function is used in the indicator to provide a responsive, low-lag smoothing of the input data. This function helps reduce the noise and fluctuations in the data, making it easier to identify and analyze underlying trends and patterns. By minimizing the lag associated with traditional moving averages, this function allows the indicator to react more quickly to changes in market conditions, providing timely signals and improving the overall effectiveness of the indicator.
Bartels Probability:
The Bartels probability function calculates the probability of a given cycle being significant in a time series. It uses a mathematical test called the Bartels test to assess the significance of cycles detected in the data. The function calculates coefficients for each detected cycle and computes an average amplitude and an expected amplitude. By comparing these values, the Bartels probability is derived, indicating the likelihood of a cycle's significance. This information can help in identifying and analyzing dominant cycles in financial markets.
The Bartels probability function is incorporated into the indicator to assess the significance of detected cycles in the input data. By calculating the Bartels probability for each cycle, the indicator can prioritize the most significant cycles and focus on the market dynamics that are most relevant to the current trading environment. This function enhances the indicator's ability to identify dominant market cycles, improving its predictive power and aiding in the development of effective trading strategies.
Detrend Logarithmic Zero-Lag Regression:
The detrend logarithmic zero-lag regression function is used for detrending data while minimizing lag. It combines a zero-lag moving average with a linear regression detrending method. The function first calculates the zero-lag moving average of the logarithm of input data and then applies a linear regression to remove the trend. By detrending the data, the function isolates the cyclical components, making it easier to analyze and interpret the underlying market dynamics.
The detrend logarithmic zero-lag regression function is used in the indicator to isolate the cyclical components of the input data. By detrending the data, the function enables the indicator to focus on the cyclical movements in the market, making it easier to analyze and interpret market dynamics. This function is essential for identifying cyclical patterns and understanding the interactions between different market cycles, which can inform trading decisions and enhance overall market understanding.
Bartels Cycle Significance Test:
The Bartels cycle significance test is a function that combines the Bartels probability function and the detrend logarithmic zero-lag regression function to assess the significance of detected cycles. The function calculates the Bartels probability for each cycle and stores the results in an array. By analyzing the probability values, traders and analysts can identify the most significant cycles in the data, which can be used to develop trading strategies and improve market understanding.
The Bartels cycle significance test function is integrated into the indicator to provide a comprehensive analysis of the significance of detected cycles. By combining the Bartels probability function and the detrend logarithmic zero-lag regression function, this test evaluates the significance of each cycle and stores the results in an array. The indicator can then use this information to prioritize the most significant cycles and focus on the most relevant market dynamics. This function enhances the indicator's ability to identify and analyze dominant market cycles, providing valuable insights for trading and market analysis.
Hodrick-Prescott Filter:
The Hodrick-Prescott filter is a popular technique used to separate the trend and cyclical components of a time series. The function applies a smoothing parameter to the input data and calculates a smoothed series using a two-sided filter. This smoothed series represents the trend component, which can be subtracted from the original data to obtain the cyclical component. The Hodrick-Prescott filter is commonly used in economics and finance to analyze economic data and financial market trends.
The Hodrick-Prescott filter is incorporated into the indicator to separate the trend and cyclical components of the input data. By applying the filter to the data, the indicator can isolate the trend component, which can be used to analyze long-term market trends and inform trading decisions. Additionally, the cyclical component can be used to identify shorter-term market dynamics and provide insights into potential trading opportunities. The inclusion of the Hodrick-Prescott filter adds another layer of analysis to the indicator, making it more versatile and comprehensive.
Detrending Options: Detrend Centered Moving Average:
The detrend centered moving average function provides different detrending methods, including the Hodrick-Prescott filter and the zero-lag moving average, based on the selected detrending method. The function calculates two sets of smoothed values using the chosen method and subtracts one set from the other to obtain a detrended series. By offering multiple detrending options, this function allows traders and analysts to select the most appropriate method for their specific needs and preferences.
The detrend centered moving average function is integrated into the indicator to provide users with multiple detrending options, including the Hodrick-Prescott filter and the zero-lag moving average. By offering multiple detrending methods, the indicator allows users to customize the analysis to their specific needs and preferences, enhancing the indicator's overall utility and adaptability. This function ensures that the indicator can cater to a wide range of trading styles and objectives, making it a valuable tool for a diverse group of market participants.
The auxiliary functions functions discussed in this section demonstrate the power and versatility of mathematical techniques in analyzing financial markets. By understanding and implementing these functions, traders and analysts can gain valuable insights into market dynamics, improve their trading strategies, and make more informed decisions. The combination of zero-lag moving averages, Bartels probability, detrending methods, and the Hodrick-Prescott filter provides a comprehensive toolkit for analyzing and interpreting financial data. The integration of advanced functions in a financial indicator creates a powerful and versatile analytical tool that can provide valuable insights into financial markets. By combining the zero-lag moving average,
█ In-Depth Analysis of the Goertzel Browser Code
The Goertzel Browser code is an implementation of the Goertzel Algorithm, an efficient technique to perform spectral analysis on a signal. The code is designed to detect and analyze dominant cycles within a given financial market data set. This section will provide an extremely detailed explanation of the code, its structure, functions, and intended purpose.
Function signature and input parameters:
The Goertzel Browser function accepts numerous input parameters for customization, including source data (src), the current bar (forBar), sample size (samplesize), period (per), squared amplitude flag (squaredAmp), addition flag (useAddition), cosine flag (useCosine), cycle strength flag (UseCycleStrength), past and future window sizes (WindowSizePast, WindowSizeFuture), Bartels filter flag (FilterBartels), Bartels-related parameters (BartNoCycles, BartSmoothPer, BartSigLimit), sorting flag (SortBartels), and output buffers (goeWorkPast, goeWorkFuture, cyclebuffer, amplitudebuffer, phasebuffer, cycleBartelsBuffer).
Initializing variables and arrays:
The code initializes several float arrays (goeWork1, goeWork2, goeWork3, goeWork4) with the same length as twice the period (2 * per). These arrays store intermediate results during the execution of the algorithm.
Preprocessing input data:
The input data (src) undergoes preprocessing to remove linear trends. This step enhances the algorithm's ability to focus on cyclical components in the data. The linear trend is calculated by finding the slope between the first and last values of the input data within the sample.
Iterative calculation of Goertzel coefficients:
The core of the Goertzel Browser algorithm lies in the iterative calculation of Goertzel coefficients for each frequency bin. These coefficients represent the spectral content of the input data at different frequencies. The code iterates through the range of frequencies, calculating the Goertzel coefficients using a nested loop structure.
Cycle strength computation:
The code calculates the cycle strength based on the Goertzel coefficients. This is an optional step, controlled by the UseCycleStrength flag. The cycle strength provides information on the relative influence of each cycle on the data per bar, considering both amplitude and cycle length. The algorithm computes the cycle strength either by squaring the amplitude (controlled by squaredAmp flag) or using the actual amplitude values.
Phase calculation:
The Goertzel Browser code computes the phase of each cycle, which represents the position of the cycle within the input data. The phase is calculated using the arctangent function (math.atan) based on the ratio of the imaginary and real components of the Goertzel coefficients.
Peak detection and cycle extraction:
The algorithm performs peak detection on the computed amplitudes or cycle strengths to identify dominant cycles. It stores the detected cycles in the cyclebuffer array, along with their corresponding amplitudes and phases in the amplitudebuffer and phasebuffer arrays, respectively.
Sorting cycles by amplitude or cycle strength:
The code sorts the detected cycles based on their amplitude or cycle strength in descending order. This allows the algorithm to prioritize cycles with the most significant impact on the input data.
Bartels cycle significance test:
If the FilterBartels flag is set, the code performs a Bartels cycle significance test on the detected cycles. This test determines the statistical significance of each cycle and filters out the insignificant cycles. The significant cycles are stored in the cycleBartelsBuffer array. If the SortBartels flag is set, the code sorts the significant cycles based on their Bartels significance values.
Waveform calculation:
The Goertzel Browser code calculates the waveform of the significant cycles for both past and future time windows. The past and future windows are defined by the WindowSizePast and WindowSizeFuture parameters, respectively. The algorithm uses either cosine or sine functions (controlled by the useCosine flag) to calculate the waveforms for each cycle. The useAddition flag determines whether the waveforms should be added or subtracted.
Storing waveforms in matrices:
The calculated waveforms for each cycle are stored in two matrices - goeWorkPast and goeWorkFuture. These matrices hold the waveforms for the past and future time windows, respectively. Each row in the matrices represents a time window position, and each column corresponds to a cycle.
Returning the number of cycles:
The Goertzel Browser function returns the total number of detected cycles (number_of_cycles) after processing the input data. This information can be used to further analyze the results or to visualize the detected cycles.
The Goertzel Browser code is a comprehensive implementation of the Goertzel Algorithm, specifically designed for detecting and analyzing dominant cycles within financial market data. The code offers a high level of customization, allowing users to fine-tune the algorithm based on their specific needs. The Goertzel Browser's combination of preprocessing, iterative calculations, cycle extraction, sorting, significance testing, and waveform calculation makes it a powerful tool for understanding cyclical components in financial data.
█ Generating and Visualizing Composite Waveform
The indicator calculates and visualizes the composite waveform for both past and future time windows based on the detected cycles. Here's a detailed explanation of this process:
Updating WindowSizePast and WindowSizeFuture:
The WindowSizePast and WindowSizeFuture are updated to ensure they are at least twice the MaxPer (maximum period).
Initializing matrices and arrays:
Two matrices, goeWorkPast and goeWorkFuture, are initialized to store the Goertzel results for past and future time windows. Multiple arrays are also initialized to store cycle, amplitude, phase, and Bartels information.
Preparing the source data (srcVal) array:
The source data is copied into an array, srcVal, and detrended using one of the selected methods (hpsmthdt, zlagsmthdt, logZlagRegression, hpsmth, or zlagsmth).
Goertzel function call:
The Goertzel function is called to analyze the detrended source data and extract cycle information. The output, number_of_cycles, contains the number of detected cycles.
Initializing arrays for past and future waveforms:
Three arrays, epgoertzel, goertzel, and goertzelFuture, are initialized to store the endpoint Goertzel, non-endpoint Goertzel, and future Goertzel projections, respectively.
Calculating composite waveform for past bars (goertzel array):
The past composite waveform is calculated by summing the selected cycles (either from the user-defined cycle list or the top cycles) and optionally subtracting the noise component.
Calculating composite waveform for future bars (goertzelFuture array):
The future composite waveform is calculated in a similar way as the past composite waveform.
Drawing past composite waveform (pvlines):
The past composite waveform is drawn on the chart using solid lines. The color of the lines is determined by the direction of the waveform (green for upward, red for downward).
Drawing future composite waveform (fvlines):
The future composite waveform is drawn on the chart using dotted lines. The color of the lines is determined by the direction of the waveform (fuchsia for upward, yellow for downward).
Displaying cycle information in a table (table3):
A table is created to display the cycle information, including the rank, period, Bartel value, amplitude (or cycle strength), and phase of each detected cycle.
Filling the table with cycle information:
The indicator iterates through the detected cycles and retrieves the relevant information (period, amplitude, phase, and Bartel value) from the corresponding arrays. It then fills the table with this information, displaying the values up to six decimal places.
To summarize, this indicator generates a composite waveform based on the detected cycles in the financial data. It calculates the composite waveforms for both past and future time windows and visualizes them on the chart using colored lines. Additionally, it displays detailed cycle information in a table, including the rank, period, Bartel value, amplitude (or cycle strength), and phase of each detected cycle.
█ Enhancing the Goertzel Algorithm-Based Script for Financial Modeling and Trading
The Goertzel algorithm-based script for detecting dominant cycles in financial data is a powerful tool for financial modeling and trading. It provides valuable insights into the past behavior of these cycles and potential future impact. However, as with any algorithm, there is always room for improvement. This section discusses potential enhancements to the existing script to make it even more robust and versatile for financial modeling, general trading, advanced trading, and high-frequency finance trading.
Enhancements for Financial Modeling
Data preprocessing: One way to improve the script's performance for financial modeling is to introduce more advanced data preprocessing techniques. This could include removing outliers, handling missing data, and normalizing the data to ensure consistent and accurate results.
Additional detrending and smoothing methods: Incorporating more sophisticated detrending and smoothing techniques, such as wavelet transform or empirical mode decomposition, can help improve the script's ability to accurately identify cycles and trends in the data.
Machine learning integration: Integrating machine learning techniques, such as artificial neural networks or support vector machines, can help enhance the script's predictive capabilities, leading to more accurate financial models.
Enhancements for General and Advanced Trading
Customizable indicator integration: Allowing users to integrate their own technical indicators can help improve the script's effectiveness for both general and advanced trading. By enabling the combination of the dominant cycle information with other technical analysis tools, traders can develop more comprehensive trading strategies.
Risk management and position sizing: Incorporating risk management and position sizing functionality into the script can help traders better manage their trades and control potential losses. This can be achieved by calculating the optimal position size based on the user's risk tolerance and account size.
Multi-timeframe analysis: Enhancing the script to perform multi-timeframe analysis can provide traders with a more holistic view of market trends and cycles. By identifying dominant cycles on different timeframes, traders can gain insights into the potential confluence of cycles and make better-informed trading decisions.
Enhancements for High-Frequency Finance Trading
Algorithm optimization: To ensure the script's suitability for high-frequency finance trading, optimizing the algorithm for faster execution is crucial. This can be achieved by employing efficient data structures and refining the calculation methods to minimize computational complexity.
Real-time data streaming: Integrating real-time data streaming capabilities into the script can help high-frequency traders react to market changes more quickly. By continuously updating the cycle information based on real-time market data, traders can adapt their strategies accordingly and capitalize on short-term market fluctuations.
Order execution and trade management: To fully leverage the script's capabilities for high-frequency trading, implementing functionality for automated order execution and trade management is essential. This can include features such as stop-loss and take-profit orders, trailing stops, and automated trade exit strategies.
While the existing Goertzel algorithm-based script is a valuable tool for detecting dominant cycles in financial data, there are several potential enhancements that can make it even more powerful for financial modeling, general trading, advanced trading, and high-frequency finance trading. By incorporating these improvements, the script can become a more versatile and effective tool for traders and financial analysts alike.
█ Understanding the Limitations of the Goertzel Algorithm
While the Goertzel algorithm-based script for detecting dominant cycles in financial data provides valuable insights, it is important to be aware of its limitations and drawbacks. Some of the key drawbacks of this indicator are:
Lagging nature:
As with many other technical indicators, the Goertzel algorithm-based script can suffer from lagging effects, meaning that it may not immediately react to real-time market changes. This lag can lead to late entries and exits, potentially resulting in reduced profitability or increased losses.
Parameter sensitivity:
The performance of the script can be sensitive to the chosen parameters, such as the detrending methods, smoothing techniques, and cycle detection settings. Improper parameter selection may lead to inaccurate cycle detection or increased false signals, which can negatively impact trading performance.
Complexity:
The Goertzel algorithm itself is relatively complex, making it difficult for novice traders or those unfamiliar with the concept of cycle analysis to fully understand and effectively utilize the script. This complexity can also make it challenging to optimize the script for specific trading styles or market conditions.
Overfitting risk:
As with any data-driven approach, there is a risk of overfitting when using the Goertzel algorithm-based script. Overfitting occurs when a model becomes too specific to the historical data it was trained on, leading to poor performance on new, unseen data. This can result in misleading signals and reduced trading performance.
No guarantee of future performance: While the script can provide insights into past cycles and potential future trends, it is important to remember that past performance does not guarantee future results. Market conditions can change, and relying solely on the script's predictions without considering other factors may lead to poor trading decisions.
Limited applicability: The Goertzel algorithm-based script may not be suitable for all markets, trading styles, or timeframes. Its effectiveness in detecting cycles may be limited in certain market conditions, such as during periods of extreme volatility or low liquidity.
While the Goertzel algorithm-based script offers valuable insights into dominant cycles in financial data, it is essential to consider its drawbacks and limitations when incorporating it into a trading strategy. Traders should always use the script in conjunction with other technical and fundamental analysis tools, as well as proper risk management, to make well-informed trading decisions.
█ Interpreting Results
The Goertzel Browser indicator can be interpreted by analyzing the plotted lines and the table presented alongside them. The indicator plots two lines: past and future composite waves. The past composite wave represents the composite wave of the past price data, and the future composite wave represents the projected composite wave for the next period.
The past composite wave line displays a solid line, with green indicating a bullish trend and red indicating a bearish trend. On the other hand, the future composite wave line is a dotted line with fuchsia indicating a bullish trend and yellow indicating a bearish trend.
The table presented alongside the indicator shows the top cycles with their corresponding rank, period, Bartels, amplitude or cycle strength, and phase. The amplitude is a measure of the strength of the cycle, while the phase is the position of the cycle within the data series.
Interpreting the Goertzel Browser indicator involves identifying the trend of the past and future composite wave lines and matching them with the corresponding bullish or bearish color. Additionally, traders can identify the top cycles with the highest amplitude or cycle strength and utilize them in conjunction with other technical indicators and fundamental analysis for trading decisions.
This indicator is considered a repainting indicator because the value of the indicator is calculated based on the past price data. As new price data becomes available, the indicator's value is recalculated, potentially causing the indicator's past values to change. This can create a false impression of the indicator's performance, as it may appear to have provided a profitable trading signal in the past when, in fact, that signal did not exist at the time.
The Goertzel indicator is also non-endpointed, meaning that it is not calculated up to the current bar or candle. Instead, it uses a fixed amount of historical data to calculate its values, which can make it difficult to use for real-time trading decisions. For example, if the indicator uses 100 bars of historical data to make its calculations, it cannot provide a signal until the current bar has closed and become part of the historical data. This can result in missed trading opportunities or delayed signals.
█ Conclusion
The Goertzel Browser indicator is a powerful tool for identifying and analyzing cyclical patterns in financial markets. Its ability to detect multiple cycles of varying frequencies and strengths make it a valuable addition to any trader's technical analysis toolkit. However, it is important to keep in mind that the Goertzel Browser indicator should be used in conjunction with other technical analysis tools and fundamental analysis to achieve the best results. With continued refinement and development, the Goertzel Browser indicator has the potential to become a highly effective tool for financial modeling, general trading, advanced trading, and high-frequency finance trading. Its accuracy and versatility make it a promising candidate for further research and development.
█ Footnotes
What is the Bartels Test for Cycle Significance?
The Bartels Cycle Significance Test is a statistical method that determines whether the peaks and troughs of a time series are statistically significant. The test is named after its inventor, George Bartels, who developed it in the mid-20th century.
The Bartels test is designed to analyze the cyclical components of a time series, which can help traders and analysts identify trends and cycles in financial markets. The test calculates a Bartels statistic, which measures the degree of non-randomness or autocorrelation in the time series.
The Bartels statistic is calculated by first splitting the time series into two halves and calculating the range of the peaks and troughs in each half. The test then compares these ranges using a t-test, which measures the significance of the difference between the two ranges.
If the Bartels statistic is greater than a critical value, it indicates that the peaks and troughs in the time series are non-random and that there is a significant cyclical component to the data. Conversely, if the Bartels statistic is less than the critical value, it suggests that the peaks and troughs are random and that there is no significant cyclical component.
The Bartels Cycle Significance Test is particularly useful in financial analysis because it can help traders and analysts identify significant cycles in asset prices, which can in turn inform investment decisions. However, it is important to note that the test is not perfect and can produce false signals in certain situations, particularly in noisy or volatile markets. Therefore, it is always recommended to use the test in conjunction with other technical and fundamental indicators to confirm trends and cycles.
Deep-dive into the Hodrick-Prescott Fitler
The Hodrick-Prescott (HP) filter is a statistical tool used in economics and finance to separate a time series into two components: a trend component and a cyclical component. It is a powerful tool for identifying long-term trends in economic and financial data and is widely used by economists, central banks, and financial institutions around the world.
The HP filter was first introduced in the 1990s by economists Robert Hodrick and Edward Prescott. It is a simple, two-parameter filter that separates a time series into a trend component and a cyclical component. The trend component represents the long-term behavior of the data, while the cyclical component captures the shorter-term fluctuations around the trend.
The HP filter works by minimizing the following objective function:
Minimize: (Sum of Squared Deviations) + λ (Sum of Squared Second Differences)
Where:
The first term represents the deviation of the data from the trend.
The second term represents the smoothness of the trend.
λ is a smoothing parameter that determines the degree of smoothness of the trend.
The smoothing parameter λ is typically set to a value between 100 and 1600, depending on the frequency of the data. Higher values of λ lead to a smoother trend, while lower values lead to a more volatile trend.
The HP filter has several advantages over other smoothing techniques. It is a non-parametric method, meaning that it does not make any assumptions about the underlying distribution of the data. It also allows for easy comparison of trends across different time series and can be used with data of any frequency.
However, the HP filter also has some limitations. It assumes that the trend is a smooth function, which may not be the case in some situations. It can also be sensitive to changes in the smoothing parameter λ, which may result in different trends for the same data. Additionally, the filter may produce unrealistic trends for very short time series.
Despite these limitations, the HP filter remains a valuable tool for analyzing economic and financial data. It is widely used by central banks and financial institutions to monitor long-term trends in the economy, and it can be used to identify turning points in the business cycle. The filter can also be used to analyze asset prices, exchange rates, and other financial variables.
The Hodrick-Prescott filter is a powerful tool for analyzing economic and financial data. It separates a time series into a trend component and a cyclical component, allowing for easy identification of long-term trends and turning points in the business cycle. While it has some limitations, it remains a valuable tool for economists, central banks, and financial institutions around the world.
SPY 4 Hour Swing TraderThe purpose of this script is to spot 4 hour pivots that indicate ~30 trading day swings. As VIX starts to drop options trading will get more boring and as we get back on the bull and can benefit from swing trading strategy. Swing trading doesn't make a whole lot of sense when VIX is above 28. Seems to get best results on 4 hour chart for this one. This indicator spots a go long opportunity when the 5 ema crosses the 13 ema on the 4 hour along with the RSI > 50 and the ADX > 20 and Stoichastic values (smoothed line < 80 or line < 90) and close > last candle close and the True Range < 6. It also spots uses a couple different means to determine when to exit the trade. Sell condition is primarily when the 13 ema crosses the 5 ema and the MACD line crosses below the signal line and the smoothed Stoichastic appears oversold (greater than 60) and slop of RSI < -.2. Stop Losses and Take Profits are configurable in Inputs along with ability to include short trades plus other MACD and Stoichastic settings. If a stop loss is encountered the trade will close. Also once twice the expected move is encountered partial profits will taken and stop losses and take profits will be re-established based on most recent close. Also a VIX above 28 will trigger any open positions to close. If trying to use this for something other than SPXL it is best to update stop losses and take profit percentages and check backtest results to ensure proper levels have been selected and the script gives satisfactory results.
SPY 1 Hour Swing TraderThe purpose of this script is to spot 1 hour pivots that indicate ~5 to 6 trading day swings. Results indicate that swings are held approximately 5 to 6 trading days on average, over the last 6 years. This indicator spots a go long opportunity when the 5 ema crosses the 13 ema on the 1 hour along with the RSI > 50. It also spots uses a couple different means to determine when to exit the trade. Sell condition is primarily when the 13 ema crosses the 5 ema and the MACD line crosses below the signal line and the smoothed Stoichastic appears oversold (greater than 60). Stop Losses and Take Profits are configurable in Inputs along with ability to include short trades plus other MACD and Stoichastic settings. If a stop loss is encountered the trade will close. Also once twice the expected move is encountered partial profits will taken and stop losses and take profits will be re-established based on most recent close. Once long trades are exited, short trades will be initiated if recent conditions appeared oversold and input option for short trading is enabled. If trying to use this for something other than SPXL it is best to update stop losses and take profit percentages and check backtest results to ensure proper levels have been selected and the script gives satisfactory results.
Antares_messages_publicLibrary "Antares_messages_public"
This library add messages for yours strategy for use in Antares trading system for binance and bybit exchanges.
Данная библиотека позволяет формировать сообщения в алертах стратегий для Antares в более упрощенном для пользователя режиме, включая всплывающие подсказки и т.д.
set_leverage(token, market, ticker_id, leverage)
Set leverage for ticker on specified market.
Parameters:
token (string) : (integer or 0) token for trade in system, if = 0 then token part mess is empty. Токен, При значениb = 0 не включается в формирование строки алерта.
market (string) : (string) Spot 'binance' , 'bybit' . Futures ('binancefru','binancefro','bybitfu', 'bybitfi'). Строковая переменная названия биржи.
ticker_id (string) : (string) ticker in market ('btcusdt', 'ethusdt' etc...). Строковая переменная названия тикера (пары).
leverage (float) : (float) leverage level. Устанавливаемое плечо.
Returns: 'Set leverage message'.
pause(time_pause)
Set pause in message. '::' -left and '::' -right included.
Parameters:
time_pause (int)
LongLimit(token, market, ticker_id, type_qty, quantity, price, orderId, leverageforqty)
Buy order with limit price and quantity.
Лимитный ордер на покупку(в лонг).
Parameters:
token (string) : (integer or 0) token for trade in system, if = 0 then token part mess is empty. Токен, При значениb = 0 не включается в формирование строки алерта.
market (string) : (string) Spot 'binance' , 'bybit' . Futures ('binancefru','binancefro','bybitfu', 'bybitfi'). Строковая переменная названия биржи.
ticker_id (string) : (string) ticker in market ('btcusdt', 'ethusdt' etc...). Строковая переменная названия тикера (пары).
type_qty (string) : (string) type of quantity: 1. 'qty' or '' or na - standart (in coins), 2. 'quqty'- in assets (usdt,btc,etc..), 3.open% - open position(futures) or buy (spot) in % of base 4. close% - close in % of position (futures) or sell (spot) coins in % for current quantity
quantity (float) : (float) orders size, see at 'type_qty'. Размер ордера, базы или % в соответствии с 'type_qty'
price (float) : (float) price for limit order. Цена по которой должен быть установлен лимитный ордер.
orderId (string) : (string) if use order id you may change or cancel your order after or set it ''. Используйте OrderId если хотите изменить или отменить ордер в будущем.
leverageforqty (bool) : (bool) use leverage in qty. Использовать плечо при расчете количества или нет.
Returns: 'Limit Buy order'. Лимитный ордер на покупку (лонг).
LongMarket(token, market, ticker_id, type_qty, quantity, leverageforqty)
Market Buy order with quantity.
Рыночный ордер на покупку (в лонг).
Parameters:
token (string) : (integer or 0) token for trade in system, if = 0 then token part mess is empty. Токен, При значениb = 0 не включается в формирование строки алерта.
market (string) : (string) Spot 'binance' , 'bybit' . Futures ('binancefru','binancefro','bybitfu', 'bybitfi'). Строковая переменная названия биржи.
ticker_id (string) : (string) ticker in market ('btcusdt', 'ethusdt' etc...). Строковая переменная названия тикера (пары).
type_qty (string) : (string) type of quantity: 1. 'qty' or '' or na - standart (in coins), 2. 'quqty'- in assets (usdt,btc,etc..), 3.open% - open position(futures) or buy (spot) in % of base 4. close% - close in % of position (futures) or sell (spot) coins in % for current quantity
quantity (float) : (float) orders size, see at 'type_qty'. Размер ордера, базы или % в соответствии с 'type_qty'
leverageforqty (int) : (bool) use leverage in qty. Использовать плечо при расчете количества или нет.
Returns: 'Market Buy order'. Маркетный ордер на покупку (лонг).
ShortLimit(token, market, ticker_id, type_qty, quantity, price, leverageforqty, orderId)
Sell order with limit price and quantity.
Лимитный ордер на продажу(в шорт).
Parameters:
token (string) : (integer or 0) token for trade in system, if = 0 then token part mess is empty. Токен, При значениb = 0 не включается в формирование строки алерта.
market (string) : (string) Spot 'binance' , 'bybit' . Futures ('binancefru','binancefro','bybitfu', 'bybitfi'). Строковая переменная названия биржи.
ticker_id (string) : (string) ticker in market ('btcusdt', 'ethusdt' etc...). Строковая переменная названия тикера (пары).
type_qty (string) : (string) type of quantity: 1. 'qty' or '' or na - standart (in coins), 2. 'quqty'- in assets (usdt,btc,etc..), 3.open% - open position(futures) or buy (spot) in % of base 4. close% - close in % of position (futures) or sell (spot) coins in % for current quantity
quantity (float) : (float) orders size, see at 'type_qty'. Размер ордера, базы или % в соответствии с 'type_qty'
price (float) : (float) price for limit order. Цена по которой должен быть установлен лимитный ордер.
leverageforqty (bool) : (bool) use leverage in qty. Использовать плечо при расчете количества или нет.
orderId (string) : (string) if use order id you may change or cancel your order after or set it ''. Используйте OrderId если хотите изменить или отменить ордер в будущем.
Returns: 'Limit Sell order'. Лимитный ордер на продажу (шорт).
ShortMarket(token, market, ticker_id, type_qty, quantity, leverageforqty)
Sell by market price and quantity.
Рыночный ордер на продажу(в шорт).
Parameters:
token (string) : (integer or 0) token for trade in system, if = 0 then token part mess is empty. Токен, При значениb = 0 не включается в формирование строки алерта.
market (string) : (string) Spot 'binance' , 'bybit' . Futures ('binancefru','binancefro','bybitfu', 'bybitfi'). Строковая переменная названия биржи.
ticker_id (string) : (string) ticker in market ('btcusdt', 'ethusdt' etc...). Строковая переменная названия тикера (пары).
type_qty (string) : (string) type of quantity: 1. 'qty' or '' or na - standart (in coins), 2. 'quqty'- in assets (usdt,btc,etc..), 3.open% - open position(futures) or buy (spot) in % of base 4. close% - close in % of position (futures) or sell (spot) coins in % for current quantity
quantity (float) : (float) orders size, see at 'type_qty'. Размер ордера, базы или % в соответствии с 'type_qty'
leverageforqty (int) : (bool) use leverage in qty. Использовать плечо при расчете количества или нет.
Returns: 'Market Sell order'. Маркетный ордер на продажу (шорт).
Cancel_by_ticker(token, market, ticker_id)
Cancel all orders for market and ticker in setups. Отменяет все ордера на заданной бирже и заданном токене(паре).
Parameters:
token (string)
market (string) : (string) Spot 'binance' , 'bybit' . Futures ('binancefru','binancefro','bybitfu', 'bybitfi'). Строковая переменная названия биржи.
ticker_id (string) : (string) ticker in market ('btcusdt', 'ethusdt' etc...). Строковая переменная названия тикера (пары).
Returns: 'Cancel all orders'. Отмена всех ордеров на заданной бирже и заданном токене(паре).
Cancel_by_id(token, market, ticker_id, orderId)
Cancel order by Id for market and ticker in setups. Отменяет ордер по Id на заданной бирже и заданном токене(паре).
Parameters:
token (string)
market (string) : (string) Spot 'binance' , 'bybit' . Futures ('binancefru','binancefro','bybitfu', 'bybitfi'). Строковая переменная названия биржи.
ticker_id (string) : (string) ticker in market ('btcusdt', 'ethusdt' etc...). Строковая переменная названия тикера (пары).
orderId (string)
Returns: 'Cancel order'. Отмена ордера по Id на заданной бирже и заданном токене(паре).
Close_positions(token, market, ticker_id)
Close all positions for market and ticker in setups. Закрывает все позиции на заданной бирже и заданном токене(паре).
Parameters:
token (string)
market (string) : (string) Spot 'binance' , 'bybit' . Futures ('binancefru','binancefro','bybitfu', 'bybitfi'). Строковая переменная названия биржи.
ticker_id (string) : (string) ticker in market ('btcusdt', 'ethusdt' etc...). Строковая переменная названия тикера (пары).
Returns: 'Close positions'
CloseLongLimit(token, market, ticker_id, type_qty, quantity, price, orderId, leverageforqty)
Close limit order for long position. (futures)
Лимитный ордер на продажу(в шорт) для закрытия лонговой позиции(reduceonly).
Parameters:
token (string) : (integer or 0) token for trade in system, if = 0 then token part mess is empty. Токен, При значениb = 0 не включается в формирование строки алерта.
market (string) : (string) Spot 'binance' , 'bybit' . Futures ('binancefru','binancefro','bybitfu', 'bybitfi'). Строковая переменная названия биржи.
ticker_id (string) : (string) ticker in market ('btcusdt', 'ethusdt' etc...). Строковая переменная названия тикера (пары).
type_qty (string) : (string) type of quantity: 1. 'qty' or '' or na - standart (in coins), 2. 'quqty'- in assets (usdt,btc,etc..), 3.open% - open position(futures) or buy (spot) in % of base 4. close% - close in % of position (futures) or sell (spot) coins in % for current quantity
quantity (float) : (float) orders size, see at 'type_qty'. Размер ордера, базы или % в соответствии с 'type_qty'
price (float) : (float) price for limit order. Цена по которой должен быть установлен лимитный ордер.
orderId (string) : (string) if use order id you may change or cancel your order after or set it ''. Используйте OrderId если хотите изменить или отменить ордер в будущем.
leverageforqty (bool) : (bool) use leverage in qty. Использовать плечо при расчете количества или нет.
Returns: 'Limit Sell order reduce only (close long position)'. Лимитный ордер на продажу для снижения текущего лонга(в шорт не входит).
CloseLongMarket(token, market, ticker_id, type_qty, quantity, leverageforqty)
Close market order for long position.
Рыночный ордер на продажу(в шорт) для закрытия лонговой позиции(reduceonly).
Parameters:
token (string) : (integer or 0) token for trade in system, if = 0 then token part mess is empty. Токен, При значениb = 0 не включается в формирование строки алерта.
market (string) : (string) Spot 'binance' , 'bybit' . Futures ('binancefru','binancefro','bybitfu', 'bybitfi'). Строковая переменная названия биржи.
ticker_id (string) : (string) ticker in market ('btcusdt', 'ethusdt' etc...). Строковая переменная названия тикера (пары).
type_qty (string) : (string) type of quantity: 1. 'qty' or '' or na - standart (in coins), 2. 'quqty'- in assets (usdt,btc,etc..), 3.open% - open position(futures) or buy (spot) in % of base 4. close% - close in % of position (futures) or sell (spot) coins in % for current quantity
quantity (float) : (float) orders size, see at 'type_qty'. Размер ордера, базы или % в соответствии с 'type_qty'
leverageforqty (bool) : (bool) use leverage in qty. Использовать плечо при расчете количества или нет.
Returns: 'Market Sell order reduce only (close long position)'. Ордер на снижение/закрытие текущего лонга(в шорт не входит) по рыночной цене.
CloseShortLimit(token, market, ticker_id, type_qty, quantity, price, orderId, leverageforqty)
Close limit order for short position.
Лимитный ордер на покупку(в лонг) для закрытия шортовой позиции(reduceonly).
Parameters:
token (string) : (integer or 0) token for trade in system, if = 0 then token part mess is empty. Токен, При значениb = 0 не включается в формирование строки алерта.
market (string) : (string) Spot 'binance' , 'bybit' . Futures ('binancefru','binancefro','bybitfu', 'bybitfi'). Строковая переменная названия биржи.
ticker_id (string) : (string) ticker in market ('btcusdt', 'ethusdt' etc...). Строковая переменная названия тикера (пары).
type_qty (string) : (string) type of quantity: 1. 'qty' or '' or na - standart (in coins), 2. 'quqty'- in assets (usdt,btc,etc..), 3.open% - open position(futures) or buy (spot) in % of base 4. close% - close in % of position (futures) or sell (spot) coins in % for current quantity
quantity (float) : (float) orders size, see at 'type_qty'. Размер ордера, базы или % в соответствии с 'type_qty'
price (float) : (float) price for limit order. Цена по которой должен быть установлен лимитный ордер.
orderId (string) : (string) if use order id you may change or cancel your order after or set it ''. Используйте OrderId если хотите изменить или отменить ордер в будущем.
leverageforqty (bool) : (bool) use leverage in qty. Использовать плечо при расчете количества или нет.
Returns: 'Limit Buy order reduce only (close short position)' . Лимитный ордер на покупку (лонг) для сокращения/закрытия текущего шорта.
CloseShortMarket(token, market, ticker_id, type_qty, quantity, leverageforqty)
Set Close limit order for long position.
Рыночный ордер на покупку(в лонг) для сокращения/закрытия шортовой позиции(reduceonly).
Parameters:
token (string) : (integer or 0) token for trade in system, if = 0 then token part mess is empty. Токен, При значениb = 0 не включается в формирование строки алерта.
market (string) : (string) Spot 'binance' , 'bybit' . Futures ('binancefru','binancefro','bybitfu', 'bybitfi'). Строковая переменная названия биржи.
ticker_id (string) : (string) ticker in market ('btcusdt', 'ethusdt' etc...). Строковая переменная названия тикера (пары).
type_qty (string) : (string) type of quantity: 1. 'qty' or '' or na - standart (in coins), 2. 'quqty'- in assets (usdt,btc,etc..), 3.open% - open position(futures) or buy (spot) in % of base 4. close% - close in % of position (futures) or sell (spot) coins in % for current quantity
quantity (float) : (float) orders size, see at 'type_qty'. Размер ордера, базы или % в соответствии с 'type_qty'
leverageforqty (bool) : (bool) use leverage in qty. Использовать плечо при расчете количества или нет.
Returns: 'Market Buy order reduce only (close short position)'. Маркетного ордера на покупку (лонг) для сокращения/закрытия текущего шорта.
cancel_all_close(token, market, ticker_id)
Parameters:
token (string)
market (string)
ticker_id (string)
limit_tpsl_bybitfu(token, ticker_id, order_id, side, type_qty, quantity, price, tp_price, sl_price, leverageforqty)
Set multi order for Bybit : limit + takeprofit + stoploss
Выставление тройного ордера на Bybit лимитка со стоплоссом и тейкпрофитом
Parameters:
token (string) : (integer or 0) token for trade in system, if = 0 then token part mess is empty. Токен, При значениb = 0 не включается в формирование строки алерта.
ticker_id (string) : (string) ticker in market ('btcusdt', 'ethusdt' etc...). Строковая переменная названия тикера (пары).
order_id (string)
side (bool) : (bool) "buy side" if true or "sell side" if false. true для лонга, false для шорта.
type_qty (string) : (string) type of quantity: 1. 'qty' or '' or na - standart (in coins), 2. 'quqty'- in assets (usdt,btc,etc..), 3.open% - open position(futures) or buy (spot) in % of base 4. close% - close in % of position (futures) or sell (spot) coins in % for current quantity
quantity (float) : (float) orders size, see at 'type_qty'. Размер ордера, базы или % в соответствии с 'type_qty'
price (float) : (float) price for limit order by 'side'. Цена лимитного ордера
tp_price (float) : (float) price for take profit order.
sl_price (float) : (float) price for stoploss order
leverageforqty (bool) : (bool) use leverage in qty. Использовать плечо при расчете количества или нет.
Returns: Set multi order for Bybit : limit + takeprofit + stoploss.
replace_limit_tpsl_bybitfu(token, ticker_id, order_id, side, type_qty, quantity, price, tp_price, sl_price, leverageforqty)
Change multi order for Bybit : limit + takeprofit + stoploss
Изменение тройного ордера на Bybit лимитка со стоплоссом и тейкпрофитом
Parameters:
token (string) : (integer or 0) token for trade in system, if = 0 then token part mess is empty. Токен, При значениb = 0 не включается в формирование строки алерта.
ticker_id (string) : (string) ticker in market ('btcusdt', 'ethusdt' etc...). Строковая переменная названия тикера (пары).
order_id (string)
side (bool) : (bool) "buy side" if true or "sell side" if false. true для лонга, false для шорта.
type_qty (string) : (string) type of quantity: 1. 'qty' or '' or na - standart (in coins), 2. 'quqty'- in assets (usdt,btc,etc..), 3.open% - open position(futures) or buy (spot) in % of base 4. close% - close in % of position (futures) or sell (spot) coins in % for current quantity
quantity (float) : (float) orders size, see at 'type_qty'. Размер ордера, базы или % в соответствии с 'type_qty'
price (float) : (float) price for limit order by 'side'. Цена лимитного ордера
tp_price (float) : (float) price for take profit order.
sl_price (float) : (float) price for stoploss order
leverageforqty (bool) : (bool) use leverage in qty. Использовать плечо при расчете количества или нет.
Returns: Set multi order for Bybit : limit + takeprofit + stoploss.
long_stop(token, market, ticker_id, type_qty, quantity, l_stop, leverageforqty)
Stop market order for long position
Рыночный стоп-ордер на продажу для закрытия лонговой позиции.
Parameters:
token (string)
market (string) : (string) 'binance' , 'binancefru' etc.. Строковая переменная названия биржи.
ticker_id (string) : (string) ticker in market ('btcusdt', 'ethusdt' etc...). Строковая переменная названия тикера (пары).
type_qty (string) : (string) type of quantity: 1. 'qty' or '' or na - standart (in coins), 2. 'quqty'- in assets (usdt,btc,etc..), 3.open% - open position(futures) or buy (spot) in % of base 4. close% - close in % of position (futures) or sell (spot) coins in % for current quantity
quantity (float) : (float) orders size. Размер ордера.
l_stop (float) : (float) price for activation stop order. Цена активации стоп-ордера.
leverageforqty (bool) : (bool) use leverage in qty. Использовать плечо при расчете количества или нет.
Returns: 'Stop Market Sell order (close long position)'. Маркетный стоп-ордер на снижения/закрытия текущего лонга.
short_stop(token, market, ticker_id, type_qty, quantity, s_stop, leverageforqty)
Stop market order for short position
Рыночный стоп-ордер на покупку(в лонг) для закрытия шорт позиции.
Parameters:
token (string)
market (string) : (string) 'binance' , 'binancefru' etc.. Строковая переменная названия биржи.
ticker_id (string) : (string) ticker in market ('btcusdt', 'ethusdt' etc...). Строковая переменная названия тикера (пары).
type_qty (string) : (string) type of quantity: 1. 'qty' or '' or na - standart (in coins), 2. 'quqty'- in assets (usdt,btc,etc..), 3.open% - open position(futures) or buy (spot) in % of base 4. close% - close in % of position (futures) or sell (spot) coins in % for current quantity
quantity (float) : (float) orders size. Размер ордера.
s_stop (float) : (float) price for activation stop order. Цена активации стоп-ордера.
leverageforqty (bool) : (bool) use leverage in qty. Использовать плечо при расчете количества или нет.
Returns: 'Stop Market Buy order (close short position)'. Маркетный стоп-ордер на снижения/закрытия текущего шорта.
change_stop_l(token, market, ticker_id, type_qty, quantity, l_stop, leverageforqty)
Change Stop market order for long position
Изменяем стоп-ордер на продажу(в шорт) для закрытия лонг позиции.
Parameters:
token (string)
market (string) : (string) 'binance' , 'binancefru' etc.. Строковая переменная названия биржи.
ticker_id (string) : (string) ticker in market ('btcusdt', 'ethusdt' etc...). Строковая переменная названия тикера (пары).
type_qty (string) : (string) type of quantity: 1. 'qty' or '' or na - standart (in coins), 2. 'quqty'- in assets (usdt,btc,etc..), 3.open% - open position(futures) or buy (spot) in % of base 4. close% - close in % of position (futures) or sell (spot) coins in % for current quantity
quantity (float) : (float) orders size. Размер ордера.
l_stop (float) : (float) price for activation stop order. Цена активации стоп-ордера.
leverageforqty (bool) : (bool) use leverage in qty. Использовать плечо при расчете количества или нет.
Returns: 'Change Stop Market Buy order (close long position)'. Смещает цену активации Маркетного стоп-ордер на снижения/закрытия текущего лонга.
change_stop_s(token, market, ticker_id, type_qty, quantity, s_stop, leverageforqty)
Change Stop market order for short position
Смещает цену активации Рыночного стоп-ордера на покупку(в лонг) для закрытия шорт позиции.
Parameters:
token (string)
market (string) : (string) 'binance' , 'binancefru' etc.. Строковая переменная названия биржи.
ticker_id (string) : (string) ticker in market ('btcusdt', 'ethusdt' etc...). Строковая переменная названия тикера (пары).
type_qty (string)
quantity (float) : (float) orders size. Размер ордера.
s_stop (float) : (float) price for activation stop order. Цена активации стоп-ордера.
leverageforqty (bool) : (bool) use leverage in qty. Использовать плечо при расчете количества или нет.
Returns: 'Change Stop Market Buy order (close short position)'. Смещает цену активации Маркетного стоп-ордер на снижения/закрытия текущего шорта.
open_long_position(token, market, ticker_id, type_qty, quantity, l_stop, leverageforqty)
Cancel and close all orders and positions by ticker , then open Long position by market price with stop order
Отменяет все лимитки и закрывает все позы по тикеру, затем открывает лонг по маркету с выставлением стопа (переворот позиции, при необходимости).
Parameters:
token (string)
market (string) : (string) 'binance' , 'binancefru' etc.. Строковая переменная названия биржи.
ticker_id (string) : (string) ticker in market ('btcusdt', 'ethusdt' etc...). Строковая переменная названия тикера (пары).
type_qty (string) : (string) type of quantity: 1. 'qty' or '' or na - standart (in coins), 2. 'quqty'- in assets (usdt,btc,etc..), 3.open% - open position(futures) or buy (spot) in % of base 4. close% - close in % of position (futures) or sell (spot) coins in % for current quantity
quantity (float) : (float) orders size. Размер ордера.
l_stop (float) : (float). Price for activation stop loss. Цена активации стоп-лосса.
leverageforqty (int) : (bool) use leverage in qty. Использовать плечо при расчете количества или нет.
Returns: 'command_all_close + LongMarket + long_stop.
open_short_position(token, market, ticker_id, type_qty, quantity, s_stop, leverageforqty)
Cancel and close all orders and positions , then open Short position by market price with stop order
Отменяет все лимитки и закрывает все позы по тикеру, затем открывает шорт по маркету с выставлением стопа(переворот позиции, при необходимости).
Parameters:
token (string)
market (string) : (string) 'binance' , 'binancefru' etc.. Строковая переменная названия биржи.
ticker_id (string) : (string) ticker in market ('btcusdt', 'ethusdt' etc...). Строковая переменная названия тикера (пары).
type_qty (string) : (string) type of quantity: 1. 'qty' or '' or na - standart (in coins), 2. 'quqty'- in assets (usdt,btc,etc..), 3.open% - open position(futures) or buy (spot) in % of base 4. close% - close in % of position (futures) or sell (spot) coins in % for current quantity
quantity (float) : (float) orders size. Размер ордера.
s_stop (float) : (float). Price for activation stop loss. Цена активации стоп-лосса.
leverageforqty (int) : (bool) use leverage in qty. Использовать плечо при расчете количества или нет.
Returns: 'command_all_close + ShortMarket + short_stop'.
open_long_trade(token, market, ticker_id, type_qty, quantity, l_stop, qty_ex1, price_ex1, qty_ex2, price_ex2, qty_ex3, price_ex3, leverageforqty)
Cancell and close all orders and positions , then open Long position by market price with stop order and take 1 ,take 2, take 3
Отменяет все лимитки и закрывает все позы по тикеру, затем открывает лонг по маркету с выставлением стопа и 3 тейками (переворот позиции, при необходимости).
Parameters:
token (string)
market (string) : (string) 'binance' , 'binancefru' etc.. Строковая переменная названия биржи.
ticker_id (string) : (string) ticker in market ('btcusdt', 'ethusdt' etc...). Строковая переменная названия тикера (пары).
type_qty (string) : (string) type of quantity: 1. 'qty' or '' or na - standart (in coins), 2. 'quqty'- in assets (usdt,btc,etc..), 3.open% - open position(futures) or buy (spot) in % of base 4. close% - close in % of position (futures) or sell (spot) coins in % for current quantity
quantity (float) : (float) enter order size, see at type_qty. Размер ордера входа, согласно type_qty.
l_stop (float) : (float). Price for activation stop loss. Цена активации стоп-лосса.
qty_ex1 (float) : (float). Quantity for 1th take see at type_qty, if = 0 string for order dont set. Размер лимитного ордера для 1го тейка, согласно type_qty.. Если 0, то строка для этого тейка не формируется
price_ex1 (float) : (float). Price for 1th take , if = 0 string for order dont set. Цена лимитного ордера для 1го тейка. Если 0, то строка для этого тейка не формируется
qty_ex2 (float) : (float). Quantity for 2th take see at type_qty, if = 0 string for order dont set. Размер лимитного ордера для 2го тейка, согласно type_qty..Если 0, то строка для этого тейка не формируется
price_ex2 (float) : (float). Price for 2th take, if = 0 string for order dont set. Цена лимитного ордера для 2го тейка. Если 0, то строка для этого тейка не формируется
qty_ex3 (float) : (float). Quantity for 3th take see at type_qty, if = 0 string for order dont set. Размер лимитного ордера для 2го тейка, согласно type_qty..Если 0, то строка для этого тейка не формируется
price_ex3 (float) : (float). Price for 3th take, if = 0 string for order dont set. Цена лимитного ордера для 3го тейка. Если 0, то строка для этого тейка не формируется
leverageforqty (int)
Returns: 'cancel_all_close + LongMarket + long_stop + CloseLongLimit1 + CloseLongLimit2+CloseLongLimit3'.
open_short_trade(token, market, ticker_id, type_qty, quantity, s_stop, qty_ex1, price_ex1, qty_ex2, price_ex2, qty_ex3, price_ex3, leverageforqty)
Cancell and close all orders and positions , then open Short position by market price with stop order and take 1 and take 2
Отменяет все лимитки и закрывает все позы по тикеру, затем открывает шорт по маркету с выставлением стопа и 3 тейками (переворот позиции, при необходимости).
Parameters:
token (string)
market (string) : (string) 'binance' , 'binancefru' etc.. Строковая переменная названия биржи.
ticker_id (string) : (string) ticker in market ('btcusdt', 'ethusdt' etc...). Строковая переменная названия тикера (пары).
type_qty (string)
quantity (float)
s_stop (float) : (float). Price for activation stop loss. Цена активации стоп-лосса.
qty_ex1 (float) : (float). Quantity for 1th take see at type_qty, if = 0 string for order dont set. Размер лимитного ордера для 1го тейка, согласно type_qty.. Если 0, то строка для этого тейка не формируется
price_ex1 (float) : (float). Price for 1th take , if = 0 string for order dont set. Цена лимитного ордера для 1го тейка. Если 0, то строка для этого тейка не формируется
qty_ex2 (float) : (float). Quantity for 2th take see at type_qty, if = 0 string for order dont set. Размер лимитного ордера для 2го тейка, согласно type_qty..Если 0, то строка для этого тейка не формируется
price_ex2 (float) : (float). Price for 2th take, if = 0 string for order dont set. Цена лимитного ордера для 2го тейка. Если 0, то строка для этого тейка не формируется
qty_ex3 (float) : (float). Quantity for 3th take see at type_qty, if = 0 string for order dont set. Размер лимитного ордера для 2го тейка, согласно type_qty..Если 0, то строка для этого тейка не формируется
price_ex3 (float) : (float). Price for 3th take, if = 0 string for order dont set. Цена лимитного ордера для 3го тейка. Если 0, то строка для этого тейка не формируется
leverageforqty (int)
Returns: 'command_all_close + ShortMarket + short_stop + CloseShortLimit + CloseShortLimit(2)'.
Multi_LongLimit(token, market, ticker_id, type_qty, qty1, price1, qty2, price2, qty3, price3, qty4, price4, qty5, price5, qty6, price6, qty7, price7, qty8, price8, leverageforqty)
8 or less Buy orders with limit price and quantity.
До 8 Лимитных ордеров на покупку(в лонг).
Parameters:
token (string) : (integer or 0) token for trade in system, if = 0 then token part mess is empty. Токен, При значениb = 0 не включается в формирование строки алерта.
market (string) : (string) Spot 'binance' , 'bybit' . Futures ('binancefru','binancefro','bybitfu', 'bybitfi'). Строковая переменная названия биржи.
ticker_id (string) : (string) ticker in market ('btcusdt', 'ethusdt' etc...). Строковая переменная названия тикера (пары).
type_qty (string) : (string) type of quantity: 1. 'qty' or '' or na - standart (in coins), 2. 'quqty'- in assets (usdt,btc,etc..), 3.open% - open position(futures) or buy (spot) in % of base 4. close% - close in % of position (futures) or sell (spot) coins in % for current quantity
qty1 (float)
price1 (float)
qty2 (float)
price2 (float)
qty3 (float)
price3 (float)
qty4 (float)
price4 (float)
qty5 (float)
price5 (float)
qty6 (float)
price6 (float)
qty7 (float)
price7 (float)
qty8 (float)
price8 (float)
leverageforqty (bool) : (bool) use leverage in qty. Использовать плечо при расчете количества или нет.
Returns: 'Limit Buy order'. Лимитный ордер на покупку (лонг).
Open Interest Profile (OI)- By LeviathanThis script implements the concept of Open Interest Profile, which can help you analyze the activity of traders and identify the price levels where they are opening/closing their positions. This data can serve as a confluence for finding the areas of support and resistance , targets and placing stop losses. OI profiles can be viewed in the ranges of days, weeks, months, Tokyo sessions, London sessions and New York sessions.
A short introduction to Open Interest
Open Interest is a metric that measures the total amount of open derivatives contracts in a specific market at a given time. A valid contract is formed by both a buyer who opens a long position and a seller who opens a short position. This means that OI represents the total value of all open longs and all open shorts, divided by two. For example, if Open Interest is showing a value of $1B, it means that there is $1B worth of long and $1B worth of short contracts currently open/unsettled in a given market.
OI increasing = new long and short contracts are entering the market
OI decreasing = long and short contracts are exiting the market
OI unchanged = the net amount of positions remains the same (no new entries/exits or just a transfer of contracts occurring)
About this indicator
*This script is basically a modified version of my previous "Market Sessions and Volume Profile by @LeviathanCapital" indicator but this time, profiles are generated from Tradingview Open Interest data instead of volume (+ some other changes).
The usual representation of OI shows Open Interest value and its change based on time (for a particular day, time frame or each given candle). This indicator takes the data and plots it in a way where you can see the OI activity (change in OI) based on price levels. To put it simply, instead of observing WHEN (time) positions are entering/exiting the market, you can now see WHERE (price) positions are entering/exiting the market. This is the same concept as when it comes to Volume and Volume profile and therefore, similar strategies and ways of understanding the given data can be applied here. You can even combine the two to gain an edge (eg. high OI increase + Volume Profile showing dominant market selling = possible aggressive shorts taking place)
Green nodes = OI increase
Red nodes = OI decrease
A cluster of large green nodes can be used for support and resistance levels (*trapped traders theory) or targets (lots of liquidations and stop losses above/below), OI Profile gaps can present an objective for the price to fill them (liquidity gaps, imbalances, inefficiencies, etc), and more.
Indicator settings
1. Session/Lookback - Choose the range from where the OI Profile will be generated
2. OI Profile Mode - Mode 1 (shows only OI increase), Mode 2 (shows both OI increase and decrease), Mode 3 (shows OI decrease on left side and OI increase on the right side).
3. Show OI Value Area - Shows the area where most OI activity took place (useful as a range or S/R level )
4. Show Session Box - Shows the box around chosen sessions/lookback
5. Show Profile - Show/hide OI Profile
6. Show Current Session - Show/hide the ongoing session
7. Show Session Labels - Show/hide the text labels for each session
8. Resolution - The higher the value, the more refined a profile is, but fewer profiles are shown on the chart
9. OI Value Area % - Choose the percentage of VA (same as in Volume Profile's VA)
10. Smooth OI Data - Useful for assets that have very large spikes in OI over large bars, helps create better profiles
11. OI Increase - Pick the color of OI increase nodes in the profile
12. OI Decrease - Pick the color of OI decrease nodes in the profile
13. Value Area Box - Pick the color of the Value Area Box
14. Session Box Thickness - Pick the thickness of the lines surrounding the chosen sessions
Advice
The indicator calculates the profile based on candles - the more candles you can show, the better profile will be formed. This means that it's best to view most sessions on timeframes like 15min or lower. The only exception is the Monthly profile, where timeframes above 15min should be used. Just take a few minutes and switch between timeframes and sessions and you will figure out the optimal settings.
This is the first version of Open Interest Profile script so please understand that it will be improved in future updates.
Thank you for your support.
** Some profile generation elements are inspired by @LonesomeTheBlue's volume profile script
Ultimate Strategy Template (Advanced Edition)Hello traders
This script is an upgraded version of that one below
New features
- Upgraded to Pinescript version 5
- Added the exit SL/TP now in real-time
- Added text fields for the alerts - easier to send the commands to your trading bots
Step 1: Create your connector
Adapt your indicator with only 2 lines of code and then connect it to this strategy template.
For doing so:
1) Find in your indicator where are the conditions printing the long/buy and short/sell signals.
2) Create an additional plot as below
I'm giving an example with a Two moving averages cross.
Please replicate the same methodology for your indicator wether it's a MACD , ZigZag , Pivots , higher-highs, lower-lows or whatever indicator with clear buy and sell conditions.
//@version=5
indicator(title='Moving Average Cross', shorttitle='Moving Average Cross', overlay=true, precision=6, max_labels_count=500, max_lines_count=500)
type_ma1 = input.string(title='MA1 type', defval='SMA', options= )
length_ma1 = input(10, title=' MA1 length')
type_ma2 = input.string(title='MA2 type', defval='SMA', options= )
length_ma2 = input(100, title=' MA2 length')
// MA
f_ma(smoothing, src, length) =>
rma_1 = ta.rma(src, length)
sma_1 = ta.sma(src, length)
ema_1 = ta.ema(src, length)
iff_1 = smoothing == 'EMA' ? ema_1 : src
iff_2 = smoothing == 'SMA' ? sma_1 : iff_1
smoothing == 'RMA' ? rma_1 : iff_2
MA1 = f_ma(type_ma1, close, length_ma1)
MA2 = f_ma(type_ma2, close, length_ma2)
// buy and sell conditions
buy = ta.crossover(MA1, MA2)
sell = ta.crossunder(MA1, MA2)
plot(MA1, color=color.new(color.green, 0), title='Plot MA1', linewidth=3)
plot(MA2, color=color.new(color.red, 0), title='Plot MA2', linewidth=3)
plotshape(buy, title='LONG SIGNAL', style=shape.circle, location=location.belowbar, color=color.new(color.green, 0), size=size.normal)
plotshape(sell, title='SHORT SIGNAL', style=shape.circle, location=location.abovebar, color=color.new(color.red, 0), size=size.normal)
/////////////////////////// SIGNAL FOR STRATEGY /////////////////////////
Signal = buy ? 1 : sell ? -1 : 0
plot(Signal, title='🔌Connector🔌', display = display.data_window)
Basically, I identified my buy, sell conditions in the code and added this at the bottom of my indicator code
Signal = buy ? 1 : sell ? -1 : 0
plot(Signal, title="🔌Connector🔌", transp=100)
Important Notes
🔥 The Strategy Template expects the value to be exactly 1 for the bullish signal, and -1 for the bearish signal
Now you can connect your indicator to the Strategy Template using the method below or that one
Step 2: Connect the connector
1) Add your updated indicator to a TradingView chart
2) Add the Strategy Template as well to the SAME chart
3) Open the Strategy Template settings and in the Data Source field select your 🔌Connector🔌 (which comes from your indicator)
From then, you should start seeing the signals and plenty of other stuff on your chart
🔥 Note that whenever you'll update your indicator values, the strategy statistics and visual on your chart will update in real-time
Settings
- Color Candles: Color the candles based on the trade state ( bullish , bearish , neutral)
- Close positions at market at the end of each session: useful for everything but cryptocurrencies
- Session time ranges: Take the signals from a starting time to an ending time
- Close Direction: Choose to close only the longs, shorts, or both
- Date Filter: Take the signals from a starting date to an ending date
- Set the maximum losing streak length with an input
- Set the maximum winning streak length with an input
- Set the maximum consecutive days with a loss
- Set the maximum drawdown (in % of strategy equity)
- Set the maximum intraday loss in percentage
- Limit the number of trades per day
- Limit the number of trades per week
- Stop-loss: None or Percentage or Trailing Stop Percentage or ATR - I'll add shortly multiple options for the trailing stop loss
- Take-Profit: None or Percentage or ATR - I'll add also a trailing take profit
- Risk-Reward based on ATR multiple for the Stop-Loss and Take-Profit
Special Thanks
Special thanks to @JosKodify as I borrowed a few risk management snippets from his website: kodify.net
Best
Dave
BB Signal v2.1 [ABA Invest]About
This signal appears based on 2nd candle break out of Bollinger Bands (called Momentum) with additional EMA 50 and EMA 200 as trend filters. so the concept is to take advantage of candle breakout by following trends.
How to use
Buy: When signal 'Buy' appears (following trend of upper timeframe)
Recommended stop loss: previous swing low
Sell: When signal 'Sell' appears (following trend of upper timeframe)
Recommended stop loss: previous swing high
Rules
1. use a good risk-reward ratio (minimum 1.5)
2. Please do backtest before using this signal
3. Don't always take every signal (must know when to stop)
Take Profit On Trend v2 (by BHD_Trade_Bot)The purpose of strategy is to detect long-term uptrend and short-term downtrend so that you can easy to take profit.
The strategy also using BHD unit to detect how big you win and lose, so that you can use this strategy for all coins without worry about it have different percentage of price change.
ENTRY
The buy order is placed on assets that have long-term uptrend and short-term downtrend:
- Long-term uptrend condition: ema200 is going up
- Short-term downtrend condition: 2 last candles are down price (use candlestick for less delay)
CLOSE
The sell order is placed when take profit or stop loss:
- Take profit: price increase 2 BHD unit
- Stop loss: price decrease 3 BHD unit
The strategy use $1000 for initial capital and trading fee is 0.1% for each order.
Pro tip: The 1-hour time frame for ETH/USDT has the best results on average.
CHN BUY SELLCHN BUY SELL is formed from two RSI indicators, those are RSI 14 and RSI 7 . I use RSI 14 to determine the trend and RSI 7 to find entry points.
+ Long (BUY) Signal:
- RSI 14 will give a "BUY" signal, then RSI 7 will give entry point to LONG when the candle turns yellow.
+ Short (SELL) Signal:
- RSI 14 will give a "EXIT" signal, then RSI 7 will give entry point to SHORT when the candle turns purple.
+ About Take Profit and Stop Loss:
- With Gold, I usually set Stop Loss and Take Profit at 50 pips
- With currency pairs, I usually keep my Stop Loss and Take Profit at 30 pips
- With crypto, I usually keep Stop Loss and Take Profit at 1.5%
Recommended to use in time frame M15 and above .
This method can be used to trade Forex, Gold and Crypto.
My idea is formed on the view that when the price is moving strongly, the RSI 14 will tell us what the current trend is through a "BUY" or "EXIT" signal. When RSI 14 reaches the oversold area it will form a "BUY" signal and when it reaches the overbought area it will give an "EXIT" signal. I believe that when the price reaches the oversold or overbought area, the price momentum has also decreased and is about to reverse.
After receiving a signal from RSI 14, my job is to wait for an Entry signal from RSI 7. When RSI 7 reaches the overbought area, a yellow candle will appear and that's when we enter a LONG order. When the RSI 7 reaches the oversold area, a purple candle will appear and that's when we enter a SHORT order.
Big Snapper Alerts R3.0 + Chaiking Volatility condition + TP RSI//@version=5
//
// Bannos
// #NotTradingAdvice #DYOR
// Disclaimer.
// I AM NOT A FINANCIAL ADVISOR.
// THESE IDEAS ARE NOT ADVICE AND ARE FOR EDUCATION PURPOSES ONLY.
// ALWAYS DO YOUR OWN RESEARCH
//
// Author: Adaptation from JustUncleL Big Snapper by Bannos
// Date: May-2022
// Version: R1.0
//Description of this addon - Script using several new conditions to give Long/short and SL levels which was not proposed in the Big Snapper strategy "Big Snapper Alerts R3.0"
//"
//This strategy is based on the use of the Big Snapper outputs from the JustUncleL script and the addition of several conditions to define filtered conditions selecting signal synchrones with a trend and a rise of the volatility.
//Also the strategy proposes to define proportional stop losses and dynamic Take profit using an RSI strategy.
// After delivering the temporary ong/short signal and ploting a green or purple signal, several conditions are defined to consider a Signal is Long or short.
//Let s take the long signal as example(this is the same process with the opposite values for a short).
//step 1 - Long Definition:
// Snapper long signal stored in the buffer variable Longbuffer to say that in a close future, we could have all conditions for a long
// Now we need some conditions to combine with it:
//the second one is to be over the Ma_medium(55)
//and because this is not selective enough, the third one is a Volatility indicator "Chaikin Volatility" indicator giving an indication about the volatility of the price compared to the 10 last values
// -> Using the volatility indicator gives the possibility to increase the potential rise if the volatility is higher compared to the last periods.
//With these 3 signals, we get a robust indication about a potential long signal which is then stored in the variable "Longe"
//Now we have a long signal and can give a long signal with its Stop Loss
// The Long Signal is automatically given as the 3 conditions above are satisfied.
// The Stop loss is a function of the last Candle sizes giving a stop below the 70% of the overall candle which can be assimilated to a Fibonacci level. Below this level it makes sense to stop the trade as the chance to recover the complete Candle is more than 60%
//Now we are in an open Long and can use all the mentioned Stop loss condition but still need a Take Profit condition
//The take profit condition is based on a RSI strategy consisting in taking profit as soon as the RSI come back from the overbought area (which is here defined as a rsi over 70) and reaching the 63.5 level to trigger the Take Profit
//This TP condition is only active when Long is active and when an entry value as been defined.
//Entry and SL level appreas as soon as a Long or short arrow signal does appears. The Take profit will be conidtioned to the RSI.
//The final step in the cycle is a reinitialization of all the values giving the possibility to detect and treat any long new signal coming from the Big Snapper signal.
