Bozz Strategy
Booz Backtesting : Booz Backtesting is a method for analyzing the performance of your current trading strategy . Booz Backtesting aims to help you generate results and evaluate risk and return without risking real capital.
The Booz Backtesting is the Booz Super Swing Indicator equivalent but gives you the ability to backtest data on different charts.
This is an Indicator created for the purpose of identifying trends in Multiple Markets, it is based on Moving Average Crossover and extra features.
Swing Trading: This function allows you to navigate the entire trend until it is not strong enough, so you can compare it with fixed parameters such as Take Profit and Stop Loss.
Take Profit and Stop Loss function: With this function you will be able to choose the most optimal parameters and see in real time the results in order to choose the best combination of parameters.
Leverage : We have this function for the futures markets where you can check which is the most appropriate leverage for your operation.
Trend Filter: allows you to take multiple entries in the same direction of the market.
If the market crosses below the 200 moving average, it will take only short entries.
If the market crosses above the 200 moving average, it will take only long entries.
Timeframes
Charting from 1 Hour, 4 Hour, Daily, Weekly, Weekly
Markets :Booz Backtesting can be tested in Cryptocurrency, Stocks and Futures markets.
Background Color : at a glance, you can see what cycle the market is in.
Green background : Shows that the market is in a bullish cycle.
Red background: Shows that the market is in a bearish cycle.
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EDMA Scalping Strategy (Exponentially Deviating Moving Average)This strategy uses crossover of Exponentially Deviating Moving Average (MZ EDMA ) along with Exponential Moving Average for trades entry/exits. Exponentially Deviating Moving Average (MZ EDMA ) is derived from Exponential Moving Average to predict better exit in top reversal case.
EDMA Philosophy
EDMA is calculated in following steps:
In first step, Exponentially expanding moving line is calculated with same code as of EMA but with different smoothness (1 instead of 2).
In 2nd step, Exponentially contracting moving line is calculated using 1st calculated line as source input and also using same code as of EMA but with different smoothness (1 instead of 2).
In 3rd step, Hull Moving Average with 2/3 of EDMA length is calculated using final line as source input. This final HMA will be equal to Exponentially Deviating Moving Average.
EDMA Defaults
Currently default EDMA and EMA length is set to 20 period which I've found better for higher timeframes but this can be adjusted according to user's timeframe. I would soon add Multi Timeframe option in script too. Chikou filter's period is set to 25.
Additional Features
EMA Band: EMA band is shown on chart to better visualize EMA cross with EDMA .
Dynamic Coloring: Chikou Filter library is used for derivation of dynamic coloring of EDMA and its band.
Trade Confirmation with Chikou Filter: Trend filteration from Chikou filter library is used as an option to enhance trades signals accuracy.
Strategy Default Test Settings
For backtesting purpose, following settings are used:
Initial capital=10000 USD
Default quantity value = 5 % of total capital
Commission value = 0.1 %
Pyramiding isn't included.
Backtesting data never assures that the same results would occur in future and also above settings use very less of total portfolio for trades, which in a way results less maximum drawdown along with less total profit on initial capital too. For example, increasing default quantity value will definity increase maximum drawdown value. The other way is also to use fix contracts in backtesting but it all depends on users general practice. Best option is to explore backtesting results with manually modified settings on different charts, before trusting them for other uses in future.
Usage and In-Detail Backtesting
This strategy has built-in option to enable trade confirmations with Chikou filter which will reduce the total number of trades increasing profit factor.
Symmetrically Weighted Moving Average (SWMA) on input source, may risk repainting in real-time data. Better option is to run a trade on bar close or simply left this optin unchecked.
I've set Chikou filter unchecked to increase number of trades (greater than 100) on higher timeframe (12H) and this can be changed according to your precision requirement and timeframe.
Timeframes lower than 4H usually have more noise. So its better to use higher EDMA and EMA length on lower timeframes which will decrease total number of offsetting trades increasing average total number of bars within a single trade.
Original "Exponentially Deviating Moving Average (MZ EDMA )" Indicator can be found here.
Advanced Multi-Timeframe Trading System (Risk Managed)Description:
This strategy is an original approach that combines two main analytical components to identify potential trade opportunities while simulating realistic trading conditions:
1. Market Trend Analysis via an Approximate Hurst Exponent
• What It Does:
The strategy computes a rough measure of market trending using an approximate Hurst exponent. A value above 0.5 suggests persistent, trending behavior, while a value below 0.5 indicates a tendency toward mean-reversion.
• How It’s Used:
The Hurst exponent is calculated on both the chart’s current timeframe and a higher timeframe (default: Daily) to capture both local and broader market dynamics.
2. Fibonacci Retracement Levels
• What It Does:
Using daily high and low data from a selected timeframe (default: Daily), the script computes key Fibonacci retracement levels.
• How It’s Used:
• The 61.8% level (Golden Ratio) serves as a key threshold:
• A long entry is signaled when the price crosses above this level if the daily Hurst exponent confirms a trending market.
• The 38.2% level is used to identify short-entry opportunities when the price crosses below it and the daily Hurst indicates non-trending conditions.
Signal Logic:
• Long Entry:
When the price crosses above the 61.8% Fibonacci level (Golden Ratio) and the daily Hurst exponent is greater than 0.5, suggesting a trending market.
• Short Entry:
When the price crosses below the 38.2% Fibonacci level and the daily Hurst exponent is less than 0.5, indicating a less trending or potentially reversing market.
Risk Management & Trade Execution:
• Stop-Loss:
Each trade is risk-managed with a stop-loss set at 2% below (for longs) or above (for shorts) the entry price. This ensures that no single trade risks more than a small, sustainable portion of the account.
• Take Profit:
A take profit order targets a risk-reward ratio of 1:2 (i.e., the target profit is twice the amount risked).
• Position Sizing:
Trades are executed with a fixed position size equal to 10% of account equity.
• Trade Frequency Limits:
• Daily Limit: A maximum of 5 trades per day
• Overall Limit: No more than 510 trades during the backtesting period (e.g., since 2019)
These limits are imposed to simulate realistic trading frequency and to avoid overtrading in backtest results.
Backtesting Parameters:
• Initial Capital: $10,000
• Commission: 0.1% per trade
• Slippage: 1 tick per bar
These settings aim to reflect the conditions faced by the average trader and help ensure that the backtesting results are realistic and not misleading.
Chart Overlays & Visual Aids:
• Fibonacci Levels:
The key Fibonacci retracement levels are plotted on the chart, and the zone between the 61.8% and 38.2% levels is highlighted to show a key retracement area.
• Market Trend Background:
The chart background is tinted green when the daily Hurst exponent indicates a trending market (value > 0.5) and red otherwise.
• Information Table:
An on-chart table displays key parameters such as the current Hurst exponent, daily Hurst value, the number of trades executed today, and the global trade count.
Disclaimer:
Past performance is not indicative of future results. This strategy is experimental and provided solely for educational purposes. It is essential that you backtest and paper trade using your own settings before considering any live deployment. The Hurst exponent calculation is an approximation and should be interpreted as a rough gauge of market behavior. Adjust the parameters and risk management settings according to your personal risk tolerance and market conditions.
Additional Notes:
• Originality & Usefulness:
This script is an original mashup that combines trend analysis with Fibonacci retracement methods. The description above explains how these components work together to provide trading signals.
• Realistic Results:
The strategy uses realistic account sizes, commission rates, slippage, and risk management rules to generate backtesting results that are representative of real-world trading.
• Educational Purpose:
This script is intended to support the TradingView community by offering insights into combining multiple analysis techniques in one strategy. It is not a “get-rich-quick” system but rather an educational tool to help traders understand risk management and trade signal logic.
By using this script, you acknowledge that trading involves risk and that you are responsible for testing and adjusting the strategy to fit your own trading environment. This publication is fully open source, and any modifications should include proper attribution if significant portions of the code are reused.
ICT Judas Swing | Flux Charts💎 GENERAL OVERVIEW
Introducing our new ICT Judas Swing Indicator! This indicator is built around the ICT's "Judas Swing" strategy. The strategy looks for a liquidity grab around NY 9:30 session and a Fair Value Gap for entry confirmation. For more information about the process, check the "HOW DOES IT WORK" section.
Features of the new ICT Judas Swing :
Implementation of ICT's Judas Swing Strategy
2 Different TP / SL Methods
Customizable Execution Settings
Customizable Backtesting Dashboard
Alerts for Buy, Sell, TP & SL Signals
📌 HOW DOES IT WORK ?
The strategy begins by identifying the New York session from 9:30 to 9:45 and marking recent liquidity zones. These liquidity zones are determined by locating high and low pivot points: buyside liquidity zones are identified using high pivots that haven't been invalidated, while sellside liquidity zones are found using low pivots. A break of either buyside or sellside liquidity must occur during the 9:30-9:45 session, which is interpreted as a liquidity grab by smart money. The strategy assumes that after this liquidity grab, the price will reverse and move in the opposite direction. For entry confirmation, a fair value gap (FVG) in the opposite direction of the liquidity grab is required. A buyside liquidity grab calls for a bearish FVG, while a sellside grab requires a bullish FVG. Based on the type of FVG—bullish for buys and bearish for sells—the indicator will then generate a Buy or Sell signal.
After the Buy or Sell signal, the indicator immediately draws the take-profit (TP) and stop-loss (SL) targets. The indicator has three different TP & SL modes, explained in the "Settings" section of this write-up.
You can set up alerts for entry and TP & SL signals, and also check the current performance of the indicator and adjust the settings accordingly to the current ticker using the backtesting dashboard.
🚩 UNIQUENESS
This indicator is an all-in-one suit for the ICT's Judas Swing concept. It's capable of plotting the strategy, giving signals, a backtesting dashboard and alerts feature. Different and customizable algorithm modes will help the trader fine-tune the indicator for the asset they are currently trading. Three different TP / SL modes are available to suit your needs. The backtesting dashboard allows you to see how your settings perform in the current ticker. You can also set up alerts to get informed when the strategy is executable for different tickers.
⚙️ SETTINGS
1. General Configuration
Swing Length -> The swing length for pivot detection. Higher settings will result in
FVG Detection Sensitivity -> You may select between Low, Normal, High or Extreme FVG detection sensitivity. This will essentially determine the size of the spotted FVGs, with lower sensitivies resulting in spotting bigger FVGs, and higher sensitivies resulting in spotting all sizes of FVGs.
2. TP / SL
TP / SL Method ->
a) Dynamic: The TP / SL zones will be auto-determined by the algorithm based on the Average True Range (ATR) of the current ticker.
b) Fixed : You can adjust the exact TP / SL ratios from the settings below.
Dynamic Risk -> The risk you're willing to take if "Dynamic" TP / SL Method is selected. Higher risk usually means a better winrate at the cost of losing more if the strategy fails. This setting is has a crucial effect on the performance of the indicator, as different tickers may have different volatility so the indicator may have increased performance when this setting is correctly adjusted.
ICT Unicorn | Flux Charts💎 GENERAL OVERVIEW
Introducing our new ICT Unicorn Indicator! This indicator is built around the ICT's "Unicorn" strategy. The strategy uses Breaker Blocks and Fair Value Gaps for entry confirmation. For more information about the process, check the "HOW DOES IT WORK" section.
Features of the new ICT Unicorn Indicator :
Implementation of ICT's Unicorn Strategy
Toggleable Retracement Entry Method
3 Different TP / SL Methods
Customizable Execution Settings
Customizable Backtesting Dashboard
Alerts for Buy, Sell, TP & SL Signals
📌 HOW DOES IT WORK ?
The ICT Unicorn entry model merges the concepts of Breaker Blocks and Fair Value Gaps (FVGs), offering a distinct method for identifying trade opportunities. By integrating these two elements, we can have a position entry with stop-loss and take-profit targets on the potential support & resistance zones. This model is particularly reliable for trade entry, as it combines two powerful entry techniques.
An ICT Unicorn Model consists of a FVG which is overlapping with a Breaker Block of the same type. Here is an example :
When a FVG overlaps with a Breaker Block of the same type, the indicator gives a Buy or Sell signal depending on the FVG type (Bullish & Bearish). If the "Require Retracement" option is enabled in the settings, the signals are not given immediately. Instead, the current price of the ticker will need to touch the FVG once more before the signals are given.
After the Buy or Sell signal, the indicator immediately draws the take-profit (TP) and stop-loss (SL) targets. The indicator has three different TP & SL modes, explained in the "Settings" section of this write-up.
You can set up alerts for entry and TP & SL signals, and also check the current performance of the indicator and adjust the settings accordingly to the current ticker using the backtesting dashboard.
🚩 UNIQUENESS
This indicator is an all-in-one suit for the ICT's Unicorn concept. It's capable of plotting the strategy, giving signals, a backtesting dashboard and alerts feature. Different and customizable algorithm modes will help the trader fine-tune the indicator for the asset they are currently trading. Three different TP / SL modes are available to suit your needs. The backtesting dashboard allows you to see how your settings perform in the current ticker. You can also set up alerts to get informed when the strategy is executable for different tickers.
⚙️ SETTINGS
1. General Configuration
FVG Detection Sensitivity -> You may select between Low, Normal, High or Extreme FVG detection sensitivity. This will essentially determine the size of the spotted FVGs, with lower sensitivies resulting in spotting bigger FVGs, and higher sensitivies resulting in spotting all sizes of FVGs.
Swing Length -> Swing length is used when finding order block formations. Smaller values will result in finding smaller order & breaker blocks.
Require Retracement ->
a) Disabled : The entry signal is given immediately once a FVG overlaps with a Breaker Block of the same type.
b) Enabled : The current price of the ticker will need to touch the FVG once more before the entry signal is given.
2. TP / SL
TP / SL Method ->
a) Unicorn : This is the default option. The SL will be set to the lowest low of the last 100 bars with an extra offset in a Buy signal. For Sell signals, the SL will be set to the highest high of the last 100 bars with an extra offset. The TP is then set to a value using the SL value and maintaining a risk-reward ratio.
b) Dynamic: The TP / SL zones will be auto-determined by the algorithm based on the Average True Range (ATR) of the current ticker.
c) Fixed : You can adjust the exact TP / SL ratios from the settings below.
Dynamic Risk -> The risk you're willing to take if "Dynamic" TP / SL Method is selected. Higher risk usually means a better winrate at the cost of losing more if the strategy fails. This setting is has a crucial effect on the performance of the indicator, as different tickers may have different volatility so the indicator may have increased performance when this setting is correctly adjusted.
Negroni Opening Range StrategyStrategy Summary:
This tool can be used to help identify breakouts from a range during a time-zone of your choosing. It plots a pre-market range, an opening range, it also includes moving average levels that can be used as confluence, as well as plotting previous day SESSION highs and lows.
There are several options on how you wish to close out the trades, all described in more detail below.
Back-testing Inputs:
You define your timezone.
You define how many trades to open on any given day.
You decide to go: long only, short only, or long & short (CAREFUL: "Long & Short" can open trades that effectively closes-out existing ones, for better AND worse!)
You define between which times the strategy will open trades.
You define when it closes any open trades (preventing overnight trades, or leaving trades open into US data times!!).
This hopefully helps make back-testing reflect YOUR trading hours.
NOTE: Renko or Heikin-Ashi charts
For ALL strategies, don’t use Renko or Heikin-Ashi charts unless you know EXACTLY the implications.
Specific to my strategy, using a renko chart can make this 85-90% profitable (I wish it was!!) Although they can be useful, renko charts don’t always capture real wicks, so the renko chart may show your trade up-only but your broker (who is not using renko!!) will have likely stopped you out on a wick somewhere along the line.
NOTE: TradingView ‘Deep backtesting’
For ALL strategies, be cynical of all backtesting (e.g. repainting issues etc) as well as ‘Deep backtesting’ results.
Specific to this strategy, the default settings here SHOULD BE OK, but unfortunately at the time of writing, we can’t see on the chart what exactly ‘deep backtesting’ is calculating. In the past I have noted a number of trades that were not closed at the end of the day, despite my ‘end of day’ trade closing being enabled, so there were big winners and losers that would not have materialized otherwise. As I say, this seems ok at these settings but just always be cynical!!
Opening Range Inputs
You define a pre-market range (example: 08:00 - 09:00).
You define an opening range (example: 09:00 - 09:30).
The strategy will give an update at the close of the opening range to let you know if the opening range has broken out the pre-market range (OR Breakout), or if it has remained inside (OR Inside). The label appears at the end of the opening range NOT at the bar that ‘broke-out’.
This is just a visual cue for you, it has no bearing on what the strategy will do.
The strategy default will trade off the pre-market range, but you can untick this if you prefer to trade off the opening range.
Opening Trades:
Strategy goes long when the bar (CLOSE) crosses-over the ‘pre-market’ high (not the ‘opening range’ high); and the time is within your trading session, and you have not maxed out your number of trades for the day!
Strategy goes short when the bar (CLOSE) crosses-under the ‘pre-market’ low (not the ‘opening range low); and the time is within your trading session, and you have not maxed out your number of trades for the day!
Remember, you can untick this if you prefer to trade off the opening range instead.
NOTES:
Using momentum indicators can help (RSI and MACD): especially to trade range plays in failed breakouts, when momentum shifts… but the strategy won’t do this for you!
Using an anchored vwap at the session open can also provide nice confluence, as well as take-profit levels at the upper/lower of 3x standard deviation.
CLOSING TRADES:
You have 6 take-profit (TP) options:
1) Full TP: uses ATR Multiplier - Full TP at the ATR parameters as defined in inputs.
2) Take Partial profits: ATR Multiplier - Takes partial profits based on parameters as defined in inputs (i.e close 40% of original trade at TP1, close another 40% of original trade at TP2, then the remainder at Full TP as set in option 1.).
3) Full TP: Trailing Stop - Applies a Trailing Stop at the number of points, as defined in inputs.
4) Full TP: MA cross - Takes profit when price crosses ‘Trend MA’ as defined in inputs.
5) Scalp: Points - closes at a set number of points, as defined in inputs.
6) Full TP: PMKT Multiplier - places a SL at opposite pre-market Hi/Low (we go long at a break-out of the pre-market high, 50% would place a SL at the pre-market range mid-point; 100% would place a SL at the pre-market low)'. This takes profit at the input set in option 1).
Dual Chain StrategyDual Chain Strategy - Technical Overview
How It Works:
The Dual Chain Strategy is a unique approach to trading that utilizes Exponential Moving Averages (EMAs) across different timeframes, creating two distinct "chains" of trading signals. These chains can work independently or together, capturing both long-term trends and short-term price movements.
Chain 1 (Longer-Term Focus):
Entry Signal: The entry signal for Chain 1 is generated when the closing price crosses above the EMA calculated on a weekly timeframe. This suggests the start of a bullish trend and prompts a long position.
bullishChain1 = enableChain1 and ta.crossover(src1, entryEMA1)
Exit Signal: The exit signal is triggered when the closing price crosses below the EMA on a daily timeframe, indicating a potential bearish reversal.
exitLongChain1 = enableChain1 and ta.crossunder(src1, exitEMA1)
Parameters: Chain 1's EMA length is set to 10 periods by default, with the flexibility for user adjustment to match various trading scenarios.
Chain 2 (Shorter-Term Focus):
Entry Signal: Chain 2 generates an entry signal when the closing price crosses above the EMA on a 12-hour timeframe. This setup is designed to capture quicker, shorter-term movements.
bullishChain2 = enableChain2 and ta.crossover(src2, entryEMA2)
Exit Signal: The exit signal occurs when the closing price falls below the EMA on a 9-hour timeframe, indicating the end of the shorter-term trend.
exitLongChain2 = enableChain2 and ta.crossunder(src2, exitEMA2)
Parameters: Chain 2's EMA length is set to 9 periods by default, and can be customized to better align with specific market conditions or trading strategies.
Key Features:
Dual EMA Chains: The strategy's originality shines through its dual-chain configuration, allowing traders to monitor and react to both long-term and short-term market trends. This approach is particularly powerful as it combines the strengths of trend-following with the agility of momentum trading.
Timeframe Flexibility: Users can modify the timeframes for both chains, ensuring the strategy can be tailored to different market conditions and individual trading styles. This flexibility makes it versatile for various assets and trading environments.
Independent Trade Logic: Each chain operates independently, with its own set of entry and exit rules. This allows for simultaneous or separate execution of trades based on the signals from either or both chains, providing a robust trading system that can handle different market phases.
Backtesting Period: The strategy includes a configurable backtesting period, enabling thorough performance assessment over a historical range. This feature is crucial for understanding how the strategy would have performed under different market conditions.
time_cond = time >= startDate and time <= finishDate
What It Does:
The Dual Chain Strategy offers traders a distinctive trading tool that merges two separate EMA-based systems into one cohesive framework. By integrating both long-term and short-term perspectives, the strategy enhances the ability to adapt to changing market conditions. The originality of this script lies in its innovative dual-chain design, providing traders with a unique edge by allowing them to capitalize on both significant trends and smaller, faster price movements.
Whether you aim to capture extended market trends or take advantage of more immediate price action, the Dual Chain Strategy provides a comprehensive solution with a high degree of customization and strategic depth. Its flexibility and originality make it a valuable tool for traders seeking to refine their approach to market analysis and execution.
How to Use the Dual Chain Strategy
Step 1: Access the Strategy
Add the Script: Start by adding the Dual Chain Strategy to your TradingView chart. You can do this by searching for the script by name or using the link provided.
Select the Asset: Apply the strategy to your preferred trading pair or asset, such as #BTCUSD, to see how it performs.
Step 2: Configure the Settings
Enable/Disable Chains:
The strategy is designed with two independent chains. You can choose to enable or disable each chain depending on your trading style and the market conditions.
enableChain1 = input.bool(true, title='Enable Chain 1')
enableChain2 = input.bool(true, title='Enable Chain 2')
By default, both chains are enabled. If you prefer to focus only on longer-term trends, you might disable Chain 2, or vice versa if you prefer shorter-term trades.
Set EMA Lengths:
Adjust the EMA lengths for each chain to match your trading preferences.
Chain 1: The default EMA length is 10 periods. This chain uses a weekly timeframe for entry signals and a daily timeframe for exits.
len1 = input.int(10, minval=1, title='Length Chain 1 EMA', group="Chain 1")
Chain 2: The default EMA length is 9 periods. This chain uses a 12-hour timeframe for entries and a 9-hour timeframe for exits.
len2 = input.int(9, minval=1, title='Length Chain 2 EMA', group="Chain 2")
Customize Timeframes:
You can customize the timeframes used for entry and exit signals for both chains.
Chain 1:
Entry Timeframe: Weekly
Exit Timeframe: Daily
tf1_entry = input.timeframe("W", title='Chain 1 Entry Timeframe', group="Chain 1")
tf1_exit = input.timeframe("D", title='Chain 1 Exit Timeframe', group="Chain 1")
Chain 2:
Entry Timeframe: 12 Hours
Exit Timeframe: 9 Hours
tf2_entry = input.timeframe("720", title='Chain 2 Entry Timeframe (12H)', group="Chain 2")
tf2_exit = input.timeframe("540", title='Chain 2 Exit Timeframe (9H)', group="Chain 2")
Set the Backtesting Period:
Define the period over which you want to backtest the strategy. This allows you to see how the strategy would have performed historically.
startDate = input.time(timestamp('2015-07-27'), title="StartDate")
finishDate = input.time(timestamp('2026-01-01'), title="FinishDate")
Step 3: Analyze the Signals
Understand the Entry and Exit Signals:
Buy Signals: When the price crosses above the entry EMA, the strategy generates a buy signal.
bullishChain1 = enableChain1 and ta.crossover(src1, entryEMA1)
Sell Signals: When the price crosses below the exit EMA, the strategy generates a sell signal.
bearishChain2 = enableChain2 and ta.crossunder(src2, entryEMA2)
Review the Visual Indicators:
The strategy plots buy and sell signals on the chart with labels for easy identification:
BUY C1/C2 for buy signals from Chain 1 and Chain 2.
SELL C1/C2 for sell signals from Chain 1 and Chain 2.
This visual aid helps you quickly understand when and why trades are being executed.
Step 4: Optimize the Strategy
Backtest Results:
Review the strategy’s performance over the backtesting period. Look at key metrics like net profit, drawdown, and trade statistics to evaluate its effectiveness.
Adjust the EMA lengths, timeframes, and other settings to see how changes affect the strategy’s performance.
Customize for Live Trading:
Once satisfied with the backtest results, you can apply the strategy settings to live trading. Remember to continuously monitor and adjust as needed based on market conditions.
Step 5: Implement Risk Management
Use Realistic Position Sizing:
Keep your risk exposure per trade within a comfortable range, typically between 1-2% of your trading capital.
Set Alerts:
Set up alerts for buy and sell signals, so you don’t miss trading opportunities.
Paper Trade First:
Consider running the strategy in a paper trading account to understand its behavior in real market conditions before committing real capital.
This dual-layered approach offers a distinct advantage: it enables the strategy to adapt to varying market conditions by capturing both broad trends and immediate price action without one chain's activity impacting the other's decision-making process. The independence of these chains in executing transactions adds a level of sophistication and flexibility that is rarely seen in more conventional trading systems, making the Dual Chain Strategy not just unique, but a powerful tool for traders seeking to navigate complex market environments.
CCI and MACD Auto Trading Strategy with Risk/RewardOverview:
This strategy combines the Commodity Channel Index (CCI) and the Moving Average Convergence Divergence (MACD) indicators to automate trading decisions. It dynamically sets stop-loss and take-profit levels based on recent lows and highs, ensuring a risk/reward ratio of 1:1.5. This script aims to leverage trend and momentum signals while maintaining effective risk management.
Originality and Usefulness:
This script is not just a simple mashup of CCI and MACD indicators; it incorporates dynamic risk management by setting stop-loss and take-profit levels based on recent price action. This approach helps traders to:
・Identify potential trend reversals using the combination of CCI and MACD signals.
・Manage trades effectively by setting realistic stop-loss and take-profit levels based on recent market data.
・Maintain a balanced risk/reward ratio, which is essential for sustainable trading.
Indicators Used:
・CCI (Commodity Channel Index):
・Measures the deviation of the price from its average over a specified period, typically ranging from -100 to +100.
・Helps identify overbought and oversold conditions.
・MACD (Moving Average Convergence Divergence):
・Utilizes the difference between short-term and long-term moving averages to indicate trend strength and direction.
・Provides momentum signals that can be used for timing entries and exits.
How It Works:
Entry Conditions:
Long Entry:
・The MACD histogram is above zero.
・The CCI crosses above the -100 line.
Short Entry:
・The MACD histogram is below zero.
・The CCI crosses below the +100 line.
Exit Conditions:
Long Positions:
・The stop-loss is set at the recent low.
・The take-profit is set at 1.5 times the distance between the entry price and the stop-loss.
Short Positions:
・The stop-loss is set at the recent high.
・The take-profit is set at 1.5 times the distance between the entry price and the stop-loss.
Risk Management:
・The script dynamically adjusts stop-loss and take-profit levels based on recent market data, ensuring that the risk/reward ratio is maintained at 1:1.5.
・This approach helps in managing the risk effectively while aiming for consistent profits.
Strategy Properties:
・Account Size: Configured for a realistic account size suitable for the average trader.
・Commission and Slippage: Includes settings for realistic commission and slippage to reflect real market conditions.
・Risk per Trade: Designed to risk no more than 5-10% of equity per trade, aligning with sustainable trading practices.
・Backtesting Results: Configured to generate a sufficient sample size (ideally more than 100 trades) for reliable backtesting results.
Revised Backtesting Settings
Ensure that your backtesting settings are realistic:
・Account Size: Set a realistic initial capital suitable for the average trader.
・Commission and Slippage: Include realistic commission fees and slippage.
・Risk Management: Ensure that each trade risks no more than 5-10% of the account equity.
・Sufficient Sample Size: Choose a dataset that will generate more than 100 trades to provide a robust sample size.
Versatile Moving Average StrategyVersatile Moving Average Strategy (VMAS)
Overview:
The Versatile Moving Average Strategy (VMAS) is designed to provide traders with a flexible approach to trend-following, utilizing multiple types of moving averages. This strategy allows for customization in choosing the moving average type and length, catering to various market conditions and trading styles.
Key Features:
- Multiple Moving Average Types: Choose from SMA, EMA, SMMA (RMA), WMA, VWMA, HULL, LSMA, and ALMA to best suit your trading needs.
- Customizable Inputs: Adjust the moving average length, source of price data, and stop-loss source to fine-tune the strategy.
- Target Percent: Set the percentage difference between successive profit targets to manage your risk and rewards effectively.
- Position Management: Enable or disable long and short positions, allowing for versatility in different market conditions.
- Commission and Slippage: The strategy includes realistic commission settings to ensure accurate backtesting results.
Strategy Logic:
1. Moving Average Calculation: The selected moving average is calculated based on user-defined parameters.
2. Entry Conditions:
- A long position is entered when the entry source crosses over the moving average, if long positions are enabled.
- A short position is entered when the entry source crosses under the moving average, if short positions are enabled.
3. Stop-Loss: Positions are closed if the stop-loss source crosses the moving average in the opposite direction.
4. Profit Targets: Multiple profit targets are defined, with each target set at an incremental percentage above (for long positions) or below (for short positions) the entry price.
Default Properties:
- Account Size: $10000
- Commission: 0.01% per trade
- Risk Management: Positions are sized to risk 80% of the equity per trade, because we get very tight stoploss when position is open.
- Sample Size: Backtesting has been conducted to ensure a sufficient sample size of trades, ideally more than 100 trades.
How to Use:
1. Configure Inputs: Set your preferred moving average type, length, and other input parameters.
2. Enable Positions: Choose whether to enable long, short, or both types of positions.
3. Backtest and Analyze: Run backtests with realistic settings and analyze the results to ensure the strategy aligns with your trading goals.
4. Deploy and Monitor: Once satisfied with the backtesting results, deploy the strategy in a live environment and monitor its performance.
This strategy is suitable for traders looking to leverage moving averages in a versatile and customizable manner. Adjust the parameters to match your trading style and market conditions for optimal results.
Note: Ensure the strategy settings used for publication are the same as those described here. Always conduct thorough backtesting before deploying any strategy in a live trading environment.
CNTLibraryLibrary "CNTLibrary"
Custom Functions To Help Code In Pinescript V5
Coded By Christian Nataliano
First Coded In 10/06/2023
Last Edited In 22/06/2023
Huge Shout Out To © ZenAndTheArtOfTrading and his ZenLibrary V5, Some Of The Custom Functions Were Heavily Inspired By Matt's Work & His Pine Script Mastery Course
Another Shout Out To The TradingView's Team Library ta V5
//====================================================================================================================================================
// Custom Indicator Functions
//====================================================================================================================================================
GetKAMA(KAMA_lenght, Fast_KAMA, Slow_KAMA)
Calculates An Adaptive Moving Average Based On Perry J Kaufman's Calculations
Parameters:
KAMA_lenght (int) : Is The KAMA Lenght
Fast_KAMA (int) : Is The KAMA's Fastes Moving Average
Slow_KAMA (int) : Is The KAMA's Slowest Moving Average
Returns: Float Of The KAMA's Current Calculations
GetMovingAverage(Source, Lenght, Type)
Get Custom Moving Averages Values
Parameters:
Source (float) : Of The Moving Average, Defval = close
Lenght (simple int) : Of The Moving Average, Defval = 50
Type (string) : Of The Moving Average, Defval = Exponential Moving Average
Returns: The Moving Average Calculation Based On Its Given Source, Lenght & Calculation Type (Please Call Function On Global Scope)
GetDecimals()
Calculates how many decimals are on the quote price of the current market © ZenAndTheArtOfTrading
Returns: The current decimal places on the market quote price
Truncate(number, decimalPlaces)
Truncates (cuts) excess decimal places © ZenAndTheArtOfTrading
Parameters:
number (float)
decimalPlaces (simple float)
Returns: The given number truncated to the given decimalPlaces
ToWhole(number)
Converts pips into whole numbers © ZenAndTheArtOfTrading
Parameters:
number (float)
Returns: The converted number
ToPips(number)
Converts whole numbers back into pips © ZenAndTheArtOfTrading
Parameters:
number (float)
Returns: The converted number
GetPctChange(value1, value2, lookback)
Gets the percentage change between 2 float values over a given lookback period © ZenAndTheArtOfTrading
Parameters:
value1 (float)
value2 (float)
lookback (int)
BarsAboveMA(lookback, ma)
Counts how many candles are above the MA © ZenAndTheArtOfTrading
Parameters:
lookback (int)
ma (float)
Returns: The bar count of how many recent bars are above the MA
BarsBelowMA(lookback, ma)
Counts how many candles are below the MA © ZenAndTheArtOfTrading
Parameters:
lookback (int)
ma (float)
Returns: The bar count of how many recent bars are below the EMA
BarsCrossedMA(lookback, ma)
Counts how many times the EMA was crossed recently © ZenAndTheArtOfTrading
Parameters:
lookback (int)
ma (float)
Returns: The bar count of how many times price recently crossed the EMA
GetPullbackBarCount(lookback, direction)
Counts how many green & red bars have printed recently (ie. pullback count) © ZenAndTheArtOfTrading
Parameters:
lookback (int)
direction (int)
Returns: The bar count of how many candles have retraced over the given lookback & direction
GetSwingHigh(Lookback, SwingType)
Check If Price Has Made A Recent Swing High
Parameters:
Lookback (int) : Is For The Swing High Lookback Period, Defval = 7
SwingType (int) : Is For The Swing High Type Of Identification, Defval = 1
Returns: A Bool - True If Price Has Made A Recent Swing High
GetSwingLow(Lookback, SwingType)
Check If Price Has Made A Recent Swing Low
Parameters:
Lookback (int) : Is For The Swing Low Lookback Period, Defval = 7
SwingType (int) : Is For The Swing Low Type Of Identification, Defval = 1
Returns: A Bool - True If Price Has Made A Recent Swing Low
//====================================================================================================================================================
// Custom Risk Management Functions
//====================================================================================================================================================
CalculateStopLossLevel(OrderType, Entry, StopLoss)
Calculate StopLoss Level
Parameters:
OrderType (int) : Is To Determine A Long / Short Position, Defval = 1
Entry (float) : Is The Entry Level Of The Order, Defval = na
StopLoss (float) : Is The Custom StopLoss Distance, Defval = 2x ATR Below Close
Returns: Float - The StopLoss Level In Actual Price As A
CalculateStopLossDistance(OrderType, Entry, StopLoss)
Calculate StopLoss Distance In Pips
Parameters:
OrderType (int) : Is To Determine A Long / Short Position, Defval = 1
Entry (float) : Is The Entry Level Of The Order, NEED TO INPUT PARAM
StopLoss (float) : Level Based On Previous Calculation, NEED TO INPUT PARAM
Returns: Float - The StopLoss Value In Pips
CalculateTakeProfitLevel(OrderType, Entry, StopLossDistance, RiskReward)
Calculate TakeProfit Level
Parameters:
OrderType (int) : Is To Determine A Long / Short Position, Defval = 1
Entry (float) : Is The Entry Level Of The Order, Defval = na
StopLossDistance (float)
RiskReward (float)
Returns: Float - The TakeProfit Level In Actual Price
CalculateTakeProfitDistance(OrderType, Entry, TakeProfit)
Get TakeProfit Distance In Pips
Parameters:
OrderType (int) : Is To Determine A Long / Short Position, Defval = 1
Entry (float) : Is The Entry Level Of The Order, NEED TO INPUT PARAM
TakeProfit (float) : Level Based On Previous Calculation, NEED TO INPUT PARAM
Returns: Float - The TakeProfit Value In Pips
CalculateConversionCurrency(AccountCurrency, SymbolCurrency, BaseCurrency)
Get The Conversion Currecny Between Current Account Currency & Current Pair's Quoted Currency (FOR FOREX ONLY)
Parameters:
AccountCurrency (simple string) : Is For The Account Currency Used
SymbolCurrency (simple string) : Is For The Current Symbol Currency (Front Symbol)
BaseCurrency (simple string) : Is For The Current Symbol Base Currency (Back Symbol)
Returns: Tuple Of A Bollean (Convert The Currency ?) And A String (Converted Currency)
CalculateConversionRate(ConvertCurrency, ConversionRate)
Get The Conversion Rate Between Current Account Currency & Current Pair's Quoted Currency (FOR FOREX ONLY)
Parameters:
ConvertCurrency (bool) : Is To Check If The Current Symbol Needs To Be Converted Or Not
ConversionRate (float) : Is The Quoted Price Of The Conversion Currency (Input The request.security Function Here)
Returns: Float Price Of Conversion Rate (If In The Same Currency Than Return Value Will Be 1.0)
LotSize(LotSizeSimple, Balance, Risk, SLDistance, ConversionRate)
Get Current Lot Size
Parameters:
LotSizeSimple (bool) : Is To Toggle Lot Sizing Calculation (Simple Is Good Enough For Stocks & Crypto, Whilst Complex Is For Forex)
Balance (float) : Is For The Current Account Balance To Calculate The Lot Sizing Based Off
Risk (float) : Is For The Current Risk Per Trade To Calculate The Lot Sizing Based Off
SLDistance (float) : Is The Current Position StopLoss Distance From Its Entry Price
ConversionRate (float) : Is The Currency Conversion Rate (Used For Complex Lot Sizing Only)
Returns: Float - Position Size In Units
ToLots(Units)
Converts Units To Lots
Parameters:
Units (float) : Is For How Many Units Need To Be Converted Into Lots (Minimun 1000 Units)
Returns: Float - Position Size In Lots
ToUnits(Lots)
Converts Lots To Units
Parameters:
Lots (float) : Is For How Many Lots Need To Be Converted Into Units (Minimun 0.01 Units)
Returns: Int - Position Size In Units
ToLotsInUnits(Units)
Converts Units To Lots Than Back To Units
Parameters:
Units (float) : Is For How Many Units Need To Be Converted Into Lots (Minimun 1000 Units)
Returns: Float - Position Size In Lots That Were Rounded To Units
ATRTrail(OrderType, SourceType, ATRPeriod, ATRMultiplyer, SwingLookback)
Calculate ATR Trailing Stop
Parameters:
OrderType (int) : Is To Determine A Long / Short Position, Defval = 1
SourceType (int) : Is To Determine Where To Calculate The ATR Trailing From, Defval = close
ATRPeriod (simple int) : Is To Change Its ATR Period, Defval = 20
ATRMultiplyer (float) : Is To Change Its ATR Trailing Distance, Defval = 1
SwingLookback (int) : Is To Change Its Swing HiLo Lookback (Only From Source Type 5), Defval = 7
Returns: Float - Number Of The Current ATR Trailing
DangerZone(WinRate, AvgRRR, Filter)
Calculate Danger Zone Of A Given Strategy
Parameters:
WinRate (float) : Is The Strategy WinRate
AvgRRR (float) : Is The Strategy Avg RRR
Filter (float) : Is The Minimum Profit It Needs To Be Out Of BE Zone, Defval = 3
Returns: Int - Value, 1 If Out Of Danger Zone, 0 If BE, -1 If In Danger Zone
IsQuestionableTrades(TradeTP, TradeSL)
Checks For Questionable Trades (Which Are Trades That Its TP & SL Level Got Hit At The Same Candle)
Parameters:
TradeTP (float) : Is The Trade In Question Take Profit Level
TradeSL (float) : Is The Trade In Question Stop Loss Level
Returns: Bool - True If The Last Trade Was A "Questionable Trade"
//====================================================================================================================================================
// Custom Strategy Functions
//====================================================================================================================================================
OpenLong(EntryID, LotSize, LimitPrice, StopPrice, Comment, CommentValue)
Open A Long Order Based On The Given Params
Parameters:
EntryID (string) : Is The Trade Entry ID, Defval = "Long"
LotSize (float) : Is The Lot Size Of The Trade, Defval = 1
LimitPrice (float) : Is The Limit Order Price To Set The Order At, Defval = Na / Market Order Execution
StopPrice (float) : Is The Stop Order Price To Set The Order At, Defval = Na / Market Order Execution
Comment (string) : Is The Order Comment, Defval = Long Entry Order
CommentValue (string) : Is For Custom Values In The Order Comment, Defval = Na
Returns: Void
OpenShort(EntryID, LotSize, LimitPrice, StopPrice, Comment, CommentValue)
Open A Short Order Based On The Given Params
Parameters:
EntryID (string) : Is The Trade Entry ID, Defval = "Short"
LotSize (float) : Is The Lot Size Of The Trade, Defval = 1
LimitPrice (float) : Is The Limit Order Price To Set The Order At, Defval = Na / Market Order Execution
StopPrice (float) : Is The Stop Order Price To Set The Order At, Defval = Na / Market Order Execution
Comment (string) : Is The Order Comment, Defval = Short Entry Order
CommentValue (string) : Is For Custom Values In The Order Comment, Defval = Na
Returns: Void
TP_SLExit(FromID, TPLevel, SLLevel, PercentageClose, Comment, CommentValue)
Exits Based On Predetermined TP & SL Levels
Parameters:
FromID (string) : Is The Trade ID That The TP & SL Levels Be Palced
TPLevel (float) : Is The Take Profit Level
SLLevel (float) : Is The StopLoss Level
PercentageClose (float) : Is The Amount To Close The Order At (In Percentage) Defval = 100
Comment (string) : Is The Order Comment, Defval = Exit Order
CommentValue (string) : Is For Custom Values In The Order Comment, Defval = Na
Returns: Void
CloseLong(ExitID, PercentageClose, Comment, CommentValue, Instant)
Exits A Long Order Based On A Specified Condition
Parameters:
ExitID (string) : Is The Trade ID That Will Be Closed, Defval = "Long"
PercentageClose (float) : Is The Amount To Close The Order At (In Percentage) Defval = 100
Comment (string) : Is The Order Comment, Defval = Exit Order
CommentValue (string) : Is For Custom Values In The Order Comment, Defval = Na
Instant (bool) : Is For Exit Execution Type, Defval = false
Returns: Void
CloseShort(ExitID, PercentageClose, Comment, CommentValue, Instant)
Exits A Short Order Based On A Specified Condition
Parameters:
ExitID (string) : Is The Trade ID That Will Be Closed, Defval = "Short"
PercentageClose (float) : Is The Amount To Close The Order At (In Percentage) Defval = 100
Comment (string) : Is The Order Comment, Defval = Exit Order
CommentValue (string) : Is For Custom Values In The Order Comment, Defval = Na
Instant (bool) : Is For Exit Execution Type, Defval = false
Returns: Void
BrokerCheck(Broker)
Checks Traded Broker With Current Loaded Chart Broker
Parameters:
Broker (string) : Is The Current Broker That Is Traded
Returns: Bool - True If Current Traded Broker Is Same As Loaded Chart Broker
OpenPC(LicenseID, OrderType, UseLimit, LimitPrice, SymbolPrefix, Symbol, SymbolSuffix, Risk, SL, TP, OrderComment, Spread)
Compiles Given Parameters Into An Alert String Format To Open Trades Using Pine Connector
Parameters:
LicenseID (string) : Is The Users PineConnector LicenseID
OrderType (int) : Is The Desired OrderType To Open
UseLimit (bool) : Is If We Want To Enter The Position At Exactly The Previous Closing Price
LimitPrice (float) : Is The Limit Price Of The Trade (Only For Pending Orders)
SymbolPrefix (string) : Is The Current Symbol Prefix (If Any)
Symbol (string) : Is The Traded Symbol
SymbolSuffix (string) : Is The Current Symbol Suffix (If Any)
Risk (float) : Is The Trade Risk Per Trade / Fixed Lot Sizing
SL (float) : Is The Trade SL In Price / In Pips
TP (float) : Is The Trade TP In Price / In Pips
OrderComment (string) : Is The Executed Trade Comment
Spread (float) : is The Maximum Spread For Execution
Returns: String - Pine Connector Order Syntax Alert Message
ClosePC(LicenseID, OrderType, SymbolPrefix, Symbol, SymbolSuffix)
Compiles Given Parameters Into An Alert String Format To Close Trades Using Pine Connector
Parameters:
LicenseID (string) : Is The Users PineConnector LicenseID
OrderType (int) : Is The Desired OrderType To Close
SymbolPrefix (string) : Is The Current Symbol Prefix (If Any)
Symbol (string) : Is The Traded Symbol
SymbolSuffix (string) : Is The Current Symbol Suffix (If Any)
Returns: String - Pine Connector Order Syntax Alert Message
//====================================================================================================================================================
// Custom Backtesting Calculation Functions
//====================================================================================================================================================
CalculatePNL(EntryPrice, ExitPrice, LotSize, ConversionRate)
Calculates Trade PNL Based On Entry, Eixt & Lot Size
Parameters:
EntryPrice (float) : Is The Trade Entry
ExitPrice (float) : Is The Trade Exit
LotSize (float) : Is The Trade Sizing
ConversionRate (float) : Is The Currency Conversion Rate (Used For Complex Lot Sizing Only)
Returns: Float - The Current Trade PNL
UpdateBalance(PrevBalance, PNL)
Updates The Previous Ginve Balance To The Next PNL
Parameters:
PrevBalance (float) : Is The Previous Balance To Be Updated
PNL (float) : Is The Current Trade PNL To Be Added
Returns: Float - The Current Updated PNL
CalculateSlpComm(PNL, MaxRate)
Calculates Random Slippage & Commisions Fees Based On The Parameters
Parameters:
PNL (float) : Is The Current Trade PNL
MaxRate (float) : Is The Upper Limit (In Percentage) Of The Randomized Fee
Returns: Float - A Percentage Fee Of The Current Trade PNL
UpdateDD(MaxBalance, Balance)
Calculates & Updates The DD Based On Its Given Parameters
Parameters:
MaxBalance (float) : Is The Maximum Balance Ever Recorded
Balance (float) : Is The Current Account Balance
Returns: Float - The Current Strategy DD
CalculateWR(TotalTrades, LongID, ShortID)
Calculate The Total, Long & Short Trades Win Rate
Parameters:
TotalTrades (int) : Are The Current Total Trades That The Strategy Has Taken
LongID (string) : Is The Order ID Of The Long Trades Of The Strategy
ShortID (string) : Is The Order ID Of The Short Trades Of The Strategy
Returns: Tuple Of Long WR%, Short WR%, Total WR%, Total Winning Trades, Total Losing Trades, Total Long Trades & Total Short Trades
CalculateAvgRRR(WinTrades, LossTrades)
Calculates The Overall Strategy Avg Risk Reward Ratio
Parameters:
WinTrades (int) : Are The Strategy Winning Trades
LossTrades (int) : Are The Strategy Losing Trades
Returns: Float - The Average RRR Values
CAGR(StartTime, StartPrice, EndTime, EndPrice)
Calculates The CAGR Over The Given Time Period © TradingView
Parameters:
StartTime (int) : Is The Starting Time Of The Calculation
StartPrice (float) : Is The Starting Price Of The Calculation
EndTime (int) : Is The Ending Time Of The Calculation
EndPrice (float) : Is The Ending Price Of The Calculation
Returns: Float - The CAGR Values
//====================================================================================================================================================
// Custom Plot Functions
//====================================================================================================================================================
EditLabels(LabelID, X1, Y1, Text, Color, TextColor, EditCondition, DeleteCondition)
Edit / Delete Labels
Parameters:
LabelID (label) : Is The ID Of The Selected Label
X1 (int) : Is The X1 Coordinate IN BARINDEX Xloc
Y1 (float) : Is The Y1 Coordinate IN PRICE Yloc
Text (string) : Is The Text Than Wants To Be Written In The Label
Color (color) : Is The Color Value Change Of The Label Text
TextColor (color)
EditCondition (int) : Is The Edit Condition of The Line (Setting Location / Color)
DeleteCondition (bool) : Is The Delete Condition Of The Line If Ture Deletes The Prev Itteration Of The Line
Returns: Void
EditLine(LineID, X1, Y1, X2, Y2, Color, EditCondition, DeleteCondition)
Edit / Delete Lines
Parameters:
LineID (line) : Is The ID Of The Selected Line
X1 (int) : Is The X1 Coordinate IN BARINDEX Xloc
Y1 (float) : Is The Y1 Coordinate IN PRICE Yloc
X2 (int) : Is The X2 Coordinate IN BARINDEX Xloc
Y2 (float) : Is The Y2 Coordinate IN PRICE Yloc
Color (color) : Is The Color Value Change Of The Line
EditCondition (int) : Is The Edit Condition of The Line (Setting Location / Color)
DeleteCondition (bool) : Is The Delete Condition Of The Line If Ture Deletes The Prev Itteration Of The Line
Returns: Void
//====================================================================================================================================================
// Custom Display Functions (Using Tables)
//====================================================================================================================================================
FillTable(TableID, Column, Row, Title, Value, BgColor, TextColor, ToolTip)
Filling The Selected Table With The Inputed Information
Parameters:
TableID (table) : Is The Table ID That Wants To Be Edited
Column (int) : Is The Current Column Of The Table That Wants To Be Edited
Row (int) : Is The Current Row Of The Table That Wants To Be Edited
Title (string) : Is The String Title Of The Current Cell Table
Value (string) : Is The String Value Of The Current Cell Table
BgColor (color) : Is The Selected Color For The Current Table
TextColor (color) : Is The Selected Color For The Current Table
ToolTip (string) : Is The ToolTip Of The Current Cell In The Table
Returns: Void
DisplayBTResults(TableID, BgColor, TextColor, StartingBalance, Balance, DollarReturn, TotalPips, MaxDD)
Filling The Selected Table With The Inputed Information
Parameters:
TableID (table) : Is The Table ID That Wants To Be Edited
BgColor (color) : Is The Selected Color For The Current Table
TextColor (color) : Is The Selected Color For The Current Table
StartingBalance (float) : Is The Account Starting Balance
Balance (float)
DollarReturn (float) : Is The Account Dollar Reture
TotalPips (float) : Is The Total Pips Gained / loss
MaxDD (float) : Is The Maximum Drawdown Over The Backtesting Period
Returns: Void
DisplayBTResultsV2(TableID, BgColor, TextColor, TotalWR, QTCount, LongWR, ShortWR, InitialCapital, CumProfit, CumFee, AvgRRR, MaxDD, CAGR, MeanDD)
Filling The Selected Table With The Inputed Information
Parameters:
TableID (table) : Is The Table ID That Wants To Be Edited
BgColor (color) : Is The Selected Color For The Current Table
TextColor (color) : Is The Selected Color For The Current Table
TotalWR (float) : Is The Strategy Total WR In %
QTCount (int) : Is The Strategy Questionable Trades Count
LongWR (float) : Is The Strategy Total WR In %
ShortWR (float) : Is The Strategy Total WR In %
InitialCapital (float) : Is The Strategy Initial Starting Capital
CumProfit (float) : Is The Strategy Ending Cumulative Profit
CumFee (float) : Is The Strategy Ending Cumulative Fee (Based On Randomized Fee Assumptions)
AvgRRR (float) : Is The Strategy Average Risk Reward Ratio
MaxDD (float) : Is The Strategy Maximum DrawDown In Its Backtesting Period
CAGR (float) : Is The Strategy Compounded Average GRowth In %
MeanDD (float) : Is The Strategy Mean / Average Drawdown In The Backtesting Period
Returns: Void
//====================================================================================================================================================
// Custom Pattern Detection Functions
//====================================================================================================================================================
BullFib(priceLow, priceHigh, fibRatio)
Calculates A Bullish Fibonacci Value (From Swing Low To High) © ZenAndTheArtOfTrading
Parameters:
priceLow (float)
priceHigh (float)
fibRatio (float)
Returns: The Fibonacci Value Of The Given Ratio Between The Two Price Points
BearFib(priceLow, priceHigh, fibRatio)
Calculates A Bearish Fibonacci Value (From Swing High To Low) © ZenAndTheArtOfTrading
Parameters:
priceLow (float)
priceHigh (float)
fibRatio (float)
Returns: The Fibonacci Value Of The Given Ratio Between The Two Price Points
GetBodySize()
Gets The Current Candle Body Size IN POINTS © ZenAndTheArtOfTrading
Returns: The Current Candle Body Size IN POINTS
GetTopWickSize()
Gets The Current Candle Top Wick Size IN POINTS © ZenAndTheArtOfTrading
Returns: The Current Candle Top Wick Size IN POINTS
GetBottomWickSize()
Gets The Current Candle Bottom Wick Size IN POINTS © ZenAndTheArtOfTrading
Returns: The Current Candle Bottom Wick Size IN POINTS
GetBodyPercent()
Gets The Current Candle Body Size As A Percentage Of Its Entire Size Including Its Wicks © ZenAndTheArtOfTrading
Returns: The Current Candle Body Size IN PERCENTAGE
GetTopWickPercent()
Gets The Current Top Wick Size As A Percentage Of Its Entire Body Size
Returns: Float - The Current Candle Top Wick Size IN PERCENTAGE
GetBottomWickPercent()
Gets The Current Bottom Wick Size As A Percentage Of Its Entire Bodu Size
Returns: Float - The Current Candle Bottom Size IN PERCENTAGE
BullishEC(Allowance, RejectionWickSize, EngulfWick, NearSwings, SwingLookBack)
Checks If The Current Bar Is A Bullish Engulfing Candle
Parameters:
Allowance (int) : To Give Flexibility Of Engulfing Pattern Detection In Markets That Have Micro Gaps, Defval = 0
RejectionWickSize (float) : To Filter Out long (Upper And Lower) Wick From The Bullsih Engulfing Pattern, Defval = na
EngulfWick (bool) : To Specify If We Want The Pattern To Also Engulf Its Upper & Lower Previous Wicks, Defval = false
NearSwings (bool) : To Specify If We Want The Pattern To Be Near A Recent Swing Low, Defval = true
SwingLookBack (int) : To Specify How Many Bars Back To Detect A Recent Swing Low, Defval = 10
Returns: Bool - True If The Current Bar Matches The Requirements of a Bullish Engulfing Candle
BearishEC(Allowance, RejectionWickSize, EngulfWick, NearSwings, SwingLookBack)
Checks If The Current Bar Is A Bearish Engulfing Candle
Parameters:
Allowance (int) : To Give Flexibility Of Engulfing Pattern Detection In Markets That Have Micro Gaps, Defval = 0
RejectionWickSize (float) : To Filter Out long (Upper And Lower) Wick From The Bearish Engulfing Pattern, Defval = na
EngulfWick (bool) : To Specify If We Want The Pattern To Also Engulf Its Upper & Lower Previous Wicks, Defval = false
NearSwings (bool) : To Specify If We Want The Pattern To Be Near A Recent Swing High, Defval = true
SwingLookBack (int) : To Specify How Many Bars Back To Detect A Recent Swing High, Defval = 10
Returns: Bool - True If The Current Bar Matches The Requirements of a Bearish Engulfing Candle
Hammer(Fib, ColorMatch, NearSwings, SwingLookBack, ATRFilterCheck, ATRPeriod)
Checks If The Current Bar Is A Hammer Candle
Parameters:
Fib (float) : To Specify Which Fibonacci Ratio To Use When Determining The Hammer Candle, Defval = 0.382 Ratio
ColorMatch (bool) : To Filter Only Bullish Closed Hammer Candle Pattern, Defval = false
NearSwings (bool) : To Specify If We Want The Doji To Be Near A Recent Swing Low, Defval = true
SwingLookBack (int) : To Specify How Many Bars Back To Detect A Recent Swing Low, Defval = 10
ATRFilterCheck (float) : To Filter Smaller Hammer Candles That Might Be Better Classified As A Doji Candle, Defval = 1
ATRPeriod (simple int) : To Change ATR Period Of The ATR Filter, Defval = 20
Returns: Bool - True If The Current Bar Matches The Requirements of a Hammer Candle
Star(Fib, ColorMatch, NearSwings, SwingLookBack, ATRFilterCheck, ATRPeriod)
Checks If The Current Bar Is A Hammer Candle
Parameters:
Fib (float) : To Specify Which Fibonacci Ratio To Use When Determining The Hammer Candle, Defval = 0.382 Ratio
ColorMatch (bool) : To Filter Only Bullish Closed Hammer Candle Pattern, Defval = false
NearSwings (bool) : To Specify If We Want The Doji To Be Near A Recent Swing Low, Defval = true
SwingLookBack (int) : To Specify How Many Bars Back To Detect A Recent Swing Low, Defval = 10
ATRFilterCheck (float) : To Filter Smaller Hammer Candles That Might Be Better Classified As A Doji Candle, Defval = 1
ATRPeriod (simple int) : To Change ATR Period Of The ATR Filter, Defval = 20
Returns: Bool - True If The Current Bar Matches The Requirements of a Hammer Candle
Doji(MaxWickSize, MaxBodySize, DojiType, NearSwings, SwingLookBack)
Checks If The Current Bar Is A Doji Candle
Parameters:
MaxWickSize (float) : To Specify The Maximum Lenght Of Its Upper & Lower Wick, Defval = 2
MaxBodySize (float) : To Specify The Maximum Lenght Of Its Candle Body IN PERCENT, Defval = 0.05
DojiType (int)
NearSwings (bool) : To Specify If We Want The Doji To Be Near A Recent Swing High / Low (Only In Dragonlyf / Gravestone Mode), Defval = true
SwingLookBack (int) : To Specify How Many Bars Back To Detect A Recent Swing High / Low (Only In Dragonlyf / Gravestone Mode), Defval = 10
Returns: Bool - True If The Current Bar Matches The Requirements of a Doji Candle
BullishIB(Allowance, RejectionWickSize, EngulfWick, NearSwings, SwingLookBack)
Checks If The Current Bar Is A Bullish Harami Candle
Parameters:
Allowance (int) : To Give Flexibility Of Harami Pattern Detection In Markets That Have Micro Gaps, Defval = 0
RejectionWickSize (float) : To Filter Out long (Upper And Lower) Wick From The Bullsih Harami Pattern, Defval = na
EngulfWick (bool) : To Specify If We Want The Pattern To Also Engulf Its Upper & Lower Previous Wicks, Defval = false
NearSwings (bool) : To Specify If We Want The Pattern To Be Near A Recent Swing Low, Defval = true
SwingLookBack (int) : To Specify How Many Bars Back To Detect A Recent Swing Low, Defval = 10
Returns: Bool - True If The Current Bar Matches The Requirements of a Bullish Harami Candle
BearishIB(Allowance, RejectionWickSize, EngulfWick, NearSwings, SwingLookBack)
Checks If The Current Bar Is A Bullish Harami Candle
Parameters:
Allowance (int) : To Give Flexibility Of Harami Pattern Detection In Markets That Have Micro Gaps, Defval = 0
RejectionWickSize (float) : To Filter Out long (Upper And Lower) Wick From The Bearish Harami Pattern, Defval = na
EngulfWick (bool) : To Specify If We Want The Pattern To Also Engulf Its Upper & Lower Previous Wicks, Defval = false
NearSwings (bool) : To Specify If We Want The Pattern To Be Near A Recent Swing High, Defval = true
SwingLookBack (int) : To Specify How Many Bars Back To Detect A Recent Swing High, Defval = 10
Returns: Bool - True If The Current Bar Matches The Requirements of a Bearish Harami Candle
//====================================================================================================================================================
// Custom Time Functions
//====================================================================================================================================================
BarInSession(sess, useFilter)
Determines if the current price bar falls inside the specified session © ZenAndTheArtOfTrading
Parameters:
sess (simple string)
useFilter (bool)
Returns: A boolean - true if the current bar falls within the given time session
BarOutSession(sess, useFilter)
Determines if the current price bar falls outside the specified session © ZenAndTheArtOfTrading
Parameters:
sess (simple string)
useFilter (bool)
Returns: A boolean - true if the current bar falls outside the given time session
DateFilter(startTime, endTime)
Determines if this bar's time falls within date filter range © ZenAndTheArtOfTrading
Parameters:
startTime (int)
endTime (int)
Returns: A boolean - true if the current bar falls within the given dates
DayFilter(monday, tuesday, wednesday, thursday, friday, saturday, sunday)
Checks if the current bar's day is in the list of given days to analyze © ZenAndTheArtOfTrading
Parameters:
monday (bool)
tuesday (bool)
wednesday (bool)
thursday (bool)
friday (bool)
saturday (bool)
sunday (bool)
Returns: A boolean - true if the current bar's day is one of the given days
AUSSess()
Checks If The Current Australian Forex Session In Running
Returns: Bool - True If Currently The Australian Session Is Running
ASIASess()
Checks If The Current Asian Forex Session In Running
Returns: Bool - True If Currently The Asian Session Is Running
EURSess()
Checks If The Current European Forex Session In Running
Returns: Bool - True If Currently The European Session Is Running
USSess()
Checks If The Current US Forex Session In Running
Returns: Bool - True If Currently The US Session Is Running
UNIXToDate(Time, ConversionType, TimeZone)
Converts UNIX Time To Datetime
Parameters:
Time (int) : Is The UNIX Time Input
ConversionType (int) : Is The Datetime Output Format, Defval = DD-MM-YYYY
TimeZone (string) : Is To Convert The Outputed Datetime Into The Specified Time Zone, Defval = Exchange Time Zone
Returns: String - String Of Datetime
Optimized Zhaocaijinbao strategyIntroduction:
The Optimized Zhaocaijinbao strategy is a mid and long-term quantitative trading strategy that combines momentum and trend factors. It generates buy and sell signals by using a combination of exponential moving averages, moving averages, volume and slope indicators. It generates buy signals when the stock is above the 35-day moving average, the trading volume is higher than the 20-day moving average, and the stock is in an upward trend on a weekly timeframe."招财进宝" is a Chinese phrase that can be translated to "Attract Wealth and Bring in Treasure" in English. It is a common expression used to wish for good luck and prosperity in various contexts, such as in business or personal finances.
Highlights:
The strategy has several special optimizations that make it unique.
Firstly, the strategy is optimized for T+1 trading in the Chinese stock market and is only suitable for long positions. The optimizations are also applicable to international stock markets.
Secondly, the trend strategy is optimized to only show indicators on the right side and oscillations. This helps to prevent false signals in choppy markets.
Thirdly, the strategy uses a risk factor for dynamic position sizing to ensure position sizes are adjusted according to the current net asset value and risk preferences. This helps to lower drawdown risks.
The strategy has good resilience even without using stop loss modules in backtesting, making it suitable for trading hourly, 2-hourly, and daily K-line charts (depending on the stock being traded). We recommend experimenting with backtesting using SSE 1-hour or 2-hour or daily Kline charts.
Backtesting outcomes:
The strategy was backtested over the period from October 13th, 2005 to April 14th, 2023, using daily candlestick charts for the commodity code SSE:600763, with a currency of CNY and tick size of 0.01. The strategy used an initial capital of 1,000,000 CNY, with order sizes set to 10% equity and a pyramid of 1 order. The strategy also had a Max Position Size of 0.01 and a Risk Factor of 2.
Here is a summary of the performance of the trading strategy:
Total net profit: 288,577.32 CNY, representing a return of 128.86%
Total number of closed trades: 61
Winning trades: 37, representing a win rate of 60.66%
Profit factor: 2.415
Largest losing trade: 222,021.46 CNY, representing a loss of 14.08%
Average trade: 21,124.22 CNY, representing a return of 3.1%
Average holding period for all trades: 12 days
Conclusion:
In conclusion, the Optimized Zhaocaijinbao strategy is a mid and long-term quantitative trading strategy that combines momentum and trend factors. It is suitable for both Chinese stocks and global stocks. While the Optimized Zhaocaijinbao strategy has performed well in backtesting, it is important to note that past performance is not a guarantee of future results. Traders should conduct their own research and analysis and exercise caution when using any trading strategy.
Heikin Ashi Candle Startegy for Long PositionThis strategy utilize Heikin-Ashi candlestick chart.
Heikin-Ashi technique is a Japanese candlestick-based technical trading tool that uses candlestick charts to represent and visualize market price data.
Heikin-Ashi candle is essentially taking an average of the movement.
There is a tendency with Heikin-Ashi for the candles to stay red during a downtrend and green during an uptrend.
This strategy only apply for long trading position.
The idea is trader will waiting 3 green candles for validation period (confirmation) before entering long position.
Different timeframe will result different result.
Number of validation period can be changed to see different result
This strategy has parameter for take profit percentage, trailing stop and stop loss.
User can set maximum active position to minimize risk and qty order.
This tool is useful for user who wants to backtest Heikin-Ashi trading strategy.
Script will emit alert when long position is opened and closed.
Warning of Backtesting
Backtesting is backward-looking. As the name implies, you are testing how something would have worked if you traded it perfectly in the past.
Past performance does not indicate future performance and you should not assume it does.
Backtesting assumes you never miss-fire, that you get in and out at the exactly perfect moment each time.
Backtesting assumes you have perfect liquidity, and your limit orders fill at a specific, pre-defined price every time (either the open, close, low, high, or some average of these).
Disclaimer
Do your own research and consider fundamental price of asset.
The indicators provided on this script is for educational purposes only.
Author does not offer advisory or brokerage services, nor does it recommend or advise users to buy or sell particular stocks or securities.
Please examined script and give feedback for further improvement.
Script are open to public, everyone see and clone source code or just apply to chart. Please make comment for improvement.
Patient Trendfollower (7)(alpha)Patient Trendfollower consists of 21 and 55 EMA, Commodity Channel Index and Supertrend indicator. It confirms a trend and gives you a signal on a pullback. Original creation worked on 1h EURUSD chart.
►Long setup:
• 21 EMA is above 55 EMA, which is above the Supertrend indicator.
• Commodity Channel Index is an oscillator, which prints into the chart if extreme levels are reached. Green is for a level above 100 or below -100, red is above 140 or below -140 and black is above 180 or below -180.
• If 21 EMA > 55EMA > Supertrend and an oversold signal appear, you can buy into the trend.
• When backtesting on 1h EURUSD, profit target 400 pips worked best with a stop-loss below Supertrend's bottom and the size of your spread.
• A picture shows two valid entries.
: This part still malfunctions and shows red dots over some green ones. It is important to disable red ones in the settings to see green ones.
Some more long signals:
Some short signals:
►Backtesting data with default settings and trading only green CCI signals with mentioned risk management strategy:
• 212 closed trades
• 58.96% profitable with average win trade 348 USD and average loss trade 263 USD when only green signals are followed.
• Profit factor 1.903, Sharpee 0.792
• 20 bars is average for all trades, short trades were 18 bars long on average.
With given data, you can see the strategy is profitable by itself. However, original risk management settings do work only on 1h charts of EURUSD and would need to be adjusted for other instruments based on average volatility.
Even though the profitability is low, you can increase your odds by a great margin, if you properly use price action (impulsive and corrective moves, patterns, bar analysis), if you trade when major exchanges are open, you may also use wave analysis such as Elliot Waves or Market Profiles to predict whether the next day might be a trending day. My backtesting program didn't consider these ideas.
Unfortunately, I won't be making backtesting strategy public with it anytime soon, because it still has some parts that do not work. I am ok with that since I understand the code and know what does malfunction and how. Then, there are parts which I am not sure how to fix yet. This is why the indicator is still considered alpha.
In the future when a strategy is published, you will also be able to set your own overbought/oversold values without entering the code itself and probably some other features. But I am not in a hurry for that. You can give me feedback on UX and try to figure out the best setups for other symbols, it might help to improve the automatic testing script when I know what I should achieve. My main point is to make this public for friends who can already be using it on EURUSD at least.
Close doesn't always have to be 400 pips, you might want to close on a logical level such as strong resistance or a trendline too.
Thanks to:
• @everget for providing Supertrend solution.
• Satik FX who hand-tested the system by hand and reported results in this article . He is my main inspiration for creating the complete indicator as one because I want to be able to show and hide it with a single click. My future scripts will also work as a whole strategy each by itself.
• The number in the script's name comes from Satik's numbering. A mentioned article was his seventh shared strategy.
HatiKO EnvelopesPublished source code is subject to the terms of the GNU Affero General Public License v3.0
This script describes and provides backtesting functionality to internal strategy of algorithmic crypto trading software "HatiKO bot".
Suitable for backtesting any Cryptocurrency Pair on any Exchange/Platform, any Timeframe.
Core Mechanics of this strategy are based on theory of price always returning to Moving Average + Envelopes indicator (Moving_average_envelope from Wiki)
Developement of this script and trading software is inspired by:
"Essential Technical Analysis: Tools and Techniques to Spot Market Trends" by Leigh Stevens (published on 12th of April 2002)
"Moving Average Envelopes" by ChartSchool, StockCharts platform (published on 13th of April 2015 or earlier)
"Коля Колеснік" from Crypto Times channel ("Метод сетка", published on 19th of August 2018)
"3 ways to use Moving Average Envelopes" by Rich Fitton, published on Trader's Nest (published on 28st of November 2018 or earlier)
noro's "Robot WhiteBox ShiftMA" strategy v1 script, published on TradingView platform (published on 29th of August 2018)
"Moving Average Envelopes: A Popular Trading Tool" Investopedia article (published 25th of June 2019)
and KROOL1980's blogpost on Argolabs ("Гридерство или Сетка как источник прибыли на форекс", published on 27th of February 2015)
Core Features:
1) Up to 4 Envelopes in each direction (Long/Short)
2) Use any of 6 different basis MAs, optionally use different MAs for Opening and Closure
3) Use different Timeframes for MA calculation, without any repainting and lookahead bias.
4) Fixed order size, not Martingale strategy
5) Close open position earlier by using Deviation parameter
6) PineScript v4 code
Options description:
Lot - % from your initial balance to use for order size calculation
Timeframe Short - Timeframe to use for Short Opening MA calculation, can be chosen from dropdown list, default is Current Graph Timeframe
MA Type Short - Type of MA to use for Short Opening MA calculation, can be chosen from dropdown list, default is SMA
Data Short - Source of Price for Short Opening MA calculation, can be chosen from dropdown list, default is OHLC4
MA Length Short - Period used for Short Opening MA calculation, should be >=1, default is 3
MA offset Short - Offset for MA value used for Short Envelopes calculation, should be >= 0, default is 0
Timeframe Long - Timeframe to use for Long Opening MA calculation, can be chosen from dropdown list, default is Current Graph Timeframe
MA Type Long - Type of MA to use for Long Opening MA calculation, can be chosen from dropdown list, default is SMA
Data Long - Source of Price for Long Opening MA calculation, can be chosen from dropdown list, default is OHLC4
MA Length Long - Period used for Long Opening MA calculation, should be >=1, default is 3
MA offset Long - Offset for MA value used for Long Envelopes calculation, should be >= 0, default is 0
Mode close MA Short - Enable different MA for Short position Closure, default is "false". If false, Closure MA = Opening MA
Timeframe Short Close - Timeframe to use for Short Position Closure MA calculation, can be chosen from dropdown list, default is Current Graph Timeframe
MA Type Close Short - Type of MA to use for Short Position Closure MA calculation, can be chosen from dropdown list, default is SMA
Data Short Close - Source of Price for Short Closure MA calculation, can be chosen from dropdown list, default is OHLC4
MA Length Short Close - Period used for Short Opening MA calculation, should be >=1, default is 3
Short Deviation - % to move from MA value, used to close position above or beyond MA, can be negative, default is 0
MA offset Short Close - Offset for MA value used for Short Position Closure calculation, should be >= 0, default is 0
Mode close MA Long - Enable different MA for Long position Closure, default is "false". If false, Closure MA = Opening MA
Timeframe Long Close - Timeframe to use for Long Position Closure MA calculation, can be chosen from dropdown list, default is Current Graph Timeframe
MA Type Close Long - Type of MA to use for Long Position Closure MA calculation, can be chosen from dropdown list, default is SMA
Data Long Close - Source of Price for Long Closure MA calculation, can be chosen from dropdown list, default is OHLC4
MA Length Long Close - Period used for Long Opening MA calculation, should be >=1, default is 3
Long Deviation - % to move from MA value, used to close position above or beyond MA, can be negative, default is 0
MA offset Long Close - Offset for MA value used for Long Position Closure calculation, should be >= 0, default is 0
Short Shift 1..4 - % from MA value to put Envelopes at, for Shorts numbers should be positive, the higher is number, the higher should be Shift position, example: "Shift 1 = 1, shift 2 = 2, etc."
Long Shift 1..4 - % from MA value to put Envelopes at, for Longs numbers should be negative, the lower is number, the lower should be Shift position, example: "Shift 1 = -1, shift 2 = -2, etc."
From Year 20XX - Backtesting Starting Year number, only 20xx supported as script is cryptocurrency-oriented.
To Year 20XX - Backtesting Final Year number, only 20xx supported as script is cryptocurrency-oriented.
From Month - Years starting Month, optional tweaking, changing not recommended
To Month - Years ending Month, optional tweaking, changing not recommended
From day - Months starting day, optional tweaking, changing not recommended
To day - Months ending day, optional tweaking, changing not recommended
Graph notes:
Green lines - Long Envelopes.
Red lines - Short Envelopes.
Orange line - MA for closing of Short positions.
Lime line - MA for closing of Long positions.
**************************************************************************************************************************************************************************************************************
Опубликованный исходный код регулируется Условиями Стандартной Общественной Лицензии GNU Affero v3.0
Этот скрипт описывает и предоставляет функции бектеста для внутренней стратегии алгоритмического программного обеспечения "HatiKO bot".
Подходит для тестирования любой криптовалютной пары на любой бирже/платформе, на любом таймфрейме.
Кор-механика этой стратегии основана на теории всегда возвращающейся к значению МА цены с использованием индикатора Envelopes (Moving_average_envelope from Wiki)
Разработка этого скрипта и программного обеспечения для торговли вдохновлена следующими источниками:
Книга "Essential Technical Analysis: Tools and Techniques to Spot Market Trends" Ли Стивенса (опубликовано 12 апреля 2002 года)
«Moving Average Envelopes» от ChartSchool, платформа StockCharts (опубликовано 13 апреля 2015 года или раньше)
«Коля Колеснік» с канала Crypto Times («Метод сетка», опубликовано 19 августа 2018 года)
«3 ways to use Moving Average Envelopes» Рича Фиттона, опубликованные в «Trader's Nest» (опубликовано 28 ноября 2018 года или раньше)
Скрипт стратегии noro "Robot WhiteBox ShiftMA" v1, опубликованный на платформе TradingView(опубликовано 29 августа 2018 года)
«Moving Average Envelopes: A Popular Trading Tool», статья Investopedia (опубликовано 25 июня 2019 года)
Блог KROOL1980 из Argolabs («Гридерство или Сетка как источник прибыли на форекс», опубликовано 27 февраля 2015 года)
Основные особенности:
1) До 4-х Ордеров в каждом из направлении (Лонг / Шорт)
2) Выбор из 6-ти разных базовых МА, опционально используйте разные МА для открытия и закрытия.
3) Используйте разные таймфреймы для расчета MA, без перерисовки и "эффекта стеклянного шара".
4) Фиксированный размер ордера, а не стратегия Мартингейла
5) Возможность закрытия открытой позиции заблаговременно, используя параметр Deviation
6) Код реализован на PineScript v4
Описание параметров:
Lot - % от вашего первоначального баланса, используется при расчете размера Ордера
Timeframe Short - таймфрейм, используемый для расчета МА Открытия Шорт позиций, может быть выбран из списка, по умолчанию - таймфрейм текущего графика
MA Type Short - тип MA, используемый для расчета МА Открытия Шорт позиций, может быть выбран из списка, по умолчанию SMA
Data Short - источник цены для расчета МА Открытия Шорт позиций, может быть выбран из списка, по умолчанию OHLC4
MA Length Short - период, используемый для расчета МА Открытия Шорт позиций, должен быть >= 1, по умолчанию 3
MA Offset Short - смещение значения MA, используемого для расчета Шорт Ордеров, должно быть >= 0, по умолчанию 0
Timeframe Long - таймфрейм, используемый для расчета МА Открытия Лонг позиций, может быть выбран из списка, по умолчанию - таймфрейм текущего графика
MA Type Long - тип MA, используемый для расчета МА Открытия Лонг позиций, может быть выбран из списка, по умолчанию SMA
Data Long - источник цены для расчета МА Открытия Лонг позиций, может быть выбран из списка, по умолчанию OHLC4
MA Length Long - период, используемый для расчета МА Открытия Лонг позиций, должен быть >= 1, по умолчанию 3
MA Offset Long - смещение значения MA, используемого для расчета Лонг Ордеров, должно быть >= 0, по умолчанию 0
Mode close MA Short - Включает отдельное MA для закрытия Шорт позиции, по умолчанию «false». Если false, MA Закрытия = MA Открытия
Timeframe Short Close - таймфрейм, используемый для расчета МА Закрытия Шорт позиций, может быть выбран из списка, по умолчанию - таймфрейм текущего графика
MA Type Close Short - тип MA, используемый при расчете МА Закрытия Шорт позиции. Mожно выбрать из списка, по умолчанию SMA
Data Short Close - источник цены для расчета МА Закрытия Шорт позиций, может быть выбран из списка, по умолчанию OHLC4
MA Length Short Close - период, используемый для расчета МА Закрытия Шорт позиции, должен быть >= 1, по умолчанию 3
Short Deviation - % отклонения от значения MA, используется для закрытия позиции выше или ниже рассчитанного значения MA, может быть отрицательным, по умолчанию 0
MA Offset Short Close - смещение значения MA, используемого для расчета закрытия Шорт позиции, должно быть >= 0, по умолчанию 0
Mode close MA Long - Включает разные MA для закрытия Лонг позиции, по умолчанию «false». Если false, MA Закрытия = MA Открытия
Timeframe Long Close - таймфрейм, используемый для расчета МА Закрытия Лонг позиций, может быть выбран из списка, по умолчанию - таймфрейм текущего графика
MA Type Close Long - тип MA, используемый при расчете МА Закрытия Лонг позиции. Mожно выбрать из списка, по умолчанию SMA
Data Long Close - источник цены для расчета МА Закрытия Лонг позиций, может быть выбран из списка, по умолчанию OHLC4
MA Length Long Close - период, используемый для расчета МА Закрытия Лонг позиции, должен быть >= 1, по умолчанию 3
Long Deviation -% для перехода от значения MA, используется для закрытия позиции выше или ниже рассчитанного значения MA, может быть отрицательным, по умолчанию 0
MA Offset Long Close - смещение значения MA, используемого для расчета закрытия Лонг позиции, должно быть >= 0, по умолчанию 0
Short Shift 1..4 - % от значения MA для размещения Ордеров, для Шорт Ордеров должен быть положительным, чем выше номер, тем выше должна располагаться позиция Shift, например: «Shift 1 = 1, Shift 2 = 2 и т.д. "
Long Shift 1..4 - % от значения MA для размещения Ордеров, для Лонг Ордеров должно быть отрицательным, чем ниже число, тем ниже должна располагаться позиция Shift, например: «Shift 1 = -1, Shift 2 = -2, и т.д."
From Year 20XX - Год начала тестирования, из-за ориентированности на криптовалюты поддерживаются только значения формата 20хх.
To Year 20XX - Год окончания тестирования, из-за ориентированности на криптовалюты поддерживаются только значения формата 20хх.
From Month - Начальный месяц, опционально, менять не рекомендуется
To Month - Конечный месяц, опционально, менять не рекомендуется
From day - Начальный день месяца, опционально, менять не рекомендуется
To day - Конечный день месяца, опционально, менять не рекомендуется
Пояснения к графику:
Зеленые линии - Лонг Ордера.
Красные линии - Шорт Ордера.
Оранжевая линия - MA Закрытия Шорт позиций.
Лаймовая линия - MA Закрытия Лонг позиций.
SMA Cross Entry & Exit StrategyThis is a TradingView Strategy Script meaning you can't execute real trades using your exchange API connected to your TradingView account, it is designed for backtesting only
This is a basic backtesting script for charting the bullish and bearish cross of two user defined simple moving averages, select the cog next to the name of the script ON the price chart in the left hand corner. The script will print to the screen either "Long Entry" or "Short Entry" depending on the direction of the cross. The script using TradingView strategies will subsequently close the opposite of the position that is executed when the bullish or bearish cross occurs. Simply put, if you are short and a bullish cross occurs, your short trade will close and be logged in strategies and the long will fire. You can pyramid the long and short positions to continue entering as long as the trend doesn't flip. You will find this in the script settings. Since this script is for backtesting you can manually set the "backtesting range" for TradingView Strategies and firing the "Long Entry" and "Short Entry". This as well, is in the settings.
Notice: When the SMA cross occurs, you have to wait till the next candle before TradingView Strategy will print the "Long Entry" or "Short Entry" to the screen
TradingView - How To Use Strategies: www.tradingview.com
Noro's SILA v1.6L StrategyBacktesting
Backtesting (for all the time of existence of couple) only with software configurations to default (without optimization of parameters):
US = Uptrend-Sensivity
DS = Downtrend-Sensivity
It is recommended and by default:
- the normal market requires US=DS (for example US=5, DS=5)
- very bear market requires US DS, (for example US=5, DS=0)
- very bull market requires US DS, (US=0, DS=5)
Cryptocurrencies it is very bull market (US=0, DS=5)
Backtesting BTC/FIAT
D1 timeframe
identical parameters for all pairs
BTC/USD (Bitstamp) profit of +41805%
BTC/EUR (BTC-e) profit of +1147%
BTC/RUB (BTC-e) profit of +1162%
BTC/JPY (Bitflyer) profit of +215%
BTC/CNY (BTCChina) profit of 54948%
Backtesting ALTCOIN/BTC
D1 timeframe
identical parameters for all pairs
the exchange Poloniex
top-10 of cryptocurrencies on capitalization at the time of this text
NA = TradingView can't make backtest because of too low price of this cryptocurrency, or on the website there are no quotations of this cryptocurrency
ETH/BTC (Etherium) profit of +11690%
XRP/BTC (Ripple) loss of-100%
LTC/BTC (Litecoin) NA
ETC/BTC (Etherium Classic) profit of +214%
NEM/BTC loss of-49%
DASH/BTC profit of +106%
IOTA/BTC NA
XMR/BTC (Monero) profit of +96%
STRAT/BTC (Stratis) loss of-31%
ALTCOIN/ALTCOIN - not recomended
I don't need your money, I need reputation and likes.
Arbitrage Spot-Futures Don++Strategy: Spot-Futures Arbitrage Don++
This strategy has been designed to detect and exploit arbitrage opportunities between the Spot and Futures markets of the same trading pair (e.g. BTC/USDT). The aim is to take advantage of price differences (spreads) between the two markets, while minimizing risk through dynamic position management.
[Operating principle
The strategy is based on calculating the spread between Spot and Futures prices. When this spread exceeds a certain threshold (positive or negative), reverse positions are opened simultaneously on both markets:
- i] Long Spot + Short Futures when the spread is positive.
- i] Short Spot + Long Futures when the spread is negative.
Positions are closed when the spread returns to a value close to zero or after a user-defined maximum duration.
[Strategy strengths
1. Adaptive thresholds :
- Entry/exit thresholds can be dynamic (based on moving averages and standard deviations) or fixed, offering greater flexibility to adapt to market conditions.
2. Robust data management :
- The script checks the validity of data before executing calculations, thus avoiding errors linked to missing or invalid data.
3. Risk limitation :
- A position size based on a percentage of available capital (default 10%) limits exposure.
- A time filter limits the maximum duration of positions to avoid losses due to persistent spreads.
4. Clear visualization :
- Charts include horizontal lines for entry/exit thresholds, as well as visual indicators for spread and Spot/Futures prices.
5. Alerts and logs :
- Alerts are triggered on entries and exits to inform the user in real time.
[Points for improvement or completion
Although this strategy is functional and robust, it still has a few limitations that could be addressed in future versions:
1. [Limited historical data :
- TradingView does not retrieve real-time data for multiple symbols simultaneously. This can limit the accuracy of calculations, especially under conditions of high volatility.
2. [Lack of liquidity management :
- The script does not take into account the volumes available on the order books. In conditions of low liquidity, it may be difficult to execute orders at the desired prices.
3. [Non-dynamic transaction costs :
- Transaction costs (exchange fees, slippage) are set manually. A dynamic integration of these costs via an external API would be more realistic.
4. User-dependency for symbols :
- Users must manually specify Spot and Futures symbols. Automatic symbol validation would be useful to avoid configuration errors.
5. Lack of advanced backtesting :
- Backtesting is based solely on historical data available on TradingView. An implementation with third-party data (via an API) would enable the strategy to be tested under more realistic conditions.
6. [Parameter optimization :
- Certain parameters (such as analysis period or spread thresholds) could be optimized for each specific trading pair.
[How can I contribute?
If you'd like to help improve this strategy, here are a few ideas:
1. Add additional filters:
- For example, a filter based on volume or volatility to avoid false signals.
2. Integrate dynamic costs:
- Use an external API to retrieve actual costs and adjust thresholds accordingly.
3. Improve position management:
- Implement hedging or scalping mechanisms to maximize profits.
4. Test on other pairs:
- Evaluate the strategy's performance on other assets (ETH, SOL, etc.) and adjust parameters accordingly.
5. Publish backtesting results :
- Share detailed analyses of the strategy's performance under different market conditions.
[Conclusion
This Spot-Futures arbitrage strategy is a powerful tool for exploiting price differentials between markets. Although it is already functional, it can still be improved to meet more complex trading scenarios. Feel free to test, modify and share your ideas to make this strategy even more effective!
[Thank you for contributing to this open-source community!
If you have any questions or suggestions, please feel free to comment or contact me directly.
Reversal & Breakout Strategy with ORB### Reversal & Breakout Strategy with ORB
This strategy combines three distinct trading approaches—reversals, trend breakouts, and opening range breakouts (ORB)—into a single, cohesive system. The goal is to capture high-probability setups across different market conditions, leveraging a mashup of technical indicators for confirmation and risk management. Below, I’ll explain why this combination works, how the components interact, and how to use it effectively.
#### Why the Mashup?
- **Reversals**: Identifies overextended moves using RSI (overbought/oversold) and SMA50 crosses, filtered by VWAP and SMA200 trend direction. This targets mean-reversion opportunities in trending markets.
- **Breakouts**: Uses EMA9/EMA20 crossovers with VWAP and SMA200 confirmation to catch momentum-driven trend continuations.
- **Opening Range Breakout (ORB)**: Detects early momentum by breaking the high/low of a user-defined opening range (default: 15 bars) with volume confirmation. This adds a time-based edge, ideal for intraday trading.
The synergy comes from blending these methods: reversals catch pullbacks, breakouts ride trends, and ORB exploits early volatility—all filtered by trend (SMA200) and anchored by VWAP for context.
#### How It Works
1. **Indicators**:
- **EMA9/EMA20**: Fast-moving averages for breakout signals.
- **SMA50**: Medium-term trend filter for reversals.
- **SMA200**: Long-term trend direction to align trades.
- **RSI (14)**: Measures overbought (>70) or oversold (<30) conditions.
- **VWAP**: Acts as a dynamic support/resistance level.
- **ATR (14)**: Sets stop-loss distance (default: 1.5x ATR).
- **Volume**: Confirms ORB breakouts (1.5x average volume of opening range).
2. **Entry Conditions**:
- **Long**: Triggers on reversal (SMA50 cross + RSI < 30 + below VWAP + uptrend), breakout (EMA9 > EMA20 + above VWAP + uptrend), or ORB (break above opening range high + volume).
- **Short**: Triggers on reversal (SMA50 cross + RSI > 70 + above VWAP + downtrend), breakout (EMA9 < EMA20 + below VWAP + downtrend), or ORB (break below opening range low + volume).
3. **Risk Management**:
- Risks 5% of equity per trade (based on the initial capital set in the strategy tester).
- Stop-loss: Based on lowest low/highest high over 7 bars ± 1.5x ATR.
- Targets: Two exits at 1:1 and 1:2 risk:reward (50% of position at each).
- Break-even: Stop moves to entry price after the first target is hit.
4. **Backtesting Settings**:
- Commission: Hardcoded at 0.1% per trade (realistic for most brokers).
- Slippage: Hardcoded at 2 ticks (realistic for most markets).
- Tested on datasets yielding 100+ trades (e.g., 2-min or 5-min charts over months).
#### How to Use It
- **Timeframe**: Works best on intraday (2-min, 5-min) or daily charts. Adjust `Opening Range Bars` (e.g., 15 bars = 30 min on 2-min chart) for your timeframe.
- **Settings**:
- Set your initial equity in the TradingView strategy tester’s "Properties" tab under "Initial Capital" (e.g., $10,000). The script automatically risks 5% of this equity per trade.
- Adjust `Stop Loss ATR Multiplier` or `Risk:Reward Targets` based on your risk tolerance.
- Note that commission (0.1%) and slippage (2 ticks) are fixed in the script for backtesting consistency.
- **Execution**: Enter on signal, monitor plotted stop (red) and targets (green/blue). The strategy supports pyramiding (up to 2 positions) for scaling into trends.
#### Backtesting Notes
Results are realistic with commission (0.1%) and slippage (2 ticks) included. For a sufficient sample, test on volatile instruments (e.g., stocks, forex) over 3-6 months on lower timeframes. The default 1.5x ATR stop may seem wide, but it’s justified to avoid premature exits in volatile markets—feel free to tweak it with justification. The script assumes an initial capital of $10,000 in the strategy tester for the 5% risk calculation (e.g., $500 risk per trade); adjust this in the "Properties" tab as needed.
This mashup isn’t just a random mix; it’s a deliberate fusion of complementary strategies, offering traders flexibility across market phases. Questions? Let me know!
Naive Bayes Candlestick Pattern Classifier v1.1 BETAAn intermezzo on why i made this script publication..
A : Candlestick Pattern took hours to backtest, why not using Machine Learning techniques?
B : Machine Learning, no that's gonna be really heavy bro!
A : Not really, because we use Naive Bayes.
B : The simplest, yet powerful machine learning algorithm to separate (a.k.a classify) multivariate data.
----------------------------------------------------------------------------------------------------------------------
Hello, everyone!
After deep research in extracting meaningful information from the market, I ended up building this powerful machine learning indicator based on the evolution of Bayesian Statistics. This indicator not only leverages the simplicity of Naive Bayes but also extends its application to candlestick pattern analysis, making it an invaluable tool for traders who are looking to enhance their technical analysis without spending countless hours manually backtesting each pattern on each market!.
What most interesting part is actually after learning all of likely useless methods like fibonacci, supply and demand, volume profile, etc. We always ended up back to basic like support and resistance and candlestick patterns, but with a slight twist on strategy algorithm design and statistical approach. Thus, the only reason why i made this, because i exactly know that you guys will ended up in this position as time goes by.
The essence of this indicator lies in its ability to automate the recognition and statistical evaluation of various candlestick patterns. Traditionally, traders have relied on visual inspection and manual backtesting to determine the effectiveness of patterns like Bullish Engulfing, Bearish Engulfing, Harami variations, Hammer formations, and even more complex multi-candle patterns such as Three White Soldiers, Three Black Crows, Dark Cloud Cover, and Piercing Pattern. However, these conventional methods are both time-consuming and prone to subjective bias.
To address these challenges, I employed Naive Bayes—a probabilistic classifier that, despite its simplicity, offers robust performance in various domains. Naive Bayes assumes that each feature is independent of the others given the class label, which, although a strong assumption, works remarkably well in practice, especially when the dataset is large like market data and the feature space is high-dimensional. In our case, each candlestick pattern acts as a feature that can be statistically evaluated based on its historical performance. The indicator calculates a probability that a given pattern will lead to a price reversal, by comparing the pattern’s close price to the highest or lowest price achieved in a lookahead window.
One of the standout features of this script is its flexibility. Each candlestick pattern is not only coded into the system but also comes with individual toggles to enable or disable them based on your trading strategy. This means you can choose to focus on single-candle patterns like Bullish Engulfing or more complex multi-candle formations such as Three White Soldiers, without modifying the core code. The built-in customization options allow you to adjust colors and labels for each pattern, giving you the freedom to tailor the visual output to your preference. This level of customization ensures that the indicator integrates seamlessly into your existing TradingView setup.
Moreover, the indicator isn’t just about pattern recognition—it also incorporates outcome-based learning. Every time a pattern is detected, it looks ahead a predefined number of bars to evaluate if the expected reversal actually materialized. This outcome is then stored in arrays, and over time, the script dynamically calculates the probability of success for each pattern. These probabilities are presented in a real-time updating table on your chart, which shows not only the percentage probability but also the count of historical occurrences. With this information at your fingertips, you can quickly gauge the reliability of each pattern in your chosen market and timeframe.
Another significant advantage of this approach is its speed and efficiency. While more complex machine learning models like neural networks might require heavy computational resources and longer training times, the Naive Bayes classifier in this script is lightweight, instantaneous and can be updated on the fly with each new bar. This real-time capability is essential for modern traders who need to make quick decisions in fast-paced markets.
Furthermore, by automating the process of backtesting, the indicator frees up your time to focus on other aspects of trading strategy development. Instead of manually analyzing hundreds or even thousands of candles, you can rely on the statistical power of Naive Bayes to provide you with insights on which patterns are most likely to result in profitable moves. This not only enhances your efficiency but also helps to eliminate the cognitive biases that often plague manual analysis.
In summary, this indicator represents a fusion of traditional candlestick analysis with modern machine learning techniques. It harnesses the simplicity and effectiveness of Naive Bayes to deliver a dynamic, real-time evaluation of various candlestick patterns. Whether you are a seasoned trader looking to refine your technical analysis or a beginner eager to understand market dynamics, this tool offers a powerful, customizable, and efficient solution. Welcome to a new era where advanced statistical methods meet practical trading insights—happy trading and may your patterns always be in your favor!
Note : On this current released beta version, you must manually adjust reversal percentage move based on each market. Further updates may include automated best range detection and probability.
Multi-indicator Signal Builder [Skyrexio]Overview
Multi-Indicator Signal Builder is a versatile, all-in-one script designed to streamline your trading workflow by combining multiple popular technical indicators under a single roof. It features a single-entry, single-exit logic, intrabar stop-loss/take-profit handling, an optional time filter, a visually accessible condition table, and a built-in statistics label. Traders can choose any combination of 12+ indicators (RSI, Ultimate Oscillator, Bollinger %B, Moving Averages, ADX, Stochastic, MACD, PSAR, MFI, CCI, Heikin Ashi, and a “TV Screener” placeholder) to form entry or exit conditions. This script aims to simplify strategy creation and analysis, making it a powerful toolkit for technical traders.
Indicators Overview
1. RSI (Relative Strength Index)
Measures recent price changes to evaluate overbought or oversold conditions on a 0–100 scale.
2. Ultimate Oscillator (UO)
Uses weighted averages of three different timeframes, aiming to confirm price momentum while avoiding false divergences.
3. Bollinger %B
Expresses price relative to Bollinger Bands, indicating whether price is near the upper band (overbought) or lower band (oversold).
4. Moving Average (MA)
Smooths price data over a specified period. The script supports both SMA and EMA to help identify trend direction and potential crossovers.
5. ADX (Average Directional Index)
Gauges the strength of a trend (0–100). Higher ADX signals stronger momentum, while lower ADX indicates a weaker trend.
6. Stochastic
Compares a closing price to a price range over a given period to identify momentum shifts and potential reversals.
7. MACD (Moving Average Convergence/Divergence)
Tracks the difference between two EMAs plus a signal line, commonly used to spot momentum flips through crossovers.
8. PSAR (Parabolic SAR)
Plots a trailing stop-and-reverse dot that moves with the trend. Often used to signal potential reversals when price crosses PSAR.
9. MFI (Money Flow Index)
Similar to RSI but incorporates volume data. A reading above 80 can suggest overbought conditions, while below 20 may indicate oversold.
10. CCI (Commodity Channel Index)
Identifies cyclical trends or overbought/oversold levels by comparing current price to an average price over a set timeframe.
11. Heikin Ashi
A type of candlestick charting that filters out market noise. The script uses a streak-based approach (multiple consecutive bullish or bearish bars) to gauge mini-trends.
12. TV Screener
A placeholder condition designed to integrate external buy/sell logic (like a TradingView “Buy” or “Sell” rating). Users can override or reference external signals if desired.
Unique Features
1. Multi-Indicator Entry and Exit
You can selectively enable any subset of 12+ classic indicators, each with customizable parameters and conditions. A position opens only if all enabled entry conditions are met, and it closes only when all enabled exit conditions are satisfied, helping reduce false triggers.
2. Single-Entry / Single-Exit with Intrabar SL/TP
The script supports a single position at a time. Once a position is open, it monitors intrabar to see if the price hits your stop-loss or take-profit levels before the bar closes, making results more realistic for fast-moving markets.
3. Time Window Filter
Users may specify a start/end date range during which trades are allowed, making it convenient to focus on specific market cycles for backtesting or live trading.
4. Condition Table and Statistics
A table at the bottom of the chart lists all active entry/exit indicators. Upon each closed trade, an integrated statistics label displays net profit, total trades, win/loss count, average and median PnL, etc.
5. Seamless Alerts and Automation
Configure alerts in TradingView using “Any alert() function call.”
The script sends JSON alert messages you can route to your own webhook.
The indicator can be integrated with Skyrexio alert bots to automate execution on major cryptocurrency exchanges
6. Optional MA/PSAR Plots
For added visual clarity, optionally plot the chosen moving averages or PSAR on the chart to confirm signals without stacking multiple indicators.
Methodology
1. Multi-Indicator Entry Logic
When multiple entry indicators are enabled (e.g., RSI + Stochastic + MACD), the script requires all signals to align before generating an entry. Each indicator can be set for crossovers, crossunders, thresholds (above/below), etc. This “AND” logic aims to filter out low-confidence triggers.
2. Single-Entry Intrabar SL/TP
One Position At a Time: Once an entry signal triggers, a trade opens at the bar’s close.
Intrabar Checks: Stop-loss and take-profit levels (if enabled) are monitored on every tick. If either is reached, the position closes immediately, without waiting for the bar to end.
3. Exit Logic
All Conditions Must Agree: If the trade is still open (SL/TP not triggered), then all enabled exit indicators must confirm a closure before the script exits on the bar’s close.
4. Time Filter
Optional Trading Window: You can activate a date/time range to constrain entries and exits strictly to that interval.
Justification of Methodology
Indicator Confluence: Combining multiple tools (RSI, MACD, etc.) can reduce noise and false signals.
Intrabar SL/TP: Capturing real-time spikes or dips provides a more precise reflection of typical live trading scenarios.
Single-Entry Model: Straightforward for both manual and automated tracking (especially important in bridging to bots).
Custom Date Range: Helps refine backtesting for specific market conditions or to avoid known irregular data periods.
How to Use
1. Add the Script to Your Chart
In TradingView, open Indicators , search for “Multi-indicator Signal Builder”.
Click to add it to your chart.
2. Configure Inputs
Time Filter: Set a start and end date for trades.
Alerts Messages: Input any JSON or text payload needed by your external service or bot.
Entry Conditions: Enable and configure any indicators (e.g., RSI, MACD) for a confluence-based entry.
Close Conditions: Enable exit indicators, along with optional SL (negative %) and TP (positive %) levels.
3. Set Up Alerts
In TradingView, select “Create Alert” → Condition = “Any alert() function call” → choose this script.
Entry Alert: Triggers on the script’s entry signal.
Close Alert: Triggers on the script’s close signal (or if SL/TP is hit).
Skyrexio Alert Bots: You can route these alerts via webhook to Skyrexio alert bots to automate order execution on major crypto exchanges (or any other supported broker).
4. Visual Reference
A condition table at the bottom summarizes active signals.
Statistics Label updates automatically as trades are closed, showing PnL stats and distribution metrics.
Backtesting Guidelines
Symbol/Timeframe: Works on multiple assets and timeframes; always do thorough testing.
Realistic Costs: Adjust commissions and potential slippage to match typical exchange conditions.
Risk Management: If using the built-in stop-loss/take-profit, set percentages that reflect your personal risk tolerance.
Longer Test Horizons: Verify performance across diverse market cycles to gauge reliability.
Example of statistic calculation
Test Period: 2023-01-01 to 2025-12-31
Initial Capital: $1,000
Commission: 0.1%, Slippage ~5 ticks
Trade Count: 468 (varies by strategy conditions)
Win rate: 76% (varies by strategy conditions)
Net Profit: +96.17% (varies by strategy conditions)
Disclaimer
This indicator is provided strictly for informational and educational purposes .
It does not constitute financial or trading advice.
Past performance never guarantees future results.
Always test thoroughly in demo environments before using real capital.
Enjoy exploring the Multi-Indicator Signal Builder! Experiment with different indicator combinations and adjust parameters to align with your trading preferences, whether you trade manually or link your alerts to external automation services. Happy trading and stay safe!
SPY/TLT Strategy█ STRATEGY OVERVIEW
The "SPY/TLT Strategy" is a trend-following crossover strategy designed to trade the relationship between TLT and its Simple Moving Average (SMA). The default configuration uses TLT (iShares 20+ Year Treasury Bond ETF) with a 20-period SMA, entering long positions on bullish crossovers and exiting on bearish crossunders. **This strategy is NOT optimized and performs best in trending markets.**
█ KEY FEATURES
SMA Crossover System: Uses price/SMA relationship for signal generation (Default: 20-period)
Dynamic Time Window: Configurable backtesting period (Default: 2014-2099)
Equity-Based Position Sizing: Default 100% equity allocation per trade
Real-Time Visual Feedback: Price/SMA plot with trend-state background coloring
Event-Driven Execution: Processes orders at bar close for accurate backtesting
█ SIGNAL GENERATION
1. LONG ENTRY CONDITION
TLT closing price crosses ABOVE SMA
Occurs within specified time window
Generates market order at next bar open
2. EXIT CONDITION
TLT closing price crosses BELOW SMA
Closes all open positions immediately
█ ADDITIONAL SETTINGS
SMA Period: Simple Moving Average length (Default: 20)
Start Time and End Time: The time window for trade execution (Default: 1 Jan 2014 - 1 Jan 2099)
Security Symbol: Ticker for analysis (Default: TLT)
█ PERFORMANCE OVERVIEW
Ideal Market Conditions: Strong trending environments
Potential Drawbacks: Whipsaws in range-bound markets
Backtesting results should be analyzed to optimize the MA Period and EMA Filter settings for specific instruments
CauchyTrend [InvestorUnknown]The CauchyTrend is an experimental tool that leverages a Cauchy-weighted moving average combined with a modified Supertrend calculation. This unique approach provides traders with insight into trend direction, while also offering an optional ATR-based range analysis to understand how often the market closes within, above, or below a defined volatility band.
Core Concepts
Cauchy Distribution and Gamma Parameter
The Cauchy distribution is a probability distribution known for its heavy tails and lack of a defined mean or variance. It is characterized by two parameters: a location parameter (x0, often 0 in our usage) and a scale parameter (γ, "gamma").
Gamma (γ): Determines the "width" or scale of the distribution. Smaller gamma values produce a distribution more concentrated near the center, giving more weight to recent data points, while larger gamma values spread the weight more evenly across the sample.
In this indicator, gamma influences how much emphasis is placed on values closer to the current price versus those further away in time. This makes the resulting weighted average either more reactive or smoother, depending on gamma’s value.
// Cauchy PDF formula used for weighting:
// f(x; γ) = (1/(π*γ)) *
f_cauchyPDF(offset, gamma) =>
numerator = gamma * gamma
denominator = (offset * offset) + (gamma * gamma)
pdf = (1 / (math.pi * gamma)) * (numerator / denominator)
pdf
A chart showing different Cauchy PDFs with various gamma values, illustrating how gamma affects the weight distribution.
Cauchy-Weighted Moving Average (CWMA)
Using the Cauchy PDF, we calculate normalized weights to create a custom Weighted Moving Average. Each bar in the lookback period receives a weight according to the Cauchy PDF. The result is a Cauchy Weighted Average (cwm_avg) that differs from typical moving averages, potentially offering unique sensitivity to price movements.
// Summation of weighted prices using Cauchy distribution weights
cwm_avg = 0.0
for i = 0 to length - 1
w_norm = array.get(weights, i) / sum_w
cwm_avg += array.get(values, i) * w_norm
Supertrend with a Cauchy Twist
The indicator integrates a modified Supertrend calculation using the cwm_avg as its reference point. The Supertrend logic typically sets upper and lower bands based on volatility (ATR), and flips direction when price crosses these bands.
In this case, the Cauchy-based average replaces the usual baseline, aiming to capture trend direction via a different weighting mechanism.
When price closes above the upper band, the trend is considered bullish; closing below the lower band signals a bearish trend.
ATR Stats Range (Optional)
Beyond the fundamental trend detection, the indicator optionally computes ATR-based stats to understand price distribution relative to a volatility corridor centered on the cwm_avg line:
Volatility Range:
Defined as cwm_avg ± (ATR * atr_mult), this range creates upper and lower bands. Turning on atr_stats computes how often the daily close falls: Within the range, Above the upper ATR boundary, Below the lower ATR boundary, Within the range but above cwm_avg, Within the range but below cwm_avg
These statistics can help traders gauge how the market behaves relative to this volatility envelope and possibly identify if the market tends to revert to the mean or break out more often.
Backtesting and Performance Metrics
The code is integrated with a backtesting library that allows users to assess strategy performance historically:
Equity Curve Calculation: Compares CauchyTrend-based signals against the underlying asset.
Performance Metrics Table: Once enabled, displays key metrics such as mean returns, Sharpe Ratio, Sortino Ratio, and more, comparing the strategy to a simple Buy & Hold approach.
Alerts and Notifications
The indicator provides Alerts for key events:
Long Alert: Triggered when the trend flips bullish.
Short Alert: Triggered when the trend flips bearish.
Customization and Calibration
Important: The default parameters are not optimized for any specific instrument or time frame. Traders should:
Adjust the length and gamma parameters to influence how sharply or broadly the cwm_avg reacts to price changes.
Tune the atr_len and atr_mult for the Supertrend logic to better match the asset’s volatility characteristics.
Experiment with atr_stats on/off to see if that additional volatility distribution information provides helpful insights.
Traders may find certain sets of parameters that align better with their preferred trading style, risk tolerance, or asset volatility profile.
Disclaimer: This indicator is for educational and informational purposes only. Past performance in backtesting does not guarantee future results. Always perform due diligence, and consider consulting a qualified financial advisor before trading.
AadTrend [InvestorUnknown]The AadTrend indicator is an experimental trading tool that combines a user-selected moving average with the Average Absolute Deviation (AAD) from this moving average. This combination works similarly to the Supertrend indicator but offers additional flexibility and insights. In addition to generating Long and Short signals, the AadTrend indicator identifies RISK-ON and RISK-OFF states for each trade direction, highlighting areas where taking on more risk may be considered.
Core Concepts and Features
Moving Average (User-Selected Type)
The indicator allows users to select from various types of moving averages to suit different trading styles and market conditions:
Simple Moving Average (SMA)
Exponential Moving Average (EMA)
Hull Moving Average (HMA)
Double Exponential Moving Average (DEMA)
Triple Exponential Moving Average (TEMA)
Relative Moving Average (RMA)
Fractal Adaptive Moving Average (FRAMA)
Average Absolute Deviation (AAD)
The Average Absolute Deviation measures the average distance between each data point and the mean, providing a robust estimation of volatility.
aad(series float src, simple int length, simple string avg_type) =>
avg = // Moving average as selected by the user
abs_deviations = math.abs(src - avg)
ta.sma(abs_deviations, length)
This provides a volatility measure that adapts to recent market conditions.
Combining Moving Average and AAD
The indicator creates upper and lower bands around the moving average using the AAD, similar to how the Supertrend indicator uses Average True Range (ATR) for its bands.
AadTrend(series float src, simple int length, simple float aad_mult, simple string avg_type) =>
// Calculate AAD (volatility measure)
aad_value = aad(src, length, avg_type)
// Calculate the AAD-based moving average by scaling the price data with AAD
avg = switch avg_type
"SMA" => ta.sma(src, length)
"EMA" => ta.ema(src, length)
"HMA" => ta.hma(src, length)
"DEMA" => ta.dema(src, length)
"TEMA" => ta.tema(src, length)
"RMA" => ta.rma(src, length)
"FRAMA" => ta.frama(src, length)
avg_p = avg + (aad_value * aad_mult)
avg_m = avg - (aad_value * aad_mult)
var direction = 0
if ta.crossover(src, avg_p)
direction := 1
else if ta.crossunder(src, avg_m)
direction := -1
A chart displaying the moving average with upper and lower AAD bands enveloping the price action.
Signals and Trade States
1. Long and Short Signals
Long Signal: Generated when the price crosses above the upper AAD band,
Short Signal: Generated when the price crosses below the lower AAD band.
2. RISK-ON and RISK-OFF States
These states provide additional insight into the strength of the current trend and potential opportunities for taking on more risk.
RISK-ON Long: When the price moves significantly above the upper AAD band after a Long signal.
RISK-OFF Long: When the price moves back below the upper AAD band, suggesting caution.
RISK-ON Short: When the price moves significantly below the lower AAD band after a Short signal.
RISK-OFF Short: When the price moves back above the lower AAD band.
Highlighted areas on the chart representing RISK-ON and RISK-OFF zones for both Long and Short positions.
A chart showing the filled areas corresponding to trend directions and RISK-ON zones
Backtesting and Performance Metrics
While the AadTrend indicator focuses on generating signals and highlighting risk areas, it can be integrated with backtesting frameworks to evaluate performance over historical data.
Integration with Backtest Library:
import InvestorUnknown/BacktestLibrary/1 as backtestlib
Customization and Calibration
1. Importance of Calibration
Default Settings Are Experimental: The default parameters are not optimized for any specific market condition or asset.
User Calibration: Traders should adjust the length, aad_mult, and avg_type parameters to align the indicator with their trading strategy and the characteristics of the asset being analyzed.
2. Factors to Consider
Market Volatility: Higher volatility may require adjustments to the aad_mult to avoid false signals.
Trading Style: Short-term traders might prefer faster-moving averages like EMA or HMA, while long-term traders might opt for SMA or FRAMA.
Alerts and Notifications
The AadTrend indicator includes built-in alert conditions to notify traders of significant market events:
Long and Short Alerts:
alertcondition(long_alert, "LONG (AadTrend)", "AadTrend flipped ⬆LONG⬆")
alertcondition(short_alert, "SHORT (AadTrend)", "AadTrend flipped ⬇Short⬇")
RISK-ON and RISK-OFF Alerts:
alertcondition(risk_on_long, "RISK-ON LONG (AadTrend)", "RISK-ON LONG (AadTrend)")
alertcondition(risk_off_long, "RISK-OFF LONG (AadTrend)", "RISK-OFF LONG (AadTrend)")
alertcondition(risk_on_short, "RISK-ON SHORT (AadTrend)", "RISK-ON SHORT (AadTrend)")
alertcondition(risk_off_short, "RISK-OFF SHORT (AadTrend)", "RISK-OFF SHORT (AadTrend)")
Important Notes and Disclaimer
Experimental Nature: The AadTrend indicator is experimental and should be used with caution.
No Guaranteed Performance: Past performance is not indicative of future results. Backtesting results may not reflect real trading conditions.
User Responsibility: Traders and investors should thoroughly test and calibrate the indicator settings before applying it to live trading.
Risk Management: Always use proper risk management techniques, including stop-loss orders and position sizing.
