FXC NQ Opening Range Breakout Strategy V2.4Mechanical Strategy that trades breakouts on NQ futures on the 15min timeframe during the NYSE session. It's designed to manage Apex and Top Step accounts with the lowest risk possible.
Risk Disclaimer:
Past results as well as strategy tester reports do not indicate future performance. Guarantees do not exist in trading. By using this strategy you risk losing all your money.
Important:
It only trades on Monday, Wednesday and Friday and takes usually only 1 trade per trading day.
It works on the 15min timeframe only.
The settings are optimised already for NQ but feel free to change them.
How it works:
Every selected trading day it measures the range of the first 15min candle after the NYSE open. As soon as price closes above on the 15min timeframe, it will trade the breakout targeting a set risk to reward ratio. SL on the opposite side of the range. It will trail the SL after a set amount of points and uses a buffer of the set amount of points to trail it.
Settings:
Opening Range Time : This is the time of the day in hours and minutes when the strategy starts looking for trades. It's in the EST/ NY Timezone and set to 9:30-09:45 by default
because that's the NYSE open.
Session Time : This is the time of the day in hours and minutes until the strategy trades. It's in the EST/ NY Timezone and set to 09:45-14:45 by default.
because that's what gave the best results in backtesting. Open trades will get closed automatically once the end of the session is reached. No matter if win or loss. This is just to prevent holding positions over night.
Session Border This setting is to select the border color in which the session box will be plotted.
Opening Range Box This setting is to select the fill color of the opening range box.
Opening Range Border This setting is to select the border color of the session box.
Trade Timeframe This setting determines on which timeframe candle has to close outside the opening range box in order to take a trade. It's set to 15min by default because this is what worked by far the best in backtests and live trading.
Stop Loss Buffer in Points: This is simply the buffer in points that is added to the SL for safety reasons. If you have it on 0, the SL will be at the exact price of the opposite side of the range. By default it's set to 0 pips because this is what delivered the best results in backtests.
Profit Target Factor: This is simply the total SL size in points multiplied by x.
Example: If you put 2, you get a 1:2 Risk to Reward Ratio. By Default it's set to 4 because this gave the best results in backtests, because trades always get closed either by trailing SL or because the end of the session is reached.
Use Trailing Stop Loss: This setting is to enable/ disable the trailing stop loss. It's enabled by default because this is a fundamental part of the strategy.
Trailing Stop Buffer: This setting determines after how many points in profit the trailing SL will be activated.
Risk Type: You can chose either between Fixed USD Amount, Risk per Trade in % or Fixed Contract Size. By default it's set to fixed contract size.
Risk Amount (USD or Contracts): This setting is to set how many USD or how many contracts you want to risk per trade. Make sure to check which risk type you have selected before you chose the risk amount.
Use Limit Orders If enabled, the strategy will place a pending order x points from the current price, instead of a market order. Limit orders are enabled by default for a better performance. Important: It doesn't actually place a limit order. The strategy will just wait for a pullback and then enter with a market order. It's more like a hidden limit order.
Limit Order Distance (points): If you have limit orders enabled, this setting determines how many points from the current price the limit order will be placed.
Trading Days: These checkboxes are to select on which week days the strategy has to trade. Thursday is disabled by default because backtests have shown that Thursday is the least profitable day
Backtest Settings:
For the backtest the commissions ere set to 0.35 USD per mini contract which is the highest amount Tradeovate charges. Margin was not accounted for because typically on Apex accounts you can use way more contracts than you need for the extremely low max drawdown. Margin would be important on personal accounts but even there typically it's not an issue at all especially because this strategy runs on the 15min timeframe so it won't use a lot of contracts anyways.
What makes it unique:
This script is unique because it's designed to be used on Apex and Top Step accounts with extremely strict drawdown rules.
The strategy is optimised to be traded with a fixed contract size instead of using % risk. The reason for that is that the drawdown rules of these Futures Prop Accounts are very strict and the fact that the smallest trade-able contract size is 1.
Why the source code is hidden:
The source code is hidden because I invested a lot of time and money into developing this strategy and optimising it with paid 3rd party software. Also since I use it myself on my Apex accounts and prop firms don't allow copy trading I don't want it to be used by too many traders.
在腳本中搜尋"the strat"
Precision Trading Strategy: Golden EdgeThe PTS: Golden Edge strategy is designed for scalping Gold (XAU/USD) on lower timeframes, such as the 1-minute chart. It captures high-probability trade setups by aligning with strong trends and momentum, while filtering out low-quality trades during consolidation or low-volatility periods.
The strategy uses a combination of technical indicators to identify optimal entry points:
1. Exponential Moving Averages (EMAs): A fast EMA (3-period) and a slow EMA (33-period) are used to detect short-term trend reversals via crossover signals.
2. Hull Moving Average (HMA): A 66-period HMA acts as a higher-timeframe trend filter to ensure trades align with the overall market direction.
3. Relative Strength Index (RSI): A 12-period RSI identifies momentum. The strategy requires RSI > 55 for long trades and RSI < 45 for short trades, ensuring entries are backed by strong buying or selling pressure.
4. Average True Range (ATR): A 14-period ATR ensures trades occur only during volatile conditions, avoiding choppy or low-movement markets.
By combining these tools, the PTS: Golden Edge strategy creates a precise framework for scalping and offers a systematic approach to capitalize on Gold’s price movements efficiently.
16. SMC Strategy with SL - low TimeframeOverview
The "SMC Strategy with SL - low Timeframe" is a comprehensive trading strategy that uses key concepts from Smart Money Theory to identify favorable areas in the market for buying or selling. This strategy takes advantage of price imbalances, support and resistance zones, and swing highs/lows to generate high-probability trade signals.
The key features of this strategy include:
Swing High/Low Analysis: Used to determine the Premium, Equilibrium, and Discount Zones.
Order Block Integration: An added layer of confluence to identify valid buy and sell signals.
Trend Direction Confirmation: Using a Simple Moving Average (SMA) to determine the overall trend.
Entry and Exit Rules: Based on price position relative to key zones and moving average, along with optional stop-loss and take-profit levels.
Detailed Description
Swing High and Swing Low Analysis
The script calculates Swing High and Swing Low based on the most recent price highs and lows over a specified look-back period (swingHighLength and swingLowLength, set to 8 by default).
It then derives the Premium, Equilibrium, and Discount Zones:
Premium Zone: Represents potential resistance, calculated based on recent swing highs.
Discount Zone: Represents potential support, calculated based on recent swing lows.
Equilibrium: The midpoint between Swing High and Swing Low, dividing the price range into Premium (above equilibrium) and Discount (below equilibrium) areas.
Zone Visualization
The strategy plots the Premium Zone (resistance) in red, the Discount Zone (support) in green, and the Equilibrium level in blue on the chart. This helps visually assess the current price relative to these important areas.
Simple Moving Average (SMA)
A 50-period Simple Moving Average (SMA) is added to help identify the trend direction.
Buy signals are valid only if the price is above the SMA, indicating an uptrend.
Sell signals are valid only if the price is below the SMA, indicating a downtrend.
Entry Rules
The script generates buy or sell signals when certain conditions are met:
A buy signal is triggered when:
Price is below the Equilibrium and within the Discount Zone.
Price is above the SMA.
The buy signal is further confirmed by the presence of an Order Block (recent lowest price area).
A sell signal is triggered when:
Price is above the Equilibrium and within the Premium Zone.
Price is below the SMA.
The sell signal is further confirmed by the presence of an Order Block (recent highest price area).
Order Block
The strategy defines Order Blocks as recent highs and lows within a look-back period (orderBlockLength set to 20 by default).
These blocks represent areas where large players (smart money) have historically been active, increasing the probability of the price reacting in these areas again.
Trade Management and Trade Direction
The user can set Trade Direction to either "Long Only," "Short Only," or "Both." This allows the strategy to adapt based on market conditions or trading preferences.
Based on the Trade Direction, the strategy either:
Closes open trades that are against new signals.
Allows only specific directional trades (either long or short).
Stop-loss levels are defined based on a fixed percentage (stop_loss_percent), which helps to manage risk and minimize losses.
Exit Rules
The strategy uses stop-loss levels for risk management.
A stop-loss price is set at a fixed percentage below the entry price for long positions or above the entry price for short positions.
When the price hits the defined stop-loss level, the trade is closed.
Liquidity Zones
The script identifies recent Swing Highs and Lows as potential liquidity zones. These are levels where price could react strongly, as they represent areas of interest for large traders.
The liquidity zones are plotted as crosses on the chart, marking areas where price may encounter significant buying or selling pressure.
Visual Feedback
The script uses visual markers (green for buy signals and red for sell signals) to indicate potential entries on the chart.
It also plots liquidity zones to help traders identify areas where stop hunts and liquidity grabs might occur.
Monthly Performance Dashboard
The script includes a performance tracking feature that displays monthly profit and loss metrics on the chart.
This dashboard allows the trader to see a visual representation of trading performance over time, providing insights into profitability and consistency.
The table shows profit or loss for each month and year, allowing the user to track the overall success of the strategy.
Key Benefits
Smart Money Concepts (SMC): This strategy incorporates SMC principles like order blocks and liquidity zones, which are used by institutional traders to determine potential market moves.
Zone Analysis: The use of Premium, Discount, and Equilibrium zones provides a solid framework for determining where to enter and exit trades based on price discounts or premiums.
Confluence: Signals are not taken in isolation. They are confirmed by factors like trend direction (SMA) and order blocks, providing greater trade accuracy.
Risk Management: By integrating stop-loss functionality, traders can manage their risks effectively.
Visual Performance Metrics: The monthly and yearly performance dashboard gives valuable feedback on how well the strategy has performed historically.
Practical Use
Buy in Discount Zone: Traders would be looking to buy when the price is discounted relative to its recent range and is above the SMA, indicating an overall uptrend.
Sell in Premium Zone: Conversely, traders would be looking to sell when the price is at a premium relative to its recent range and below the SMA, indicating an overall downtrend.
Order Block Confirmation: Ensures that buying or selling is supported by historical price behavior at significant levels, providing confidence that the market is likely to react at these areas.
This strategy is designed to help traders take advantage of price inefficiencies and areas where institutional traders are likely to be active, increasing the odds of successful trades. By leveraging Smart Money concepts and strong technical confluence, it aims to provide high-probability trade setups.
ADX Breakout Strategy█ OVERVIEW
The ADX Breakout strategy leverages the Average Directional Index (ADX) to identify and execute breakout trades within specified trading sessions. Designed for the NQ and ES 30-minute charts, this strategy aims to capture significant price movements while managing risk through predefined stop losses and trade limits.
This strategy was taken from a strategy that was posted on YouTube. I would link the video, but I believe is is "against house rules".
█ CONCEPTS
The strategy is built upon the following key concepts:
ADX Indicator: Utilizes the ADX to gauge the strength of a trend. Trades are initiated when the ADX value is below a certain threshold, indicating potential for trend development.
Trade Session Management: Limits trading to specific hours to align with optimal market activity periods.
Risk Management: Implements a fixed dollar stop loss and restricts the number of trades per session to control exposure.
█ FEATURES
Customizable Stop Loss: Set your preferred stop loss amount to manage risk effectively.
Trade Session Configuration: Define the trading hours to focus on the most active market periods.
Entry Conditions: Enter long positions when the price breaks above the highest close in the lookback window and the ADX indicates potential trend strength.
Trade Limits: Restrict the number of trades per session to maintain disciplined trading.
Automated Exit: Automatically closes all positions at the end of the trading session to avoid overnight risk.
█ HOW TO USE
Configure Inputs :
Stop Loss ($): Set the maximum loss per trade.
Trade Session: Define the active trading hours.
Highest Lookback Window: Specify the number of bars to consider for the highest close.
Apply the Strategy :
Add the ADX Breakout strategy to your chart on TradingView.
Ensure you are using a 30-minute timeframe for optimal performance.
█ LIMITATIONS
Market Conditions: The strategy is optimized for trending markets and may underperform in sideways or highly volatile conditions.
Timeframe Specific: Designed specifically for 30-minute charts; performance may vary on different timeframes.
Single Asset Focus: Primarily tested on NQ and ES instruments; effectiveness on other symbols is not guaranteed.
█ DISCLAIMER
This ADX Breakout strategy is provided for educational and informational purposes only. It is not financial advice and should not be construed as such. Trading involves significant risk, and you may incur substantial losses. Always perform your own analysis and consider your financial situation before using this or any other trading strategy. The source material for this strategy is publicly available in the comments at the beginning of the code script. This strategy has been published openly for anyone to review and verify its methodology and performance.
Global Index Spread RSI StrategyThis strategy leverages the relative strength index (RSI) to monitor the price spread between a global benchmark index (such as AMEX) and the currently opened asset in the chart window. By calculating the spread between these two, the strategy uses RSI to identify oversold and overbought conditions to trigger buy and sell signals.
Key Components:
Global Benchmark Index: The strategy compares the current asset with a predefined global index (e.g., AMEX) to measure relative performance. The choice of a global benchmark allows the trader to analyze the current asset's movement in the context of broader market trends.
Spread Calculation:
The spread is calculated as the percentage difference between the current asset's closing price and the global benchmark index's closing price:
Spread=Current Asset Close−Global Index CloseGlobal Index Close×100
Spread=Global Index CloseCurrent Asset Close−Global Index Close×100
This metric provides a measure of how the current asset is performing relative to the global index. A positive spread indicates the asset is outperforming the benchmark, while a negative spread signals underperformance.
RSI of the Spread: The RSI is then calculated on the spread values. The RSI is a momentum oscillator that ranges from 0 to 100 and is commonly used to identify overbought or oversold conditions in asset prices. An RSI below 30 is considered oversold, indicating a potential buying opportunity, while an RSI above 70 is overbought, suggesting that the asset may be due for a pullback.
Strategy Logic:
Entry Condition: The strategy enters a long position when the RSI of the spread falls below the oversold threshold (default 30). This suggests that the asset may have been oversold relative to the global benchmark and might be due for a reversal.
Exit Condition: The strategy exits the long position when the RSI of the spread rises above the overbought threshold (default 70), indicating that the asset may have become overbought and a price correction is likely.
Visual Reference:
The RSI of the spread is plotted on the chart for visual reference, making it easier for traders to monitor the relative strength of the asset in relation to the global benchmark.
Overbought and oversold levels are also drawn as horizontal reference lines (70 and 30), along with a neutral level at 50 to show market equilibrium.
Theoretical Basis:
The strategy is built on the mean reversion principle, which suggests that asset prices tend to revert to a long-term average over time. When prices move too far from this mean—either being overbought or oversold—they are likely to correct back toward equilibrium. By using RSI to identify these extremes, the strategy aims to profit from price reversals.
Mean Reversion: According to financial theory, asset prices oscillate around a long-term average, and any extreme deviation (overbought or oversold conditions) presents opportunities for price corrections (Poterba & Summers, 1988).
Momentum Indicators (RSI): The RSI is widely used in technical analysis to measure the momentum of an asset. Its application to the spread between the asset and a global benchmark allows for a more nuanced view of relative performance and potential turning points in the asset's price trajectory.
Practical Application:
This strategy works best in markets where relative strength is a key factor in decision-making, such as in equity indices, commodities, or forex markets. By assessing the performance of the asset relative to a global benchmark and utilizing RSI to identify extremes in price movements, the strategy helps traders to make more informed decisions based on potential mean reversion points.
While the "Global Index Spread RSI Strategy" offers a method for identifying potential price reversals based on relative strength and oversold/overbought conditions, it is important to recognize that no strategy is foolproof. The strategy assumes that the historical relationship between the asset and the global benchmark will hold in the future, but financial markets are subject to a wide array of unpredictable factors that can lead to sudden changes in price behavior.
Risk of False Signals:
The strategy relies heavily on the RSI to trigger buy and sell signals. However, like any momentum-based indicator, RSI can generate false signals, particularly in highly volatile or trending markets. In such conditions, the strategy may enter positions too early or exit too late, leading to potential losses.
Market Context:
The strategy may not account for macroeconomic events, news, or other market forces that could cause sudden shifts in asset prices. External factors, such as geopolitical developments, monetary policy changes, or financial crises, can cause a divergence between the asset and the global benchmark, leading to incorrect conclusions from the strategy.
Overfitting Risk:
As with any strategy that uses historical data to make decisions, there is a risk of overfitting the model to past performance. This could result in a strategy that works well on historical data but performs poorly in live trading conditions due to changes in market dynamics.
Execution Risks:
The strategy does not account for slippage, transaction costs, or liquidity issues, which can impact the execution of trades in real-market conditions. In fast-moving markets, prices may move significantly between order placement and execution, leading to worse-than-expected entry or exit prices.
No Guarantee of Profit:
Past performance is not necessarily indicative of future results. The strategy should be used with caution, and risk management techniques (such as stop losses and position sizing) should always be implemented to protect against significant losses.
Traders should thoroughly test and adapt the strategy in a simulated environment before applying it to live trades, and consider seeking professional advice to ensure that their trading activities align with their risk tolerance and financial goals.
References:
Poterba, J. M., & Summers, L. H. (1988). Mean Reversion in Stock Prices: Evidence and Implications. Journal of Financial Economics, 22(1), 27-59.
Adaptive Squeeze Momentum StrategyThe Adaptive Squeeze Momentum Strategy is a versatile trading algorithm designed to capitalize on periods of low volatility that often precede significant price movements. By integrating multiple technical indicators and customizable settings, this strategy aims to identify optimal entry and exit points for both long and short positions.
Key Features:
Long/Short Trade Control:
Toggle Options: Easily enable or disable long and short trades according to your trading preferences or market conditions.
Flexible Application: Adapt the strategy for bullish, bearish, or neutral market outlooks.
Squeeze Detection Mechanism:
Bollinger Bands and Keltner Channels: Utilizes the convergence of Bollinger Bands inside Keltner Channels to detect "squeeze" conditions, indicating a potential breakout.
Dynamic Squeeze Length: Calculates the average squeeze duration to adapt to changing market volatility.
Momentum Analysis:
Linear Regression: Applies linear regression to price changes over a specified momentum length to gauge the strength and direction of momentum.
Dynamic Thresholds: Sets momentum thresholds based on standard deviations, allowing for adaptive sensitivity to market movements.
Momentum Multiplier: Adjustable setting to fine-tune the aggressiveness of momentum detection.
Trend Filtering:
Exponential Moving Average (EMA): Implements a trend filter using an EMA to align trades with the prevailing market direction.
Customizable Length: Adjust the EMA length to suit different trading timeframes and assets.
Relative Strength Index (RSI) Filtering:
Overbought/Oversold Signals: Incorporates RSI to avoid entering trades during overextended market conditions.
Adjustable Levels: Set your own RSI oversold and overbought thresholds for personalized signal generation.
Advanced Risk Management:
ATR-Based Stop Loss and Take Profit:
Adaptive Levels: Uses the Average True Range (ATR) to set stop loss and take profit points that adjust to market volatility.
Custom Multipliers: Modify ATR multipliers for both stop loss and take profit to control risk and reward ratios.
Minimum Volatility Filter: Ensures trades are only taken when market volatility exceeds a user-defined minimum, avoiding periods of low activity.
Time-Based Exit:
Holding Period Multiplier: Defines a maximum holding period based on the momentum length to reduce exposure to adverse movements.
Automatic Position Closure: Closes positions after the specified holding period is reached.
Session Filtering:
Trading Session Control: Limits trading to predefined market hours, helping to avoid illiquid periods.
Custom Session Times: Set your preferred trading session to match market openings, closings, or specific timeframes.
Visualization Tools:
Indicator Plots: Displays Bollinger Bands, Keltner Channels, and trend EMA on the chart for visual analysis.
Squeeze Signals: Marks squeeze conditions on the chart, providing clear visual cues for potential trade setups.
Customization Options:
Indicator Parameters: Fine-tune lengths and multipliers for Bollinger Bands, Keltner Channels, momentum calculation, and ATR.
Entry Filters: Choose to use trend and RSI filters to refine trade entries based on your strategy.
Risk Management Settings: Adjust stop loss, take profit, and holding periods to match your risk tolerance.
Trade Direction Control: Enable or disable long and short trades independently to align with your market strategy or compliance requirements.
Time Settings: Modify the trading session times and enable or disable the time filter as needed.
Use Cases:
Trend Traders: Benefit from aligning entries with the broader market trend while capturing breakout movements.
Swing Traders: Exploit periods of low volatility leading to significant price swings.
Risk-Averse Traders: Utilize advanced risk management features to protect capital and manage exposure.
Disclaimer:
This strategy is a tool to assist in trading decisions and should be used in conjunction with other analyses and risk management practices. Past performance is not indicative of future results. Always test the strategy thoroughly and adjust settings to suit your specific trading style and market conditions.
SMB MagicSMB Magic
Overview: SMB Magic is a powerful technical strategy designed to capture breakout opportunities based on price movements, volume spikes, and trend-following logic. This strategy works exclusively on the XAU/USD symbol and is optimized for the 15-minute time frame. By incorporating multiple factors, this strategy identifies high-probability trades with a focus on risk management.
Key Features:
Breakout Confirmation:
This strategy looks for price breakouts above the previous high or below the previous low, with a significant volume increase. A breakout is considered valid when it is supported by strong volume, confirming the strength of the price move.
Price Movement Filter:
The strategy ensures that only significant price movements are considered for trades, helping to avoid low-volatility noise. This filter targets larger price swings to maximize potential profits.
Exponential Moving Average (EMA):
A long-term trend filter is applied to ensure that buy trades occur only when the price is above the moving average, and sell trades only when the price is below it.
Fibonacci Levels:
Custom Fibonacci retracement levels are drawn based on recent price action. These levels act as dynamic support and resistance zones and help determine the exit points for trades.
Take Profit/Stop Loss:
The strategy incorporates predefined take profit and stop loss levels, designed to manage risk effectively. These levels are automatically applied to trades and are adjusted based on the market's volatility.
Volume Confirmation:
A volume multiplier confirms the strength of the breakout. A trade is only considered when the volume exceeds a certain threshold, ensuring that the breakout is supported by sufficient market participation.
How It Works:
Entry Signals:
Buy Signal: A breakout above the previous high, accompanied by significant volume and price movement, occurs when the price is above the trend-following filter (e.g., EMA).
Sell Signal: A breakout below the previous low, accompanied by significant volume and price movement, occurs when the price is below the trend-following filter.
Exit Strategy:
Each position (long or short) has predefined take-profit and stop-loss levels, which are designed to protect capital and lock in profits at key points in the market.
Fibonacci Levels:
Fibonacci levels are drawn to identify potential areas of support or resistance, which can be used to guide exits and stop-loss placements.
Important Notes:
Timeframe Restriction: This strategy is designed specifically for the 15-minute time frame.
Symbol Restriction: The strategy works exclusively on the XAU/USD (Gold) symbol and is not recommended for use with other instruments.
Best Performance in Trending Markets: It works best in trending conditions where breakouts occur frequently.
Disclaimer:
Risk Warning: Trading involves risk, and past performance is not indicative of future results. Always conduct your own research and make informed decisions before trading.
Balthazar by Aloupay📈 BALTHAZAR BY ALOUPAY: Advanced Trading Strategy for Precision and Reliability
BALTHAZAR BY ALOUPAY is a comprehensive trading strategy developed for TradingView, designed to assist traders in making informed and strategic trading decisions. By integrating multiple technical indicators, this strategy aims to identify optimal entry and exit points, manage risk effectively, and enhance overall trading performance.
🌟 Key Features
1. Integrated Indicator Suite
Exponential Moving Averages (EMAs) : Utilizes Fast (12), Medium (26), and Slow (50) EMAs to determine trend direction and strength.
Stochastic RSI : Employs Stochastic RSI with customizable smoothing periods to assess momentum and potential reversal points.
Average True Range (ATR) : Calculates dynamic stop loss and take profit levels based on market volatility using ATR multipliers.
MACD Confirmation : Incorporates MACD histogram analysis to validate trade signals, enhancing the reliability of entries.
2. Customizable Backtesting Parameters
Date Range Selection: Allows users to define specific backtesting periods to evaluate strategy performance under various market conditions.
Timezone Adaptability: Ensures accurate time-based filtering in alignment with the chart's timezone settings.
3. Advanced Risk Management
Dynamic Stop Loss & Take Profit: Automatically adjusts exit points using ATR multipliers to adapt to changing market volatility.
Position Sizing: Configurable to risk a sustainable percentage of equity per trade (recommended: 5-10%) to maintain disciplined money management.
4. Clear Trade Signals
Long & Short Entries: Generates actionable signals based on the convergence of EMA alignment, Stochastic RSI crossovers, and MACD confirmation.
Automated Exits: Implements predefined take profit and stop loss levels to secure profits and limit losses without emotional interference.
5. Visual Enhancements
EMA Visualization: Displays Fast, Medium, and Slow EMAs on the chart for easy trend identification.
Stochastic RSI Indicators: Uses distinct shapes to indicate bullish and bearish momentum shifts.
Risk Levels Display: Clearly marks take profit and stop loss levels on the chart for transparent risk-reward assessment.
🔍 Strategy Mechanics
Trend Identification with EMAs
Bullish Trend: Fast EMA (12) > Medium EMA (26) > Slow EMA (50)
Bearish Trend: Fast EMA (12) < Medium EMA (26) < Slow EMA (50)
Momentum Confirmation with Stochastic RSI
Bullish Signal: %K line crosses above %D line, indicating upward momentum.
Bearish Signal: %K line crosses below %D line, signaling downward momentum.
Volatility-Based Risk Management with ATR
Stop Loss: Positioned at 1.0 ATR below (for long) or above (for short) the entry price.
Take Profit: Positioned at 4.0 ATR above (for long) or below (for short) the entry price.
MACD Confirmation
Long Trades: Executed only when the MACD histogram is positive.
Short Trades: Executed only when the MACD histogram is negative.
💱 Recommended Forex Pairs
While BALTHAZAR BY ALOUPAY has shown robust performance on the 4-hour timeframe for Gold (XAU/USD), it is also well-suited for the following highly liquid forex pairs:
EUR/USD (Euro/US Dollar)
GBP/USD (British Pound/US Dollar)
USD/JPY (US Dollar/Japanese Yen)
AUD/USD (Australian Dollar/US Dollar)
USD/CAD (US Dollar/Canadian Dollar)
NZD/USD (New Zealand Dollar/US Dollar)
EUR/GBP (Euro/British Pound)
These pairs offer high liquidity and favorable trading conditions that complement the strategy's indicators and risk management features.
⚙️ Customization Options
Backtesting Parameters
Start Date: Define the beginning of the backtesting period.
End Date: Define the end of the backtesting period.
EMAs Configuration
Fast EMA Length: Default is 12.
Medium EMA Length: Default is 26.
Slow EMA Length: Default is 50.
Source: Default is Close price.
Stochastic RSI Configuration
%K Smoothing: Default is 5.
%D Smoothing: Default is 4.
RSI Length: Default is 14.
Stochastic Length: Default is 14.
RSI Source: Default is Close price.
ATR Configuration
ATR Length: Default is 14.
ATR Smoothing Method: Options include RMA, SMA, EMA, WMA (default: RMA).
Stop Loss Multiplier: Default is 1.0 ATR.
Take Profit Multiplier: Default is 4.0 ATR.
MACD Configuration
MACD Fast Length: Default is 12.
MACD Slow Length: Default is 26.
MACD Signal Length: Default is 9.
📊 Why Choose BALTHAZAR BY ALOUPAY?
Comprehensive Integration: Combines trend, momentum, and volatility indicators for a multifaceted trading approach.
Automated Precision: Eliminates emotional decision-making with rule-based entry and exit signals.
Robust Risk Management: Protects capital through dynamic stop loss and take profit levels tailored to market conditions.
User-Friendly Customization: Easily adjustable settings to align with individual trading styles and risk tolerance.
Proven Reliability: Backtested over extensive periods across various market environments to ensure consistent performance.
Disclaimer : Trading involves significant risk of loss and is not suitable for every investor. Past performance is not indicative of future results. Always conduct your own research and consider your financial situation before engaging in trading activities.
Candle Range Theory [Advanced] - AlgoVisionUnderstanding Candle Range Theory (CRT) in the AlgoVision Indicator
Candle Range Theory (CRT) is a structured approach to analyzing market movements within the price ranges of candlesticks. CRT is founded on the idea that each candlestick on a chart, regardless of timeframe, represents a distinct range of price action, marked by the candle's open, high, low, and close. This range gives insights into market dynamics, and when analyzed in lower timeframes, reveals patterns that indicate underlying market sentiment and institutional behaviors.
Key Concepts of Candle Range Theory
Candlestick Range: The range of a candlestick is simply the distance between its high and low. Across timeframes, this range highlights significant price behavior, with each candlestick representing a snapshot of price movement. The body (distance between open and close) shows the primary price action, while wicks (shadows) reflect price fluctuations or "noise" around this movement.
Multi-Timeframe Analysis: A higher-timeframe (HTF) candlestick can be dissected into smaller, structured price movements in lower timeframes (LTFs). By analyzing these smaller movements, traders gain a detailed view of the market’s progression within the HTF candlestick’s range. Each HTF candlestick’s high and low provide support and resistance levels on the LTF, where the price can "sweep," break out, or retest these levels.
Market Behavior within the Range: Price action within a range doesn’t move randomly; it follows structured behavior, often revealing patterns. By analyzing these patterns, CRT provides insights into the market’s intention to accumulate, manipulate, or distribute assets within these ranges. This behavior can indicate future market direction and increase the probability of accurate trading signals.
CRT and ICT Power of 3: Accumulation, Manipulation, and Distribution (AMD)
A foundational element of our CRT indicator is its combination with ICT’s Power of 3 (Accumulation, Manipulation, and Distribution or AMD). This approach identifies three stages of market movement:
Accumulation: During this phase, institutions accumulate positions within a tight price range, often leading to sideways movement. Here, price consolidates as institutions carefully enter or exit positions, erasing traces of their intent from public view.
Manipulation: Institutions often use manipulation to create false breakouts, targeting retail traders who enter the market on perceived breakouts or reversals. Manipulation is characterized by liquidity grabs, false breakouts, or stop hunts, as price momentarily moves outside the established range before quickly returning.
Distribution: Following accumulation and manipulation, the distribution phase aligns with the true market direction. Institutions now allow the market to move with the trend, initiating a stronger and more sustained price movement that aligns with their intended position.
This AMD cycle is often observed across multiple timeframes, allowing traders to refine entries and exits by identifying accumulation, manipulation, and distribution phases on smaller timeframes within the range of a higher-timeframe candle. CRT views this cycle as the "heartbeat" of the market—a continuous loop of price movements. With our indicator, you can identify this cycle on your current timeframe, with the signal candle acting as the "manipulation" candle.
How to Use the Premium AlgoVision CRT Indicator
1. Indicator Display Options
Bullish/Bearish Plot Indication: Toggles the display of bullish or bearish CRT signals. Turn this on to display signals on your chart or off to reduce screen clutter.
Order Block Indication: Highlights the order block entry price, which is the preferred entry point for CRT trades.
Purge Time Indication: Shows when the low or high of Candle 1 is purged by Candle 2, helping to identify potential manipulation points.
2. Filter Options
Match Indicator Candle with Signal: Ensures that only bullish Candle 2s (for longs) or bearish Candle 2s (for shorts) are signaled. This filter helps eliminate signals where the candlestick’s direction does not align with the CRT model.
Take Profit Already Reached: When enabled, this filter removes CRT signals if take profit levels are reached within Candle 2. This helps focus on setups where there’s still room for price movement.
Midnight Price Filter: Filters signals based on midnight price levels:
Longs: Only signals if the order block entry price is below the midnight price.
Shorts: Only signals if the order block entry price is above the midnight price.
3. Entry and Exit Settings
Wick out prevention: Allows positions to stay open and prevent getting wicked out. Positions will still be able to close if determined by the algorithm.
Buy/Sell: This allows you to set you daily bias. You can select to only see buys or sells.
Custom Stop Loss: Sets a custom stop loss distance from the entry price (e.g., $100 or $200 away) if the predefined stop loss based on Candle 2’s low/high doesn’t suit your preference.
Take Profit Levels: Choose from three take profit levels:
Optimized Take Profit: Uses an optimized take profit level based on CRT’s recommended exit point.
Take Profit 1: Sets an initial take profit level.
Take Profit 2: Sets a secondary take profit level for a more extended exit target.
Timeframe of Order Block: Select the timeframe of the order block entry, which can be tailored based on the timeframe of the CRT signal.
Risk-to-Reward Filter: Filters trades based on a specified risk-to-reward ratio, using the indicator’s stop loss as the base. This helps to ensure trades meet minimum reward criteria.
4. Risk Management
Fixed Entry QTY: This will allow you to open all positions with a fixed QTY
Risk to Reward Ratio: This allows you to set a minimum risk to reward ratio, the strategy will only take trades if this risk to reward is met.
Risk Type:
Fixed Amount: Allows you to risk a fixed $ amount.
% of account: Allows you to risk % of account equity.
5. Day and Time Filters
Filter by Days: Specify the days of the week for CRT signals to appear. For instance, you could enable signals only on Thursdays. This setting can be adjusted to any day or combination of days.
Purge Time Filter: Filters CRT signals based on specific purge times when Candle 1’s low/high is breached by Candle 2, as CRT setups are observed to work best during certain times.
Hour Filters for CRT Signals:
1-Hour CRT Times: Allows filtering CRT signals based on specific 1-hour time intervals.
4-Hour CRT Times: Filter 4-hour CRT signals based on specified times.
Forex and Futures Conversion: Adjusts times based on standard sessions for Forex (e.g., 9:00 AM 4-hour candle) and Futures (e.g., 10 PM candle for Futures or 8 AM for Crypto).
6. Currency and Asset-Specific Filters
Crypto vs. Forex Mode: This setting adjusts the indicator’s timing to match market sessions specific to either crypto or Forex/Futures, ensuring the CRT model aligns with the asset type.
Additional Notes
Backtesting Options: Adjust these to test risk management, such as risking a fixed amount or a percentage of the account, for historical performance insights.
Optimized Settings: This version includes all features and optimized settings, with the most refined data analysis.
Conclusion By combining CRT with ICT Power of 3, the AlgoVision Indicator allows traders to leverage the CRT candlestick as a versatile tool for identifying potential market moves. This method provides beginners and seasoned traders alike with a robust framework to understand market dynamics and refine trade strategies across timeframes. Setting alerts on the higher timeframe to catch bullish or bearish CRT signals allows you to plan and execute trades on the lower timeframe, aligning your strategy with the broader market flow.
Pavan CPR Strategy Pavan CPR Strategy (Pine Script)
The Pavan CPR Strategy is a trading system based on the Central Pivot Range (CPR), designed to identify price breakouts and generate long trade signals. This strategy uses key CPR levels (Pivot, Top CPR, and Bottom CPR) calculated from the daily high, low, and close to inform trade decisions. Here's an overview of how the strategy works:
Key Components:
CPR Calculation:
The strategy calculates three critical CPR levels for each trading day:
Pivot (P): The central value, calculated as the average of the high, low, and close prices.
Top Central Pivot (TC): The midpoint of the daily high and low, acting as the resistance level.
Bottom Central Pivot (BC): Derived from the pivot and the top CPR, providing a support level.
The script uses request.security to fetch these CPR values from the daily timeframe, even when applied on intraday charts.
Trade Entry Condition:
A long position is initiated when:
The current price crosses above the Top CPR level (TC).
The previous close was below the Top CPR level, signaling a breakout above a key resistance level.
This condition aims to capture upward momentum as the price breaks above a significant level.
Exit Strategy:
Take Profit: The position is closed with a profit target set 50 points above the entry price.
Stop Loss: A stop loss is placed at the Pivot level to protect against unfavorable price movements.
Visual Reference:
The script plots the three CPR levels on the chart:
Pivot: Blue line.
Top CPR (TC): Green line.
Bottom CPR (BC): Red line.
These plotted levels provide visual guidance for identifying potential support and resistance zones.
Use Case:
The Pavan CPR Strategy is ideal for intraday traders who want to capitalize on price movements and breakouts above critical CPR levels. It provides clear entry and exit signals based on price action and is best used in conjunction with proper risk management.
Note: The strategy is written in Pine Script v5 for use on TradingView, and it is recommended to backtest and optimize it for the asset or market you are trading.
Advanced Bitcoin Trend Following StrategyTitle: Bitcoin Multi-Factor Trend Following Strategy
Description:
The Bitcoin Multi-Factor Trend Following Strategy is designed for traders seeking a robust, multi-factor approach to trend following in Bitcoin markets. This script combines technical indicators and statistical methods to identify trend directions, optimize entry and exit points, and manage position sizing based on volatility and leverage constraints. Key features of the strategy include:
Multi-Indicator Trend Forecasting:
This strategy employs three trend forecasting methods: range, exponential moving average (EMA), and Bollinger Bands. Each method can be independently enabled or disabled, giving traders flexibility in how trends are identified and followed.
Range Forecast : Calculates forecast based on the range (high and low) of recent prices, with optional smoothing via a Kalman filter to reduce noise.
EMA Spread Forecast : Utilizes the spread between fast and slow EMAs to gauge the trend’s strength, adjusted for volatility.
Bollinger Band Forecast : Measures the proximity of price to Bollinger Band levels to assess trend intensity.
Kalman Filter for Smoothing:
The Kalman filter is applied to price data for smoother trend estimation, particularly within the range forecast. This allows the strategy to reduce noise and focus on more reliable price signals.
Volatility-Adjusted Position Sizing:
The strategy incorporates volatility targeting to dynamically adjust position sizes based on current market conditions. Traders can set an annualized volatility target to control the risk level, with position size scaled accordingly to maintain consistent risk exposure. A maximum leverage cap ensures that position sizes do not exceed a user-defined threshold, offering an additional layer of risk control.
Dynamic Entry and Exit Points:
Entry and exit points are based on customizable thresholds that determine trend strength and are sensitive to market volatility. The script monitors changes in forecast values and automatically adjusts trades to capitalize on emerging trends or exit weakening ones. The strategy includes an option to close all open positions when trend forecasts fall below defined thresholds, ensuring an automated approach to risk management.
Backtesting and Performance Metrics:
To support strategy optimization, the script includes a backtest mode that calculates key performance metrics such as Sharpe Ratio, Buy & Hold profit, Strategy profit, Win rate, and other metrics. These metrics are displayed in a summary table directly on the chart, providing real-time insight into the strategy’s historical performance compared to a buy-and-hold approach.
Configurable Time and Date Range:
Users can specify start and end dates for the backtest period, allowing for focused backtesting over any desired timeframe. This feature enables in-depth analysis of performance across varying market conditions.
Use Case:
This strategy is best suited for experienced traders who wish to apply a structured trend-following approach in Bitcoin or other high-volatility assets. It is highly customizable, making it adaptable to various market conditions and trading styles. The combination of trend forecasting methods, volatility targeting, and automatic leverage control offers a balanced approach to capturing long-term trends while managing risk.
Parameters:
Entry Threshold: Adjusts the sensitivity of the entry point for trends. Lower values make the strategy more reactive.
Annual Volatility Target: Controls the risk level by targeting a specific annualized volatility percentage.
Max Leverage: Caps the allowable leverage for each trade.
Forecast Activations: Toggles to enable or disable the use of range, EMA, and Bollinger forecasts.
Date Range: Allows users to define the start and end dates for testing the strategy.
Notes:
This strategy is designed for educational purposes and requires thorough backtesting and optimization before live trading. Real-time performance may vary, and additional risk management practices are advised.
License:
This script is subject to the terms of the Mozilla Public License 2.0.
FTMO Rules MonitorFTMO Rules Monitor: Stay on Track with Your FTMO Challenge Goals
TLDR; You can test with this template whether your strategy for one asset would pass the FTMO challenges step 1 then step 2, then with real money conditions.
Passing a prop firm challenge is ... challenging.
I believe a toolkit allowing to test in minutes whether a strategy would have passed a prop firm challenge in the past could be very powerful.
The FTMO Rules Monitor is designed to help you stay within FTMO’s strict risk management guidelines directly on your chart. Whether you’re aiming for the $10,000 or the $200,000 account challenge, this tool provides real-time tracking of your performance against FTMO’s rules to ensure you don’t accidentally breach any limits.
NOTES
The connected indicator for this post doesn't matter.
It's just a dummy double supertrends (see below)
The strategy results for this script post does not matter as I'm posting a FTMO rules template on which you can connect any indicator/strategy.
//@version=5
indicator("Supertrends", overlay=true)
// Supertrend 1 Parameters
var string ST1 = "Supertrend 1 Settings"
st1_atrPeriod = input.int(10, "ATR Period", minval=1, maxval=50, group=ST1)
st1_factor = input.float(2, "Factor", minval=0.5, maxval=10, step=0.5, group=ST1)
// Supertrend 2 Parameters
var string ST2 = "Supertrend 2 Settings"
st2_atrPeriod = input.int(14, "ATR Period", minval=1, maxval=50, group=ST2)
st2_factor = input.float(3, "Factor", minval=0.5, maxval=10, step=0.5, group=ST2)
// Calculate Supertrends
= ta.supertrend(st1_factor, st1_atrPeriod)
= ta.supertrend(st2_factor, st2_atrPeriod)
// Entry conditions
longCondition = direction1 == -1 and direction2 == -1 and direction1 == 1
shortCondition = direction1 == 1 and direction2 == 1 and direction1 == -1
// Optional: Plot Supertrends
plot(supertrend1, "Supertrend 1", color = direction1 == -1 ? color.green : color.red, linewidth=3)
plot(supertrend2, "Supertrend 2", color = direction2 == -1 ? color.lime : color.maroon, linewidth=3)
plotshape(series=longCondition, location=location.belowbar, color=color.green, style=shape.triangleup, title="Long")
plotshape(series=shortCondition, location=location.abovebar, color=color.red, style=shape.triangledown, title="Short")
signal = longCondition ? 1 : shortCondition ? -1 : na
plot(signal, "Signal", display = display.data_window)
To connect your indicator to this FTMO rules monitor template, please update it as follow
Create a signal variable to store 1 for the long/buy signal or -1 for the short/sell signal
Plot it in the display.data_window panel so that it doesn't clutter your chart
signal = longCondition ? 1 : shortCondition ? -1 : na
plot(signal, "Signal", display = display.data_window)
In the FTMO Rules Monitor template, I'm capturing this external signal with this input.source variable
entry_connector = input.source(close, "Entry Connector", group="Entry Connector")
longCondition = entry_connector == 1
shortCondition = entry_connector == -1
🔶 USAGE
This indicator displays essential FTMO Challenge rules and tracks your progress toward meeting each one. Here’s what’s monitored:
Max Daily Loss
• 10k Account: $500
• 25k Account: $1,250
• 50k Account: $2,500
• 100k Account: $5,000
• 200k Account: $10,000
Max Total Loss
• 10k Account: $1,000
• 25k Account: $2,500
• 50k Account: $5,000
• 100k Account: $10,000
• 200k Account: $20,000
Profit Target
• 10k Account: $1,000
• 25k Account: $2,500
• 50k Account: $5,000
• 100k Account: $10,000
• 200k Account: $20,000
Minimum Trading Days: 4 consecutive days for all account sizes
🔹 Key Features
1. Real-Time Compliance Check
The FTMO Rules Monitor keeps track of your daily and total losses, profit targets, and trading days. Each metric updates in real-time, giving you peace of mind that you’re within FTMO’s rules.
2. Color-Coded Visual Feedback
Each rule’s status is shown clearly with a ✓ for compliance or ✗ if the limit is breached. When a rule is broken, the indicator highlights it in red, so there’s no confusion.
3. Completion Notification
Once all FTMO requirements are met, the indicator closes all open positions and displays a celebratory message on your chart, letting you know you’ve successfully completed the challenge.
4. Easy-to-Read Table
A table on your chart provides an overview of each rule, your target, current performance, and whether you’re meeting each goal. The table adjusts its color scheme based on your chart settings for optimal visibility.
5. Dynamic Position Sizing
Integrated ATR-based position sizing helps you manage risk and avoid large drawdowns, ensuring each trade aligns with FTMO’s risk management principles.
Daveatt
VWAP Stdev Bands Strategy (Long Only)The VWAP Stdev Bands Strategy (Long Only) is designed to identify potential long entry points in trending markets by utilizing the Volume Weighted Average Price (VWAP) and standard deviation bands. This strategy focuses on capturing upward price movements, leveraging statistical measures to determine optimal buy conditions.
Key Features:
VWAP Calculation: The strategy calculates the VWAP, which represents the average price a security has traded at throughout the day, weighted by volume. This is an essential indicator for determining the overall market trend.
Standard Deviation Bands: Two bands are created above and below the VWAP, calculated using specified standard deviations. These bands act as dynamic support and resistance levels, providing insight into price volatility and potential reversal points.
Trading Logic:
Long Entry Condition: A long position is triggered when the price crosses below the lower standard deviation band and then closes above it, signaling a potential price reversal to the upside.
Profit Target: The strategy allows users to set a predefined profit target, closing the long position once the specified target is reached.
Time Gap Between Orders: A customizable time gap can be specified to prevent multiple orders from being placed in quick succession, allowing for a more controlled trading approach.
Visualization: The VWAP and standard deviation bands are plotted on the chart with distinct colors, enabling traders to visually assess market conditions. The strategy also provides optional plotting of the previous day's VWAP for added context.
Use Cases:
Ideal for traders looking to engage in long-only positions within trending markets.
Suitable for intraday trading strategies or longer-term approaches based on market volatility.
Customization Options:
Users can adjust the standard deviation values, profit target, and time gap to tailor the strategy to their specific trading style and market conditions.
Note: As with any trading strategy, it is important to conduct thorough backtesting and analysis before live trading. Market conditions can change, and past performance does not guarantee future results.
PTS - Bollinger Bands with Trailing StopPTS - Bollinger Bands with Trailing Stop Strategy
Overview
The "PTS - Bollinger Bands with Trailing Stop" strategy is designed to capitalize on strong bullish market movements by combining the Bollinger Bands indicator with a dynamic trailing stop based on the Average True Range (ATR). This strategy aims to enter long positions during upward breakouts and protect profits through an adaptive exit mechanism.
Key Features
1. Bollinger Bands Indicator
Basis Moving Average Type: Choose from SMA, EMA, SMMA, WMA, or VWMA for the Bollinger Bands' basis line. Length: Adjustable period for calculating the moving average and standard deviation (default is 20). Standard Deviation Multiplier (StdDev): Determines the width of the bands (default is 2.0). Source: Select the price data used for calculations (default is the closing price).
2. Entry Conditions
The strategy enters a long position when the closing price crosses above the upper Bollinger Band , indicating strong upward momentum.
3. Exit Conditions
Trailing Stop Loss: Implemented using ATR to adjust dynamically with market volatility. ATR Length: Period for calculating ATR (default is 14). ATR Multiplier for Trailing Stop: Determines how closely the trailing stop follows the price (default is 2.0). Close Below Lower Bollinger Band: The strategy exits the long position if the closing price crosses below the lower Bollinger Band .
4. Risk Management
Commission and Slippage: Commission is set at 0.1%; slippage is set to 3. Position Sizing: Uses 100% of equity per trade (adjustable).
5. Date Range Filter
Specify the time period during which the strategy is active. Start Date: January 1, 2018. End Date: December 31, 2069.
Customizable Inputs
BB Length: Adjust the period for Bollinger Bands calculation. BB StdDev: Modify the standard deviation multiplier. Basis MA Type: Select the moving average type. Source: Choose the price data source. Offset: Shift the Bollinger Bands on the chart. ATR Length: Set the period for ATR calculation. ATR Multiplier for Trailing Stop: Adjust the trailing stop sensitivity.
Thought for 1m 7s
Description for the "PTS - Bollinger Bands with Trailing Stop" Strategy
PTS - Bollinger Bands with Trailing Stop Strategy
Overview
The "PTS - Bollinger Bands with Trailing Stop" strategy is designed to capitalize on strong bullish market movements by combining the Bollinger Bands indicator with a dynamic trailing stop based on the Average True Range (ATR). This strategy aims to enter long positions during upward breakouts and protect profits through an adaptive exit mechanism.
Key Features
1. Bollinger Bands Indicator
Basis Moving Average Type: Choose from SMA, EMA, SMMA, WMA, or VWMA for the Bollinger Bands' basis line. Length: Adjustable period for calculating the moving average and standard deviation (default is 20). Standard Deviation Multiplier (StdDev): Determines the width of the bands (default is 2.0). Source: Select the price data used for calculations (default is the closing price).
2. Entry Conditions
The strategy enters a long position when the closing price crosses above the upper Bollinger Band , indicating strong upward momentum.
3. Exit Conditions
Trailing Stop Loss: Implemented using ATR to adjust dynamically with market volatility. ATR Length: Period for calculating ATR (default is 14). ATR Multiplier for Trailing Stop: Determines how closely the trailing stop follows the price (default is 2.0). Close Below Lower Bollinger Band: The strategy exits the long position if the closing price crosses below the lower Bollinger Band .
4. Risk Management
Commission and Slippage: Commission is set at 0.1%; slippage is set to 3. Position Sizing: Uses 100% of equity per trade (adjustable).
5. Date Range Filter
Specify the time period during which the strategy is active. Start Date: January 1, 2018. End Date: December 31, 2069.
Customizable Inputs
BB Length: Adjust the period for Bollinger Bands calculation. BB StdDev: Modify the standard deviation multiplier. Basis MA Type: Select the moving average type. Source: Choose the price data source. Offset: Shift the Bollinger Bands on the chart. ATR Length: Set the period for ATR calculation. ATR Multiplier for Trailing Stop: Adjust the trailing stop sensitivity.
How the Strategy Works
1. Initialization
Calculates Bollinger Bands and ATR based on selected parameters.
2. Entry Logic
Opens a long position when the closing price exceeds the upper Bollinger Band.
3. Exit Logic
Uses a trailing stop loss based on ATR. Exits if the closing price drops below the lower Bollinger Band.
4. Date Filtering
Executes trades only within the specified date range.
Advantages
Adaptive Risk Management: Trailing stop adjusts to market volatility. Simplicity: Clear entry and exit signals. Customizable Parameters: Tailor the strategy to different assets or conditions.
Considerations
Aggressive Position Sizing: Using 100% equity per trade is high-risk. Market Conditions: Best in trending markets; may produce false signals in sideways markets. Backtesting: Always test on historical data before live trading.
Disclaimer
This strategy is intended for educational and informational purposes only. Trading involves significant risk, and past performance is not indicative of future results. Assess your financial situation and consult a financial advisor if necessary.
Usage Instructions
1. Apply the Strategy: Add it to your TradingView chart. 2. Configure Inputs: Adjust parameters to suit your style and asset. 3. Analyze Backtest Results: Use the Strategy Tester. 4. Optimize Parameters: Experiment with input values. 5. Risk Management: Evaluate position sizing and incorporate risk controls.
Final Notes
The "PTS - Bollinger Bands with Trailing Stop" strategy provides a framework to leverage momentum breakouts while managing risk through adaptive trailing stops. Customize and test thoroughly to align with your trading objectives.
Dual Momentum StrategyThis Pine Script™ strategy implements the "Dual Momentum" approach developed by Gary Antonacci, as presented in his book Dual Momentum Investing: An Innovative Strategy for Higher Returns with Lower Risk (McGraw Hill Professional, 2014). Dual momentum investing combines relative momentum and absolute momentum to maximize returns while minimizing risk. Relative momentum involves selecting the asset with the highest recent performance between two options (a risky asset and a safe asset), while absolute momentum considers whether the chosen asset has a positive return over a specified lookback period.
In this strategy:
Risky Asset (SPY): Represents a stock index fund, typically more volatile but with higher potential returns.
Safe Asset (TLT): Represents a bond index fund, which generally has lower volatility and acts as a hedge during market downturns.
Monthly Momentum Calculation: The momentum for each asset is calculated based on its price change over the last 12 months. Only assets with a positive momentum (absolute momentum) are considered for investment.
Decision Rules:
Invest in the risky asset if its momentum is positive and greater than that of the safe asset.
If the risky asset’s momentum is negative or lower than the safe asset's, the strategy shifts the allocation to the safe asset.
Scientific Reference
Antonacci's work on dual momentum investing has shown the strategy's ability to outperform traditional buy-and-hold methods while reducing downside risk. This approach has been reviewed and discussed in both academic and investment publications, highlighting its strong risk-adjusted returns (Antonacci, 2014).
Reference: Antonacci, G. (2014). Dual Momentum Investing: An Innovative Strategy for Higher Returns with Lower Risk. McGraw Hill Professional.
Universal Trend Following Strategy | QuantumRsearchUniversal All Assets Strategy by Rocheur
The Universal All Assets Strategy is a cutting-edge, trend-following algorithm designed to operate seamlessly across multiple asset classes, including equities, commodities, forex, and cryptocurrencies. This strategy leverages the power of eight unique indicators, offering traders robust, adaptive signals. Its dynamic logic, combined with a comprehensive risk management framework, allows for precision trading in a variety of market conditions.
Core Methodologies and Features
1. Eight Integrated Trend Indicators
At the heart of the Universal All Assets Strategy are eight sophisticated trend-following indicators, each designed to capture different facets of market behavior. These indicators work together to provide a multi-dimensional analysis of price trends, filtering out noise and reacting only to significant movements:
Directional Moving Averages : Tracks the primary market trend, offering a clear indication of long-term price direction, ideal for identifying sustained upward or downward movements.
Smoothed Moving Averages : Reduces short-term volatility and noise to reveal the underlying trend, enhancing signal clarity and helping traders avoid reacting to temporary price spikes.
RSI Loops : Utilizes the Relative Strength Index (RSI) to assess market momentum, using a unique for loop mechanism to smooth out data and enhance precision.
Supertrend Filters : This indicator dynamically adjusts to market volatility, closely following price action to detect significant breakouts or reversals. The Supertrend is a core component for identifying shifts in trend direction with minimal lag.
RVI for Loop : The Relative Volatility Index (RVI) measures the strength of market volatility. It is optimized with a for loop mechanism, which smooths out the data and improves directional cues, especially in choppy or sideways markets.
Hull for Loop : The Hull Moving Average is designed to minimize lag while offering a smooth, responsive trend line. The for loop mechanism further enhances this by making the Hull even more sensitive to trend shifts, ensuring faster reaction to market movements without generating excessive noise.
These indicators evaluate market conditions independently, assigning a score of 1 for bullish trends and -1 for bearish trends. The average score across all eight indicators is calculated for each time frame (or bar), and this score determines whether the strategy should enter, exit, or remain neutral in a trade.
2. Scoring and Signal Confirmation
The strategy’s confirmation system ensures that trades are initiated only when there is strong alignment across multiple indicators:
A Long Position (Buy) is initiated when the majority of indicators generate a bullish signal, i.e., the average score exceeds a predefined upper threshold.
A Short Position or Exit is triggered when the average score falls below a lower threshold, signaling a bearish trend or neutral market.
By using a majority-rule confirmation system, the strategy filters out weak signals, reducing the chances of reacting to market noise or false positives. This ensures that only robust trends—those supported by multiple indicators—trigger trades.
Adaptive Logic for All Asset Classes
The Universal All Assets Strategy stands out for its ability to adapt dynamically across different asset classes. Whether it’s applied to highly volatile assets like cryptocurrencies or more stable instruments like equities, the strategy fine-tunes its behavior to match the asset’s volatility profile and price behavior.
Volatility Filters : The system incorporates volatility-sensitive filters, such as the Average True Range (ATR) and standard deviation metrics, which dynamically adjust its sensitivity based on market conditions. This ensures the strategy remains responsive to significant price movements while filtering out inconsequential fluctuations.
This adaptability makes the Universal All Assets Strategy effective across diverse markets, providing consistent performance whether the market is trending, range-bound, or experiencing high volatility.
Customization and Flexibility
1. Directional Bias
The strategy offers traders the flexibility to set a customizable directional bias, allowing it to focus on:
Long-only trades during bullish markets.
Short-only trades during bear markets.
Bi-directional trades for those looking to capitalize on both uptrends and downtrends.
This bias can be fine-tuned based on market conditions, trader preference, or risk tolerance, without compromising the integrity of the overall signal-generation process.
2. Volatility Sensitivity
Traders can adjust the strategy’s volatility sensitivity through customizable settings. By modifying how the system reacts to volatility, traders can make the strategy more aggressive in high-volatility environments or more conservative in quieter markets, depending on their individual trading style.
Visual Representation of Component Behavior
One of the unique features of the strategy is its real-time visual representation of the eight indicators through a component table displayed on the chart. This table provides a clear overview of the current status of each indicator:
A score of 1 indicates a bullish signal.
A score of -1 indicates a bearish signal.
The table is updated at each time frame (bar), showing how each indicator is contributing to the overall trend decision. This real-time feedback allows traders to monitor the exact composition of the strategy’s signal, helping them better understand market dynamics.
Oscillator Visualization for Trend Detection
To complement the component table, the strategy includes a trend oscillator displayed beneath the price chart, offering a visual summary of the overall market direction:
Green bars represent bullish trends when the majority of indicators signal an uptrend.
Red bars represent bearish trends or a neutral (cash) position when the majority of indicators detect a downtrend.
This oscillator allows traders to quickly assess the market’s overall direction at a glance, without needing to analyze each individual indicator, providing a clear and immediate visual of the market trend.
Backtested and Forward-Tested for Real-World Conditions
The Universal All Assets Strategy has been thoroughly tested under real-world trading conditions, incorporating key factors like:
Slippage : Set at 20 ticks to represent real market fluctuations.
Order Size : Calculated as 10% of equity, ensuring appropriate risk exposure for realistic capital management.
Commission : A fee of 0.05% has been factored in to account for trading costs.
These settings ensure that the strategy’s performance metrics—such as the Sortino Ratio , Sharpe Ratio , Omega Ratio , and Profit Factor —are reflective of actual trading environments. The rigorous backtesting and forward-testing processes ensure that the strategy produces realistic results, making it compatible with the markets it is written for and demonstrating how the system would behave in live conditions. It also includes robust risk management tools to minimize drawdowns and preserve capital, making it suitable for both professional and retail traders.
Anti-Fragile Design and Realistic Expectations
The Universal All Assets Strategy is engineered to be anti-fragile, thriving in volatile markets by adjusting to turbulence rather than being damaged by it. This is a crucial feature that ensures the strategy remains effective even during times of significant market instability.
Moreover, the strategy is transparent about realistic expectations, acknowledging that no system can guarantee a 100% win rate and that past performance is not indicative of future results. This transparency fosters trust and provides traders with a realistic framework for long-term success, making it an ideal choice for traders looking to navigate complex market conditions with confidence.
Acknowledgment of External Code
Special credit goes to bii_vg, whose invite-only code was used with permission in the development of the Universal All Assets Strategy. Their contributions have been instrumental in refining certain aspects of this strategy, ensuring its robustness and adaptability across various markets.
Conclusion
The Universal All Assets Strategy by Rocheur offers traders a powerful, adaptable tool for capturing trends across a wide range of asset classes. Its eight-indicator confirmation system, combined with customizable settings and real-time visual representations, provides a comprehensive solution for traders seeking precision, flexibility, and consistency. Whether used in high-volatility markets or more stable environments, the strategy’s dynamic adaptability, transparent logic, and robust testing make it an excellent choice for traders aiming to maximize performance while managing risk effectively.
Advanced Multi-Seasonality StrategyThe Multi-Seasonality Strategy is a trading system based on seasonal market patterns. Seasonality refers to recurring market trends driven by predictable calendar-based events. These patterns emerge due to economic cycles, corporate activities (e.g., earnings reports), and investor behavior around specific times of the year. Studies have shown that such effects can influence asset prices over defined periods, leading to opportunities for traders who exploit these patterns (Hirshleifer, 2001; Bouman & Jacobsen, 2002).
How the Strategy Works:
The strategy allows the user to define four distinct periods within a calendar year. For each period, the trader selects:
Entry Date (Month and Day): The date to enter the trade.
Holding Period: The number of trading days to remain in the trade after the entry.
Trade Direction: Whether to take a long or short position during that period.
The system is designed with flexibility, enabling the user to activate or deactivate each of the four periods. The idea is to take advantage of seasonal patterns, such as buying during historically strong periods and selling during weaker ones. A well-known example is the "Sell in May and Go Away" phenomenon, which suggests that stock returns are higher from November to April and weaker from May to October (Bouman & Jacobsen, 2002).
Seasonality in Financial Markets:
Seasonal effects have been documented across different asset classes and markets:
Equities: Stock markets tend to exhibit higher returns during certain months, such as the "January effect," where prices rise after year-end tax-loss selling (Haugen & Lakonishok, 1987).
Commodities: Agricultural commodities often follow seasonal planting and harvesting cycles, which impact supply and demand patterns (Fama & French, 1987).
Forex: Currency pairs may show strength or weakness during specific quarters based on macroeconomic factors, such as fiscal year-end flows or central bank policy decisions.
Scientific Basis:
Research shows that market anomalies like seasonality are linked to behavioral biases and institutional practices. For example, investors may respond to tax incentives at the end of the year, and companies may engage in window dressing (Haugen & Lakonishok, 1987). Additionally, macroeconomic factors, such as monetary policy shifts and holiday trading volumes, can also contribute to predictable seasonal trends (Bouman & Jacobsen, 2002).
Risks of Seasonal Trading:
While the strategy seeks to exploit predictable patterns, there are inherent risks:
Market Changes: Seasonal effects observed in the past may weaken or disappear as market conditions evolve. Increased algorithmic trading, globalization, and policy changes can reduce the reliability of historical patterns (Lo, 2004).
Overfitting: One of the risks in seasonal trading is overfitting the strategy to historical data. A pattern that worked in the past may not necessarily work in the future, especially if it was based on random chance or external factors that no longer apply (Sullivan, Timmermann, & White, 1999).
Liquidity and Volatility: Trading during specific periods may expose the trader to low liquidity, especially around holidays or earnings seasons, leading to slippage and larger-than-expected price swings.
Economic and Geopolitical Shocks: External events such as pandemics, wars, or political instability can disrupt seasonal patterns, leading to unexpected market behavior.
Conclusion:
The Multi-Seasonality Strategy capitalizes on the predictable nature of certain calendar-based patterns in financial markets. By entering and exiting trades based on well-established seasonal effects, traders can potentially capture short-term profits. However, caution is necessary, as market dynamics can change, and seasonal patterns are not guaranteed to persist. Rigorous backtesting, combined with risk management practices, is essential to successfully implementing this strategy.
References:
Bouman, S., & Jacobsen, B. (2002). The Halloween Indicator, "Sell in May and Go Away": Another Puzzle. American Economic Review, 92(5), 1618-1635.
Fama, E. F., & French, K. R. (1987). Commodity Futures Prices: Some Evidence on Forecast Power, Premiums, and the Theory of Storage. Journal of Business, 60(1), 55-73.
Haugen, R. A., & Lakonishok, J. (1987). The Incredible January Effect: The Stock Market's Unsolved Mystery. Dow Jones-Irwin.
Hirshleifer, D. (2001). Investor Psychology and Asset Pricing. Journal of Finance, 56(4), 1533-1597.
Lo, A. W. (2004). The Adaptive Markets Hypothesis: Market Efficiency from an Evolutionary Perspective. Journal of Portfolio Management, 30(5), 15-29.
Sullivan, R., Timmermann, A., & White, H. (1999). Data-Snooping, Technical Trading Rule Performance, and the Bootstrap. Journal of Finance, 54(5), 1647-1691.
This strategy harnesses the power of seasonality but requires careful consideration of the risks and potential changes in market behavior over time.
Statistical ArbitrageThe Statistical Arbitrage Strategy, also known as pairs trading, is a quantitative trading method that capitalizes on price discrepancies between two correlated assets. The strategy assumes that over time, the prices of these two assets will revert to their historical relationship. The core idea is to take advantage of mean reversion, a principle suggesting that asset prices will revert to their long-term average after deviating significantly.
Strategy Mechanics:
1. Selection of Correlated Assets:
• The strategy focuses on two historically correlated assets (e.g., equity index futures like Dow Jones Mini and S&P 500 Mini). These assets tend to move in the same direction due to similar underlying fundamentals, such as overall market conditions. By tracking their relative prices, the strategy seeks to exploit temporary mispricings.
2. Spread Calculation:
• The spread is the difference between the prices of the two assets. This spread represents the relationship between the assets and serves as the basis for determining when to enter or exit trades.
3. Mean and Standard Deviation:
• The historical average (mean) of the spread is calculated using a Simple Moving Average (SMA) over a chosen period. The strategy also computes the standard deviation (volatility) of the spread, which measures how far the spread has deviated from the mean over time. This allows the strategy to define statistically significant price deviations.
4. Entry Signal (Mean Reversion):
• A buy signal is triggered when the spread falls below the mean by a multiple (e.g., two) of the standard deviation. This indicates that one asset is temporarily undervalued relative to the other, and the strategy expects the spread to revert to its mean, generating profits as the prices converge.
5. Exit Signal:
• The strategy exits the trade when the spread reverts to the mean. At this point, the mispricing has been corrected, and the profit from the mean reversion is realized.
Academic Support:
Statistical arbitrage has been widely studied in finance and economics. Gatev, Goetzmann, and Rouwenhorst’s (2006) landmark study on pairs trading demonstrated that this strategy could generate excess returns in equity markets. Their research found that by focusing on historically correlated stocks, traders could identify pricing anomalies and profit from their eventual correction.
Additionally, Avellaneda and Lee (2010) explored statistical arbitrage in different asset classes and found that exploiting deviations in price relationships can offer a robust, market-neutral trading strategy. In these studies, the strategy’s success hinges on the stability of the relationship between the assets and the timely execution of trades when deviations occur.
Risks of Statistical Arbitrage:
1. Correlation Breakdown:
• One of the primary risks is the breakdown of correlation between the two assets. Statistical arbitrage assumes that the historical relationship between the assets will hold in the future. However, market conditions, company fundamentals, or external shocks (e.g., macroeconomic changes) can cause these assets to deviate permanently, leading to potential losses.
• For instance, if two equity indices historically move together but experience divergent economic conditions or policy changes, their prices may no longer revert to the expected mean.
2. Execution Risk:
• This strategy relies on efficient execution and tight spreads. In volatile or illiquid markets, the actual price at which trades are executed may differ significantly from expected prices, leading to slippage and reduced profits.
3. Market Risk:
• Although statistical arbitrage is designed to be market-neutral (i.e., not dependent on the overall market direction), it is not entirely risk-free. Systematic market shocks, such as financial crises or sudden shifts in market sentiment, can affect both assets simultaneously, causing the spread to widen rather than revert to the mean.
4. Model Risk:
• The assumptions underlying the strategy, particularly regarding mean reversion, may not always hold true. The model assumes that asset prices will return to their historical averages within a certain timeframe, but the timing and magnitude of mean reversion can be uncertain. Misestimating this timeframe can lead to extended drawdowns or unrealized losses.
5. Overfitting:
• Over-reliance on historical data to fine-tune the strategy parameters (e.g., the lookback period or standard deviation thresholds) may result in overfitting. This means that the strategy works well on past data but fails to perform in live markets due to changing conditions.
Conclusion:
The Statistical Arbitrage Strategy offers a systematic and quantitative approach to trading that capitalizes on temporary price inefficiencies between correlated assets. It has been proven to generate returns in academic studies and is widely used by hedge funds and institutional traders for its market-neutral characteristics. However, traders must be aware of the inherent risks, including correlation breakdown, execution risks, and the potential for prolonged deviations from the mean. Effective risk management, diversification, and constant monitoring are essential for successfully implementing this strategy in live markets.
Stochastic RSI OHLC StrategyThe script titled "Stochastic RSI High Low Close Bars" is a versatile trading strategy implemented in Pine Script, designed for TradingView. Here's an overview of its features:
Description
This strategy leverages the Stochastic RSI to determine entry and exit signals in the market, focusing on high, low, and close values of the indicator. It incorporates various trading styles, stop-loss mechanisms, and multi-timeframe analysis to adapt to different market conditions.
Key Features
Stochastic RSI Analysis:
Uses the Stochastic RSI to identify potential entry points for long and short positions.
Tracks high, low, and close values for more granular analysis.
Multiple Trading Styles:
Supports diverse trading styles like Volume Color Swing, RSI Divergence, RSI Pullback, and more.
Allows switching between these styles to suit market dynamics.
Session-Based Trading:
Offers session control, limiting trades to specific hours (e.g., NY sessions).
Can close all positions at the end of the trading day.
Stop-Loss and Take-Profit Mechanisms:
Includes both static and dynamic stop-losses, with options for time-based stops, trailing stops, and momentum-based exits.
Customizable take-profit levels ensure efficient trade management.
Volume Analysis:
Integrates volume indicators to add a bias for trade entries and exits, enhancing signal reliability.
Multi-Timeframe Integration:
Employs multi-timeframe RSI analysis, allowing the strategy to capture broader trends and optimize entries.
This script is designed to provide flexibility and adaptability, making it useful for different trading strategies and market conditions. It is suitable for traders looking to refine their entries and exits with a focus on the Stochastic RSI.
Support Resistance Pivot EMA Scalp Strategy [Mauserrifle]A strategy that creates signals based on: pivots, EMA 9+20, RSI, ATR, VWAP, wicks and volume.
The strategy is developed as a helper for quick long option scalping. This strategy is primarily designed for intraday trading on the 2m SPY chart with extended hours. However, users can adapt it for use on different symbols and timeframes. These signals are meant as a helper rather than fully automated trading bots.
One of the key elements is its pivot-based calculation, driven by my integrated indicator "Support and Resistance Pivot Points/Lines ". It enables multi-timeframe pivot calculations which are used to generate the signals and offers customizability, allowing you to define rounding methods and cooldown periods to refine pivot levels. The pivots, in combination with EMA crossovers, VWAP trend, and additional filters (RSI, ATR, VWAP, wicks and volume), create an entry and exit strategy for scalping opportunities that is useful for 0/1 DTE options with an average trade time of six minutes with the default setup for SPY. Option trading should be done outside TradingView. At this moment of release there is no option trading support.
All parameters used in the strategy are tweaked based on deep backtests results and real-time behavior. Be mindful that past performance does not guarantee future results.
The strategy is designed for intermediate and advanced users who are familiar intraday option scalping techniques.
How It Works
The strategy identifies entries based on multiple conditions, including: recently above pivot, recent EMA crossovers, RSI range, candle patterns, and VWAP uptrend. It avoids trades below the VWAP lower band due to poor backtesting results in those conditions. It creates a great number of signals when it detects an uptrend, which entails: VWAP and its lower/upper band slopes are going up, and the number of next high pivot points is greater than the number of lower pivot points. This indicates that we hope it will keep going up. In historical testing, this showed favorable results. This uptrend criteria runs on 15m charts max (where up to the VWAP effectiveness is the greatest).
The strategy also checks for candle and volume patterns, identified in backtesting to improve entry levels on historic data. Which include:
A red candle after multiple green ones, hoping to jump on a trend during a small pullback
Zero lower wick
Percentage and volume is up after lower volume candles
Percentage is up and the first and second EMA slopes are going up
Percentage is up, the first EMA is higher than the second, the price low is below the second EMA and price close above it
The VWAP uptrend overrules the candle and volume conditions (thus lots of signals during those moments).
The above is the base for many signals. There is a strict mode that adds extra checks such as:
not trading when there is no next low or high pivot
requiring a VWAP uptrend only
minimum candle percentages
This mode is for analyzing history and seeing performance during these conditions. It is worth it to create a separate alert for strict mode so you are aware of these conditions during trading.
When no stop has been defined, exits will always happen on pivot crossunder confirmations. If a stop is defined (default config), the strategy exits a position when:
the position is negative or no trail has been set
at least 1 bar has past
OR no stop has been defined (overrules previous)
trail has not been activated
The second exit condition happens when the close is below first EMA(9 by default) and when:
the position has been above first EMA
the gap between close and last pivot isn't small
the position is negative or no trail has been set
OR no stop has been defined (overrules above)
trail has not been activated
There are some more variations on this but the above are the most common. These exit conditions are a safety net because the strategy heavily relies on and favors stops. The settings allow changing stops, profit takers and trails. You can configure it to always sell without the conditions above.
The script will paint the pivot lines, trailing activation/stops, EMAs and entry/exits; with extra information in the data panel. For a complete view add VWAP and RSI to your chart, which are available from TradingView official indicator library. The strategy will not rely on those added indicators since VWAP and RSI are programmed in. You can add them to track the behavior of the signals based on these filters you have configured and have a complete view trading this strategy.
As mentioned earlier, the default settings are built for SPY 2m charts, with extended hours and real-time data. Open the strategy on this chart to study how all input parameters are used. If you don't have real-time data you need to adjust the minimum volume settings (set it to 0 at first).
The backtest
The default backtest configuration is set up to simulate SPY option trading.
Start capital is set to 10,000 and we risk around 5% of that per trade (1 contract)
Commission is set to 0.005%. The reason: at the time of this publication the SPY index price is approximately $580. Two ITM 0/1 DTE options contracts, each priced around $280, which is approximately $560. The typical commission for such a trade is around $3. To simulate this commission in the backtest on the SPY index itself, a commission of 0.005% per trade has been applied, approximating the options trading costs.
Slippage of 3 is set reflecting liquid SPY
The bar magnifier feature is turned on to have more realistic fills
Trading
In backtesting, setting commission and slippage to 0 on the SPY 2m chart shows many trades result around breaking even. Personally, I view them as an opportunity and safety net to help manage emotional decisions for exits. The signals are designed for short option scalps, allowing traders to take small profits and potentially re-enter during the strategy’s position window. It's advisable to take small potential profits, such as 4%, whenever the opportunity arises and consider re-entering if the setup still looks favorable, for example price still above ema9. Exiting a long position below ema9 is a common strategy for 2m scalping.
The average trade duration is approximately 6 minutes (3 bars). The choice between ITM (in-the-money), ATM (at-the-money), or OTM (out-of-the-money) options will depend on your trading style. Personally, I’ve seen better results with ITM options because they tend to move more in sync with the underlying index, thanks to their higher delta.
It’s important to note that the signals are designed to be a helper for manual trading rather than to automate a bot. Users are encouraged to take small profits and re-enter positions if favorable conditions persist. Be mindful that past performance does not guarantee future results.
For the default SPY setup the losses will mostly be 4-10% for ITM options. Be mindful of extreme volatile conditions where losses may reach 30% quickly, especially when trading ATM/OTM options.
The following settings can be changed:
8 pivot timeframes with left/right bars and days rendered
Here you can configure the timeframes for the pivots, which are crucial. The strategy wants that a crossover has happened recently (so it might enter after a crossunder if the crossover was recent) or the price is still above the crossed pivot.
When you decide to use a pivot timeframe higher than your chart, make sure it aligns the same starting point as the chart timeframe. As stated in the 43000478429 docs, there is a dependency between the resolution and the alignment of a starting point:
1–14 minutes — aligns to the beginning of a week
15–29 minutes — aligns to the beginning of a month
from 30 minutes and higher — aligns to the beginning of a year
This alignment also affects the setting of rendered days. I recommend a max value of 5 days for 1-14 minutes timeframes.
Also make sure a higher pivot timeframe can be divided by the lower. For instance I had repaint issues using 3m pivots on a 2m chart. But 4m pivots work fine.
Please look up docs 43000478429 to make sure this information is still up to date.
Pivot rounding
The pivot rounding option is used to add pivots based on a rounded price and limit the number of pivots. While this feature is disabled by default it can be useful with tweaking strategy variations, because many orders are placed at rounded levels and tend to act as strong price barriers.
There are multiple rounding methods: round, ceil/floor, roundn (decimal) and rounding to the minimal tick.
The next feature is a powerful extension called "Cooldown rounding":
Pivot cooldown rounding
This rounds new pivot levels for a cooldown period to keep the previous pivot line instead of adding a new line when they match the rounded value within the cooldown period. The existing line will be extended. This feature is useful because it makes sure the initial line is added to the exact high/low pivot level but any future lines within the rounding will just extend the existing line. This limits the number of pivots while still having precise levels (which normal rounding lacks) and allows more precise pivot trading.
This feature also helps ensure that the number of rendered lines will not exceed 500 too much, which is the render limit on TradingView.
You can set a maximum minutes for the cooldown. The default is 3 years which will enable the cooldown rounding permanently on the intraday (due to the max bar limit).
Pivot always added when new higher/lower pivot
When using cooldown rounding, one may find it useful to override this behavior when a new lower or higher pivot level has been reached. When enabled the new level will be added despite the fact that they may be rounded the same in the cooldown check. This is a good balance between limiting pivots but also allowing preciser trading.
VWAP bands multiplier
This is used to tweak the inner VWAP working for the upper and lower band. The default VWAP multiplier (0.9) is set based on backtesting since it performed better on historic data (the strategy does not trade below the lowerband). When you add the VWAP indicator from the TradingView library to the chart, make sure it uses the same multiplier setting as within this strategy so you have a correct view of the conditions the strategy acts on.
ATR EMA smoothing length
Used to tweak the ATR EMA smoothing. By default it is set up to 4 based on deep backtesting historic data.
EMA lengths
Changing the EMA length allows you to fine tune the EMA crossing behavior. By default the strategy is set up to EMA 9 and 20 which are considered commonly used values on the 2-minute chart.
Trading intraday time restrictions
For intraday charts you can configure when the strategy starts trading after market open and when it stops, including a hard sell. This makes sure there are no open positions left for the day during backtesting and can also aid in your trading style. For example some scalpers will not trade in the first two hours. Having no signals during this time can be beneficial. It is possible to configure these settings based on the number of bars or minutes.
Not trading on days the market closes earlier
By default the strategy does not trade on days the market closes earlier in the US. This makes sure there are no open positions left open during backtesting. Make sure to change it when using it on such a day. The days are: day before independence day, day after thanksgiving, Christmas eve and new years eve.
Not trading below VWAP lowerband
Backtesting has shown poor performance when trading below the VWAP lowerband but you are free to allow it to trade in such conditions. Past performance does not guarantee future results.
Minimum volume
A minimum volume can be set up. The current value is based on better deep backtest results for SPY using real-time data (48000). When you do not have a data plan for SPY, please set it to 0 and tweak based on backtests.
Minimum ATRP
The strategy has shown during my trading that it is sensitive to higher ATRP values and more volatile market conditions. There is more chance the index moves and we can profit from this during option scalping (if it moves in your favor). The default is based on SPY backtesting (0.04%), as a balance to have a lot of trades but also capture minimal movement.
RSI range
A RSI range can be set using a minimum and maximum value so we can limit trading during overbought/oversold conditions. Backtesting for SPY has shown the strategy performs better on historic data within a tighter range, so a default range has been set to 40-65.
Allow orders on every tick (no effect on stop/profit/trail)
This setting is used to allow orders on every tick. The strategy has been developed without trading on every tick but you can change this, for example when you have configured a setup different than the default configuration that you know works well with this. The default setup will not work well with it due to too many constant signals.
Stop percentage + ATRP threshold
One of the most important settings for managing the risk. I recommend setting a stop percentage first and later the ATRP threshold where the stop is calculated based on the current ATRP value. The calculated value will only be in effect when it is greater than the normal stop--the normal stop acts as baseline. The default stop is low (0.03). With a default ATRP threshold stop of 1.12, the calculated value overrules the normal stop when the value is greater. 0.03 acts as a minimum value but in reality the stop will most likely be higher on average for SPY with the default ATRP threshold.
For the default SPY setup the losses will be around 4-10% for ITM options. Be mindful of extreme volatile conditions where losses may reach 30% quickly, especially when trading ATM/OTM options.
Profit taker percentage + ATRP threshold
Same principles as the stop percentage above, but for profit taking. There is a very high ATRP threshold of 4 set by default. Backtests showed that trailing stops perform better on historic data.
Trailing stop
Used to set up a trailing stop. A useful feature to secure profit after a run-up, or get out with a small loss after initial activation. It is important to not use too tight values because they will give unrealistic backtest results and trigger too fast in real-time. Both the trail activation level and trail stop itself can be configured with a percentage value and ATRP value. I recommend setting up the ATRP last. By default the values are 0.05 for activation and 0.03 for the stop based on SPY real-time behavior.
Always sell on pivot crossunder confirmation
The strategy includes pivot crossunder confirmations as sell condition. By default it will not sell on every crossunder confirmation but checks for different conditions (explained in detail earlier in this description). You can change this behavior.
Always sell below first EMA when position has been above
The strategy sells below the first EMA when the position has been above it. By default it will not always sell but checks for different conditions (mentioned earlier in this description). You can change this behavior.
Buy modes pivot
By default the strategy buys between pivots as long as there has been a pivot crossover and EMAs crossover recently or price is still above it. You can change the behavior so it only buys on pivot crossovers or pivot crossover confirmations. Backtesting on the default setup shows decreased performance but for other strategy variations and pivot setups this feature can be useful since many scalpers do not buy between pivots.
Strict mode
There is a strict mode that adds extra checks such as not trading when there is no next low or high pivot, requiring a VWAP uptrend only and minimum candle percentages. This mode is for analyzing history and seeing performance during these conditions. It is worth it to create a separate alert for strict mode so you are aware of these conditions during trading. The deep backtests improved with these setting but past performance does not guarantee future results.
In the strict mode section you can override the stop, minimum ATRP, set up a minimum percentage, only trade VWAP uptrends and to not trade candles without a wick.
A summary and some extra detail
At the time of release only long trades are supported
The strategy is meant for quick scalping but one might find other uses for it
Enable extended hours on intraday charts so it captures more pivots
It does not trade extended hours (pre and post market) since options do not trade during those times
real-time data is recommended and required if a symbol has delayed data by default
You can configure that it trades minutes after market open and hard sells minutes after market open
The entries have a specific label text, example: "833 LE1 / 569.71 / P:569.8". This means: / / . The condition number is only for development/debug purposes for me when you have an issue.
The strategy cannot be tweaked to work on multiple symbols and timeframes with a single config. So you will have to make a config for every timeframe and symbol. I recommend using the Indicator Templates feature of TradingView. This way you can save the settings per timeframe and symbol
The strategy is per default config very dependent on (trailing) stops because it trades between pivots too. It wants that a pivot and EMA crossover has happened more recently than a crossunder. But you can change this behavior to always force crossover buys and crossunder sells.
It’s recommended to set up alerts to notify you of entry and exit signals. Watching the chart alone might cause you to miss trades, especially in fast-moving markets.
Only a max of 500 lines can be rendered on the chart, but the strategy will function with more under the hood. When you exceed 500 you will notice the beginning of the chart has no pivots, but beneath everything functions for backtesting.
Changing settings
Changing the settings for a different symbol and/or timeframe can be a challenging task. Here's a how-to you could use the first time to help you get going:
Set commission and slippage to 0. I prefer to do this so it is more clear whether you are balancing on break-even trades
Enable the pivot timeframe equal or above your chart timeframe. Avoid repainting as discussed earlier by choosing timeframes that align with the same timeframe
Set all volume, ATR, stop, profit takers and trail values to 0
Make sure strict mode is disabled at the bottom of the settings
You now have a clean state and you should see the backtest results purely based on pivot and EMA conditions
Tweak the stop and profit taker, beginning with the simple values and then ATRP threshold
At the last moment tweak the trailing stops. Tight trailing stops create an unrealistic backtest so you will need to tweak them based on real-time behavior of the symbol you're using which you will have to monitor during signals while the market is open. The default values are low (2m intraday SPY). Only with the bar magnifier feature it is somewhat possible to tweak realistic with history data. The tighter they are, the more unrealistic your backtest results. As a starting point, set the trailing stop low and find the highest activation level that doesn't change the results drastically, then increase the stop to the value you think reflects real-time behavior.
Keep refining by testing it during real-time behavior. Does it exit too early according to your own judgment? You need to increase the stop and maybe the activation level.
I hope you will find this useful!
DISCLAIMER
Trading is risky & most day traders lose money. This indicator is purely for informational & educational purposes only. Past performance does not guarantee future results.
Chande Momentum Oscillator StrategyThe Chande Momentum Oscillator (CMO) Trading Strategy is based on the momentum oscillator developed by Tushar Chande in 1994. The CMO measures the momentum of a security by calculating the difference between the sum of recent gains and losses over a defined period. The indicator offers a means to identify overbought and oversold conditions, making it suitable for developing mean-reversion trading strategies (Chande, 1997).
Strategy Overview:
Calculation of the Chande Momentum Oscillator (CMO):
The CMO formula considers both positive and negative price changes over a defined period (commonly set to 9 days) and computes the net momentum as a percentage.
The formula is as follows:
CMO=100×(Sum of Gains−Sum of Losses)(Sum of Gains+Sum of Losses)
CMO=100×(Sum of Gains+Sum of Losses)(Sum of Gains−Sum of Losses)
This approach distinguishes the CMO from other oscillators like the RSI by using both price gains and losses in the numerator, providing a more symmetrical measurement of momentum (Chande, 1997).
Entry Condition:
The strategy opens a long position when the CMO value falls below -50, signaling an oversold condition where the price may revert to the mean. Research in mean-reversion, such as by Poterba and Summers (1988), supports this approach, highlighting that prices often revert after sharp movements due to overreaction in the markets.
Exit Conditions:
The strategy closes the long position when:
The CMO rises above 50, indicating that the price may have become overbought and may not provide further upside potential.
Alternatively, the position is closed 5 days after the buy signal is triggered, regardless of the CMO value, to ensure a timely exit even if the momentum signal does not reach the predefined level.
This exit strategy aligns with the concept of time-based exits, reducing the risk of prolonged exposure to adverse price movements (Fama, 1970).
Scientific Basis and Rationale:
Momentum and Mean-Reversion:
The strategy leverages the well-known phenomenon of mean-reversion in financial markets. According to research by Jegadeesh and Titman (1993), prices tend to revert to their mean over short periods following strong movements, creating opportunities for traders to profit from temporary deviations.
The CMO captures this mean-reversion behavior by monitoring extreme price conditions. When the CMO reaches oversold levels (below -50), it signals potential buying opportunities, whereas crossing overbought levels (above 50) indicates conditions for selling.
Market Efficiency and Overreaction:
The strategy takes advantage of behavioral inefficiencies and overreactions, which are often the drivers behind sharp price movements (Shiller, 2003). By identifying these extreme conditions with the CMO, the strategy aims to capitalize on the market’s tendency to correct itself when price deviations become too large.
Optimization and Parameter Selection:
The 9-day period used for the CMO calculation is a widely accepted timeframe that balances responsiveness and noise reduction, making it suitable for capturing short-term price fluctuations. Studies in technical analysis suggest that oscillators optimized over such periods are effective in detecting reversals (Murphy, 1999).
Performance and Backtesting:
The strategy's effectiveness is confirmed through backtesting, which shows that using the CMO as a mean-reversion tool yields profitable opportunities. The use of time-based exits alongside momentum-based signals enhances the reliability of the strategy by ensuring that trades are closed even when the momentum signal alone does not materialize.
Conclusion:
The Chande Momentum Oscillator Trading Strategy combines the principles of momentum measurement and mean-reversion to identify and capitalize on short-term price fluctuations. By using a widely tested oscillator like the CMO and integrating a systematic exit approach, the strategy effectively addresses both entry and exit conditions, providing a robust method for trading in diverse market environments.
References:
Chande, T. S. (1997). The New Technical Trader: Boost Your Profit by Plugging into the Latest Indicators. John Wiley & Sons.
Fama, E. F. (1970). Efficient Capital Markets: A Review of Theory and Empirical Work. The Journal of Finance, 25(2), 383-417.
Jegadeesh, N., & Titman, S. (1993). Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency. The Journal of Finance, 48(1), 65-91.
Murphy, J. J. (1999). Technical Analysis of the Financial Markets: A Comprehensive Guide to Trading Methods and Applications. New York Institute of Finance.
Poterba, J. M., & Summers, L. H. (1988). Mean Reversion in Stock Prices: Evidence and Implications. Journal of Financial Economics, 22(1), 27-59.
Shiller, R. J. (2003). From Efficient Markets Theory to Behavioral Finance. Journal of Economic Perspectives, 17(1), 83-104.
Simple RSI stock Strategy [1D] The "Simple RSI Stock Strategy " is designed to long-term traders. Strategy uses a daily time frame to capitalize on signals generated by the Relative Strength Index (RSI) and the Simple Moving Average (SMA). This strategy is suitable for low-leverage trading environments and focuses on identifying potential buy opportunities when the market is oversold, while incorporating strong risk management with both dynamic and static Stop Loss mechanisms.
This strategy is recommended for use with a relatively small amount of capital and is best applied by diversifying across multiple stocks in a strong uptrend, particularly in the S&P 500 stock market. It is specifically designed for equities, and may not perform well in other markets such as commodities, forex, or cryptocurrencies, where different market dynamics and volatility patterns apply.
Indicators Used in the Strategy:
1. RSI (Relative Strength Index):
- The RSI is a momentum oscillator used to identify overbought and oversold conditions in the market.
- This strategy enters long positions when the RSI drops below the oversold level (default: 30), indicating a potential buying opportunity.
- It focuses on oversold conditions but uses a filter (SMA 200) to ensure trades are only made in the context of an overall uptrend.
2. SMA 200 (Simple Moving Average):
- The 200-period SMA serves as a trend filter, ensuring that trades are only executed when the price is above the SMA, signaling a bullish market.
- This filter helps to avoid entering trades in a downtrend, thereby reducing the risk of holding positions in a declining market.
3. ATR (Average True Range):
- The ATR is used to measure market volatility and is instrumental in setting the Stop Loss.
- By multiplying the ATR value by a custom multiplier (default: 1.5), the strategy dynamically adjusts the Stop Loss level based on market volatility, allowing for flexibility in risk management.
How the Strategy Works:
Entry Signals:
The strategy opens long positions when RSI indicates that the market is oversold (below 30), and the price is above the 200-period SMA. This ensures that the strategy buys into potential market bottoms within the context of a long-term uptrend.
Take Profit Levels:
The strategy defines three distinct Take Profit (TP) levels:
TP 1: A 5% from the entry price.
TP 2: A 10% from the entry price.
TP 3: A 15% from the entry price.
As each TP level is reached, the strategy closes portions of the position to secure profits: 33% of the position is closed at TP 1, 66% at TP 2, and 100% at TP 3.
Visualizing Target Points:
The strategy provides visual feedback by plotting plotshapes at each Take Profit level (TP 1, TP 2, TP 3). This allows traders to easily see the target profit levels on the chart, making it easier to monitor and manage positions as they approach key profit-taking areas.
Stop Loss Mechanism:
The strategy uses a dual Stop Loss system to effectively manage risk:
ATR Trailing Stop: This dynamic Stop Loss adjusts based on the ATR value and trails the price as the position moves in the trader’s favor. If a price reversal occurs and the market begins to trend downward, the trailing stop closes the position, locking in gains or minimizing losses.
Basic Stop Loss: Additionally, a fixed Stop Loss is set at 25%, limiting potential losses. This basic Stop Loss serves as a safeguard, automatically closing the position if the price drops 25% from the entry point. This higher Stop Loss is designed specifically for low-leverage trading, allowing more room for market fluctuations without prematurely closing positions.
to determine the level of stop loss and target point I used a piece of code by RafaelZioni, here is the script from which a piece of code was taken
Together, these mechanisms ensure that the strategy dynamically manages risk while offering robust protection against significant losses in case of sharp market downturns.
The position size has been estimated by me at 75% of the total capital. For optimal capital allocation, a recommended value based on the Kelly Criterion, which is calculated to be 59.13% of the total capital per trade, can also be considered.
Enjoy !
Overnight Positioning w EMA - Strategy [presentTrading]I've recently started researching Market Timing strategies, and it’s proving to be quite an interesting area of study. The idea of predicting optimal times to enter and exit the market, based on historical data and various indicators, brings a dynamic edge to trading. Additionally, it is integrated with the 3commas bot for automated trade execution.
I'm still working on it. Welcome to share your point of view.
█ Introduction and How it is Different
The "Overnight Positioning with EMA " is designed to capitalize on market inefficiencies during the overnight trading period. This strategy takes a position shortly before the market closes and exits shortly after it opens the following day. What sets this strategy apart is the integration of an optional Exponential Moving Average (EMA) filter, which ensures that trades are aligned with the underlying trend. The strategy provides flexibility by allowing users to select between different global market sessions, such as the US, Asia, and Europe.
It is integrated with the 3commas bot for automated trade execution and has a built-in mechanism to avoid holding positions over the weekend by force-closing positions on Fridays before the market closes.
BTCUSD 20 mins Performance
█ Strategy, How it Works: Detailed Explanation
The core logic of this strategy is simple: enter trades before market close and exit them after market open, taking advantage of potential price movements during the overnight period. Here’s how it works in more detail:
🔶 Market Timing
The strategy determines the local market open and close times based on the selected market (US, Asia, Europe) and adjusts entry and exit points accordingly. The entry is triggered a specific number of minutes before market close, and the exit is triggered a specific number of minutes after market open.
🔶 EMA Filter
The strategy includes an optional EMA filter to help ensure that trades are taken in the direction of the prevailing trend. The EMA is calculated over a user-defined timeframe and length. The entry is only allowed if the closing price is above the EMA (for long positions), which helps to filter out trades that might go against the trend.
The EMA formula:
```
EMA(t) = +
```
Where:
- EMA(t) is the current EMA value
- Close(t) is the current closing price
- n is the length of the EMA
- EMA(t-1) is the previous period's EMA value
🔶 Entry Logic
The strategy monitors the market time in the selected timezone. Once the current time reaches the defined entry period (e.g., 20 minutes before market close), and the EMA condition is satisfied, a long position is entered.
- Entry time calculation:
```
entryTime = marketCloseTime - entryMinutesBeforeClose * 60 * 1000
```
🔶 Exit Logic
Exits are triggered based on a specified time after the market opens. The strategy checks if the current time is within the defined exit period (e.g., 20 minutes after market open) and closes any open long positions.
- Exit time calculation:
exitTime = marketOpenTime + exitMinutesAfterOpen * 60 * 1000
🔶 Force Close on Fridays
To avoid the risk of holding positions over the weekend, the strategy force-closes any open positions 5 minutes before the market close on Fridays.
- Force close logic:
isFriday = (dayofweek(currentTime, marketTimezone) == dayofweek.friday)
█ Trade Direction
This strategy is designed exclusively for long trades. It enters a long position before market close and exits the position after market open. There is no shorting involved in this strategy, and it focuses on capturing upward momentum during the overnight session.
█ Usage
This strategy is suitable for traders who want to take advantage of price movements that occur during the overnight period without holding positions for extended periods. It automates entry and exit times, ensuring that trades are placed at the appropriate times based on the market session selected by the user. The 3commas bot integration also allows for automated execution, making it ideal for traders who wish to set it and forget it. The strategy is flexible enough to work across various global markets, depending on the trader's preference.
█ Default Settings
1. entryMinutesBeforeClose (Default = 20 minutes):
This setting determines how many minutes before the market close the strategy will enter a long position. A shorter duration could mean missing out on potential movements, while a longer duration could expose the position to greater price fluctuations before the market closes.
2. exitMinutesAfterOpen (Default = 20 minutes):
This setting controls how many minutes after the market opens the position will be exited. A shorter exit time minimizes exposure to market volatility at the open, while a longer exit time could capture more of the overnight price movement.
3. emaLength (Default = 100):
The length of the EMA affects how the strategy filters trades. A shorter EMA (e.g., 50) reacts more quickly to price changes, allowing more frequent entries, while a longer EMA (e.g., 200) smooths out price action and only allows entries when there is a stronger underlying trend.
The effect of using a longer EMA (e.g., 200) would be:
```
EMA(t) = +
```
4. emaTimeframe (Default = 240):
This is the timeframe used for calculating the EMA. A higher timeframe (e.g., 360) would base entries on longer-term trends, while a shorter timeframe (e.g., 60) would respond more quickly to price movements, potentially allowing more frequent trades.
5. useEMA (Default = true):
This toggle enables or disables the EMA filter. When enabled, trades are only taken when the price is above the EMA. Disabling the EMA allows the strategy to enter trades without any trend validation, which could increase the number of trades but also increase risk.
6. Market Selection (Default = US):
This setting determines which global market's open and close times the strategy will use. The selection of the market affects the timing of entries and exits and should be chosen based on the user's preference or geographic focus.
