Discount/Premium OTE LevelsThis indicator is created to identify discount/premium areas to provide additional confluence to trades taken. The underlying theory is that the trades taken in discounted areas are likely to have less risk due to a smaller stop loss and a higher reward/risk ratio.
The indicator operates by first identifying a zone between the last major swing high and low. These highs and lows are determined as price points that at the extremes within the number of bars to the left, as defined by the "Swing Sensitivity" setting.
Once a price zone is established, the indicator verifies that the zone meets the minimum size in points as configured via the "Minimum size" setting to be considered tradable. Zones that are too small may not provide a sufficient range even for scalping. The default value is 42 points based on Nasdaq, which means that the distance between inner most OTE levels (0.382 and 0.618) is at least 10 points.
When a valid zone is identified, it is then subdivided into areas of interest based on OTE levels, which can be configured/adjusted via the "Levels to Draw" setting. These levels represent the midpoint (50%), which distinguishes between premium and discount, and the three OTE levels 0.79, 0.705, 0.618, above the 50% for discount and below the 50% for premium.
For example, if a zone is formed initially by a swing low followed by a swing high with the assumption that the draw is higher, the indicator can be used to formulate long positions from below the 50% level starting at 0.38 OTE level, or ideally at 0.295 OTE level using 0 as a stop loss. Alternatively, if the 50% level is not yet tapped, short scalp positions can be made from 0.79-0.618 OTE levels with 50% as a partial or TP target.
See for long/short example
Typically, the indicator will show only a single zone. However, there may be cases with two zones: one larger parent zone containing a smaller, valid price zone within itself.
The indicator will automatically invalidate and remove the zone once the high/low of the zone is invalidated.
Configuration:
The indicator provides several visualization options for customization, including:
Color settings for OTE levels, with separate settings for edge/50% color, premium, and discount levels.
Settings for line style for OTE levels.
Settings to determine whether to show prices on level labels.
Settings to decide if lines should be extended to the right.
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Ultimate T3 Fibonacci for BTC Scalping. Look at backtest report!Hey Everyone!
I created another script to add to my growing library of strategies and indicators that I use for automated crypto trading! This strategy is for BITCOIN on the 30 minute chart since I designed it to be a scalping strategy. I calculated for trading fees, and use a small amount of capital in the backtest report. But feel free to modify the capital and how much per order to see how it changes the results:)
It is called the "Ultimate T3 Fibonacci Indicator by NHBprod" that computes and displays two T3-based moving averages derived from price data. The t3_function calculates the Tilson T3 indicator by applying a series of exponential moving averages to a combined price metric and then blending these results with specific coefficients derived from an input factor.
The script accepts several user inputs that toggle the use of the T3 filter, select the buy signal method, and set parameters like lengths and volume factors for two variations of the T3 calculation. Two T3 lines, T3 and T32, are computed with different parameters, and their colors change dynamically (green/red for T3 and blue/purple for T32) based on whether the lines are trending upward or downward. Depending on the selected signal method, the script generates buy signals either when T32 crosses over T3 or when the closing price is above T3, and similarly, sell signals are generated on the respective conditions for crossing under or closing below. Finally, the indicator plots the T3 lines on the chart, adds visual buy/sell markers, and sets alert conditions to notify users when the respective trading signals occur.
The user has the ability to tune the parameters using TP/SL, date timerames for analyses, and the actual parameters of the T3 function including the buy/sell signal! Lastly, the user has the option of trading this long, short, or both!
Let me know your thoughts and check out the backtest report!
Ultimate Stochastics Strategy by NHBprod Use to Day Trade BTCHey All!
Here's a new script I worked on that's super simple but at the same time useful. Check out the backtest results. The backtest results include slippage and fees/commission, and is still quite profitable. Obviously the profitability magnitude depends on how much capital you begin with, and how much the user utilizes per order, but in any event it seems to be profitable according to backtests.
This is different because it allows you full functionality over the stochastics calculations which is designed for random datasets. This script allows you to:
Designate ANY period of time to analyze and study
Choose between Long trading, short trading, and Long & Short trading
It allows you to enter trades based on the stochastics calculations
It allows you to EXIT trades using the stochastics calculations or take profit, or stop loss, Or any combination of those, which is nice because then the user can see how one variable effects the overall performance.
As for the actual stochastics formula, you get control, and get to SEE the plot lines for slow K, slow D, and fast K, which is usually not considered.
You also get the chance to modify the smoothing method, which has not been done with regular stochastics indicators. You get to choose the standard simple moving average (SMA) method, but I also allow you to choose other MA's such as the HMA and WMA.
Lastly, the user gets the option of using a custom trade extender, which essentially allows a buy or sell signal to exist for X amount of candles after the initial signal. For example, you can use "max bars since signal" to 1, and this will allow the indicator to produce an extra sequential buy signal when a buy signal is generated. This can be useful because it is possible that you use a small take profit (TP) and quickly exit a profitable trade. With the max bars since signal variable, you're able to reenter on the next candle and allow for another opportunity.
Let me know if you have any questions! Please take a look at the performance report and let me know your thoughts! :)
9-20 EMA Crossover with TP and SL9-20 EMA Crossover: This script tracks the crossover of the 9-period EMA and the 20-period EMA.
When the 9 EMA crosses above the 20 EMA, a buy signal is triggered.
When the 9 EMA crosses below the 20 EMA, a sell signal is triggered.
Take Profit and Stop Loss Levels:
The take profit for a long position is set at 3% above the entry price (close * 1.03).
The stop loss for a long position is set at 1% below the entry price (close * 0.99).
The take profit for a short position is set at 3% below the entry price (close * 0.97).
The stop loss for a short position is set at 1% above the entry price (close * 1.01).
Leverage: The strategy uses 20x leverage for both long and short positions (leverage=20).
Alerts: Alerts are set up for the buy signal when the 9 EMA crosses above the 20 EMA and the sell signal when the 9 EMA crosses below the 20 EMA. These alerts can be used with a webhook to trigger trades on Binance Futures.
Strategy:
For long trades: The strategy enters a long position and sets a take profit at 3% above the entry price and a stop loss at 1% below the entry price.
For short trades: The strategy enters a short position and sets a take profit at 3% below the entry price and a stop loss at 1% above the entry price.
Futures Engulfing Candle Size Strategy (Ticks, TP/SL)The Futures Candle Size Strategy is designed to identify and trade significant price movements in the futures market based on candle size. It is optimized for futures instruments like ES, NQ, or CL, where precise tick-level calculations are essential. The strategy includes a customizable take profit and stop loss in ticks and operates only within a specified time window (e.g., 7:00 AM to 9:15 AM CST).
Key Features:
Candle Size Threshold: Trades are triggered when the candle's high-to-low range exceeds the defined threshold in ticks.
Time Filter: Limits trades to the most active market hours, specifically between 7:00 AM and 9:15 AM CST.
Take Profit and Stop Loss: Customizable exit levels in ticks to manage risk and lock in profits.
Long and Short Trades: Automatically places buy or sell orders based on the candle's direction (bullish or bearish).
Alerts: Sends alerts whenever a trade is triggered, helping you stay informed in real-time.
How It Works:
The strategy calculates the size of each candle in ticks and compares it to the user-defined threshold.
If the candle size meets or exceeds the threshold within the specified time range, it triggers a long or short trade.
The trade automatically exits when the price hits the take profit or stop loss levels.
IU Trailing Stop Loss MethodsThe 'IU Trailing Stop Loss Methods' it's a risk management tool which allows users to apply 12 trailing stop-loss (SL) methods for risk management of their trades and gives live alerts when the trailing Stop loss has hit. Below is a detailed explanation of each input and the working of the Script.
Main Inputs:
- bar_time: Specifies the date from which the trade begins and entry price will be the open of the first candle.
- entry_type: Choose between 'Long' or 'Short' positions.
- trailing_method: Select the trailing stop-loss method. Options include ATR, Parabolic SAR, Supertrend, Point/Pip based, Percentage, EMA, Highest/Lowest, Standard Deviation, and multiple target-based methods.
- exit_after_close: If checked, exits the trade only after the candle closes.
Optional Inputs:
ATR Settings:
- atr_Length: Length for the ATR calculation.
- atr_factor: ATR multiplier for SL calculation.
Parabolic SAR Settings:
- start, increment, maximum: Parameters for the Parabolic SAR indicator.
Supertrend Settings:
- supertrend_Length, supertrend_factor: Length and factor for the Supertrend indicator.
Point/Pip Based:
- point_base: Set trailing SL in points/pips.
Percentage Based:
- percentage_base: Set SL as a percentage of entry price.
EMA Settings:
- ema_Length: Length for EMA calculation.
Standard Deviation Settings:
- std_Length, std_factor: Length and factor for standard deviation calculation.
Highest/Lowest Settings:
- highest_lowest_Length: Length for the highest/lowest SL calculation.
Target-Based Inputs:
- ATR, Point, Percentage, and Standard Deviation based target SL settings with customizable lengths and multipliers.
Entry Logic:
- Trades initiate based on the entry_type selected and the specified bar_time.
- If Long is selected, a long trade is initiated when the conditions match, and vice versa for Short.
Trailing Stop-Loss (SL) Methods Explained:
The strategy dynamically adjusts stop-loss based on the chosen method. Each method has its calculation logic:
- ATR: Stop-loss calculated using ATR multiplied by a user-defined factor.
- Parabolic SAR: Uses the Parabolic SAR indicator for trailing stop-loss.
- Supertrend: Utilizes the Supertrend indicator as the stop-loss line.
- Point/Pip Based: Fixed point-based stop-loss.
- Percentage Based: SL set as a percentage of entry price.
- EMA: SL based on the Exponential Moving Average.
- Highest/Lowest: Uses the highest high or lowest low over a specified period.
- Standard Deviation: SL calculated using standard deviation.
Exit Conditions:
- If exit_after_close is enabled, the position will only close after the candle confirms the stop-loss hit.
- If exit_after_close is disabled, the strategy will close the trade immediately when the SL is breached.
Visualization:
The script plots the chosen trailing stop-loss method on the chart for easy visualization.
Target-Based Trailing SL Logic:
- When a position is opened, the strategy calculates the initial stop-loss and progressively adjusts it as the price moves favorably.
- Each SL adjustment is stored in an array for accurate tracking and visualization.
Alerts and Labels:
- When the Entry or trailing stop loss is hit this scripts draws a label and give alert to the user that trailing stop has been hit for the trade.
Note - on the historical data The Script will show nothing if the entry and the exit has happened on the same candle, because we don't know what was hit first SL or TP (basically how the candle was formed on the lower timeframe).
Summary:
This script offers flexible trailing stop-loss options for traders who want dynamic risk management in their strategies. By offering multiple methods like ATR, SAR, Supertrend, and EMA, it caters to various trading styles and risk preferences.
00 Averaging Down Backtest Strategy by RPAlawyer v21FOR EDUCATIONAL PURPOSES ONLY! THE CODE IS NOT YET FULLY DEVELOPED, BUT IT CAN PROVIDE INTERESTING DATA AND INSIGHTS IN ITS CURRENT STATE.
This strategy is an 'averaging down' backtester strategy. The goal of averaging/doubling down is to buy more of an asset at a lower price to reduce your average entry price.
This backtester code proves why you shouldn't do averaging down, but the code can be developed (and will be developed) further, and there might be settings even in its current form that prove that averaging down can be done effectively.
Different averaging down strategies exist:
- Linear/Fixed Amount: buy $1000 every time price drops 5%
- Grid Trading: Placing orders at price levels, often with increasing size, like $1000 at -5%, $2000 at -10%
- Martingale: doubling the position size with each new entry
- Reverse Martingale: decreasing position size as price falls: $4000, then $2000, then $1000
- Percentage-Based: position size based on % of remaining capital, like 10% of available funds at each level
- Dynamic/Adaptive: larger entries during high volatility, smaller during low
- Logarithmic: position sizes increase logarithmically as price drops
Unlike the above average costing strategies, it applies averaging down (I use DCA as a synonym) at a very strong trend reversal. So not at a certain predetermined percentage negative PNL % but at a trend reversal signaled by an indicator - hence it most closely resembles a dynamically moving grid DCA strategy.
Both entering the trade and averaging down assume a strong trend. The signals for trend detection are provided by an indicator that I published under the name '00 Parabolic SAR Trend Following Signals by RPAlawyer', but any indicator that generates numeric signals of 1 and -1 for buy and sell signals can be used.
The indicator must be connected to the strategy: in the strategy settings under 'External Source' you need to select '00 Parabolic SAR Trend Following Signals by RPAlawyer: Connector'. From this point, the strategy detects when the indicator generates buy and sell signals.
The strategy considers a strong trend when a buy signal appears above a very conservative ATR band, or a sell signal below the ATR band. The conservative ATR is chosen to filter ranging markets. This very conservative ATR setting has a default multiplier of 8 and length of 40. The multiplier can be increased up to 10, but there will be very few buy and sell signals at that level and DCA requirements will be very high. Trade entry and DCA occur at these strong trends. In the settings, the 'ATR Filter' setting determines the entry condition (e.g., ATR Filter multiplier of 9), and the 'DCA ATR' determines when DCA will happen (e.g., DCA ATR multiplier of 6).
The DCA levels and DCA amounts are determined as follows:
The first DCA occurs below the DCA Base Deviation% level (see settings, default 3%) which acts as a threshold. The thick green line indicates the long position avg price, and the thin red line below the green line indicates the 3% DCA threshold for long positions. The thick red line indicates the short position avg price, and the thin red line above the thick red line indicates the short position 3% DCA threshold. DCA size multiplier defines the DCA amount invested.
If the loss exceeds 3% AND a buy signal arrives below the lower ATR band for longs, or a sell signal arrives above the upper ATR band for shorts, then the first DCA will be executed. So the first DCA won't happen at 3%, rather 3% is a threshold where the additional condition is that the price must close above or below the ATR band (let's say the first DCA occured at 8%) – this is why the code resembles a dynamic grid strategy, where the grid moves such that alongside the first 3% threshold, a strong trend must also appear for DCA. At this point, the thick green/red line moves because the avg price is modified as a result of the DCA, and the thin red line indicating the next DCA level also moves. The next DCA level is determined by the first DCA level, meaning modified avg price plus an additional +8% + (3% * the Step Scale Multiplier in the settings). This next DCA level will be indicated by the modified thin red line, and the price must break through this level and again break through the ATR band for the second DCA to occur.
Since all this wasn't complicated enough, and I was always obsessed by the idea that when we're sitting in an underwater position for days, doing DCA and waiting for the price to correct, we can actually enter a short position on the other side, on which we can realize profit (if the broker allows taking hedge positions, Binance allows this in Europe).
This opposite position in this strategy can open from the point AFTER THE FIRST DCA OF THE BASE POSITION OCCURS. This base position first DCA actually indicates that the price has already moved against us significantly so time to earn some money on the other side. Breaking through the ATR band is also a condition for entry here, so the hedge position entry is not automatic, and the condition for further DCA is breaking through the DCA Base Deviation (default 3%) and breaking through the ATR band. So for the 'hedge' or rather opposite position, the entry and further DCA conditions are the same as for the base position. The hedge position avg price is indicated by a thick black line and the Next Hedge DCA Level is indicated by a thin black line.
The TPs are indicated by green labels for base positions and red labels for hedge positions.
No SL built into the strategy at this point but you are free to do your coding.
Summary data can be found in the upper right corner.
The fantastic trend reversal indicator Machine learning: Lorentzian Classification by jdehorty can be used as an external indicator, choose 'backtest stream' for the external source. The ATR Band multiplicators need to be reduced to 5-6 when using Lorentz.
The code can be further developed in several aspects, and as I write this, I already have a few ideas 😊
HTF Anchor DotsHigh Time Frame Anchor Dots is designed for traders who use the 15m or 1H chart and want a clear visual of higher-timeframe momentum conditions without constantly switching timeframes. The script builds on the Wave Anchor Indicator concept and is intended to complement the TP Mint Trading Strategy.
Using the momentum waves (similar to VuManChu Cipher B / Market Cipher B), the script identifies “anchor” states on higher timeframes (1H, 4H, Daily) and plots colored dots on your current chart:
• Yellow Dots: TP1 timeframe is anchored (overbought/oversold)
• Red Dots: TP2 timeframe is anchored (overbought/oversold)
• Orange Dots: Both TP1 and TP2 timeframes are simultaneously anchored, indicating a significant confluence.
Dots above the price indicate overbought anchors; dots below indicate oversold anchors. Because the indicator checks these conditions at the close of your entry timeframe candles, you may occasionally see a dot even though the higher-timeframe candle later closes out of anchor. Remember, this indicator offers no direct entry signals; it merely highlights potential confluence or caution zones.
Why Use It?
- Quickly assess if momentum on higher timeframes is stretched in one direction.
- Avoid entering a new position when a higher timeframe has just hit a potential turning point.
- Spot patterns in anchor dot clusters that can refine your trading entries or exits.
If you have questions or suggestions, feel free to share them! The script is open to improvements, and I appreciate all feedback. As with any indicator, this tool is not guaranteed to be an accurate prediction of future price action. Most successful traders combine indicators with sound risk management practices and their own personal analysis.
Fibonacci Trading Strategy (Auto Levels)How It Works
Swing Highs and Lows Detection:
The script identifies the highest high and lowest low over a specified lookback period (default: 50 candles). These points are used as the basis for Fibonacci calculations.
Fibonacci Levels:
Fibonacci retracement levels: 0%, 38.2%, 50%, 61.8%, 78.6%, and 100%.
Fibonacci extension levels: 127.2%, 161.8%, 200%, 261.8%, and 361.8%.
Each level is plotted on the chart with a specific color and labeled with the corresponding price.
Entry Zones:
Pullback Area: Between the 50% and 61.8% retracement levels. This area is highlighted in green, indicating a potential entry for conservative traders.
Full Margin Area: Between the 61.8% and 78.6% retracement levels. This area is highlighted in red, suggesting a higher-risk entry for aggressive traders.
Stop Loss (SL):
The Stop Loss is placed at the 78.6% Fibonacci retracement level. A dotted red line is drawn at this level to provide a visual reference for risk management.
Entry labels include the Stop Loss price for clarity.
Take Profit (TP) Levels:
Multiple take-profit targets are identified using Fibonacci extension levels (127.2%, 161.8%, 200%, 261.8%, and 361.8%).
Each level is labeled with the price and target percentage.
Visual Aids:
The script dynamically labels each Fibonacci level with its corresponding price.
Entry points (Pullback and Full Margin) are marked with clear labels, including the recommended Stop Loss.
Background highlights help distinguish the Pullback and Full Margin areas.
Strategy Highlights
Risk Management:
Incorporates a well-defined Stop Loss at the 78.6% level to limit downside risk.
Multiple take-profit levels help traders scale out of positions gradually.
Automation:
Automatically recalculates levels when new swing highs or lows are detected, ensuring accuracy in dynamic markets.
Customizability:
Users can adjust the lookback period to suit different timeframes or trading styles.
Clarity:
Clean visuals and detailed labels ensure the strategy is easy to interpret and apply.
When to Use
The strategy is suitable for trend-following traders looking to enter during pullbacks in an established trend.
It works best in trending markets where Fibonacci levels often act as strong support or resistance.
Example Scenario
Bullish Setup:
Price retraces to the 50%-61.8% area (Pullback Area) after a swing high.
A buy order is placed in this zone, with the Stop Loss at the 78.6% level.
Profit targets are set at the 127.2%, 161.8%, and higher Fibonacci extensions.
Bearish Setup:
In a downtrend, price retraces upward to the 50%-61.8% zone.
A sell order is placed, with the Stop Loss at the 78.6% level and take-profit levels below.
Follow Through Day (FTD) + Sweep [TrendX_]The Follow Through Day (FTD) + Sweep indicator is a Trend-following tool mixing William O'Neil's original FTD concept and Liquidity concept. This indicator helps you identify potential subsequent bullish trends with greater precision by combining volume analysis, price action, and liquidity concepts.
💎 FEATURES
Follow Through Day Candle (FTD Candle)
The FTD, pioneered by William O'Neil, serves as a reliable signal for identifying the beginning of new bull markets. It's particularly valuable because it combines multiple market factors - price action, volume, and timing - to confirm genuine market reversals rather than temporary bounces.
The power of the FTD lies in its ability to distinguish between ordinary market fluctuations and significant trend changes. By requiring specific criteria to be met across multiple sessions, it helps filter out false signals and identifies high-probability reversal points where institutional investors are likely beginning to accumulate positions.
Sweep Area
The Sweep area feature enhances the traditional FTD concept by incorporating modern liquidity analysis. This overlay identifies zones where large market participants are likely to trigger stop losses before continuing the trend. These areas often represent optimal entry points for traders looking to join the new uptrend with reduced risk.
🔎 BREAKDOWN
FTD Candle
The FTD formation process occurs in two distinct phases: Setup and Completion.
Setup Phase
Strong Market Decline
The market must first experience a significant downtrend
This selling pressure helps clear out weak hands and creates oversold conditions
The decline creates the potential energy for a powerful reversal
First Recovery Session
Marks the initial sign of buying pressure emerging
Often characterized by a strong reversal candle
Represents the first indication that selling pressure may be exhausting
Recovery Confirmation
The second and third days must maintain prices above the new pivot low
This consolidation period helps confirm the validity of the initial bounce
Shows that sellers are no longer in control of price action
Completion Phase:
Supply Test Session
Low volume indicates diminishing selling pressure
Price remains above the pivot low
Creates the foundation for institutional buyers to begin accumulating
Breakout Day
Price increase exceeds average profit of bullish candles
Volume increases by at least 15% compared to previous session
Shows strong institutional commitment to the new uptrend
Timing Window
Must occur between the 4th and 8th candle after First Recovery Session
This specific timing helps confirm the sustainability of the reversal
Based on O'Neil's research of historical market bottoms
FTD Sweep
The Post-FTD Phase introduces the Sweep concept, which is crucial for understanding how large market participants operate. This feature leverages the liquidity concept because institutional traders often need to trigger stop losses to accumulate larger positions at better prices. This helps:
Create liquidity pools for large position entries
Shake out weak hands before continuing the trend
Test the strength of the new trend by absorbing selling pressure
⚙️ USAGE
Sweep + TP & SL Strategy
Example: BTCUSDT (1D) - Replay back to 9th November 2024
After an FTD candle forms, traders can adopt a systematic approach to enhance their trading strategy. First, they should determine the swing range and convert the post-FTD zone into concrete stop loss and take profit levels, which are based on the price action during the FTD formation. Next, traders should wait for a sweep formation, as this indicates that institutional players are accumulating positions. A quick price rejection from the sweep level should be observed before executing an entry.
The reasoning behind this strategy is rooted in market microstructure. By waiting for the sweep, traders position themselves alongside institutional players who need to build large positions without causing adverse price movement. The sweep creates the liquidity they need, and the subsequent move often represents the true trend continuation.
DISCLAIMER
This indicator is not financial advice, it can only help traders make better decisions. There are many factors and uncertainties that can affect the outcome of any endeavor, and no one can guarantee or predict with certainty what will occur. Therefore, one should always exercise caution and judgment when making decisions based on past performance.
IronBot v3Introduction
IronBot V3 is a TradingView indicator that analyzes market trends, identifies potential trading opportunities, and helps manage trades by visualizing entry points, stop-loss levels, and take-profit targets.
How It Works
The indicator evaluates price action within a specified analysis window to determine market trends. It uses Fibonacci retracement levels to identify key price levels for trend detection and trading signals. Based on user-defined inputs, it calculates and displays trade levels, including entry points, stop-loss, and multiple take-profit levels.
Trend Definition:
The highest high and lowest low are calculated over a specified number of candles.
The price range is determined as the difference between the highest high and lowest low.
Three Fibonacci levels are calculated within this range:
- Fib Level 0.236
- Trend Line (0.5 level)
- Fib Level 0.786
Determining Long and Short Conditions:
Long Conditions (Buy):
The closing price must be above both the trend line (0.5 level) and the Fib Level 0.236.
Additionally, the market must not currently be in a bearish trend.
Short Conditions (Sell):
The closing price must be below both the trend line and the Fib Level 0.786.
The market must not currently be in a bullish trend.
Trend State Updates:
When a condition is met, the indicator sets the trend to bullish or bearish and turns off bearish or bullish trend conditions.
If neither buy nor sell conditions are met, the trend remains unchanged, and no new trade signals are generated.
Inputs and Their Role in the Algorithm
General Settings
Analysis Window: Specifies the number of historical candles to analyze. This influences the calculation of key levels such as highs and lows, which are critical for determining Fibonacci retracement levels.
First Trade: Defines the start date for generating trading signals.
Trade Configuration
Display TP/SL: Enables or disables the visualization of take-profit and stop-loss levels on the chart.
Leverage: Defines the leverage applied to trades for risk and position size calculations.
Initial Capital: Specifies the starting capital, which is used for calculating position sizes and profits.
Exchange Fees (%): Sets the percentage of fees applied by the exchange, which is factored into profit calculations.
Country Tax (%): Allows users to define applicable taxes, which are subtracted from net profits.
Stop-Loss Configuration
Break Even: Toggles the break-even functionality. When enabled, the stop-loss level adjusts dynamically as take-profit levels are reached.
Stop Loss (%): Defines the percentage distance from the entry price to the stop-loss level.
Take-Profit Settings
The indicator supports up to four take-profit levels:
- TP1 through TP4 Ratios: Specify the price levels for each take-profit target as a percentage of the entry price.
- Profit Percentages: Allocate a percentage of the position size to each take-profit level.
Visualization Elements
Trend Indicators: Displays Fibonacci-based trend lines and markers for bullish or bearish conditions.
Trade Levels: Entry, stop-loss, and take-profit levels are visualized on the chart by dotted lines for clarity. Additionally, a semi-transparent background is applied when a portion of the trade is closed to enhance visualization. Positive profits from a closed trade are green; otherwise, they are red.
Trade Profit Indicator: On each trade, every time a part of the trade is closed (e.g., take profit is reached), the profit indicator will be updated.
Performance Panel: Summarizes key account statistics, including net balance, profit/loss, and trading performance metrics.
Usage Guidelines
Add the indicator to your TradingView chart.
Configure the input settings based on your trading strategy.
Use the displayed levels and trend signals to make informed trading decisions.
Contact
For further assistance, including automation inquiries, feel free to contact me through TradingView’s messaging system.
Purpose and Disclaimer
IronBot V3 is designed for educational purposes and to assist in analyzing market trends. It is not financial advice, and users should perform their own due diligence before making any trading decisions.
Trading involves significant risk, and past performance is not indicative of future results. Use this indicator responsibly.
Trend Trader-Remastered StrategyOfficial Strategy for Trend Trader - Remastered
Indicator: Trend Trader-Remastered (TTR)
Overview:
The Trend Trader-Remastered is a refined and highly sophisticated implementation of the Parabolic SAR designed to create strategic buy and sell entry signals, alongside precision take profit and re-entry signals based on marked Bill Williams (BW) fractals. Built with a deep emphasis on clarity and accuracy, this indicator ensures that only relevant and meaningful signals are generated, eliminating any unnecessary entries or exits.
Please check the indicator details and updates via the link above.
Important Disclosure:
My primary objective is to provide realistic strategies and a code base for the TradingView Community. Therefore, the default settings of the strategy version of the indicator have been set to reflect realistic world trading scenarios and best practices.
Key Features:
Strategy execution date&time range.
Take Profit Reduction Rate: The percentage of progressive reduction on active position size for take profit signals.
Example:
TP Reduce: 10%
Entry Position Size: 100
TP1: 100 - 10 = 90
TP2: 90 - 9 = 81
Re-Entry When Rate: The percentage of position size on initial entry of the signal to determine re-entry.
Example:
RE When: 50%
Entry Position Size: 100
Re-Entry Condition: Active Position Size < 50
Re-Entry Fill Rate: The percentage of position size on initial entry of the signal to be completed.
Example:
RE Fill: 75%
Entry Position Size: 100
Active Position Size: 50
Re-Entry Order Size: 25
Final Active Position Size:75
Important: Even RE When condition is met, the active position size required to drop below RE Fill rate to trigger re-entry order.
Key Points:
'Process Orders on Close' is enabled as Take Profit and Re-Entry signals must be executed on candle close.
'Calculate on Every Tick' is enabled as entry signals are required to be executed within candle time.
'Initial Capital' has been set to 10,000 USD.
'Default Quantity Type' has been set to 'Percent of Equity'.
'Default Quantity' has been set to 10% as the best practice of investing 10% of the assets.
'Currency' has been set to USD.
'Commission Type' has been set to 'Commission Percent'
'Commission Value' has been set to 0.05% to reflect the most realistic results with a common taker fee value.
ToolsPosLibrary "ToolsPos"
Library for general purpose position helpers
new_pos(state, price, when, index)
Returns new PosInfo object
Parameters:
state (series PosState) : Position state
price (float) : float Entry price
when (int) : int Entry bar time UNIX. Default: time
index (int) : int Entry bar index. Default: bar_index
Returns: PosInfo
new_tp(pos, price, when, index, info)
Returns PosInfo object with new take profit info object
Parameters:
pos (PosInfo) : PosInfo object
price (float) : float Entry price
when (int) : int Entry bar time UNIX. Default: time
index (int) : int Entry bar index. Default: bar_index
info (Info type from aybarsm/Tools/14) : Info holder object. Default: na
Returns: PosInfo
new_re(pos, price, when, index, info)
Returns PosInfo object with new re-entry info object
Parameters:
pos (PosInfo) : PosInfo object
price (float) : float Entry price
when (int) : int Entry bar time UNIX. Default: time
index (int) : int Entry bar index. Default: bar_index
info (Info type from aybarsm/Tools/14) : Info holder object. Default: na
Returns: PosInfo
PosTPInfo
PosTPInfo - Position Take Profit info object
Fields:
price (series float) : float Take profit price
when (series int) : int Take profit bar time UNIX. Default: time
index (series int) : int Take profit bar index. Default: bar_index
info (Info type from aybarsm/Tools/14) : Info holder object
PosREInfo
PosREInfo - Position Re-Entry info object
Fields:
price (series float) : float Re-entry price
when (series int) : int Re-entry bar time UNIX. Default: time
index (series int) : int Take profit bar index. Default: bar_index
info (Info type from aybarsm/Tools/14) : Info holder object
PosInfo
PosInfo - Position info object
Fields:
state (series PosState) : Position state
price (series float) : float Entry price
when (series int) : int Entry bar time UNIX. Default: time
index (series int) : int Entry bar index. Default: bar_index
tp (array) : PosTPInfo Take profit info. Default: na
re (array) : PosREInfo Re-entry info. Default: na
info (Info type from aybarsm/Tools/14) : Info holder object
Wyckoff Trading Strategy for XAU/USD by KAIZVIETNAMXAU/USD TF M15 TP SL 20-30 pip
- Volume: Calculates the average volume based on the SMA to compare with the current trading volume.
- ATR (Average True Range): Calculated to determine price volatility.
- Support and Resistance Levels: Identifies support and resistance levels over the last 10 trading sessions.
Specific Point Identification
- A series of functions are defined to detect critical phases in the market structure, such as:
- Finding Preliminary Support: Recognizing signals of accumulation near support levels.
- Finding Selling Climax: Detecting signals of profit-taking near resistance levels.
- Finding Last Point of Support: Identifying points that provide stability for the price.
- Finding Preliminary Supply: Recognizing supply signals near resistance levels.
- Finding Buying Climax: Identifying strong buy signals accompanied by high trading volume.
- Finding Sign of Weakness: Determining instances of price adjustments that could lead to declines.
Market State Identification
- Accumulation: When the closing price is situated between the support and resistance levels.
- Distribution: When the closing price approaches the highest level of the previous few sessions.
- Sideways: When there is no clear bias toward either an upward or downward trend.
Buy and Sell Signals
- Buy Signals: Determined through finding preliminary support, selling climax, and last point of support.
- Sell Signals: Determined through finding preliminary supply, buying climax, and signs of weakness.
SnowdexUtilsLibrary "SnowdexUtils"
the various function that often use when create a strategy trading.
f_backtesting_date(train_start_date, train_end_date, test_date, deploy_date)
Backtesting within a specific window based on deployment and testing dates.
Parameters:
train_start_date (int) : the start date for training the strategy.
train_end_date (int) : the end date for training the strategy.
test_date (bool) : if true, backtests within the period from `train_end_date` to the current time.
deploy_date (bool) : if true, the strategy backtests up to the current time.
Returns: given time falls within the specified window for backtesting.
f_init_ma(ma_type, source, length)
Initializes a moving average based on the specified type.
Parameters:
ma_type (simple string) : the type of moving average (e.g., "RMA", "EMA", "SMA", "WMA").
source (float) : the input series for the moving average calculation.
length (simple int) : the length of the moving average window.
Returns: the calculated moving average value.
f_init_tp(side, entry_price, rr, sl_open_position)
Calculates the target profit based on entry price, risk-reward ratio, and stop loss. The formula is `tp = entry price + (rr * (entry price - stop loss))`.
Parameters:
side (bool) : the trading side (true for long, false for short).
entry_price (float) : the entry price of the position.
rr (float) : the risk-reward ratio.
sl_open_position (float) : the stop loss price for the open position.
Returns: the calculated target profit value.
f_round_up(number, decimals)
Rounds up a number to a specified number of decimals.
Parameters:
number (float)
decimals (int)
Returns: The rounded-up number.
f_get_pip_size()
Calculates the pip size for the current instrument.
Returns: Pip size adjusted for Forex instruments or 1 for others.
f_table_get_position(value)
Maps a string to a table position constant.
Parameters:
value (string) : String representing the desired position (e.g., "Top Right").
Returns: The corresponding position constant or `na` for invalid values.
Honest Volatility Grid [Honestcowboy]The Honest Volatility Grid is an attempt at creating a robust grid trading strategy but without standard levels.
Normal grid systems use price levels like 1.01;1.02;1.03;1.04... and place an order at each of these levels. In this program instead we create a grid using keltner channels using a long term moving average.
🟦 IS THIS EVEN USEFUL?
The idea is to have a more fluid style of trading where levels expand and follow price and do not stick to precreated levels. This however also makes each closed trade different instead of using fixed take profit levels. In this strategy a take profit level can even be a loss. It is useful as a strategy because it works in a different way than most strategies, making it a good tool to diversify a portfolio of trading strategies.
🟦 STRATEGY
There are 10 levels below the moving average and 10 above the moving average. For each side of the moving average the strategy uses 1 to 3 orders maximum (3 shorts at top, 3 longs at bottom). For instance you buy at level 2 below moving average and you increase position size when level 6 is reached (a cheaper price) in order to spread risks.
By default the strategy exits all trades when the moving average is reached, this makes it a mean reversion strategy. It is specifically designed for the forex market as these in my experience exhibit a lot of ranging behaviour on all the timeframes below daily.
There is also a stop loss at the outer band by default, in case price moves too far from the mean.
What are the risks?
In case price decides to stay below the moving average and never reaches the outer band one trade can create a very substantial loss, as the bands will keep following price and are not at a fixed level.
Explanation of default parameters
By default the strategy uses a starting capital of 25000$, this is realistic for retail traders.
Lot sizes at each level are set to minimum lot size 0.01, there is no reason for the default to be risky, if you want to risk more or increase equity curve increase the number at your own risk.
Slippage set to 20 points: that's a normal 2 pip slippage you will find on brokers.
Fill limit assumtion 20 points: so it takes 2 pips to confirm a fill, normal forex spread.
Commission is set to 0.00005 per contract: this means that for each contract traded there is a 5$ or whatever base currency pair has as commission. The number is set to 0.00005 because pinescript does not know that 1 contract is 100000 units. So we divide the number by 100000 to get a realistic commission.
The script will also multiply lot size by 100000 because pinescript does not know that lots are 100000 units in forex.
Extra safety limit
Normally the script uses strategy.exit() to exit trades at TP or SL. But because these are created 1 bar after a limit or stop order is filled in pinescript. There are strategy.orders set at the outer boundaries of the script to hedge against that risk. These get deleted bar after the first order is filled. Purely to counteract news bars or huge spikes in price messing up backtest.
🟦 VISUAL GOODIES
I've added a market profile feature to the edge of the grid. This so you can see in which grid zone market has been the most over X bars in the past. Some traders may wish to only turn on the strategy whenever the market profile displays specific characteristics (ranging market for instance).
These simply count how many times a high, low, or close price has been in each zone for X bars in the past. it's these purple boxes at the right side of the chart.
🟦 Script can be fully automated to MT5
There are risk settings in lot sizes or % for alerts and symbol settings provided at the bottom of the indicator. The script will send alert to MT5 broker trying to mimic the execution that happens on tradingview. There are always delays when using a bridge to MT5 broker and there could be errors so be mindful of that. This script sends alerts in format so they can be read by tradingview.to which is a bridge between the platforms.
Use the all alert function calls feature when setting up alerts and make sure you provide the right webhook if you want to use this approach.
Almost every setting in this indicator has a tooltip added to it. So if any setting is not clear hover over the (?) icon on the right of the setting.
Trade Manager 2Hi Traders,
this manager will make it easier for you to enter lots into your trading platform. Just go to the indicator settings, set your trading account amount, RRR, % risk and then give ok. If you then know where you want to put the stop loss then reopen, enter the value and hit ok again. The chart will show you exactly the stop loss and take profit as you wanted. The stop loss will always stay where you enter it and the take profit will move with the lot size as the price goes further or closer to the stop loss.
This should help when entering the number of lots, TP, SL into the platform.
Atr Target TP & Protecion Zone [Pinescriptlabs]This indicator provides an adaptive trailing stop system with dynamic price targets and protection zones, ideal for position management.
Main Features:
🚦 ADAPTIVE TRAILING STOP:
Automatically adjusts based on volatility (ATR) and volume
Two modes: "Modified" and "UnModified" for true range calculation
Displayed as a line with colored background (green for longs, red for shorts)
🎯 TARGET ZONES (T1 & T2):
Calculates two target zones (T1 and T2) based on:
Market strength (combination of RSI, volume, MFI, ADX, MACD)
Current volatility (ATR)
Distance from current price
Visualized with blue boxes (T1) and purple boxes (T2)
🛡️ PROTECTION ZONE:
Automatically activates in sideways markets
Provides an additional buffer to the trailing stop
Helps avoid premature exits in volatile markets
📊 INFORMATION PANELS:
Top Right Panel displays:
Current trend direction
Target status (T1 & T2)
Market strength
Current ATR
RSI level
Bottom Right Panel displays:
Trailing status (WIDE/NORMAL)
Volume impact
Directional strength
Protection zone status
Español:
Este indicador proporciona un sistema de trailing stop adaptativo con objetivos de precio dinámicos y zonas de protección, ideal para la gestión de posiciones.
**Características Principales**:
🚦 **TRAILING STOP ADAPTATIVO**:
- Se ajusta automáticamente según la volatilidad (ATR) y el volumen
- Dos modos: "Modified" y "UnModified" para el cálculo del rango verdadero
- Se visualiza como una línea con fondo coloreado (verde para largos, rojo para cortos)
🎯 **ZONAS OBJETIVO (T1 y T2)**:
- Calcula dos zonas objetivo (T1 y T2) basadas en:
- Fuerza del mercado (combinación de RSI, volumen, MFI, ADX, MACD)
- Volatilidad actual (ATR)
- Distancia al precio actual
- Visualización mediante cajas azules (T1) y moradas (T2)
🛡️ **ZONA DE PROTECCIÓN**:
- Se activa automáticamente en mercados laterales
- Proporciona un buffer adicional al trailing stop
- Ayuda a evitar salidas prematuras en mercados volátiles
📊 **PANELES INFORMATIVOS**:
*Panel Superior Derecho* muestra:
- Dirección de la tendencia actual
- Estado de los objetivos (T1 y T2)
- Fuerza del mercado
- ATR actual
- Nivel de RSI
*Panel Inferior Derecho* muestra:
- Estado del trailing (WIDE/NORMAL)
- Impacto del volumen
- Fuerza direccional
- Estado de la zona de protección
VIDYA ProTrend Multi-Tier ProfitHello! This time is about a trend-following system.
VIDYA is quite an interesting indicator that adjusts dynamically to market volatility, making it more responsive to price changes compared to traditional moving averages. Balancing adaptability and precision, especially with the more aggressive short trade settings, challenged me to fine-tune the strategy for a variety of market conditions.
█ Introduction and How it is Different
The "VIDYA ProTrend Multi-Tier Profit" strategy is a trend-following system that combines the VIDYA (Variable Index Dynamic Average) indicator with Bollinger Bands and a multi-step take-profit mechanism.
Unlike traditional trend strategies, this system allows for more adaptive profit-taking, adjusting for long and short positions through distinct ATR-based and percentage-based targets. The innovation lies in its dynamic multi-tier approach to profit-taking, especially for short trades, where more aggressive percentages are applied using a multiplier. This flexibility helps adapt to various market conditions by optimizing trade management and profit allocation based on market volatility and trend strength.
BTCUSD 6hr performance
█ Strategy, How it Works: Detailed Explanation
The core of the "VIDYA ProTrend Multi-Tier Profit" strategy lies in the dual VIDYA indicators (fast and slow) that analyze price trends while accounting for market volatility. These indicators work alongside Bollinger Bands to filter trade entries and exits.
🔶 VIDYA Calculation
The VIDYA indicator is calculated using the following formula:
Smoothing factor (𝛼):
alpha = 2 / (Length + 1)
VIDYA formula:
VIDYA(t) = alpha * k * Price(t) + (1 - alpha * k) * VIDYA(t-1)
Where:
k = |Chande Momentum Oscillator (MO)| / 100
🔶 Bollinger Bands as a Volatility Filter
Bollinger Bands are calculated using a rolling mean and standard deviation of price over a specified period:
Upper Band:
BB_upper = MA + (K * stddev)
Lower Band:
BB_lower = MA - (K * stddev)
Where:
MA is the moving average,
K is the multiplier (typically 2), and
stddev is the standard deviation of price over the Bollinger Bands length.
These bands serve as volatility filters to identify potential overbought or oversold conditions, aiding in the entry and exit logic.
🔶 Slope Calculation for VIDYA
The slopes of both fast and slow VIDYAs are computed to assess the momentum and direction of the trend. The slope for a given VIDYA over its length is:
Slope = (VIDYA(t) - VIDYA(t-n)) / n
Where:
n is the length of the lookback period. Positive slope indicates bullish momentum, while negative slope signals bearish momentum.
LOCAL picture
🔶 Entry and Exit Conditions
- Long Entry: Occurs when the price moves above the slow VIDYA and the fast VIDYA is trending upward. Bollinger Bands confirm the signal when the price crosses the upper band, indicating bullish strength.
- Short Entry: Happens when the price drops below the slow VIDYA and the fast VIDYA trends downward. The signal is confirmed when the price crosses the lower Bollinger Band, showing bearish momentum.
- Exit: Based on VIDYA slopes flattening or reversing, or when the price hits specific ATR or percentage-based profit targets.
🔶 Multi-Step Take Profit Mechanism
The strategy incorporates three levels of take profit for both long and short trades:
- ATR-based Take Profit: Each step applies a multiple of the ATR (Average True Range) to the entry price to define the exit point.
The first level of take profit (long):
TP_ATR1_long = Entry Price + (2.618 * ATR)
etc.
█ Trade Direction
The strategy offers flexibility in defining the trading direction:
- Long: Only long trades are considered based on the criteria for upward trends.
- Short: Only short trades are initiated in bearish trends.
- Both: The strategy can take both long and short trades depending on the market conditions.
█ Usage
To use the strategy effectively:
- Adjust the VIDYA lengths (fast and slow) based on your preference for trend sensitivity.
- Use Bollinger Bands as a filter for identifying potential breakout or reversal scenarios.
- Enable the multi-step take profit feature to manage positions dynamically, allowing for partial exits as the price reaches specified ATR or percentage levels.
- Leverage the short trade multiplier for more aggressive take profit levels in bearish markets.
This strategy can be applied to different asset classes, including equities, forex, and cryptocurrencies. Adjust the input parameters to suit the volatility and characteristics of the asset being traded.
█ Default Settings
The default settings for this strategy have been designed for moderate to trending markets:
- Fast VIDYA Length (10): A shorter length for quick responsiveness to price changes. Increasing this length will reduce noise but may delay signals.
- Slow VIDYA Length (30): The slow VIDYA is set longer to capture broader market trends. Shortening this value will make the system more reactive to smaller price swings.
- Minimum Slope Threshold (0.05): This threshold helps filter out weak trends. Lowering the threshold will result in more trades, while raising it will restrict trades to stronger trends.
Multi-Step Take Profit Settings
- ATR Multipliers (2.618, 5.0, 10.0): These values define how far the price should move before taking profit. Larger multipliers widen the profit-taking levels, aiming for larger trend moves. In higher volatility markets, these values might be adjusted downwards.
- Percentage Levels (3%, 8%, 17%): These percentage levels define how much the price must move before taking profit. Increasing the percentages will capture larger moves, while smaller percentages offer quicker exits.
- Short TP Multiplier (1.5): This multiplier applies more aggressive take profit levels for short trades. Adjust this value based on the aggressiveness of your short trade management.
Each of these settings directly impacts the performance and risk profile of the strategy. Shorter VIDYA lengths and lower slope thresholds will generate more trades but may result in more whipsaws. Higher ATR multipliers or percentage levels can delay profit-taking, aiming for larger trends but risking partial gains if the trend reverses too early.
Bitcoin Cycle Master [InvestorUnknown]The "Bitcoin Cycle Master" indicator is designed for in-depth, long-term analysis of Bitcoin's price cycles, using several key metrics to track market behavior and forecast potential price tops and bottoms. The indicator integrates multiple moving averages and on-chain metrics, offering a comprehensive view of Bitcoin’s historical and projected performance. Each of its components plays a crucial role in identifying critical cycle points:
Top Cap: This is a multiple of the Average Cap, which is calculated as the cumulative sum of Bitcoin’s price (price has a longer history than Market Cap) divided by its age in days. Top Cap serves as an upper boundary for speculative price peaks, multiplied by a factor of 35.
Time_dif() =>
date = ta.valuewhen(bar_index == 0, time, 0)
sec_r = math.floor(date / 1000)
min_r = math.floor(sec_r / 60)
h_r = math.floor(min_r / 60)
d_r = math.floor(h_r / 24)
// Launch of BTC
start = timestamp(2009, 1, 3, 00, 00)
sec_rb = math.floor(start / 1000)
min_rb = math.floor(sec_rb / 60)
h_rb = math.floor(min_rb / 60)
d_rb = math.floor(h_rb / 24)
difference = d_r - d_rb
AverageCap() =>
ta.cum(btc_price) / (Time_dif() + btc_age)
TopCap() =>
// To calculate Top Cap, it is first necessary to calculate Average Cap, which is the cumulative sum of Market Cap divided by the age of the market in days.
// This creates a constant time-based moving average of market cap.
// Once Average cap is calculated, those values are multiplied by 35. The result is Top Cap.
// For AverageCap the BTC price was used instead of the MC because it has more history
// (the result should have minimal if any deviation since MC would have to be divided by Supply)
AverageCap() * 35
Delta Top: Defined as the difference between the Realized Cap and the Average Cap, this metric is further multiplied by a factor of 7. Delta Top provides a historically reliable signal for Bitcoin market cycle tops.
DeltaTop() =>
// Delta Cap = Realized Cap - Average Cap
// Average Cap is explained in the Top Cap section above.
// Once Delta Cap is calculated, its values over time are then multiplied by 7. The result is Delta Top.
(RealizedPrice() - AverageCap()) * 7
Terminal Price: Derived from Coin Days Destroyed, Terminal Price normalizes Bitcoin’s historical price behavior by its finite supply (21 million bitcoins), offering an adjusted price forecast as all bitcoins approach being mined. The original formula for Terminal Price didn’t produce expected results, hence the calculation was adjusted slightly.
CVDD() =>
// CVDD stands for Cumulative Value Coin Days Destroyed.
// Coin Days Destroyed is a term used for bitcoin to identify a value of sorts to UTXO’s (unspent transaction outputs). They can be thought of as coins moving between wallets.
(MCR - TV) / 21000000
TerminalPrice() =>
// Theory:
// Before Terminal price is calculated, it is first necessary to calculate Transferred Price.
// Transferred price takes the sum of > Coin Days Destroyed and divides it by the existing supply of bitcoin and the time it has been in circulation.
// The value of Transferred Price is then multiplied by 21. Remember that there can only ever be 21 million bitcoin mined.
// This creates a 'terminal' value as the supply is all mined, a kind of reverse supply adjustment.
// Instead of heavily weighting later behavior, it normalizes historical behavior to today. By normalizing by 21, a terminal value is created
// Unfortunately the theoretical calculation didn't produce results it should, in pinescript.
// Therefore the calculation was slightly adjusted/improvised
TransferredPrice = CVDD() / (Supply * math.log(btc_age))
tp = TransferredPrice * 210000000 * 3
Realized Price: Calculated as the Market Cap Realized divided by the current supply of Bitcoin, this metric shows the average value of Bitcoin based on the price at which coins last moved, giving a market consensus price for long-term holders.
CVDD (Cumulative Value Coin Days Destroyed): This on-chain metric analyzes Bitcoin’s UTXOs (unspent transaction outputs) and the velocity of coins moving between wallets. It highlights key market dynamics during prolonged accumulation or distribution phases.
Balanced Price: The Balanced Price is the difference between the Realized Price and the Terminal Price, adjusted by Bitcoin's supply constraints. This metric provides a useful signal for identifying oversold market conditions during bear markets.
BalancedPrice() =>
// It is calculated by subtracting Transferred Price from Realized Price
RealizedPrice() - (TerminalPrice() / (21 * 3))
Each component can be toggled individually, allowing users to focus on specific aspects of Bitcoin’s price cycle and derive meaningful insights from its long-term behavior. The combination of these models provides a well-rounded view of both speculative peaks and long-term value trends.
Important consideration:
Top Cap did historically provide reliable signals for cycle peaks, however it may not be a relevant indication of peaks in the future.
Intramarket Difference Index StrategyHi Traders !!
The IDI Strategy:
In layman’s terms this strategy compares two indicators across markets and exploits their differences.
note: it is best the two markets are correlated as then we know we are trading a short to long term deviation from both markets' general trend with the assumption both markets will trend again sometime in the future thereby exhausting our trading opportunity.
📍 Import Notes:
This Strategy calculates trade position size independently (i.e. risk per trade is controlled in the user inputs tab), this means that the ‘Order size’ input in the ‘Properties’ tab will have no effect on the strategy. Why ? because this allows us to define custom position size algorithms which we can use to improve our risk management and equity growth over time. Here we have the option to have fixed quantity or fixed percentage of equity ATR (Average True Range) based stops in addition to the turtle trading position size algorithm.
‘Pyramiding’ does not work for this strategy’, similar to the order size input togeling this input will have no effect on the strategy as the strategy explicitly defines the maximum order size to be 1.
This strategy is not perfect, and as of writing of this post I have not traded this algo.
Always take your time to backtests and debug the strategy.
🔷 The IDI Strategy:
By default this strategy pulls data from your current TV chart and then compares it to the base market, be default BINANCE:BTCUSD . The strategy pulls SMA and RSI data from either market (we call this the difference data), standardizes the data (solving the different unit problem across markets) such that it is comparable and then differentiates the data, calling the result of this transformation and difference the Intramarket Difference (ID). The formula for the the ID is
ID = market1_diff_data - market2_diff_data (1)
Where
market(i)_diff_data = diff_data / ATR(j)_market(i)^0.5,
where i = {1, 2} and j = the natural numbers excluding 0
Formula (1) interpretation is the following
When ID > 0: this means the current market outperforms the base market
When ID = 0: Markets are at long run equilibrium
When ID < 0: this means the current market underperforms the base market
To form the strategy we define one of two strategy type’s which are Trend and Mean Revesion respectively.
🔸 Trend Case:
Given the ‘‘Strategy Type’’ is equal to TREND we define a threshold for which if the ID crosses over we go long and if the ID crosses under the negative of the threshold we go short.
The motivating idea is that the ID is an indicator of the two symbols being out of sync, and given we know volatility clustering, momentum and mean reversion of anomalies to be a stylised fact of financial data we can construct a trading premise. Let's first talk more about this premise.
For some markets (cryptocurrency markets - synthetic symbols in TV) the stylised fact of momentum is true, this means that higher momentum is followed by higher momentum, and given we know momentum to be a vector quantity (with magnitude and direction) this momentum can be both positive and negative i.e. when the ID crosses above some threshold we make an assumption it will continue in that direction for some time before executing back to its long run equilibrium of 0 which is a reasonable assumption to make if the market are correlated. For example for the BTCUSD - ETHUSD pair, if the ID > +threshold (inputs for MA and RSI based ID thresholds are found under the ‘‘INTRAMARKET DIFFERENCE INDEX’’ group’), ETHUSD outperforms BTCUSD, we assume the momentum to continue so we go long ETHUSD.
In the standard case we would exit the market when the IDI returns to its long run equilibrium of 0 (for the positive case the ID may return to 0 because ETH’s difference data may have decreased or BTC’s difference data may have increased). However in this strategy we will not define this as our exit condition, why ?
This is because we want to ‘‘let our winners run’’, to achieve this we define a trailing Donchian Channel stop loss (along with a fixed ATR based stop as our volatility proxy). If we were too use the 0 exit the strategy may print a buy signal (ID > +threshold in the simple case, market regimes may be used), return to 0 and then print another buy signal, and this process can loop may times, this high trade frequency means we fail capture the entire market move lowering our profit, furthermore on lower time frames this high trade frequencies mean we pay more transaction costs (due to price slippage, commission and big-ask spread) which means less profit.
By capturing the sum of many momentum moves we are essentially following the trend hence the trend following strategy type.
Here we also print the IDI (with default strategy settings with the MA difference type), we can see that by letting our winners run we may catch many valid momentum moves, that results in a larger final pnl that if we would otherwise exit based on the equilibrium condition(Valid trades are denoted by solid green and red arrows respectively and all other valid trades which occur within the original signal are light green and red small arrows).
another example...
Note: if you would like to plot the IDI separately copy and paste the following code in a new Pine Script indicator template.
indicator("IDI")
// INTRAMARKET INDEX
var string g_idi = "intramarket diffirence index"
ui_index_1 = input.symbol("BINANCE:BTCUSD", title = "Base market", group = g_idi)
// ui_index_2 = input.symbol("BINANCE:ETHUSD", title = "Quote Market", group = g_idi)
type = input.string("MA", title = "Differrencing Series", options = , group = g_idi)
ui_ma_lkb = input.int(24, title = "lookback of ma and volatility scaling constant", group = g_idi)
ui_rsi_lkb = input.int(14, title = "Lookback of RSI", group = g_idi)
ui_atr_lkb = input.int(300, title = "ATR lookback - Normalising value", group = g_idi)
ui_ma_threshold = input.float(5, title = "Threshold of Upward/Downward Trend (MA)", group = g_idi)
ui_rsi_threshold = input.float(20, title = "Threshold of Upward/Downward Trend (RSI)", group = g_idi)
//>>+----------------------------------------------------------------+}
// CUSTOM FUNCTIONS |
//<<+----------------------------------------------------------------+{
// construct UDT (User defined type) containing the IDI (Intramarket Difference Index) source values
// UDT will hold many variables / functions grouped under the UDT
type functions
float Close // close price
float ma // ma of symbol
float rsi // rsi of the asset
float atr // atr of the asset
// the security data
getUDTdata(symbol, malookback, rsilookback, atrlookback) =>
indexHighTF = barstate.isrealtime ? 1 : 0
= request.security(symbol, timeframe = timeframe.period,
expression = [close , // Instentiate UDT variables
ta.sma(close, malookback) ,
ta.rsi(close, rsilookback) ,
ta.atr(atrlookback) ])
data = functions.new(close_, ma_, rsi_, atr_)
data
// Intramerket Difference Index
idi(type, symbol1, malookback, rsilookback, atrlookback, mathreshold, rsithreshold) =>
threshold = float(na)
index1 = getUDTdata(symbol1, malookback, rsilookback, atrlookback)
index2 = getUDTdata(syminfo.tickerid, malookback, rsilookback, atrlookback)
// declare difference variables for both base and quote symbols, conditional on which difference type is selected
var diffindex1 = 0.0, var diffindex2 = 0.0,
// declare Intramarket Difference Index based on series type, note
// if > 0, index 2 outpreforms index 1, buy index 2 (momentum based) until equalibrium
// if < 0, index 2 underpreforms index 1, sell index 1 (momentum based) until equalibrium
// for idi to be valid both series must be stationary and normalised so both series hae he same scale
intramarket_difference = 0.0
if type == "MA"
threshold := mathreshold
diffindex1 := (index1.Close - index1.ma) / math.pow(index1.atr*malookback, 0.5)
diffindex2 := (index2.Close - index2.ma) / math.pow(index2.atr*malookback, 0.5)
intramarket_difference := diffindex2 - diffindex1
else if type == "RSI"
threshold := rsilookback
diffindex1 := index1.rsi
diffindex2 := index2.rsi
intramarket_difference := diffindex2 - diffindex1
//>>+----------------------------------------------------------------+}
// STRATEGY FUNCTIONS CALLS |
//<<+----------------------------------------------------------------+{
// plot the intramarket difference
= idi(type,
ui_index_1,
ui_ma_lkb,
ui_rsi_lkb,
ui_atr_lkb,
ui_ma_threshold,
ui_rsi_threshold)
//>>+----------------------------------------------------------------+}
plot(intramarket_difference, color = color.orange)
hline(type == "MA" ? ui_ma_threshold : ui_rsi_threshold, color = color.green)
hline(type == "MA" ? -ui_ma_threshold : -ui_rsi_threshold, color = color.red)
hline(0)
Note it is possible that after printing a buy the strategy then prints many sell signals before returning to a buy, which again has the same implication (less profit. Potentially because we exit early only for price to continue upwards hence missing the larger "trend"). The image below showcases this cenario and again, by allowing our winner to run we may capture more profit (theoretically).
This should be clear...
🔸 Mean Reversion Case:
We stated prior that mean reversion of anomalies is an standerdies fact of financial data, how can we exploit this ?
We exploit this by normalizing the ID by applying the Ehlers fisher transformation. The transformed data is then assumed to be approximately normally distributed. To form the strategy we employ the same logic as for the z score, if the FT normalized ID > 2.5 (< -2.5) we buy (short). Our exit conditions remain unchanged (fixed ATR stop and trailing Donchian Trailing stop)
🔷 Position Sizing:
If ‘‘Fixed Risk From Initial Balance’’ is toggled true this means we risk a fixed percentage of our initial balance, if false we risk a fixed percentage of our equity (current balance).
Note we also employ a volatility adjusted position sizing formula, the turtle training method which is defined as follows.
Turtle position size = (1/ r * ATR * DV) * C
Where,
r = risk factor coefficient (default is 20)
ATR(j) = risk proxy, over j times steps
DV = Dollar Volatility, where DV = (1/Asset Price) * Capital at Risk
🔷 Risk Management:
Correct money management means we can limit risk and increase reward (theoretically). Here we employ
Max loss and gain per day
Max loss per trade
Max number of consecutive losing trades until trade skip
To read more see the tooltips (info circle).
🔷 Take Profit:
By defualt the script uses a Donchain Channel as a trailing stop and take profit, In addition to this the script defines a fixed ATR stop losses (by defualt, this covers cases where the DC range may be to wide making a fixed ATR stop usefull), ATR take profits however are defined but optional.
ATR SL and TP defined for all trades
🔷 Hurst Regime (Regime Filter):
The Hurst Exponent (H) aims to segment the market into three different states, Trending (H > 0.5), Random Geometric Brownian Motion (H = 0.5) and Mean Reverting / Contrarian (H < 0.5). In my interpretation this can be used as a trend filter that eliminates market noise.
We utilize the trending and mean reverting based states, as extra conditions required for valid trades for both strategy types respectively, in the process increasing our trade entry quality.
🔷 Example model Architecture:
Here is an example of one configuration of this strategy, combining all aspects discussed in this post.
Future Updates
- Automation integration (next update)
VS (Vegas Fractal System)VS is a trading system based on the identification of fractal reaction zones within a larger, carefully identified movement. It is internally made up of 4 sub-systems.
The indicator is composed of the following parameters: Max and Min, are the largest area identified and will act as the STOPLOSS point. L1, is the price reaction level. Entry, is where to place a pending market entry order. TP, is the place to place a 100% sell order.
A valid area must be identified through the Fibonacci levels that join Highs and Lows or vice versa depending on the bullish or bearish movement. To be usable, this movement must not have a sub-movement that has already hit the 0.618 level.
Always manage Risk and Money Management in an adequate, technical and sustainable manner in relation to your capital. A fair exposure per transaction is between 1% and 2% of the capital.
Innocent Heikin Ashi Ethereum StrategyHello there, im back!
If you are familiar with my previous scripts, this one will seem like the future's nostalgia!
Functionality:
As you can see, all candles are randomly colored. This has no deeper meaning, it should remind you to switch to Heikin Ashi. The Strategy works on standard candle stick charts, but should be used with Heikin Ashi to see the actual results. (Regular OHLC calculations are included.)
Same as in my previous scripts we import our PVSRA Data from @TradersReality open source Indicator.
With this data and the help of moving averages, we have got an edge in the market.
Signal Logic:
When a "violently green" candle appears (high buy volume + tick speed) above the 50 EMA indicates a change in trend and sudden higher prices. Depending on OHLC of the candle itself and volume, Take Profit and Stop Loss is calculated. (The price margin is the only adjustable setting). Additionally, to make this script as simple and easily useable as possible, all other adjustable variables have been already set to the best suitable value and the chart was kept plain, except for the actual entries and exits.
Basic Settings and Adjustables:
Main Input 1: TP and SL combined price range. (Double, Triple R:R equally.)
Trade Inputs: All standard trade size and contract settings for testing available.
Special Settings:
Checkbox 1: Calculate Signal in Heikin Ashi chart, including regular candle OHLC („Open, High, Low, Close“)
Checkbox 2/3: Calculate by order fill or every tick.
Checkbox 4: Possible to fill orders on bar close.
Timeframe and practical usage:
Made for the 5 Minute to 1 hour timeframe.
Literally ONLY works on Ethereum and more or less on Bitcoin.
EVERY other asset has absolute 0% profitability.
Have fun and share with your friends!
Thanks for using!
Example Chart:
