IU Opening range Breakout StrategyIU Opening Range Breakout Strategy
This Pine Script strategy is designed to capitalize on the breakout of the opening range, which is a popular trading approach. The strategy identifies the high and low prices of the opening session and takes trades based on price crossing these levels, with built-in risk management and trade limits for intraday trading.
Key Features:
1. Risk Management:
- Risk-to-Reward Ratio (RTR):
Set a customizable risk-to-reward ratio to calculate target prices based on stop-loss levels.
Default: 2:1
- Max Trades in a Day:
Specify the maximum number of trades allowed per day to avoid overtrading.
Default: 2 trades in a day.
- End-of-Day Close:
Automatically closes all open positions at a user-defined session end time to ensure no overnight exposure.
Default: 3:15 PM
2. Opening Range Identification
- Opening Range High and Low:
The script detects the high and low of the first trading session using Pine Script's session functions.
These levels are plotted as visual guides on the chart:
- High: Lime-colored circles.
- Low: Red-colored circles.
3. Trade Entry Logic
- Long Entry:
A long trade is triggered when the price closes above the opening range high.
- Entry condition: Crossover of the price above the opening range high.
-Short Entry:
A short trade is triggered when the price closes below the opening range low.
- Entry condition: Crossunder of the price below the opening range low.
Both entries are conditional on the absence of an existing position.
4. Stop Loss and Take Profit
- Long Position:
- Stop Loss: Previous candle's low.
- Take Profit: Calculated based on the RTR.
- **Short Position:**
- **Stop Loss:** Previous candle's high.
- **Take Profit:** Calculated based on the RTR.
The strategy plots these levels for visual reference:
- Stop Loss: Red dashed lines.
- Take Profit: Green dashed lines.
5. Visual Enhancements
-Trade Level Highlighting:
The script dynamically shades the areas between the entry price and SL/TP levels:
- Red shading for the stop-loss region.
- Green shading for the take-profit region.
- Entry Price Line:
A silver-colored line marks the average entry price for active trades.
How to Use:
1.Input Configuration:
Adjust the Risk-to-Reward ratio, max trades per day, and session end time to suit your trading preferences.
2.Visual Cues:
Use the opening range high/low lines and shading to identify potential breakout opportunities.
3.Execution:
The strategy will automatically enter and exit trades based on the conditions. Review the plotted SL and TP levels to monitor the risk-reward setup.
Important Notes:
- This strategy is designed for intraday trading and works best in markets with high volatility during the opening session.
- Backtest the strategy on your preferred market and timeframe to ensure compatibility.
- Proper risk management and position sizing are essential when using this strategy in live markets.
Penunjuk dan strategi
Overnight Effect High Volatility Crypto (AiBitcoinTrend)👽 Overview of the Strategy
This strategy leverages the overnight effect in the cryptocurrency market, specifically targeting the two-hour window from 21:00 UTC to 23:00 UTC. The strategy is designed to be applied only during periods of high volatility, which is determined using historical volatility data. This approach, inspired by research from Padyšák and Vojtko (2022), aims to capitalize on statistically significant return patterns observed during these hours.
Deep Backtesting with a High Volatility Filter
Deep Backtesting without a High Volatility Filter
👽 How the Strategy Works
Volatility Calculation:
Each day at 00:00 UTC, the strategy calculates the 30-day historical volatility of crypto returns (typically Bitcoin). The historical volatility is the standard deviation of the log returns over the past 30 days, representing the market's recent volatility level.
Median Volatility Benchmark:
The median of the 30-day historical volatility is calculated over a 365-day period (one year). This median acts as a benchmark to classify each day as either:
👾 High Volatility: When the current 30-day volatility exceeds the median volatility.
👾 Low Volatility: When the current 30-day volatility is below the median.
Trading Rule:
If the day is classified as a High Volatility Day, the strategy executes the following trades:
👾 Buy at 21:00 UTC.
👾 Sell at 23:00 UTC.
Trade Execution Details:
The strategy uses a 0.02% fee per trade.
Each trade is executed with 25% of the available capital. This allocation helps manage risk while allowing for compounding returns.
Rationale:
The returns during the 22:00 and 23:00 UTC hours have been found to be statistically significant during high volatility periods. The overnight effect is believed to drive this phenomenon due to the asynchronous closing hours of global financial markets. This creates unique trading opportunities in the cryptocurrency market, where exchanges remain open 24/7.
👽 Market Context and Global Time Zone Impact
👾 Why 21:00 to 23:00 UTC?
During this window, major traditional financial markets are closed:
NYSE (New York) closes at 21:00 UTC.
London and European markets are closed during these hours.
Asian markets (Tokyo, Hong Kong, etc.) open later, leaving this window largely unaffected by traditional trading flows.
This global market inactivity creates a period where significant moves can occur in the cryptocurrency market, particularly during high volatility.
👽 Strategy Parameters
Volatility Period: 30 days.
The lookback period for calculating historical volatility.
Median Period: 365 days.
The lookback period for calculating the median volatility benchmark.
Entry Time: 21:00 UTC.
Adjust this to your local time if necessary (e.g., 16:00 in New York, 22:00 in Stockholm).
Exit Time: 23:00 UTC.
Adjust this to your local time if necessary (e.g., 18:00 in New York, 00:00 midnight in Stockholm).
👽 Benefits of the Strategy
Seasonality Effect:
The strategy captures consistent patterns driven by the overnight effect and high volatility periods.
Risk Reduction:
Since trades are executed during a specific window and only on high volatility days, the strategy helps mitigate exposure to broader market risk.
Simplicity and Efficiency:
The strategy is moderately complex, making it accessible for traders while offering significant returns.
Global Applicability:
Suitable for traders worldwide, with clear guidelines on adjusting for local time zones.
👽 Considerations
Market Conditions: The strategy works best in a high-volatility environment.
Execution: Requires precise timing to enter and exit trades at the specified hours.
Time Zone Adjustments: Ensure you convert UTC times accurately based on your location to execute trades at the correct local times.
Disclaimer: This information is for entertainment purposes only and does not constitute financial advice. Please consult with a qualified financial advisor before making any investment decisions.
SMA Buy/Sell Strategy with Significant Slope and Dynamic TP/SLDescription:
This strategy uses a simple moving average (SMA) to detect trading opportunities based on the slope and proximity of price action. It ensures trades are only executed during significant trends, reducing false signals caused by sideways movements. The strategy incorporates dynamic risk management with an initial ambitious Take Profit (TP) and a Trailing Stop Loss (SL) to protect profits.
Key Features:
Trend Detection with SMA:
Two SMAs are calculated: one on High values and one on Low values.
Signals are generated when the price crosses these SMAs, ensuring:
Buy: Price closes above the SMA on High, with a significant upward slope.
Sell: Price closes below the SMA on Low, with a significant downward slope.
Slope Significance Check:
The slope of the SMA is calculated over a configurable period.
Only trends with a slope variation exceeding a user-defined percentage threshold are considered significant.
Dynamic Risk Management:
Ambitious Initial TP: Positions target a high percentage gain upon entry.
Trailing SL: Automatically adjusts as the price moves in favor of the trade, locking in profits.
Automatic Position Management:
Opposing signals close existing positions to avoid conflicting trades.
Configurable position size for risk control.
Parameters:
SMA Period: Number of candles for calculating the SMA.
Initial Take Profit (%): Percentage gain for the initial TP.
Trailing Stop Loss (%): Percentage for trailing SL based on the current price.
Slope Threshold (%): Minimum percentage change in SMA slope to confirm trend significance.
How It Works:
Buy Signal:
The price closes above the SMA on High values.
The slope of the SMA (on High) is positive and exceeds the slope threshold.
Sell Signal:
The price closes below the SMA on Low values.
The slope of the SMA (on Low) is negative and exceeds the slope threshold.
Exits:
A position closes at the Take Profit level, Trailing Stop Loss, or when an opposing signal is generated.
Use Case:
This strategy is ideal for trending markets where price action respects moving averages. It can be used on any timeframe or asset but is particularly effective in markets with clear directional movements.
Recommended Settings:
Timeframe: Works well on higher timeframes (e.g., 1H, 4H, Daily).
Slope Threshold (%): Default is 5%, adjust based on market volatility.
Initial TP and Trailing SL: Tailor to your risk/reward preferences.
By utilizing this strategy, traders can capitalize on significant market trends while dynamically managing risk. Test it on historical data to optimize the parameters for your preferred market!
FXC NQ Opening Range Breakout Strategy V2.4Mechanical Strategy that trades breakouts on NQ futures on the 15min timeframe during the NYSE session. It's designed to manage Apex and Top Step accounts with the lowest risk possible.
Risk Disclaimer:
Past results as well as strategy tester reports do not indicate future performance. Guarantees do not exist in trading. By using this strategy you risk losing all your money.
Important:
It only trades on Monday, Wednesday and Friday and takes usually only 1 trade per trading day.
It works on the 15min timeframe only.
The settings are optimised already for NQ but feel free to change them.
How it works:
Every selected trading day it measures the range of the first 15min candle after the NYSE open. As soon as price closes above on the 15min timeframe, it will trade the breakout targeting a set risk to reward ratio. SL on the opposite side of the range. It will trail the SL after a set amount of points and uses a buffer of the set amount of points to trail it.
Settings:
Opening Range Time : This is the time of the day in hours and minutes when the strategy starts looking for trades. It's in the EST/ NY Timezone and set to 9:30-09:45 by default
because that's the NYSE open.
Session Time : This is the time of the day in hours and minutes until the strategy trades. It's in the EST/ NY Timezone and set to 09:45-14:45 by default.
because that's what gave the best results in backtesting. Open trades will get closed automatically once the end of the session is reached. No matter if win or loss. This is just to prevent holding positions over night.
Session Border This setting is to select the border color in which the session box will be plotted.
Opening Range Box This setting is to select the fill color of the opening range box.
Opening Range Border This setting is to select the border color of the session box.
Trade Timeframe This setting determines on which timeframe candle has to close outside the opening range box in order to take a trade. It's set to 15min by default because this is what worked by far the best in backtests and live trading.
Stop Loss Buffer in Points: This is simply the buffer in points that is added to the SL for safety reasons. If you have it on 0, the SL will be at the exact price of the opposite side of the range. By default it's set to 0 pips because this is what delivered the best results in backtests.
Profit Target Factor: This is simply the total SL size in points multiplied by x.
Example: If you put 2, you get a 1:2 Risk to Reward Ratio. By Default it's set to 4 because this gave the best results in backtests, because trades always get closed either by trailing SL or because the end of the session is reached.
Use Trailing Stop Loss: This setting is to enable/ disable the trailing stop loss. It's enabled by default because this is a fundamental part of the strategy.
Trailing Stop Buffer: This setting determines after how many points in profit the trailing SL will be activated.
Risk Type: You can chose either between Fixed USD Amount, Risk per Trade in % or Fixed Contract Size. By default it's set to fixed contract size.
Risk Amount (USD or Contracts): This setting is to set how many USD or how many contracts you want to risk per trade. Make sure to check which risk type you have selected before you chose the risk amount.
Use Limit Orders If enabled, the strategy will place a pending order x points from the current price, instead of a market order. Limit orders are enabled by default for a better performance. Important: It doesn't actually place a limit order. The strategy will just wait for a pullback and then enter with a market order. It's more like a hidden limit order.
Limit Order Distance (points): If you have limit orders enabled, this setting determines how many points from the current price the limit order will be placed.
Trading Days: These checkboxes are to select on which week days the strategy has to trade. Thursday is disabled by default because backtests have shown that Thursday is the least profitable day
Backtest Settings:
For the backtest the commissions ere set to 0.35 USD per mini contract which is the highest amount Tradeovate charges. Margin was not accounted for because typically on Apex accounts you can use way more contracts than you need for the extremely low max drawdown. Margin would be important on personal accounts but even there typically it's not an issue at all especially because this strategy runs on the 15min timeframe so it won't use a lot of contracts anyways.
What makes it unique:
This script is unique because it's designed to be used on Apex and Top Step accounts with extremely strict drawdown rules.
The strategy is optimised to be traded with a fixed contract size instead of using % risk. The reason for that is that the drawdown rules of these Futures Prop Accounts are very strict and the fact that the smallest trade-able contract size is 1.
Why the source code is hidden:
The source code is hidden because I invested a lot of time and money into developing this strategy and optimising it with paid 3rd party software. Also since I use it myself on my Apex accounts and prop firms don't allow copy trading I don't want it to be used by too many traders.
DCA Strategy with Mean Reversion and Bollinger BandDCA Strategy with Mean Reversion and Bollinger Band
The Dollar-Cost Averaging (DCA) Strategy with Mean Reversion and Bollinger Bands is a sophisticated trading strategy that combines the principles of DCA, mean reversion, and technical analysis using Bollinger Bands. This strategy aims to capitalize on market corrections by systematically entering positions during periods of price pullbacks and reversion to the mean.
Key Concepts and Principles
1. Dollar-Cost Averaging (DCA)
DCA is an investment strategy that involves regularly purchasing a fixed dollar amount of an asset, regardless of its price. The idea behind DCA is that by spreading out investments over time, the impact of market volatility is reduced, and investors can avoid making large investments at inopportune times. The strategy reduces the risk of buying all at once during a market high and can smooth out the cost of purchasing assets over time.
In the context of this strategy, the Investment Amount (USD) is set by the user and represents the amount of capital to be invested in each buy order. The strategy executes buy orders whenever the price crosses below the lower Bollinger Band, which suggests a potential market correction or pullback. This is an effective way to average the entry price and avoid the emotional pitfalls of trying to time the market perfectly.
2. Mean Reversion
Mean reversion is a concept that suggests prices will tend to return to their historical average or mean over time. In this strategy, mean reversion is implemented using the Bollinger Bands, which are based on a moving average and standard deviation. The lower band is considered a potential buy signal when the price crosses below it, indicating that the asset has become oversold or underpriced relative to its historical average. This triggers the DCA buy order.
Mean reversion strategies are popular because they exploit the natural tendency of prices to revert to their mean after experiencing extreme deviations, such as during market corrections or panic selling.
3. Bollinger Bands
Bollinger Bands are a technical analysis tool that consists of three lines:
Middle Band: The moving average, usually a 200-period Exponential Moving Average (EMA) in this strategy. This serves as the "mean" or baseline.
Upper Band: The middle band plus a certain number of standard deviations (multiplier). The upper band is used to identify overbought conditions.
Lower Band: The middle band minus a certain number of standard deviations (multiplier). The lower band is used to identify oversold conditions.
In this strategy, the Bollinger Bands are used to identify potential entry points for DCA trades. When the price crosses below the lower band, this is seen as a potential opportunity for mean reversion, suggesting that the asset may be oversold and could reverse back toward the middle band (the EMA). Conversely, when the price crosses above the upper band, it indicates overbought conditions and signals potential market exhaustion.
4. Time-Based Entry and Exit
The strategy has specific entry and exit points defined by time parameters:
Open Date: The date when the strategy begins opening positions.
Close Date: The date when all positions are closed.
This time-bound approach ensures that the strategy is active only during a specified window, which can be useful for testing specific market conditions or focusing on a particular time frame.
5. Position Sizing
Position sizing is determined by the Investment Amount (USD), which is the fixed amount to be invested in each buy order. The quantity of the asset to be purchased is calculated by dividing the investment amount by the current price of the asset (investment_amount / close). This ensures that the amount invested remains constant despite fluctuations in the asset's price.
6. Closing All Positions
The strategy includes an exit rule that closes all positions once the specified close date is reached. This allows for controlled exits and limits the exposure to market fluctuations beyond the strategy's timeframe.
7. Background Color Based on Price Relative to Bollinger Bands
The script uses the background color of the chart to provide visual feedback about the price's relationship with the Bollinger Bands:
Red background indicates the price is above the upper band, signaling overbought conditions.
Green background indicates the price is below the lower band, signaling oversold conditions.
This provides an easy-to-interpret visual cue for traders to assess the current market environment.
Postscript: Configuring Initial Capital for Backtesting
To ensure the backtest results align with the actual investment scenario, users must adjust the Initial Capital in the TradingView strategy properties. This is done by calculating the Initial Capital as the product of the Total Closed Trades and the Investment Amount (USD). For instance:
If the user is investing 100 USD per trade and has 10 closed trades, the Initial Capital should be set to 1,000 USD.
Similarly, if the user is investing 200 USD per trade and has 24 closed trades, the Initial Capital should be set to 4,800 USD.
This adjustment ensures that the backtesting results reflect the actual capital deployed in the strategy and provides an accurate representation of potential gains and losses.
Conclusion
The DCA strategy with Mean Reversion and Bollinger Bands is a systematic approach to investing that leverages the power of regular investments and technical analysis to reduce market timing risks. By combining DCA with the insights offered by Bollinger Bands and mean reversion, this strategy offers a structured way to navigate volatile markets while targeting favorable entry points. The clear entry and exit rules, coupled with time-based constraints, make it a robust and disciplined approach to long-term investing.
linreg-gridbotLinreg-GridBot
>release note version 1<
Introduction
This script is a powerful trading strategy tool designed to help users identify market reversal points and make smarter trading decisions using grid thinking.
Background
Traditional grid/martingale strategies have several drawbacks: inefficient use of capital, premature grid boundaries, and trading at fixed intervals, all of which significantly reduce profitability. Since, there is not a gridbot can trail-stop at each level, stay close with the trend, and do better capital usage, tradalive has created this advanced gridbot to address these issues, and enhance the profitability.
How does it work?
Imagine plotting closes on a graph, where the x-axis represents the time-intervals and the y-axis represents the price. Linear regression would fit a straight line through these points that best represents the trend of the data.
In this script utilize the built-in to find consecutive slopes at each moment, and combine them to a smooth trend line. When turning point censored, an entry is placed right after the next bar. Then the gridbot starts working, the upper limit and lower limit is calculated by built-in , for example 3 ATRs above and under the entry price.
There is a 0.2 trailing stop for each step level. Also, when built-in VWMA is rising, this script uses built-in ROC to find the average change of lookback length, then move the grid upwards accordingly.
Size trading is crucial, in gridbot all-in when beginning the trade is risky, because turning point does not guarantee a reversal market upcoming. As a grid trader, we believe the price is relatively cheap near the lower limit, and the price is relatively expensive near the upper limit. Properly sized orders help prevent overexposure and reduce the potential for significant losses.
Features
Trend Detection: Utilizes linear regression to differentiate between upward and downward trends, displaying them as (orange) trend lines on the chart.
Signal Generation: Provides buy or sell signals at reversal points, helping users trade at optimal times.
Adjustable Parameters: Allows users to customize different indicator parameters to fit various trading strategies.
Backtested Device Parameters (see appendix)
Grid Parameters
🔃: Cyclic Trading
💰: Capital Turnover Ratio (Grid capital difference per level: 0.5 to 2)
⬆️ / ⬇️ Expected Number of Upward and Downward Grids.
The minimum number of grids is three: one level above and below the current price.
The maximum number of grids is seven: three levels above and below the current price.
🧭: Trade Signal: Controls the trading direction, long or short;
📏: Linear regression length value.
⏳⌛Backtest Period: Set the time range for users to analyze the performance of the strategy over different periods.
Analytic Toolbox (upper right corner) :
Usage Instructions
Add this script to your TradingView account.
Apply the script to your chart.
Adjust the parameters to fit your trading needs.
Make trading decisions based on the buy and sell signals.
Manually place orders on your trading platform using the parameters provided.
Enter grid parameters according to the highest and lowest prices.
Fill in the number of grid levels (the number of grids equals the number of upward grids plus the number of downward grids plus one).
Set stop-loss and take-profit values.
Alternatively, use a webhook to connect to your trading platform for automated trading.
Important Notes
This script currently only supports 4-hour and daily charts!
This script relies on historical data for calculations and may not be suitable for all market conditions.
Trading carries risks, so please use this script cautiously for trading decisions.
User has to update the backtest period, or else the strategy might not be seen.
Demostration
Phase one, the orange line is about to turn up.
Phase two, the reversal point is located, and right after the next bar start an entry of gridbot.
Phase Three, the gridbot operates, once level touches, then a 0.2ATR trailing stop is applied on each step.
Phase four, when vwma rises, the grid window follows it by the rate of change of lookback price. If vwma does not move up, then the grid boundaries remain.
Phase five, either side when the current price breaks through the white limits, the gridbot stops. And the trading strategy is done for this round.
Precision Trading Strategy: Golden EdgeThe PTS: Golden Edge strategy is designed for scalping Gold (XAU/USD) on lower timeframes, such as the 1-minute chart. It captures high-probability trade setups by aligning with strong trends and momentum, while filtering out low-quality trades during consolidation or low-volatility periods.
The strategy uses a combination of technical indicators to identify optimal entry points:
1. Exponential Moving Averages (EMAs): A fast EMA (3-period) and a slow EMA (33-period) are used to detect short-term trend reversals via crossover signals.
2. Hull Moving Average (HMA): A 66-period HMA acts as a higher-timeframe trend filter to ensure trades align with the overall market direction.
3. Relative Strength Index (RSI): A 12-period RSI identifies momentum. The strategy requires RSI > 55 for long trades and RSI < 45 for short trades, ensuring entries are backed by strong buying or selling pressure.
4. Average True Range (ATR): A 14-period ATR ensures trades occur only during volatile conditions, avoiding choppy or low-movement markets.
By combining these tools, the PTS: Golden Edge strategy creates a precise framework for scalping and offers a systematic approach to capitalize on Gold’s price movements efficiently.
MultiLayer Acceleration/Deceleration Strategy [Skyrexio]Overview
MultiLayer Acceleration/Deceleration Strategy leverages the combination of Acceleration/Deceleration Indicator(AC), Williams Alligator, Williams Fractals and Exponential Moving Average (EMA) to obtain the high probability long setups. Moreover, strategy uses multi trades system, adding funds to long position if it considered that current trend has likely became stronger. Acceleration/Deceleration Indicator is used for creating signals, while Alligator and Fractal are used in conjunction as an approximation of short-term trend to filter them. At the same time EMA (default EMA's period = 100) is used as high probability long-term trend filter to open long trades only if it considers current price action as an uptrend. More information in "Methodology" and "Justification of Methodology" paragraphs. The strategy opens only long trades.
Unique Features
No fixed stop-loss and take profit: Instead of fixed stop-loss level strategy utilizes technical condition obtained by Fractals and Alligator to identify when current uptrend is likely to be over (more information in "Methodology" and "Justification of Methodology" paragraphs)
Configurable Trading Periods: Users can tailor the strategy to specific market windows, adapting to different market conditions.
Multilayer trades opening system: strategy uses only 10% of capital in every trade and open up to 5 trades at the same time if script consider current trend as strong one.
Short and long term trend trade filters: strategy uses EMA as high probability long-term trend filter and Alligator and Fractal combination as a short-term one.
Methodology
The strategy opens long trade when the following price met the conditions:
1. Price closed above EMA (by default, period = 100). Crossover is not obligatory.
2. Combination of Alligator and Williams Fractals shall consider current trend as an upward (all details in "Justification of Methodology" paragraph)
3. Acceleration/Deceleration shall create one of two types of long signals (all details in "Justification of Methodology" paragraph). Buy stop order is placed one tick above the candle's high of last created long signal.
4. If price reaches the order price, long position is opened with 10% of capital.
5. If currently we have opened position and price creates and hit the order price of another one long signal, another one long position will be added to the previous with another one 10% of capital. Strategy allows to open up to 5 long trades simultaneously.
6. If combination of Alligator and Williams Fractals shall consider current trend has been changed from up to downtrend, all long trades will be closed, no matter how many trades has been opened.
Script also has additional visuals. If second long trade has been opened simultaneously the Alligator's teeth line is plotted with the green color. Also for every trade in a row from 2 to 5 the label "Buy More" is also plotted just below the teeth line. With every next simultaneously opened trade the green color of the space between teeth and price became less transparent.
Strategy settings
In the inputs window user can setup strategy setting: EMA Length (by default = 100, period of EMA, used for long-term trend filtering EMA calculation). User can choose the optimal parameters during backtesting on certain price chart.
Justification of Methodology
Let's explore the key concepts of this strategy and understand how they work together. We'll begin with the simplest: the EMA.
The Exponential Moving Average (EMA) is a type of moving average that assigns greater weight to recent price data, making it more responsive to current market changes compared to the Simple Moving Average (SMA). This tool is widely used in technical analysis to identify trends and generate buy or sell signals. The EMA is calculated as follows:
1.Calculate the Smoothing Multiplier:
Multiplier = 2 / (n + 1), Where n is the number of periods.
2. EMA Calculation
EMA = (Current Price) × Multiplier + (Previous EMA) × (1 − Multiplier)
In this strategy, the EMA acts as a long-term trend filter. For instance, long trades are considered only when the price closes above the EMA (default: 100-period). This increases the likelihood of entering trades aligned with the prevailing trend.
Next, let’s discuss the short-term trend filter, which combines the Williams Alligator and Williams Fractals. Williams Alligator
Developed by Bill Williams, the Alligator is a technical indicator that identifies trends and potential market reversals. It consists of three smoothed moving averages:
Jaw (Blue Line): The slowest of the three, based on a 13-period smoothed moving average shifted 8 bars ahead.
Teeth (Red Line): The medium-speed line, derived from an 8-period smoothed moving average shifted 5 bars forward.
Lips (Green Line): The fastest line, calculated using a 5-period smoothed moving average shifted 3 bars forward.
When the lines diverge and align in order, the "Alligator" is "awake," signaling a strong trend. When the lines overlap or intertwine, the "Alligator" is "asleep," indicating a range-bound or sideways market. This indicator helps traders determine when to enter or avoid trades.
Fractals, another tool by Bill Williams, help identify potential reversal points on a price chart. A fractal forms over at least five consecutive bars, with the middle bar showing either:
Up Fractal: Occurs when the middle bar has a higher high than the two preceding and two following bars, suggesting a potential downward reversal.
Down Fractal: Happens when the middle bar shows a lower low than the surrounding two bars, hinting at a possible upward reversal.
Traders often use fractals alongside other indicators to confirm trends or reversals, enhancing decision-making accuracy.
How do these tools work together in this strategy? Let’s consider an example of an uptrend.
When the price breaks above an up fractal, it signals a potential bullish trend. This occurs because the up fractal represents a shift in market behavior, where a temporary high was formed due to selling pressure. If the price revisits this level and breaks through, it suggests the market sentiment has turned bullish.
The breakout must occur above the Alligator’s teeth line to confirm the trend. A breakout below the teeth is considered invalid, and the downtrend might still persist. Conversely, in a downtrend, the same logic applies with down fractals.
In this strategy if the most recent up fractal breakout occurs above the Alligator's teeth and follows the last down fractal breakout below the teeth, the algorithm identifies an uptrend. Long trades can be opened during this phase if a signal aligns. If the price breaks a down fractal below the teeth line during an uptrend, the strategy assumes the uptrend has ended and closes all open long trades.
By combining the EMA as a long-term trend filter with the Alligator and fractals as short-term filters, this approach increases the likelihood of opening profitable trades while staying aligned with market dynamics.
Now let's talk about Acceleration/Deceleration signals. AC indicator is calculated using the Awesome Oscillator, so let's first of all briefly explain what is Awesome Oscillator and how it can be calculated. The Awesome Oscillator (AO), developed by Bill Williams, is a momentum indicator designed to measure market momentum by contrasting recent price movements with a longer-term historical perspective. It helps traders detect potential trend reversals and assess the strength of ongoing trends.
The formula for AO is as follows:
AO = SMA5(Median Price) − SMA34(Median Price)
where:
Median Price = (High + Low) / 2
SMA5 = 5-period Simple Moving Average of the Median Price
SMA 34 = 34-period Simple Moving Average of the Median Price
The Acceleration/Deceleration (AC) Indicator, introduced by Bill Williams, measures the rate of change in market momentum. It highlights shifts in the driving force of price movements and helps traders spot early signs of trend changes. The AC Indicator is particularly useful for identifying whether the current momentum is accelerating or decelerating, which can indicate potential reversals or continuations. For AC calculation we shall use the AO calculated above is the following formula:
AC = AO − SMA5(AO), where SMA5(AO)is the 5-period Simple Moving Average of the Awesome Oscillator
When the AC is above the zero line and rising, it suggests accelerating upward momentum.
When the AC is below the zero line and falling, it indicates accelerating downward momentum.
When the AC is below zero line and rising it suggests the decelerating the downtrend momentum. When AC is above the zero line and falling, it suggests the decelerating the uptrend momentum.
Now we can explain which AC signal types are used in this strategy. The first type of long signal is when AC value is below zero line. In this cases we need to see three rising bars on the histogram in a row after the falling one. The second type of signals occurs above the zero line. There we need only two rising AC bars in a row after the falling one to create the signal. The signal bar is the last green bar in this sequence. The strategy places the buy stop order one tick above the candle's high, which corresponds to the signal bar on AC indicator.
After that we can have the following scenarios:
Price hit the order on the next candle in this case strategy opened long with this price.
Price doesn't hit the order price, the next candle set lower high. If current AC bar is increasing buy stop order changes by the script to the high of this new bar plus one tick. This procedure repeats until price finally hit buy order or current AC bar become decreasing. In the second case buy order cancelled and strategy wait for the next AC signal.
If long trades are initiated, the strategy continues utilizing subsequent signals until the total number of trades reaches a maximum of 5. All open trades are closed when the trend shifts to a downtrend, as determined by the combination of the Alligator and Fractals described earlier.
Why we use AC signals? If currently strategy algorithm considers the high probability of the short-term uptrend with the Alligator and Fractals combination pointed out above and the long-term trend is also suggested by the EMA filter as bullish. Rising AC bars after period of falling AC bars indicates the high probability of local pull back end and there is a high chance to open long trade in the direction of the most likely main uptrend. The numbers of rising bars are different for the different AC values (below or above zero line). This is needed because if AC below zero line the local downtrend is likely to be stronger and needs more rising bars to confirm that it has been changed than if AC is above zero.
Why strategy use only 10% per signal? Sometimes we can see the false signals which appears on sideways. Not risking that much script use only 10% per signal. If the first long trade has been open and price continue going up and our trend approximation by Alligator and Fractals is uptrend, strategy add another one 10% of capital to every next AC signal while number of active trades no more than 5. This capital allocation allows to take part in long trades when current uptrend is likely to be strong and use only 10% of capital when there is a high probability of sideways.
Backtest Results
Operating window: Date range of backtests is 2023.01.01 - 2024.11.01. It is chosen to let the strategy to close all opened positions.
Commission and Slippage: Includes a standard Binance commission of 0.1% and accounts for possible slippage over 5 ticks.
Initial capital: 10000 USDT
Percent of capital used in every trade: 10%
Maximum Single Position Loss: -5.15%
Maximum Single Profit: +24.57%
Net Profit: +2108.85 USDT (+21.09%)
Total Trades: 111 (36.94% win rate)
Profit Factor: 2.391
Maximum Accumulated Loss: 367.61 USDT (-2.97%)
Average Profit per Trade: 19.00 USDT (+1.78%)
Average Trade Duration: 75 hours
How to Use
Add the script to favorites for easy access.
Apply to the desired timeframe and chart (optimal performance observed on 3h BTC/USDT).
Configure settings using the dropdown choice list in the built-in menu.
Set up alerts to automate strategy positions through web hook with the text: {{strategy.order.alert_message}}
Disclaimer:
Educational and informational tool reflecting Skyrex commitment to informed trading. Past performance does not guarantee future results. Test strategies in a simulated environment before live implementation
These results are obtained with realistic parameters representing trading conditions observed at major exchanges such as Binance and with realistic trading portfolio usage parameters.
LETF Leveraged Edge Strategy v1.5Overview
The strategy is based on Stochastics to detect trends and then makes Buys and Sell based on custom entry and exit criteria as described below in the Execution Logic Rules section. It will NOT work with standard Stochastics.
This is not a standard Stochastics implementation. It has been customized and modified, and does not match any widely known Stochastics variations (like Fast, Slow, or Full Stochastics) in its smoothing and iterative calculation process with:
• A unique smoothing mechanism.
• Iterative calculations.
• Additional conditional logic for strategy execution.
This strategy is designed to focus on volatile, liquid leveraged ETFs to capture gains equal to or better than Buy and Hold, and mitigate the risk of trading with a goal of reducing drawdown to a lot less than Buy and Hold. It has had successful backtest performance to varying degrees with TQQQ, SOXL, FNGU, TECL, FAS, UPRO, NAIL and SPXL. Results have not been good on other LETFs that have been backtested.
Performance
In this backtest the Net Profit shows to be $4,561 or 45.61%. Considering the initial order size was $1,000 I have to wonder if the Strategy Tester is calculating this correctly. The Strategy Tester Performance Summary shows the Buy and Hold Return at $61,165 or 611.7%. Based on calculating the price of the last shares sold, less the price paid, times the number of initial shares purchased, my math shows the Buy and Hold Gain at $4,572 or about equal with the strategy performance in this case. The Performance Summary also states the strategy had a Max DD of 3.46% which I believe is incorrect. Based on other backtests I’ve done, I believe the strategy drawdown here was closer to 28.4% and the Buy and Hold Drawdown at 82.7%. I manually calculated the Buy and Hold drawdown.
How it Works
The author provides training and support resource materials for this at his website. The strategy execution logic is driven by these rules:
Execution Logic Rules
Buy the LETF When:
BR #1a) The Daily Fast Line (FL) crosses above the Daily Slow Line (SL) and the FL is between the Low (L*) and High (H*) Range set (often referred to as Oversold and Overbought Lines). This can execute (Buy) any trading day of the week.
BR #1b) Re-Buy the next day after any Stop or Take Profit Sell if the Buy Rule condition is true (FL is above SL), if not, remain in cash and wait for the next Buy Signal.
Sell the LETF When:
SR #1a) The Daily Fast Line (FL) crosses below Daily Slow Line (SL) within the Low (L*) and High (H*) Range (often referred to as Oversold and Overbought Lines). “Crossunder Range Exit” This can execute (Sell) any trading day of the week.
SR #1b) If the (FL) crosses Below the SL above the Exit Level*, wait. Only Sell if the FL drops down below the Exit Level* “Crossunder Level Exit” This can execute (Sell) any trading day of the week.
SR #2a) Sell at the open any day the gap-down price is at or below the 1-Day Stop%*, based on previous day’s closing price (Execute on the day it happens.)
SR #2b) Sell intraday any day the price is at or below the 1-Day Stop %*, based on previous day’s closing price (Execute on the day it happens.)
SR #3a) Sell at the open any day the price is at or below the Trailing Stop %*, based on highest intraday price since Buy date (Execute on the day it happens.)
SR #3b) Sell intraday any day the price is at or below the Trailing Stop%*, based on highest intraday price since Buy date (Execute on the day it happens.)
SR #4) Sell any day when the opening price exceeds, or intraday price meets the Profit Target % price* (Execute on the day it happens.)
SR #5) After each Sell go to Rule BR #1b to determine if a Re-Buy should occur the next day, or stay in cash until next Buy Signal
Settings:
Properties Tab – Initial Capital has been set to $10,000 and order size 10% of Equity, 0.1% commission and 3 Ticks for slippage. Net order size is $1,000
Input Tab:
Stochastic
Timeframe is selected to Daily or Weekly based on preference. Daily has more trades, but on average higher profitability.
Type: Proprietary (best selection for most LETFs, but a few will work better with the Full selection
%k Length 20, %K Smoothing 14, %D Smoothing (many LETFs work better with a specific Stoch setting, often each different) A List of these is provided for your starting point.
Trade Settings
Direction: Longs (This strategy only works on the Long side)
Stop Type: Trailing is recommended, but Fixed is an option.
Stop % (based on user risk tolerance)
PD Stop % (Suggest start at 5%. Based on volatility of LETF and is a stop percentage from prior day’s close. Designed to protect against sudden market volatility. Will need to balance between strategy performance and user risk tolerance)
Profit Target: User preference. (I can help with suggestions based on historical performance)
Entry/Exit Conditions
Enter on Tie: Default Checked – if a Fast line crosses a Slow line for a Buy signal, but doesn’t do so in the range set, this will trigger if it crosses at a tie.
Renter – Default Checked – If stopped out of a position, this tells the strategy to re-buy the position the next day if the conditions are still positive.
Exit Level: This is a exit level for a Fast cross below a Slow line that takes place above the Sell Range, but only happens if the Fast continues down to the level set. These usually don’t happen often, but can have a significant impact on performance. Unfortunately, it’s a trial and error process starting with 90 and working down to see if there’s any positive impact.
Trade Range
Buy Range: Start at typical 20 to 80. Expand the low end down first to check on performance impact. Normally a wide buying range is better for performance.
Sell Range: Start at 20 to 80 and tighten gradually to see performance impact. In some cases a very tight sell range does better. I have worked on our primary LETFs for many months to determine ranges for each that typically produce better results.
External Indicator: Some additional indicators have a positive impact on the strategy performance by increasing P/l, reducing drawdown and reducing the number of trades. This is not always the case and each LETF and time period for the LETF will have a bearing on whether the secondary indicator will help or not. Two that have helped are the MACD Histogram, and the Sloe-Velocity Indicator by Kamleshkumar43. Sometimes a couple of different indicators will have a positive impact, then it’s a personal preference which you pick to use with the strategy.
Since this strategy is focused on a very narrow selection of liquid LETFs, I have a lot of experience experimenting with the settings for the primary ones and can suggest things that will help. Additional training on the rules, working with the settings, and mitigating some of the negative trades during choppy markets is available at the website.
Chart
The strategy can be selected to use either a Daily or Weekly version of stochastic. This is important because the characteristics are different while still generating very good gains and minimal drawdowns. Generally, the daily stochastic will have a greater number of, and certainly more frequent, trades than the weekly stochastic. However, on average the daily version of the stochastic will generates greater profitability.
The Settings tabs have tooltip icons that will assist in inputting values that correspond to the written rules for the strategy, and some include specific rule detail.
Buying
The strategy generates Buy signals with the Fast line crossing over the Slow line within a “Buy Range” which is adjusted based on volatility of the leveraged ETF. This is unique in that a default is set for these entries to occur if the values are tied and doesn’t need to be within the high and low range if that occurs. The trader can select in the strategy for this to occur the same day, if he’s selected a Daily Stochastic timeframe, or at the end of the trading week if he’s selected a Weekly stochastic timeframe. The volatility of a leveraged ETF will sometimes cause a shake-out exit, a trailing stop can be hit, or there can be an exit based on taking a profit. A big part of the timing challenge was how to handle these. The strategy normally (set as a default) will immediately re-buy the next day only if the original buy conditions are still true. This helps capture gains when conditions are still favorable but keeps the trader out when they’re not.
Selling
Exits are handled in several ways. The strategy will exit if there is a fast line cross below a slow line within the “range”. The range is adjusted based on volatility of the leveraged ETF. The exit occurs at the close of the day if the trader has selected to use a Daily stochastic setting. The exit will occur at the end of the trading week if the trader has chosen a weekly stochastic strategy. The trader will set a level based on the instrument and volatility for another exit type. The level will sometimes coincide with the range exit high level but does not need to. If a fast line crosses down through a slow line above the level set, and then comes down to that level, the strategy will exit the position.
Another unique aspect of the strategy is the PD Stop setting. This is short for “Prior Day”, Rather than a normal stop based on the price paid for a position, the PD Stop is based on a percentage drop from the previous day’s closing price. This helps account for the volatility of the leveraged ETF and will cause an exit quickly if there’s a market, or index moving event. This helps capture gains and reduce risk should there be continued pullback.
Exits will also occur based on setting a trailing stop level and profit taking level. These are adjusted based on the leveraged ETFs volatility and historical performance.
Limitations
Choppy, or sideways markets are the most prone to poor performance and potential for being stopped out multiple times. If stopped out two consecutive times, make sure you’re monitoring market health and there are clear signs of a new uptrend such as a 10D and 21D MA in proper alignment and moving up. If you get a Buy signal from the strategy and you’re not confident yet about market and price direction then it’s fine to wait a day, or several days, to enter after the Buy signal when you have greater confidence about market direction. The author can help with a short list of tactical rules developed for these sideways or choppy markets.
This strategy has proven successful backtest results with a very limited set of LETFs as discussed earlier. The author does not know if it will prove successful with any others, or other types of ETFs such as 2X or plain ETFs. A lot more testing needs to be done.
The strategy buys and sells , excluding stops or take profit, at the market close. It can be very challenging to enter an order at market close.
Disclaimer
Please remember that past performance may not be indicative of future results.
Due to various factors, including changing market conditions, the strategy may no longer perform as well as in historical backtesting. This post and the script do not provide any financial advice and are for educational and entertainment purposes only.
Nifty Options Trendy Markets with TSLNifty options strategy that works on volume, technical analysis etc
Ichimoku by FarmerBTCLegal Disclaimer
This strategy, "Ichimoku by FarmerBTC," is provided for educational and informational purposes only. It does not constitute financial advice and should not be relied upon as such. Trading and investing involve substantial risk, including the potential for losing more than your initial investment. Past performance is not indicative of future results. Always consult with a qualified financial advisor before making trading or investment decisions. The author of this strategy is not responsible for any financial losses incurred through its use.
Overview
The "Ichimoku by FarmerBTC" strategy is a trend-following system built on the Ichimoku Cloud indicator, enhanced with volume analysis and a high-timeframe Simple Moving Average (HTF SMA) condition. It is designed to identify long-only trade opportunities and performs optimally on higher timeframes, such as the daily chart or above.
Core Components
1. Ichimoku Cloud
The Ichimoku Cloud is a comprehensive trend-following indicator that helps identify the overall market direction and momentum. It consists of:
Conversion Line (Tenkan-Sen): Measures short-term momentum.
Base Line (Kijun-Sen): Filters medium-term trends.
Leading Span A: The average of the Conversion and Base Lines, forming one cloud boundary.
Leading Span B: The midpoint of the highest high and lowest low over a longer period, forming the other cloud boundary.
Key Ichimoku Rules Applied:
The strategy identifies bullish trends when:
The price is above the cloud.
The cloud is bullish (Leading Span A > Leading Span B).
2. High-Timeframe Simple Moving Average (HTF SMA)
This condition ensures alignment with the broader trend:
Default SMA Length: 13 periods.
Default Timeframe: 1 day.
HTF SMA Rule:
Trades are allowed only when the price is above the HTF SMA, ensuring alignment with the larger trend.
3. Volume Analysis
The strategy uses volume to validate trade setups:
Volume MA: A 20-period moving average of volume is calculated.
Trades are allowed only when the current volume is at least 1.5x the Volume MA, indicating strong market participation.
Entry and Exit Rules
Entry Condition (Long Only):
Price above the Ichimoku Cloud: Confirms a bullish trend.
Bullish Cloud: Leading Span A > Leading Span B indicates upward momentum.
Price above the HTF SMA: Ensures alignment with the broader trend.
Volume exceeds threshold: Confirms strong market participation.
Exit Condition:
The strategy exits the position when the price moves below the Ichimoku Cloud, signaling a potential trend reversal.
Best Timeframes
This strategy is optimized for daily (1D) or higher timeframes (e.g., weekly 1W). Using it on lower timeframes may produce false signals due to increased noise in price and volume data.
Default Settings
Ichimoku Settings:
Conversion Line Period: 10
Base Line Period: 30
Lagging Span Period: 53
Displacement: 26
HTF SMA Settings:
SMA Length: 13
Timeframe: 1 Day
Volume Settings:
Volume MA Length: 20
Volume Multiplier: 1.5x
Visualization
Ichimoku Cloud:
Dynamic cloud coloring (green for bullish, red for bearish) helps identify the current trend.
HTF SMA:
A purple line overlays the chart, providing a clear representation of the high-timeframe trend.
Volume Panel:
An optional panel displays volume (blue histogram) and the Volume Moving Average (orange line) to analyze market participation.
Advantages of This Strategy
High Accuracy on Higher Timeframes:
Filtering trades using the Ichimoku Cloud, HTF SMA, and volume ensures robust trend alignment, reducing false signals.
Volume Confirmation:
Incorporates volume as a validation metric to enter trades only during strong market participation.
Easy Customization:
Parameters like Ichimoku periods, SMA length, timeframe, and volume thresholds can be adjusted to suit different assets or trading styles.
Limitations
Not Suitable for Low Timeframes:
Lower timeframes can produce excessive noise, leading to false signals.
Long-Only:
The strategy is designed only for bullish markets and does not support short trades.
Lagging Nature of Indicators:
Both the Ichimoku Cloud and SMA are lagging indicators, meaning they react to past price movements.
Conclusion
The "Ichimoku by FarmerBTC" strategy is an excellent tool for trend-following on daily or higher timeframes. Its combination of Ichimoku Cloud, high-timeframe SMA, and volume ensures a robust framework for identifying high-probability long trades in trending markets. However, users are advised to test the strategy thoroughly and manage their risk appropriately. Always consult with a financial professional before making trading decisions.
TTM Grid StrategyThis strategy uses a TTM (based on EMAs of highs and lows) to determine the market's trend direction.
It then deploys a grid trading system around a dynamically updated base price, with the grid's direction and levels adjusting based on the trend.
Trades are executed as the price crosses the predefined grid levels, with the strategy risking a set percentage of equity per trade.
Core Strategy Logic:
TTM State Calculation (ttmState() function):
* Calculates two EMAs based on the `ttmPeriod`: one for the lows (`lowMA`) and one for the highs (`highMA`).
* Defines two threshold levels: `lowThird` (1/3 from the bottom) and `highThird` (2/3 from the bottom) of the range between `highMA` and `lowMA`.
* Returns the current TTM state as an integer:
+ `1` if the close price is above `highThird` (indicating an uptrend).
+ `0` if the close price is below `lowThird` (indicating a downtrend).
+ `-1` if the close price is between `lowThird` and `highThird` (indicating a neutral state).
Liquid Pours XtremeStrategy Description: Liquid Pours Xtreme
The Liquid Pours Xtreme is an innovative trading strategy that combines the analysis of specific time-based patterns with price comparisons to identify potential opportunities in the forex market. Designed for traders seeking a structured methodology based on clear rules, this strategy offers integration with Telegram for real-time alerts and provides visual tools to enhance trade management.
Key Features:
Analysis of Specific Time Patterns: The strategy captures and compares closing prices at two key moments during the trading day, identifying recurring patterns that may indicate future market movements.
Dynamic SL and TP Levels Implementation: Utilizes tick-based calculations to set Stop-Loss and Take-Profit levels, adapting to the current market volatility.
Advanced Telegram Integration: Provides detailed alerts including information such as the asset, signal time, entry price, and SL/TP levels, facilitating real-time decision-making.
Complete Customization: Allows users to adjust key parameters, including operation schedules, weekdays, and visual settings, adapting to different trading styles.
Enhanced Chart Visualization: Includes visual elements like candle color changes based on signal state, event markers, and halos to highlight important moments.
Default Strategy Properties: Specific configuration for optimal risk management and simulation.
How the Strategy Works
Capturing Prices at Key Moments:
- The strategy records the closing price at two user-defined specific times. These times typically correspond to periods of high market volatility, such as the opening of the European session and the US pre-market.
- Rationale: Volatility and trading volume usually increase during these times, presenting opportunities for significant price movements.
Generating Signals Based on Price Comparison:
- Buy Signal: If the second closing price is lower than the first, it indicates possible accumulation and is interpreted as a bullish signal.
- Sell Signal: If the second closing price is higher than the first, it suggests possible distribution and is interpreted as a bearish signal.
- Signals are only generated on selected trading days, allowing you to avoid days with lower liquidity or higher risk.
Calculating Dynamic SL and TP Levels:
- Stop-Loss (SL) and Take-Profit (TP) levels are calculated based on the entry price and a user-defined number of ticks, adapting to market volatility.
- The strategy offers the option to base these levels on the close of the signal candle or the open of the next candle, providing flexibility according to the trader's preference.
- SL and TP boxes are drawn on the chart for visual reference, facilitating trade management.
Automatic Execution and Alerts:
- Upon signal generation, the strategy automatically executes a market order (buy or sell).
- Sends a detailed alert to your Telegram channel, including essential information for quick decision-making.
Visual Elements:
- Colors candles based on the signal state: buy, sell, or neutral, allowing for quick trend identification.
- Provides a smooth color transition between signal states and uses markers and halos to highlight important events and signals on the chart.
Trade Management:
- Manages open trades with automatic exit conditions based on the established SL and TP levels.
- Includes mechanisms to prevent exceeding TradingView's limitations on boxes and labels, ensuring optimal script performance.
Originality and utility:
- This strategy incorporates a unique approach focusing on specific time patterns and their relationship to institutional activity in the market.
How to Use the Strategy
Add the Script to the Chart:
- Go to the indicators menu in TradingView.
- Search for " Liquid Pours Xtreme " and add it to your chart.
Set Up Telegram Alerts:
- Enter your Telegram Chat ID in the script parameters to receive alerts.
- Customize the Buy and Sell alert messages as desired.
Configure Time Patterns:
- Set the hours and minutes for the two times you want to compare closing prices, aligning them with relevant market sessions or events.
Set SL and TP Parameters:
- Define the number of ticks for the Stop-Loss and Take-Profit levels, adapting them to the asset you're trading and your risk tolerance.
- Choose the basis for SL and TP calculation (close of the signal candle or open of the next candle).
Select Trading Days:
- Enable or disable trading on specific days of the week, allowing you to avoid days with lower activity or unexpected volatility.
Customize Visual Elements:
- Adjust the colors and styles of visual elements to enhance readability and suit your personal preferences.
Monitor the Strategy:
- Observe the chart for signals and use Telegram alerts to stay informed of new opportunities, even when you're not at your terminal.
Testing and Optimization:
- Use TradingView's backtesting features to evaluate the historical performance of the strategy with different parameters.
- Adjust and optimize the parameters based on the results and your own analysis.
Adjust the Strategy Properties:
- Ensure that the strategy properties (order size, commission, slippage) are aligned with your trading account and platform to obtain realistic results.
Strategy Properties (Important)
This script backtest is conducted on M30 EURUSD , using the following backtesting properties:
Initial Capital: $10,000
Order Size: 50,000 Contracts (equivalent to 0.5% of the capital)
Commission: $0.20 per order
Slippage: 1 tick
Pyramiding: 1 order
Verify Price for Limit Orders: 0 ticks
Recalculate on Order Execution: Enabled
Recalculate on Every Tick: Enabled
Recalculate After Order Filled: Enabled
Bar Magnifier for Backtesting Precision: Enabled
We use these properties to ensure a realistic preview of the backtesting system. Note that default properties may vary for different reasons:
- Order Size: It is essential to calculate the contract size according to the traded asset and desired risk level.
- Commission and Slippage: These costs can vary depending on the market and instrument; there is no default value that might return realistic results.
We strongly recommend all users adjust the Properties within the script settings to align with their accounts and trading platforms to ensure the results from the strategies are realistic.
Backtesting Results:
- Net Profit: $4,037.50 (40.37%)
- Total Closed Trades : 292
- Profitability Percentage: 26.71%
- Profit Factor: 1.369
- Max Drawdown: $769.30 (6.28%)
- Average Trade: $13.83 (0.03%)
- Average Bars in Trades: 11
These results were obtained under the mentioned conditions and properties, providing an overview of the strategy's historical performance.
Interpreting Results:
- The strategy has demonstrated profitability in the analyzed period, although with a win rate of 26.71%, indicating that success relies on a favorable risk-reward ratio.
- The profit factor of 1.369 suggests that total gains exceed total losses by that proportion.
- It is crucial to consider the maximum drawdown of 6.28% when evaluating the strategy's suitability to your risk tolerance.
Risk Warning:
Trading leveraged financial instruments carries a high level of risk and may not be suitable for all investors. Before deciding to trade, you should carefully consider your investment objectives, level of experience, and risk tolerance. Past performance does not guarantee future results. It is essential to conduct additional testing and adjust the strategy according to your needs.
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What Makes This Strategy Original?
Time-Based Pattern Approach: Unlike conventional strategies, this strategy focuses on identifying time patterns that reflect institutional activity and macroeconomic events that can influence the market.
Advanced Technological Integration: The combination of automatic execution and customized alerts via Telegram provides an efficient and modern tool for active traders.
Customization and Adaptability: The wide range of adjustable parameters allows the strategy to be tailored to different assets, time zones, and trading styles.
Enhanced Visual Tools: Incorporated visual elements facilitate quick market interpretation and informed decision-making.
Additional Considerations
Continuous Testing and Optimization: Users are encouraged to perform additional backtesting and optimize parameters according to their own observations and requirements.
Complementary Analysis: Use this strategy in conjunction with other indicators and fundamental analysis to reinforce decision-making.
Rigorous Risk Management: Ensure that SL and TP levels, as well as position sizing, align with your risk management plan.
Updates and Support: I am committed to providing updates and improvements based on community feedback. For inquiries or suggestions, feel free to contact me.
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Example Configuration
Assuming you want to use the strategy with the following parameters:
Telegram Chat ID: Your unique Telegram Chat ID
First Time (Hour:Minute): 6:30
Second Time (Hour:Minute): 7:30
SL Ticks: 100
TP Ticks: 400
SL and TP Basis: Close of the Signal Candle
Trading Days: Tuesday, Wednesday, Thursday
Simulated Initial Capital: $10,000
Risk per Trade in Simulation: $50 (-0.5% of capital)
Slippage and Commissions in Simulation: 1 tick of slippage and $0.20 commission per trade
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Conclusion
The Liquid Pours Xtreme strategy offers an innovative approach by combining specific time analysis with robust risk management and modern technological tools. Its original and adaptable design makes it valuable for traders looking to diversify their methods and capitalize on opportunities based on less conventional patterns.
Ready for immediate implementation in TradingView, this strategy can enrich your trading arsenal and contribute to a more informed and structured approach to your operations.
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Final Disclaimer:
Financial markets are volatile and can present significant risks. This strategy should be used as part of a comprehensive trading approach and does not guarantee positive results. It is always advisable to consult with a professional financial advisor before making investment decisions.
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Enhanced Swing Trading Strategy//@version=5
strategy("Enhanced Swing Trading Strategy", overlay=true)
// Input Parameters
smaShortLength = input.int(50, title="Short SMA Length")
smaLongLength = input.int(200, title="Long SMA Length")
rsiLength = input.int(14, title="RSI Length")
rsiOverbought = input.int(70, title="RSI Overbought Level")
rsiOversold = input.int(30, title="RSI Oversold Level")
takeProfitPerc = input.float(2.0, title="Take Profit (%)")
stopLossPerc = input.float(1.0, title="Stop Loss (%)")
riskPerc = input.float(1.0, title="Risk per Trade (%)")
alertOnSignal = input.bool(true, title="Enable Alerts")
// Indicators
smaShort = ta.sma(close, smaShortLength)
smaLong = ta.sma(close, smaLongLength)
rsi = ta.rsi(close, rsiLength)
= ta.macd(close, 12, 26, 9)
// Conditions
longCondition = ta.crossover(smaShort, smaLong) and rsi > rsiOversold and macdLine > signalLine
shortCondition = ta.crossunder(smaShort, smaLong) and rsi < rsiOverbought and macdLine < signalLine
// Position Sizing based on Risk
capital = strategy.equity
riskAmount = capital * (riskPerc / 100)
stopLossValue = riskAmount / (stopLossPerc / 100)
positionSize = stopLossValue / close
// Entry Signals
if (longCondition)
strategy.entry("Long", strategy.long, qty=positionSize)
if alertOnSignal
alert("Swing Trading: Long Position Opened", alert.freq_once)
if (shortCondition)
strategy.entry("Short", strategy.short, qty=positionSize)
if alertOnSignal
alert("Swing Trading: Short Position Opened", alert.freq_once)
// Exit Conditions
strategy.exit("Take Profit/Stop Loss",
from_entry="Long",
profit=takeProfitPerc,
loss=stopLossPerc)
strategy.exit("Take Profit/Stop Loss",
from_entry="Short",
profit=takeProfitPerc,
loss=stopLossPerc)
// Plotting
plot(smaShort, color=color.blue, title="SMA 50")
plot(smaLong, color=color.red, title="SMA 200")
hline(rsiOverbought, "RSI Overbought", color=color.red)
hline(rsiOversold, "RSI Oversold", color=color.green)
plot(macdLine, color=color.green, title="MACD Line")
plot(signalLine, color=color.orange, title="Signal Line")
// Draw Entry and Exit Labels
if (strategy.position_size > 0)
label.new(bar_index, high, "Long Open", style=label.style_label_up, color=color.green)
if (strategy.position_size < 0)
label.new(bar_index, low, "Short Open", style=label.style_label_down, color=color.red)
VIDYA Auto-Trading(Reversal Logic)
Purpose and Unique Features
This script leverages the Variable Index Dynamic Average (VIDYA) to implement a dynamic trend-following auto-trading strategy. By adapting to price volatility, it optimizes entry points and strengthens risk management. Key differentiators of this strategy include:
VIDYA Characteristics:
Quickly responds to price momentum changes through dynamic calculations.
Incorporates volatility adjustments for enhanced trend detection accuracy.
ATR Band Utilization:
Measures market volatility to set stop-loss levels and guide risk management.
Supports more calculated trade entries in volatile markets.
Visual Trend Representation:
Displays "green zones" for uptrends and "red zones" for downtrends.
Enables intuitive understanding of trend continuation and reversal.
Usage Instructions
Entry Conditions
Long Entry:
Enter when the price crosses above the upper band.
Close any previous short positions and initiate a new long position.
Short Entry:
Enter when the price crosses below the lower band.
Close any previous long positions and initiate a new short position.
Exit Conditions
Take Profit and Stop Loss:
Reverse Position Strategy or Position Reversal Strategy
Account Size: ¥100,0000
Commissions and Slippage: Assumed commission of 94 pips per trade and slippage of 1 pip.
Risk per Trade: 10% of account equity (adjustable based on risk tolerance).
Script Parameters
VIDYA Length: The period for calculating the trend (e.g., 14).
Momentum Period: The lookback period for calculating the Chande Momentum Oscillator (CMO).
ATR Band Distance: Adjustment coefficient for the band width (e.g., 1.5).
Price Source: Choose from close, open, high, or low prices for VIDYA calculation.
Trend Display Colors: Customize the colors for uptrend and downtrend zones.
Visualization Options: Toggle the display of trend lines, bands, and other elements on or off.
Strategy Features and Enhancements
Dynamic Momentum Adaptation:
Utilizes VIDYA's sensitivity to momentum changes for rapid trend detection.
Volatility-Aware Risk Management:
Employs ATR to dynamically adjust risk levels, ensuring resilience in volatile markets.
Enhanced Visual Indicators:
Clearly plots trend zones and entry points on the chart.
Simplifies analysis with intuitive visual cues.
Credits
This script is inspired by the innovative work of BigBeluga, whose indicators laid the foundation for this enhanced trend-following strategy. By leveraging BigBeluga’s insights, this script integrates VIDYA, ATR Bands, and other technical elements to create a more dynamic and intuitive trading tool.
We extend our gratitude to BigBeluga and the broader trading community for their invaluable contributions, which have enabled this advanced implementation.
Disclaimer:
This script is provided for educational purposes, and past performance does not guarantee future results. Always practice proper risk management in live trading scenarios.
By leveraging VIDYA, this strategy provides a precise and intuitive approach to trend-following. It is particularly effective in capturing market reversals and adapting to sudden price changes in volatile environments.
TheHorsyAlgoPROThe Horsy algo is an automated strategy that uses any minute Higher timeframe range as reference and search for a purge of liquidity on the HTF high or low where buyside or sell side liquidity is, the algo only search this at specific desired times that can be configured according to the time you usually trade, the strategy is known as Turtle soup purge and reverse or lately as CRT.
Why is useful?
The purpose of this Algorithm is to help turtle soup traders to quickly identify when the market is likely to reverse the algo evaluates if the opportunity is worth it, base on risk reward and other desired filters. Also this strategy can help to quickly backtest the trader strategy it can be configured in different timeframes and adapt to the trader personality, they can easily see the results and statistics and notice if its profitable or not.
This algo is useful for intraday traders looking for a purge and reverse at a key times and at key HTF price levels this only looks the previous HTF highs and lows but is important to also monitor Order blocks, FVGs, gaps, or wicks to have the best results.
How it works and how it does it?
The Horsy algo simply Jumps from one type of liquidity to another one buyside to sell side or vice versa. In order for the algo to trigger an entry it has to meet these conditions
1. Take HTF liquidity, trade above a HTF high or below a HTF low in the selected time window
2. Make a change in the state of delivery with a close below the previous candle low for shorts and close above previous candle high for longs.
3. Allow for a reasonable risk reward, it will use the highest high for shorts and the lowest low for longs. The default take profit is the opposite side of the range.
4. Validate others user filters this include enter only trades aligned with the HTF bias, or trades aligned with the LTF bias or booth. The algo have the option to enter only premium and discount entries. And finally, an option to allow for different contract sizes depending of the maximum percent of the account we want to risk default is 1%. For this last option is important to check the initial balance and leverage are configured correctly, is disable by default because it requires more capital to perform well.
We can see the algo performing in the picture below with a short trade, notice there are some white lines, they are the high or the low of HTF candle that start generating inside candles in the HTF meaning a possible consolidation. The algo plots the HTF ranges in a shaded boxes as you can see below
The HTF bias as you can see in the picture is calculated based on the last close of the HTF meaning close above previous HTF high is bullish close below previous HTF low is bearish. This HTF bias level is also the last HTF mid-price or 50%. By default, this line is enabled.
The LTF bias is calculated based on the range created from the expansion outside the previous HTF range is also the mid-price. If the LTF close above previous HTF high is bullish and if the LTF close below previous HTF low is bearish. By default this LTF bias line is disable.
This strategy includes an original and personal developed code that uses dealing ranges to recognize if the market is expanding, retracing, reversing or consolidating. This allow the algo to exit the position when it detects a retracement or at the end of the expansion. This is the default exit type.
You can monitor the previous dealing ranges created in history with an option than can be enable, by default is disable, this ranges are created after price takes buyside and then sell side or vice versa. So this dealing ranges can be useful also to identify minor pools of liquidity and premium and discount in the lower timeframe.
The picture below is a long example, the exit in this case is just at the high of the range. The normal take profit is in a blue line for longs.
How to use it?
First select the desired HTF timeframe recommended is from 30min to 240min then you setup the chart on the lower timeframe you want to trade recommended is from 1min to 15min to enter. By default This strategy is designed to work for intraday during key times when price take stops and then moves quickly away from them. You can select as much as 6 different times or just one. After you select the desired time window where the algo will look for the purge and reverse, They are highlighted in the candles that change colors excluding the gray ones that indicates consolidation.
Then the Algo allow to performs several additional filters in the entries you can select if you want to trade only longs or shorts trades, you can select when to move the stop loss to Break even. In deviations of the risk or you can just select to remove risk when price hits the 50% of previous HTF range.
You can select the minimum desired risk reward of the trade before is allow to be taken. Once is configured correctly the algo should trigger signals with a triangle up or down plus the strategy entry.
At the beginning of the picture there are some blue lines in the HTF high low and close, this is to easily identify that the market is in the Asia session, the time can be configured by the user, these lines are normally gray.
On the right top of the screen you can see some statistics about the strategy how many trades it took, ARR is an approximated value of the accumulated total risk reward of all the trades when they get closed in the simulation.
Profit factor and percent profitable are also shown should be green it means that the strategy makes money over time. But apart from that is important to notice how it makes money it is stable over time? it is a roller coaster? that why I Include this other measurements MxcsTps is the maximum consecutives take profits and Mxcsls is the maximum consecutive stop losses it takes, the slash number after it is the consecutive Break evens. So this way you know what to expect and what is normal in the strategy.
The algo shows all the times the stop loss, take profit and break even level if enable in the colored red lines for short and blue lines for longs. You can also select how price will manage the profit or stoploss point meaning that you can choose to wait for the candle to close to invalidate your idea or to take profit. This is good to avoid liquidity sweeps but can also lead to mayor loses if the idea is wrong. The default setting is to close the trade when price takes the high or low where the stoploss is, the take profit is taken after a retracement to allow to profit on expansions. You can select also to exit on a reversal if you want to ride all the move. This last option has to be used with caution because sometimes price just retrace or reverse very fast decreasing the trade profit and overall strategy performance.
The algo have the option to use standard deviation from the normal risk if you prefer to prevent liquidity sweeps near the stop level this make wider stops but can lead to increased loses so it has to be used carefully.
Below is a picture that show the entry stop and take profit levels with an exit on a retracement activated.
Strategy Results
The backtesting results are obtained simulating a 2000usd account in the Micro Nasdaq using 1 contract per trade. Commission are set to 2usd per contract, slippage to 1tick. You can see in list of trades we are not risking more than 1 % percent of the account. The backtested range is from august to November 2024. This strategy doesn’t generate too much trades because of the time filters and conditions that has to be meet to take an entry but you can see the results of the last 4months with the available data that are around 32 trades.
The default settings for this strategy is HTF as 240min designed to work on a LTF 5min chart, the default purge times are 245-300, 745-800, 845-900, 1045-1100 and 1245-1300 UTC-4, the algo will look for shorts or longs, with a minimum risk reward of 2.0. With an additional filter of the HTFBias. The take profit is by default taken on the first retracement after hitting the target. The default settings are optimized to work on the Nasdaq or Spy, but can also perform well in other assets with the correct adjustments.
Remember entries constitute only a small component of a complete winning strategy. Other factors like risk management, position-sizing, trading frequency, trading fees, and many others must also be properly managed to achieve profitability. Past performance doesn’t guarantee future results. To really take advantage of this strategy you have to study turtle soup and the HTF key levels use this only as a confirmation that your overall idea will play out and use it to backtest your model.
Summary of features
·Adaptable strategy to different HTF timeframes from 1-1440min
· Select up to 6 different purge time windows UTC-4, UTC-5
· Choose desired Risk Reward per trade
· Easily see the HTF high low close and 50% key levels in the LTF
· Identify HTF consolidations that generate key major liquidity pools
· HTF/LTF bias filters to trade in favor of the big trend or in sync
· Shaded boxes that indicate if the market is bullish, bearish or consolidating
· See the current midpoint of the last expansion move
· Optimal trade entry filter to trade only in a discount or premium
· Customizable trade management take profit, stop, breakeven level
· Option to exit on a close, retracement or reversal after hitting the take profit level
· Option to exit on a close or reversal after hitting stop loss
· Configurable breakeven point with standard deviations or at 50% of the HTF
· Calculate different contract sizes depending of a percentage of the initial balance
· Standard deviations from normal risk can be used to prevent liquidity sweeps
· See dealing ranges history to check minor pools of liquidity and premium or discount
· Dashboard with instant statistics about the strategy current settings
DNSE VN301!, SMA & EMA Cross StrategyDiscover the tailored Pinescript to trade VN30F1M Future Contracts intraday, the strategy focuses on SMA & EMA crosses to identify potential entry/exit points. The script closes all positions by 14:25 to avoid holding any contracts overnight.
HNX:VN301!
www.tradingview.com
Setting & Backtest result:
1-minute chart, initial capital of VND 100 million, entering 4 contracts per time, backtest result from Jan-2024 to Nov-2024 yielded a return over 40%, executed over 1,000 trades (average of 4 trades/day), winning trades rate ~ 30% with a profit factor of 1.10.
The default setting of the script:
A decent optimization is reached when SMA and EMA periods are set to 60 and 15 respectively while the Long/Short stop-loss level is set to 20 ticks (2 points) from the entry price.
Entry & Exit conditions:
Long signals are generated when ema(15) crosses over sma(60) while Short signals happen when ema(15) crosses under sma(60). Long orders are closed when ema(15) crosses under sma(60) while Short orders are closed when ema(15) crosses over sma(60).
Exit conditions happen when (whichever came first):
Another Long/Short signal is generated
The Stop-loss level is reached
The Cut-off time is reached (14:25 every day)
*Disclaimers:
Futures Contracts Trading are subjected to a high degree of risk and price movements can fluctuate significantly. This script functions as a reference source and should be used after users have clearly understood how futures trading works, accessed their risk tolerance level, and are knowledgeable of the functioning logic behind the script.
Users are solely responsible for their investment decisions, and DNSE is not responsible for any potential losses from applying such a strategy to real-life trading activities. Past performance is not indicative/guarantee of future results, kindly reach out to us should you have specific questions about this script.
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Khám phá Pinescript được thiết kế riêng để giao dịch Hợp đồng tương lai VN30F1M trong ngày, chiến lược tập trung vào các đường SMA & EMA cắt nhau để xác định các điểm vào/ra tiềm năng. Chiến lược sẽ đóng tất cả các vị thế trước 14:25 để tránh giữ bất kỳ hợp đồng nào qua đêm.
Thiết lập & Kết quả backtest:
Chart 1 phút, vốn ban đầu là 100 triệu đồng, vào 4 hợp đồng mỗi lần, kết quả backtest từ tháng 1/2024 tới tháng 11/2024 mang lại lợi nhuận trên 40%, thực hiện hơn 1.000 giao dịch (trung bình 4 giao dịch/ngày), tỷ lệ giao dịch thắng ~ 30% với hệ số lợi nhuận là 1,10.
Thiết lập mặc định của chiến lược:
Đạt được một mức tối ưu ổn khi SMA và EMA periods được đặt lần lượt là 60 và 15 trong khi mức cắt lỗ được đặt thành 20 tick (2 điểm) từ giá vào.
Điều kiện Mở và Đóng vị thế:
Tín hiệu Long được tạo ra khi ema(15) cắt trên sma(60) trong khi tín hiệu Short xảy ra khi ema(15) cắt dưới sma(60). Lệnh Long được đóng khi ema(15) cắt dưới sma(60) trong khi lệnh Short được đóng khi ema(15) cắt lên sma(60).
Điều kiện đóng vị thể xảy ra khi (tùy điều kiện nào đến trước):
Một tín hiệu Long/Short khác được tạo ra
Giá chạm mức cắt lỗ
Lệnh chưa đóng nhưng tới giờ cut-off (14:25 hàng ngày)
*Tuyên bố miễn trừ trách nhiệm:
Giao dịch hợp đồng tương lai có mức rủi ro cao và giá có thể dao động đáng kể. Chiến lược này hoạt động như một nguồn tham khảo và nên được sử dụng sau khi người dùng đã hiểu rõ cách thức giao dịch hợp đồng tương lai, đã đánh giá mức độ chấp nhận rủi ro của bản thân và hiểu rõ về logic vận hành của chiến lược này.
Người dùng hoàn toàn chịu trách nhiệm về các quyết định đầu tư của mình và DNSE không chịu trách nhiệm về bất kỳ khoản lỗ tiềm ẩn nào khi áp dụng chiến lược này vào các hoạt động giao dịch thực tế. Hiệu suất trong quá khứ không chỉ ra/cam kết kết quả trong tương lai, vui lòng liên hệ với chúng tôi nếu bạn có thắc mắc cụ thể về chiến lược giao dịch này.
Gold Friday Anomaly StrategyThis script implements the " Gold Friday Anomaly Strategy ," a well-known historical trading strategy that leverages the gold market's behavior from Thursday evening to Friday close. It is a backtesting-focused strategy designed to assess the historical performance of this pattern. Traders use this anomaly as it captures a recurring market tendency observed over the years.
What It Does:
Entry Condition: The strategy enters a long position at the beginning of the Friday trading session (Thursday evening close) within the defined backtesting period.
Exit Condition: Friday evening close.
Backtesting Controls: Allows users to set custom backtesting periods to evaluate strategy performance over specific date ranges.
Key Features:
Custom Backtest Periods: Easily configurable inputs to set the start and end date of the backtesting range.
Fixed Slippage and Commission Settings: Ensures realistic simulation of trading conditions.
Process Orders on Close: Backtesting is optimized by processing orders at the bar's close.
Important Notes:
Backtesting Only: This script is intended purely for backtesting purposes. Past performance is not indicative of future results.
Live Trading Recommendations: For live trading, it is highly recommended to use limit orders instead of market orders, especially during evening sessions, as market order slippage can be significant.
Default Settings:
Entry size: 10% of equity per trade.
Slippage: 1 tick.
Commission: 0.05% per trade.
Bollinger Breakout Strategy with Direction Control [4H crypto]Bollinger Breakout Strategy with Direction Control - User Guide
This strategy leverages Bollinger Bands, RSI, and directional filters to identify potential breakout trading opportunities. It is designed for traders looking to capitalize on significant price movements while maintaining control over trade direction (long, short, or both). Here’s how to use this strategy effectively:
How the Strategy Works
Indicators Used:
Bollinger Bands:
A volatility-based indicator with an upper and lower band around a simple moving average (SMA). The bands expand or contract based on market volatility.
RSI (Relative Strength Index):
Measures momentum to determine overbought or oversold conditions. In this strategy, RSI is used to confirm breakout strength.
Trade Direction Control:
You can select whether to trade:
Long only: Buy positions.
Short only: Sell positions.
Both: Trade in both directions depending on conditions.
Breakout Conditions:
Long Trade:
The price closes above the upper Bollinger Band.
RSI is above the midline (50), confirming upward momentum.
The "Trade Direction" setting allows either "Long" or "Both."
Short Trade:
The price closes below the lower Bollinger Band.
RSI is below the midline (50), confirming downward momentum.
The "Trade Direction" setting allows either "Short" or "Both."
Risk Management:
Stop-Loss:
Long trades: Set at 2% below the entry price.
Short trades: Set at 2% above the entry price.
Take-Profit:
Calculated using a Risk/Reward Ratio (default is 2:1).
Adjust this in the strategy settings.
Inputs and Customization
Key Parameters:
Bollinger Bands Length: Default is 20. Adjust based on the desired sensitivity.
Multiplier: Default is 2.0. Higher values widen the bands; lower values narrow them.
RSI Length: Default is 14, which is standard for RSI.
Risk/Reward Ratio: Default is 2.0. Increase for more aggressive profit targets, decrease for conservative exits.
Trade Direction:
Options: "Long," "Short," or "Both."
Example: Set to "Long" in a bullish market to focus only on buy trades.
How to Use This Strategy
Adding the Strategy:
Paste the script into TradingView’s Pine Editor and add it to your chart.
Setting Parameters:
Adjust the Bollinger Band settings, RSI, and Risk/Reward Ratio to fit the asset and timeframe you're trading.
Analyzing Signals:
Green line (Upper Band): Signals breakout potential for long trades.
Red line (Lower Band): Signals breakout potential for short trades.
Blue line (Basis): Central Bollinger Band (SMA), helpful for understanding price trends.
Testing the Strategy:
Use the Strategy Tester in TradingView to backtest performance on your chosen asset and timeframe.
Optimizing for Assets:
Forex pairs, cryptocurrencies (like BTC), or stocks with high volatility are ideal for this strategy.
Works best on higher timeframes like 4H or Daily.
Best Practices
Combine with Volume: Confirm breakouts with increased volume for higher reliability.
Avoid Sideways Markets: Use additional trend filters (like ADX) to avoid trades in low-volatility conditions.
Optimize Parameters: Regularly adjust the Bollinger Bands multiplier and RSI settings to match the asset's behavior.
By utilizing this strategy, you can effectively trade breakouts while maintaining flexibility in trade direction. Adjust the parameters to match your trading style and market conditions for optimal results!
MultiLayer Awesome Oscillator Saucer Strategy [Skyrexio]Overview
MultiLayer Awesome Oscillator Saucer Strategy leverages the combination of Awesome Oscillator (AO), Williams Alligator, Williams Fractals and Exponential Moving Average (EMA) to obtain the high probability long setups. Moreover, strategy uses multi trades system, adding funds to long position if it considered that current trend has likely became stronger. Awesome Oscillator is used for creating signals, while Alligator and Fractal are used in conjunction as an approximation of short-term trend to filter them. At the same time EMA (default EMA's period = 100) is used as high probability long-term trend filter to open long trades only if it considers current price action as an uptrend. More information in "Methodology" and "Justification of Methodology" paragraphs. The strategy opens only long trades.
Unique Features
No fixed stop-loss and take profit: Instead of fixed stop-loss level strategy utilizes technical condition obtained by Fractals and Alligator to identify when current uptrend is likely to be over (more information in "Methodology" and "Justification of Methodology" paragraphs)
Configurable Trading Periods: Users can tailor the strategy to specific market windows, adapting to different market conditions.
Multilayer trades opening system: strategy uses only 10% of capital in every trade and open up to 5 trades at the same time if script consider current trend as strong one.
Short and long term trend trade filters: strategy uses EMA as high probability long-term trend filter and Alligator and Fractal combination as a short-term one.
Methodology
The strategy opens long trade when the following price met the conditions:
1. Price closed above EMA (by default, period = 100). Crossover is not obligatory.
2. Combination of Alligator and Williams Fractals shall consider current trend as an upward (all details in "Justification of Methodology" paragraph)
3. Awesome Oscillator shall create the "Saucer" long signal (all details in "Justification of Methodology" paragraph). Buy stop order is placed one tick above the candle's high of last created "Saucer signal".
4. If price reaches the order price, long position is opened with 10% of capital.
5. If currently we have opened position and price creates and hit the order price of another one "Saucer" signal another one long position will be added to the previous with another one 10% of capital. Strategy allows to open up to 5 long trades simultaneously.
6. If combination of Alligator and Williams Fractals shall consider current trend has been changed from up to downtrend, all long trades will be closed, no matter how many trades has been opened.
Script also has additional visuals. If second long trade has been opened simultaneously the Alligator's teeth line is plotted with the green color. Also for every trade in a row from 2 to 5 the label "Buy More" is also plotted just below the teeth line. With every next simultaneously opened trade the green color of the space between teeth and price became less transparent.
Strategy settings
In the inputs window user can setup strategy setting: EMA Length (by default = 100, period of EMA, used for long-term trend filtering EMA calculation). User can choose the optimal parameters during backtesting on certain price chart.
Justification of Methodology
Let's go through all concepts used in this strategy to understand how they works together. Let's start from the easies one, the EMA. Let's briefly explain what is EMA. The Exponential Moving Average (EMA) is a type of moving average that gives more weight to recent prices, making it more responsive to current price changes compared to the Simple Moving Average (SMA). It is commonly used in technical analysis to identify trends and generate buy or sell signals. It can be calculated with the following steps:
1.Calculate the Smoothing Multiplier:
Multiplier = 2 / (n + 1), Where n is the number of periods.
2. EMA Calculation
EMA = (Current Price) × Multiplier + (Previous EMA) × (1 − Multiplier)
In this strategy uses EMA an initial long term trend filter. It allows to open long trades only if price close above EMA (by default 50 period). It increases the probability of taking long trades only in the direction of the trend.
Let's go to the next, short-term trend filter which consists of Alligator and Fractals. Let's briefly explain what do these indicators means. The Williams Alligator, developed by Bill Williams, is a technical indicator designed to spot trends and potential market reversals. It uses three smoothed moving averages, referred to as the jaw, teeth, and lips:
Jaw (Blue Line): The slowest of the three, based on a 13-period smoothed moving average shifted 8 bars ahead.
Teeth (Red Line): The medium-speed line, derived from an 8-period smoothed moving average shifted 5 bars forward.
Lips (Green Line): The fastest line, calculated using a 5-period smoothed moving average shifted 3 bars forward.
When these lines diverge and are properly aligned, the "alligator" is considered "awake," signaling a strong trend. Conversely, when the lines overlap or intertwine, the "alligator" is "asleep," indicating a range-bound or sideways market. This indicator assists traders in identifying when to act on or avoid trades.
The Williams Fractals, another tool introduced by Bill Williams, are used to pinpoint potential reversal points on a price chart. A fractal forms when there are at least five consecutive bars, with the middle bar displaying the highest high (for an up fractal) or the lowest low (for a down fractal), relative to the two bars on either side.
Key Points:
Up Fractal: Occurs when the middle bar has a higher high than the two preceding and two following bars, suggesting a potential downward reversal.
Down Fractal: Happens when the middle bar shows a lower low than the surrounding two bars, hinting at a possible upward reversal.
Traders often combine fractals with other indicators to confirm trends or reversals, improving the accuracy of trading decisions.
How we use their combination in this strategy? Let’s consider an uptrend example. A breakout above an up fractal can be interpreted as a bullish signal, indicating a high likelihood that an uptrend is beginning. Here's the reasoning: an up fractal represents a potential shift in market behavior. When the fractal forms, it reflects a pullback caused by traders selling, creating a temporary high. However, if the price manages to return to that fractal’s high and break through it, it suggests the market has "changed its mind" and a bullish trend is likely emerging.
The moment of the breakout marks the potential transition to an uptrend. It’s crucial to note that this breakout must occur above the Alligator's teeth line. If it happens below, the breakout isn’t valid, and the downtrend may still persist. The same logic applies inversely for down fractals in a downtrend scenario.
So, if last up fractal breakout was higher, than Alligator's teeth and it happened after last down fractal breakdown below teeth, algorithm considered current trend as an uptrend. During this uptrend long trades can be opened if signal was flashed. If during the uptrend price breaks down the down fractal below teeth line, strategy considered that uptrend is finished with the high probability and strategy closes all current long trades. This combination is used as a short term trend filter increasing the probability of opening profitable long trades in addition to EMA filter, described above.
Now let's talk about Awesome Oscillator's "Sauser" signals. Briefly explain what is the Awesome Oscillator. The Awesome Oscillator (AO), created by Bill Williams, is a momentum-based indicator that evaluates market momentum by comparing recent price activity to a broader historical context. It assists traders in identifying potential trend reversals and gauging trend strength.
AO = SMA5(Median Price) − SMA34(Median Price)
where:
Median Price = (High + Low) / 2
SMA5 = 5-period Simple Moving Average of the Median Price
SMA 34 = 34-period Simple Moving Average of the Median Price
Now we know what is AO, but what is the "Saucer" signal? This concept was introduced by Bill Williams, let's briefly explain it and how it's used by this strategy. Initially, this type of signal is a combination of the following AO bars: we need 3 bars in a row, the first one shall be higher than the second, the third bar also shall be higher, than second. All three bars shall be above the zero line of AO. The price bar, which corresponds to third "saucer's" bar is our signal bar. Strategy places buy stop order one tick above the price bar which corresponds to signal bar.
After that we can have the following scenarios.
Price hit the order on the next candle in this case strategy opened long with this price.
Price doesn't hit the order price, the next candle set lower low. If current AO bar is increasing buy stop order changes by the script to the high of this new bar plus one tick. This procedure repeats until price finally hit buy order or current AO bar become decreasing. In the second case buy order cancelled and strategy wait for the next "Saucer" signal.
If long trades has been opened strategy use all the next signals until number of trades doesn't exceed 5. All trades are closed when the trend changes to downtrend according to combination of Alligator and Fractals described above.
Why we use "Saucer" signals? If AO above the zero line there is a high probability that price now is in uptrend if we take into account our two trend filters. When we see the decreasing bars on AO and it's above zero it's likely can be considered as a pullback on the uptrend. When we see the stop of AO decreasing and the first increasing bar has been printed there is a high probability that this local pull back is finished and strategy open long trade in the likely direction of a main trend.
Why strategy use only 10% per signal? Sometimes we can see the false signals which appears on sideways. Not risking that much script use only 10% per signal. If the first long trade has been open and price continue going up and our trend approximation by Alligator and Fractals is uptrend, strategy add another one 10% of capital to every next saucer signal while number of active trades no more than 5. This capital allocation allows to take part in long trades when current uptrend is likely to be strong and use only 10% of capital when there is a high probability of sideways.
Backtest Results
Operating window: Date range of backtests is 2023.01.01 - 2024.11.25. It is chosen to let the strategy to close all opened positions.
Commission and Slippage: Includes a standard Binance commission of 0.1% and accounts for possible slippage over 5 ticks.
Initial capital: 10000 USDT
Percent of capital used in every trade: 10%
Maximum Single Position Loss: -5.10%
Maximum Single Profit: +22.80%
Net Profit: +2838.58 USDT (+28.39%)
Total Trades: 107 (42.99% win rate)
Profit Factor: 3.364
Maximum Accumulated Loss: 373.43 USDT (-2.98%)
Average Profit per Trade: 26.53 USDT (+2.40%)
Average Trade Duration: 78 hours
These results are obtained with realistic parameters representing trading conditions observed at major exchanges such as Binance and with realistic trading portfolio usage parameters.
How to Use
Add the script to favorites for easy access.
Apply to the desired timeframe and chart (optimal performance observed on 3h BTC/USDT).
Configure settings using the dropdown choice list in the built-in menu.
Set up alerts to automate strategy positions through web hook with the text: {{strategy.order.alert_message}}
Disclaimer:
Educational and informational tool reflecting Skyrex commitment to informed trading. Past performance does not guarantee future results. Test strategies in a simulated environment before live implementation
Kairos StrategyKairos meaning: The right or critical moment
Overview
Kairos Strategy combines multiple technical indicators and filters to create a robust framework for identifying, confirming, and managing trade entries in both trending and ranging markets. The strategy's core revolves around Hull Moving Average (HMA) variants for primary trend detection, supported by VWMA, SMMA, and ZLSMA for precision in signal confirmation. The integration of Parabolic SAR, ATR-based Stop Loss, and RSI filters ensures accurate entry points and risk control. This multi-layered approach provides flexibility and reliability across timeframes and market conditions.
Methodology
The Kairos Strategy employs a systematic approach to analyze market dynamics:
Primary Trend Identification
The Hull Moving Average (HMA) and its variants (THMA and EHMA) detect major trends.
Users can adjust sources like VWMA, SMMA, and ZLSMA for improved accuracy and trend clarity.
Multi-Indicator Integration
Parabolic SAR signals align with price direction to identify actionable trade zones.
RSI Filters ensure trades occur during optimal momentum conditions, avoiding overbought/oversold areas.
Dynamic Risk Management
ATR-based Stop Loss adapts to volatility, ensuring proper risk/reward ratios.
A customizable trailing stop follows price movements, locking profits while minimizing risk.
Signal Filtering
To enhance reliability, entries are validated by avoiding conditions where price interacts directly with the moving averages.
Customization and Flexibility
The Kairos Strategy empowers traders to adapt to different trading styles and market environments through an array of customizable settings. Each component of the strategy is fine-tuned for flexibility and precision, ensuring it meets the diverse needs of its users.
Configurable Indicators and Sources
Select from multiple moving average options, including LSMA, VWMA, SMMA, VAMA, and ZLSMA, for trend identification and crossover signals.
Adjust the length, smoothing type, and multiplier settings for each indicator to suit various market conditions and timeframes.
Incorporate higher timeframes for broader trend validation without sacrificing detail on lower timeframes.
Risk-Reward Optimization
Define distinct risk-reward ratios for both long and short trades, allowing for tailored approaches to each market scenario.
Enable ATR-based stop-loss calculations for adaptive risk management that responds to market volatility.
Utilize bar-based stop-loss levels for simpler, price-action-driven risk placement.
Strategy Logic
The Kairos Strategy's multi-layered logic is designed to maximize trading opportunities while minimizing false signals:
Entry Conditions
Crossover Signals: The strategy identifies buy or sell signals when a selected moving average crosses over the Hull MA in the direction of the trend.
Momentum Validation: RSI filters ensure that entries are aligned with favorable momentum conditions, reducing exposure to false signals during choppy markets.
Trend Alignment: Parabolic SAR confirms that entries align with the current price trend, adding an additional layer of validation.
Price Interaction Check: The strategy avoids signals when price touches key levels, such as the moving averages or crossover sources, ensuring cleaner entries.
Exit Conditions
Stop-Loss Placement: Choose between ATR-based or bar-based stop-loss calculations, ensuring exits are optimized for risk control.
Take-Profit Targets: Automatically calculated based on customizable risk-reward ratios, providing a consistent framework for locking in gains.
Trailing Stops: Optional trailing stops dynamically adjust with price movement, preserving profits as trends evolve.
Key Benefits
Versatility Across Markets: Effective in both trending and ranging conditions, with settings adaptable to any trading style.
Enhanced Signal Accuracy: Multi-indicator validation and dynamic filtering reduce noise and improve entry timing.
Robust Risk Management: Advanced stop-loss and risk-reward configurations safeguard capital while optimizing trade potential.
User-Friendly Interface: Intuitive settings and customizable visuals make the strategy accessible to both novice and experienced traders.
Use Cases
Trend Trading: Utilize the Hull MA and its variants to capture long-term trends with precision and confidence.
Scalping: Leverage shorter timeframes and the strategy's adaptive indicators for quick, high-frequency trades.
Swing Trading: Combine multi-timeframe analysis and dynamic stop-loss settings to capture medium-term market moves.
Backtest Results
Symbol: BTCUSDT.P Bitcoin / TetherUS PREPERTUAL CONTRACT Binance
Timeframe: 1h
Operating window: Date range of backtests is 2022.01.08 - 2024.11.26. It is chosen to let the strategy to close all opened positions.
Commission and Slippage: Includes a standard Binance commission of 0.07% and accounts for possible slippage over 5 ticks.
Initial capital: 1000 USDT
Percent of capital used in every trade: 100 USDT on 30x leverage = 3000 USDT
Maximum Single Profit: 463.95 USDT
Maximum Single Position Loss: 219.34 USDT
Net Profit: +7,302.60 USDT (730.26%)
Total Trades: 156 (52.56% Win rate)
Profit Factor: 2.443
Maximum Drawdown: 640.29 USDT (-29.65%)
Average Profit per Trade: 46.81 USDT (+1.56%)
I recommend this strategy for leverage trading, that's why the trading properties are set this way!
Disclaimer
This tool is designed for educational and informational purposes, reflecting Kairos dedication to empowering traders with knowledge. Keep in mind that past performance is not indicative of future results. Always test strategies in a simulated environment before applying them in live markets.
Supertrend and MACD strategyThe Supertrend and MACD Strategy is a comprehensive trading approach designed to capitalize on market trends by using a combination of the Supertrend indicator, the Exponential Moving Average (EMA), and the Moving Average Convergence Divergence (MACD). This strategy aims to identify optimal entry and exit points for both long and short trades, while incorporating strict risk management rules.
Indicators Used:
Supertrend: This indicator is used to identify the overall trend direction. It provides clear signals for trend reversals, helping traders to enter trades in the direction of the prevailing trend.
200-period EMA: This long-term moving average is used to determine the primary trend direction. The strategy only takes long trades when the price is above the 200 EMA and short trades when the price is below it.
MACD: The MACD is used to gauge the momentum and confirm the signals provided by the Supertrend and EMA. It consists of the MACD line, the signal line, and the histogram.
Entry Conditions:
Long Entry:
The Supertrend indicator shows an uptrend (direction > 0).
The MACD line is above the signal line (macd > signal).
The price is above the 200-period EMA (close > ema200).
Short Entry:
The Supertrend indicator shows a downtrend (direction < 0).
The MACD line is below the signal line (macd < signal).
The price is below the 200-period EMA (close < ema200).
Exit Conditions:
Long Exit:
Exit the long position when the MACD line crosses below the signal line (ta.crossunder(macd, signal)).
Set a stop loss (SL) below the lowest low of the last 10 periods (lowestLow - 1).
Short Exit:
Exit the short position when the MACD line crosses above the signal line (ta.crossover(macd, signal)).
Set a stop loss (SL) above the highest high of the last 10 periods (highestHigh + 1).
Risk Management:
The strategy ensures that no new positions are opened if there is already an open trade, preventing overexposure in the market.
Alerts:
Alerts are set to notify traders when the MACD crosses the signal line, providing timely updates for potential exit points.
Triple CCI Strategy MFI Confirmed [Skyrexio]Overview
Triple CCI Strategy MFI Confirmed leverages 3 different periods Commodity Channel Index (CCI) indicator in conjunction Money Flow Index (MFI) and Exponential Moving Average (EMA) to obtain the high probability setups. Fast period CCI is used for having the high probability to enter in the direction of short term trend, middle and slow period CCI are used for confirmation, if market now likely in the mid and long-term uptrend. MFI is used to confirm trade with the money inflow/outflow with the high probability. EMA is used as an additional trend filter. Moreover, strategy uses exponential moving average (EMA) to trail the price when it reaches the specific level. More information in "Methodology" and "Justification of Methodology" paragraphs. The strategy opens only long trades.
Unique Features
Dynamic stop-loss system: Instead of fixed stop-loss level strategy utilizes average true range (ATR) multiplied by user given number subtracted from the position entry price as a dynamic stop loss level.
Configurable Trading Periods: Users can tailor the strategy to specific market windows, adapting to different market conditions.
Four layers trade filtering system: Strategy utilizes two different period CCI indicators, MFI and EMA indicators to confirm the signals produced by fast period CCI.
Trailing take profit level: After reaching the trailing profit activation level scrip activate the trailing of long trade using EMA. More information in methodology.
Methodology
The strategy opens long trade when the following price met the conditions:
Fast period CCI shall crossover the zero-line.
Slow and Middle period CCI shall be above zero-lines.
Price shall close above the EMA. Crossover is not obligatory
MFI shall be above 50
When long trade is executed, strategy set the stop-loss level at the price ATR multiplied by user-given value below the entry price. This level is recalculated on every next candle close, adjusting to the current market volatility.
At the same time strategy set up the trailing stop validation level. When the price crosses the level equals entry price plus ATR multiplied by user-given value script starts to trail the price with EMA. If price closes below EMA long trade is closed. When the trailing starts, script prints the label “Trailing Activated”.
Strategy settings
In the inputs window user can setup the following strategy settings:
ATR Stop Loss (by default = 1.75)
ATR Trailing Profit Activation Level (by default = 2.25)
CCI Fast Length (by default = 14, used for calculation short term period CCI)
CCI Middle Length (by default = 25, used for calculation short term period CCI)
CCI Slow Length (by default = 50, used for calculation long term period CCI)
MFI Length (by default = 14, used for calculation MFI
EMA Length (by default = 50, period of EMA, used for trend filtering EMA calculation)
Trailing EMA Length (by default = 20)
User can choose the optimal parameters during backtesting on certain price chart.
Justification of Methodology
Before understanding why this particular combination of indicator has been chosen let's briefly explain what is CCI, MFI and EMA.
The Commodity Channel Index (CCI) is a momentum-based technical indicator that measures the deviation of a security's price from its average price over a specific period. It helps traders identify overbought or oversold conditions and potential trend reversals.
The CCI formula is:
CCI = (Typical Price − SMA) / (0.015 × Mean Deviation)
Typical Price (TP): This is calculated as the average of the high, low, and closing prices for the period.
Simple Moving Average (SMA): This is the average of the Typical Prices over a specific number of periods.
Mean Deviation: This is the average of the absolute differences between the Typical Price and the SMA.
The result is a value that typically fluctuates between +100 and -100, though it is not bounded and can go higher or lower depending on the price movement.
The Money Flow Index (MFI) is a technical indicator that measures the strength of money flowing into and out of a security. It combines price and volume data to assess buying and selling pressure and is often used to identify overbought or oversold conditions. The formula for MFI involves several steps:
1. Calculate the Typical Price (TP):
TP = (high + low + close) / 3
2. Calculate the Raw Money Flow (RMF):
Raw Money Flow = TP × Volume
3. Determine Positive and Negative Money Flow:
If the current TP is greater than the previous TP, it's Positive Money Flow.
If the current TP is less than the previous TP, it's Negative Money Flow.
4. Calculate the Money Flow Ratio (MFR):
Money Flow Ratio = Sum of Positive Money Flow (over n periods) / Sum of Negative Money Flow (over n periods)
5. Calculate the Money Flow Index (MFI):
MFI = 100 − (100 / (1 + Money Flow Ratio))
MFI above 80 can be considered as overbought, below 20 - oversold.
The Exponential Moving Average (EMA) is a type of moving average that places greater weight and significance on the most recent data points. It is widely used in technical analysis to smooth price data and identify trends more quickly than the Simple Moving Average (SMA).
Formula:
1. Calculate the multiplier
Multiplier = 2 / (n + 1) , Where n is the number of periods.
2. EMA Calculation
EMA = (Current Price) × Multiplier + (Previous EMA) × (1 − Multiplier)
This strategy leverages Fast period CCI, which shall break the zero line to the upside to say that probability of short term trend change to the upside increased. This zero line crossover shall be confirmed by the Middle and Slow periods CCI Indicators. At the moment of breakout these two CCIs shall be above 0, indicating that there is a high probability that price is in middle and long term uptrend. This approach increases chances to have a long trade setup in the direction of mid-term and long-term trends when the short-term trend starts to reverse to the upside.
Additionally strategy uses MFI to have a greater probability that fast CCI breakout is confirmed by this indicator. We consider the values of MFI above 50 as a higher probability that trend change from downtrend to the uptrend is real. Script opens long trades only if MFI is above 50. As you already know from the MFI description, it incorporates volume in its calculation, therefore we have another one confirmation factor.
Finally, strategy uses EMA an additional trend filter. It allows to open long trades only if price close above EMA (by default 50 period). It increases the probability of taking long trades only in the direction of the trend.
ATR is used to adjust the strategy risk management to the current market volatility. If volatility is low, we don’t need the large stop loss to understand the there is a high probability that we made a mistake opening the trade. User can setup the settings ATR Stop Loss and ATR Trailing Profit Activation Level to realize his own risk to reward preferences, but the unique feature of a strategy is that after reaching trailing profit activation level strategy is trying to follow the trend until it is likely to be finished instead of using fixed risk management settings. It allows sometimes to be involved in the large movements. It’s also important to make a note, that script uses another one EMA (by default = 20 period) as a trailing profit level.
Backtest Results
Operating window: Date range of backtests is 2022.04.01 - 2024.11.25. It is chosen to let the strategy to close all opened positions.
Commission and Slippage: Includes a standard Binance commission of 0.1% and accounts for possible slippage over 5 ticks.
Initial capital: 10000 USDT
Percent of capital used in every trade: 50%
Maximum Single Position Loss: -4.13%
Maximum Single Profit: +19.66%
Net Profit: +5421.21 USDT (+54.21%)
Total Trades: 108 (44.44% win rate)
Profit Factor: 2.006
Maximum Accumulated Loss: 777.40 USDT (-7.77%)
Average Profit per Trade: 50.20 USDT (+0.85%)
Average Trade Duration: 44 hours
These results are obtained with realistic parameters representing trading conditions observed at major exchanges such as Binance and with realistic trading portfolio usage parameters.
How to Use
Add the script to favorites for easy access.
Apply to the desired timeframe and chart (optimal performance observed on 2h BTC/USDT).
Configure settings using the dropdown choice list in the built-in menu.
Set up alerts to automate strategy positions through web hook with the text: {{strategy.order.alert_message}}
Disclaimer:
Educational and informational tool reflecting Skyrex commitment to informed trading. Past performance does not guarantee future results. Test strategies in a simulated environment before live implementation
BTCUSD Momentum After Abnormal DaysThis indicator identifies abnormal days in the Bitcoin market (BTCUSD) based on daily returns exceeding specific thresholds defined by a statistical approach. It is inspired by the findings of Caporale and Plastun (2020), who analyzed the cryptocurrency market's inefficiencies and identified exploitable patterns, particularly around abnormal returns.
Key Concept:
Abnormal Days:
Days where the daily return significantly deviates (positively or negatively) from the historical average.
Positive abnormal days: Returns exceed the mean return plus k times the standard deviation.
Negative abnormal days: Returns fall below the mean return minus k times the standard deviation.
Momentum Effect:
As described in the academic paper, on abnormal days, prices tend to move in the direction of the abnormal return until the end of the trading day, creating momentum effects. This can be leveraged by traders for profit opportunities.
How It Works:
Calculation:
The script calculates the daily return as the percentage difference between the open and close prices. It then derives the mean and standard deviation of returns over a configurable lookback period.
Thresholds:
The script dynamically computes upper and lower thresholds for abnormal days using the mean and standard deviation. Days exceeding these thresholds are flagged as abnormal.
Visualization:
The mean return and thresholds are plotted as dynamic lines.
Abnormal days are visually highlighted with transparent green (positive) or red (negative) backgrounds on the chart.
References:
This indicator is based on the methodology discussed in "Momentum Effects in the Cryptocurrency Market After One-Day Abnormal Returns" by Caporale and Plastun (2020). Their research demonstrates that hourly returns during abnormal days exhibit a strong momentum effect, moving in the same direction as the abnormal return. This behavior contradicts the efficient market hypothesis and suggests profitable trading opportunities.
"Prices tend to move in the direction of abnormal returns till the end of the day, which implies the existence of a momentum effect on that day giving rise to exploitable profit opportunities" (Caporale & Plastun, 2020).