Liquidity strategy tester [Influxum]This tool is based on the concept of liquidity. It includes 10 methods for identifying liquidity in the market. Although this tool is presented as a strategy, we see it more as a data-gathering instrument.
Warning: This indicator/strategy is not intended to generate profitable strategies. It is designed to identify potential market advantages and help with identifying effective entry points to capitalize on those advantages.
Once again, we have advanced the methods of effectively searching for liquidity in the market. With strategies, defined by various entry methods and risk management, you can find your edge in the market. This tool is backed by thorough testing and development, and we plan to continue improving it.
In its current form, it can also be used to test well-known ICT or Smart Money concepts. Using various methods, you can define market structure and identify areas where liquidity is located.
Fair Value Gaps - one of the entry signal options is fair value gaps, where an imbalance between buyers and sellers in the market can be expected.
Time and Price Theory - you can test this by setting liquidity from a specific session and testing entries as that liquidity is grabbed
Judas Swing - can be tested as a market reversal after a breakout during the first hours of trading.
Power of Three - accumulation can be observed as the market moving within a certain range, identified as cluster liquidity in our tool, manipulation occurs with the break of liquidity, and distribution is the direction of the entry.
🟪 Methods of Identifying Liquidity
Pivot Liquidity
This refers to liquidity formed by local extremes – the highest or lowest prices reached in the market over a certain period. The period is defined by a pivot number and determines how many candles before and after the high/low were higher/lower. Simply put, the pivot number represents the number of adjacent candles to the left and right, with a lower high for a pivot high and a higher low for a pivot low. The higher the number, the more significant the high/low is. Behind these local market extremes, we expect to find orders waiting for breakout as well as stop-losses.
Gann Swing
Similar to pivot liquidity, Gann swing identifies significant market points. However, instead of candle highs and lows, it focuses on the closing prices. A Gann swing is formed when a candle closes above (or below) several previous closes (the number is again defined by a strength parameter).
Percentage Change
Apart from ticks, percentages are also a key unit of market movement. In the search for liquidity, we monitor when a local high or low is formed. For liquidity defined by percentage change, a high must be a certain percentage higher than the last low to confirm a significant high. Similarly, a low must be a defined percentage away from the last significant high to confirm a new low. With the right percentage settings, you can eliminate market noise.
Session Range (3x)
Session range is a popular concept for finding liquidity, especially in smart money concepts (SMC). You can set up liquidity visualization for the Asian, London, or New York sessions – or even all three at once. This tool allows you to work with up to three sessions, so you can easily track how and if the market reacts to liquidity grabs during these sessions.
Tip for traders: If you want to see the reaction to liquidity grab during a specific session at a certain time (e.g., the well-known killzone), you can set the Trading session in this tool to the exact time where you want to look for potential entries.
Unfinished Auction
Based on order flow theory, an unfinished auction occurs when the market reverses sharply without filling all pending orders. In price action terms, this can be seen as two candles at a local high or low with very similar or identical highs/lows. The maximum difference between these values is defined as Tolerance, with the default setting being 3 ticks. This setting is particularly useful for filtering out noise during slower market periods, like the Asian session.
Double Tops and Bottoms
A very popular concept not only from smart money concepts but also among price pattern traders is the double bottom and double top. This occurs when the market stops and reverses at a certain price twice in a row. In the tool, you can set how many candles apart these bottoms/tops can be by adjusting the Length parameter. According to some theories, double bottoms are more effective when there is a significant peak between the two bottoms. You can set this in the tool as the Swing value, which defines how large the movement (expressed in ticks) must be between the two peaks/bottoms. The final parameter you can adjust is Tolerance, which defines the possible price difference between the two peaks/bottoms, also expressed in ticks.
Range or Cluster Liquidity
When the market stays within a certain price range, there’s a chance that breakout orders and stop-losses are accumulating outside of this range. Our tool defines ranges in two ways:
Candle balance calculates the average price within a candle (open, high, low, and close), and it defines consolidation when the centers of candles are within a certain distance from each other.
Overlap confirms consolidation when a candle overlaps with the previous one by a set percentage.
Daily, Weekly, and Monthly Highs or Lows
These options simply define liquidity as the previous day’s, week’s, or month’s highs or lows.
Visual Settings
You can easily adjust how liquidity is displayed on the chart, choosing line style, color, and thickness. To display only uncollected liquidity, select "Delete grabbed liquidity."
Liquidity Duration
This setting allows you to control how long liquidity areas remain valid. You can cancel liquidity at the end of the day, the second day, or after a specific number of candles.
🟪 Strategy
Now we come to the part of working with strategies.
Max # of bars after liquidity grab – This parameter allows you to define how many candles you can search for entry signals from the moment liquidity is grabbed. If you are using engulfing as an entry signal, which consists of 2 candles, keep in mind that this number must be at least 2. In general, if you want to test a quick and sharp reaction, set this number as low as possible. If you want to wait for a structural change after the liquidity grab, which may require more candles, set the number a bit higher.
🟪 Strategy - entries
In this section, we define the signals or situations where we can enter the market after liquidity has been taken out.
Liquidity grab - This setup triggers a trade immediately after liquidity is grabbed, meaning the trade opens as the next candle forms.
Close below, close above - This refers to situations where the price closes below liquidity, but then reverses and closes above liquidity again, suggesting the liquidity grab was a false breakout.
Over bar - This occurs when the entire candle (high and low) passes beyond the liquidity level but then experiences a pullback.
Engulfing - A popular price action pattern that is included in this tool.
2HL - weak, medium, strong - A variation of a popular candlestick pattern.
Strong bar - A strong reactionary candle that forms after a liquidity grab. If liquidity is grabbed at a low, this would be a strong long candle that closes near its high and is significantly larger compared to typical volatility.
Naked bar - A candlestick pattern we’ve tested that serves as a good confirmation of market movement.
FVG (Fair Value Gap) - A currently popular concept. This is the only signal with additional settings. “Pending FVG order valid” means if a fair value gap forms after a liquidity grab, a limit order is placed, which remains valid for a set number of candles. “FVG minimal tick size” allows you to filter based on the gap size, measured in ticks. “GAP entry model” lets you decide whether to place the limit order at the gap close or its edge.
🟪 Strategy - General
Long, short - You can choose whether to focus on long or short trades. It’s interesting to see how long and short trades yield different results across various markets.
Pyramiding - By default, the tool opens only one trade at a time. If a new signal arises while a trade is open, it won’t enter another position unless the pyramiding box is checked. You also need to set the maximum number of open trades in the Properties.
Position size - Simply set the size of the traded position.
🟪 Strategy - Time
In this section, you can set time parameters for the strategy being tested.
Test since year - As the name implies, you can limit the testing to start from a specific year.
Trading session - Define the trading session during which you want to test entries. You can also visualize the background (BG) for confirmation.
Exclude session - You can set a session period during which you prefer not to search for trades. For example, when the New York session opens, volatility can sharply increase, potentially reducing the long-term success rate of the tested setup.
🟪 Strategy - Exits
This section lets you define risk management rules.
PT & SL - Set the profit target (PT) and stop loss (SL) here.
Lowest/highest since grab - This option sets the stop loss at the lowest point after a liquidity grab at a low or at the highest point after a liquidity grab at a high. Since markets usually overshoot during liquidity grabs, it’s good practice to place the stop loss at the furthest point after the grab. You can also set your risk-reward ratio (RRR) here. A value of 1 sets an RRR of 1:1, 2 means 2:1, and so on.
Lowest/highest last # bars - Similar to the previous option, but instead of finding the extreme after a liquidity grab, it identifies the furthest point within the last number of candles. You can set how far back to look using the # bars field (for an engulfing pattern, 2 is optimal since it’s made of two candles, and the stop loss can be placed at the edge of the engulfing pattern). The RRR setting works the same way as in the previous option.
Other side liquidity grab - If this option is checked, the trade will exit when liquidity is grabbed on the opposite side (i.e., if you entered on a liquidity grab at a low, the trade will exit when liquidity is grabbed at a high).
Exit after # bars - A popular exit strategy where you close the position after a set number of candles.
Exit after # bars in profit - This option exits the trade once the position is profitable for a certain number of consecutive candles. For example, if set to 5, the position will close when 5 consecutive candles are profitable. You can also set a maximum number of candles (in the max field), ensuring the trade is closed after a certain time even if the profit condition hasn’t been met.
🟪 Alerts
Alerts are a key tool for traders to ensure they don’t miss trading opportunities. They also allow traders to manage their time effectively. Who would want to sit in front of the computer all day waiting for a trading opportunity when they could be attending to other matters? In our tool, you currently have two options for receiving alerts:
Liquidity grabs alert – if you enable this feature and set an alert, the alert will be triggered every time a candle on the current timeframe closes and intersects with the displayed liquidity line.
Entry signals alert – this feature triggers an alert when a signal for entry is generated based on the option you’ve selected in the Entry type. It’s an ideal way to be notified only when a trading opportunity appears according to your predefined rules.
Indicateurs et stratégies
DCA, Support and Resistance with RSI and Trend FilterThis script is based on
script from Kieranj with added pyramiding and DCA
The buy condition (buyCondition) is triggered when the RSI crosses above the oversold threshold (ta.crossover(rsi, oversoldThreshold)), the trend filter confirms an uptrend (isUptrend is true), and the close price is greater than or equal to the support level (close >= supportLevel).
The partial sell condition (sellCondition) is triggered when the RSI crosses below the overbought threshold (ta.crossunder(rsi, overboughtThreshold)) and profit goal is reached, the trend filter confirms a downtrend (isUptrend is false), and the close price is less than or equal to the resistance level (close <= resistanceLevel).
Full sell will be triggered if trend is broken and profit goal is reached
With this implementation, the signals will only be generated in the direction of the trend on the 4-hour timeframe. The trend is considered up when the 50-period SMA is below the 200-period SMA (ta.sma(trendFilterSource, 50) < ta.sma(trendFilterSource, 200)).
Pyramiding should be activated, values like 100, so every DCA step should be around 1%
i have best results on 5 min charts
TradeCreator Pro - Moving Averages, RSI, Volume, Trends, Levels█ Overview
TradeCreator Pro is designed to help you build successful trades by streamlining the processes of trade planning, evaluation, and execution. With a focus on data accuracy, speed, precision, and ease of use, this all-in-one tool assists in identifying optimal entry and exit points, calculating risk/reward ratios, and executing trades efficiently. Whether you’re a beginner or an experienced trader, TradeCreator Pro empowers you to make informed, data-driven decisions with real-time signals and fully customizable settings.
█ Key Benefits & Use Cases
TradeCreator Pro is designed to help you effortlessly discover profitable trades by evaluating and testing multiple setups across different assets and timeframes. Key use cases include:
Quick Strategy Testing: Rapidly test multiple setups and strategies, gaining immediate insights into their potential outcomes.
Risk/Reward Evaluation: Quickly identify which trade ideas are worth pursuing based on their profitability and associated risk.
Multi-Timeframe Testing: Seamlessly test the same trading setup across various timeframes and tickers.
Backtesting: Analyze the historical performance of specific setups to gauge their effectiveness.
Key Level Identification: Instantly spot critical support and resistance levels, improving your decision-making process.
Custom Alerts: Set personalized notifications for key levels, ensuring timely action on potential trade opportunities.
█ Core Features
Dashboard: A real-time view of critical metrics such as trend strength, support/resistance levels, volume profiles, RSI divergence, and trade scoring. Designed to provide a comprehensive snapshot of your trading environment and potential trading outcome.
Trend Analysis: Detect prevailing trends by analyzing multiple moving averages, support/resistance zones, volume profile and linear regressions for RSI and closing prices.
Support & Resistance Identification: Automatically identify support and resistance levels.
Volume Profile: Visualize volume profile and its point of control across support/resistance ranges, helping you spot key consolidation areas.
RSI & Price Divergence Detection: Identify potential divergences between RSI and price through linear regressions, providing valuable trade signals.
Risk Management Tools: Set equity loss levels based on specified leverage, allowing you to manage risk effectively for both long and short trades.
Entry & Exit Recommendations: Identify multiple options for optimal entry and exit levels based on current market conditions.
Trade Scoring: Score each trade setup on a 0-100 scale, factoring in potential ROI, ROE, P&L, and Risk-Reward Ratios to ensure high-quality trade execution.
Dynamic Execution & Monitoring: Benefit from multi-stage exit strategies, dynamic trailing stop losses, and the ability to backtest setups with historical data.
Alerts & Automation: Customize alerts for key market movements and opt for manual or automated trading through TradingView’s supported partners.
█ How to Use
Installation: Add TradeCreator Pro to your TradingView chart.
Trend Adjustment: The system automatically detects the current market trend, but you can fine-tune all trend detection parameters as needed.
Trading Parameter Configuration: Customize entry, exit, profitability, and risk-reward settings to match your trading style.
Entry and Exit Level Refinement: Use the automated suggestions, or choose from conceptual or arbitrary levels for greater control.
Stop Loss and Profit Target Fine-Tuning: Apply the system’s recommendations or adjust them by selecting from multiple available options.
Backtest Setup: Run the backtester to analyze past performance and assess how the strategy would have performed historically.
Set Alerts: Stay informed by setting alerts to notify you when a trade setup is triggered.
█ Notes
The first time you apply the indicator to a chart, it may take a few moments to compile. If it takes too long, switch timeframes temporarily to restart the process.
█ Risk Disclaimer
Trading in financial markets involves significant risk and is not suitable for all investors. The use of TradeCreator Pro, as well as any other tools provided by AlgoTrader Pro, is purely for informational and educational purposes. These tools are not intended to provide financial advice, and past performance is not indicative of future results. It is essential to do your own research, practice proper risk management, and consult with a licensed financial advisor before making any trading decisions. AlgoTrader Pro is not responsible for any financial losses you may incur through the use of these tools.
Nifty scalping 3 minutes options on Dhan
Strategy Description for Publishing: Nifty Scalping 3 Minutes Options on Dhan
Overview:
The Nifty Scalping 3 Minutes Options on Dhan strategy is an enhanced version tailored for trading Nifty Options, building on the core logic used in the previously published Nifty Scalping 3 Minutes Strategy. This strategy provides automated order execution via JSON alerts for seamless integration with the Dhan platform, enabling hands-free options trading.
This system is designed to capture short-term market moves using a combination of technical indicators like the Jurik Moving Average (JMA), Exponential Moving Average (EMA), and Bollinger Bands, while also allowing traders to manage risk effectively with custom inputs for maximum loss per lot and partial profit booking.
For more details on the core logic and performance of the strategy, please refer to our earlier published strategy:
Nifty Scalping 3 Minutes Strategy
Key Features:
JMA and EMA Crossovers: Trades are executed when the Jurik Moving Average (JMA) crosses over (for long trades) or under (for short trades) the Exponential Moving Average (EMA), signaling trend direction.
Price-Volume Spike Detection: Ensures that trades are executed only when significant market activity is detected, avoiding low-momentum conditions. Price-volume relationships are monitored to confirm the strength of market movements.
Bollinger Band Noise Filter: Filters out low-volatility periods by executing trades only when prices break through the upper or lower Bollinger Bands, confirming high volatility.
Customizable Risk Management: Traders can set their own maximum risk per lot (e.g., ₹650), and the strategy adjusts the stop-loss accordingly to ensure that no trade exceeds this threshold.
Partial Profit Booking: A predefined percentage (e.g., 60%) of the position can be booked as profit once the first profit target is reached, with the remaining position trailed using an ATR-based stop.
STBT/BTST Support: The strategy offers the flexibility to carry trades overnight, supporting Sell Today, Buy Tomorrow (STBT) and Buy Today, Sell Tomorrow (BTST).
Time-Based Exit: The strategy automatically closes any open positions by 3:20 PM to avoid the volatile end-of-day market conditions.
Inputs for Traders:
Option Quantity: Select the number of contracts to trade (e.g., 10).
Maximum Risk Per Lot: Set your maximum allowable loss per lot (e.g., ₹650), ensuring that your risk is managed effectively.
Partial Profit Booking Percentage: Define what percentage of your position to book as profit (e.g., 60%) when the first target is hit.
STBT/BTST Option: Choose whether to allow positions to be carried overnight.
Alert Secret Key: Input your secret key for the Dhan platform to trigger automated orders via JSON alerts.
Option Expiry Date: Specify the expiry date for the options being traded.
Trade Logic:
Long Trades: Triggered when JMA crosses above EMA, supported by filters like price-volume spikes and Bollinger Band breakouts. The strategy waits for momentum confirmation before entering long trades, with stop-loss and profit-taking mechanisms in place.
Short Trades: Triggered when JMA crosses below EMA, with confirmation through additional filters to ensure strong market trends before entering short positions.
Risk Management:
Stop-Loss: A dynamic stop-loss is placed for each trade based on the trader's maximum risk per lot. The stop-loss adapts to market conditions using ATR trailing stops to capture further gains as the trade progresses.
Partial Profit Booking: Once the first profit target is hit (2.1x risk for long trades and 2.5x risk for short trades), a percentage of the position is booked as profit, and the remainder is trailed using an ATR stop.
Automation via JSON Alerts:This strategy sends automated JSON alerts to the Dhan platform for seamless execution of orders. The alerts support multi-leg orders for both entry and exit, ensuring that trades are executed efficiently without manual intervention.
Why Use This Strategy?
The Nifty Scalping 3 Minutes Options on Dhan strategy is perfect for traders who want to capitalize on quick market moves in options, backed by strong risk management and automation. With automated alerts, customizable inputs, and advanced technical filters, this strategy is ideal for traders looking to engage in high-probability options trades with minimal effort.
For more detailed information about the underlying logic, you can refer to the previously published Nifty Scalping 3 Minutes Strategy here.
Disclaimer:
This strategy is provided as an educational tool, and we are not affiliated with or sponsored by Dhan. The strategy integrates with the Dhan platform for automated trading, but there is no formal relationship between this strategy and Dhan.
Larry Connors %b Strategy (Bollinger Band)Larry Connors’ %b Strategy is a mean-reversion trading approach that uses Bollinger Bands to identify buy and sell signals based on the %b indicator. This strategy was developed by Larry Connors, a renowned trader and author known for his systematic, data-driven trading methods, particularly those focusing on short-term mean reversion.
The %b indicator measures the position of the current price relative to the Bollinger Bands, which are volatility bands placed above and below a moving average. The strategy specifically targets times when prices are oversold within a long-term uptrend and aims to capture rebounds by buying at relatively low points and selling at relatively high points.
Strategy Rules
The basic rules of the %b Strategy are:
1. Trend Confirmation: The closing price must be above the 200-day moving average. This filter ensures that trades are made in alignment with a longer-term uptrend, thereby avoiding trades against the primary market trend.
2. Oversold Conditions: The %b indicator must be below 0.2 for three consecutive days. The %b value below 0.2 indicates that the price is near the lower Bollinger Band, suggesting an oversold condition.
3. Entry Signal: Enter a long position at the close when conditions 1 and 2 are met.
4. Exit Signal: Exit the position when the %b value closes above 0.8, signaling an overbought condition where the price is near the upper Bollinger Band.
How the Strategy Works
This strategy operates on the premise of mean reversion, which suggests that extreme price movements will revert to the mean over time. By entering positions when the %b value indicates an oversold condition (below 0.2) in a confirmed uptrend, the strategy attempts to capture short-term price rebounds. The exit rule (when %b is above 0.8) aims to lock in profits once the price reaches an overbought condition, often near the upper Bollinger Band.
Who Was Larry Connors?
Larry Connors is a well-known figure in the world of financial markets and trading. He co-authored several influential trading books, including “Short-Term Trading Strategies That Work” and “High Probability ETF Trading.” Connors is recognized for his quantitative approach, focusing on systematic, rules-based strategies that leverage historical data to validate trading edges.
His work primarily revolves around short-term trading strategies, often using technical indicators like RSI (Relative Strength Index), Bollinger Bands, and moving averages. Connors’ methodologies have been widely adopted by traders seeking structured approaches to exploit short-term inefficiencies in the market.
Risks of the Strategy
While the %b Strategy can be effective, particularly in mean-reverting markets, it is not without risks:
1. Mean Reversion Assumption: The strategy is based on the assumption that prices will revert to the mean. In trending or sharply falling markets, this reversion may not occur, leading to sustained losses.
2. False Signals in Choppy Markets: In volatile or sideways markets, the strategy may generate multiple false signals, resulting in whipsaw trades that can erode capital through frequent small losses.
3. No Stop Loss: The basic implementation of the strategy does not include a stop loss, which increases the risk of holding losing trades longer than intended, especially if the market continues to move against the position.
4. Performance During Market Crashes: During major market downturns, the strategy’s buy signals could be triggered frequently as prices decline, compounding losses without the presence of a risk management mechanism.
Scientific References and Theoretical Basis
The %b Strategy relies on the concept of mean reversion, which has been extensively studied in finance literature. Studies by Avellaneda and Lee (2010) and Bouchaud et al. (2018) have demonstrated that mean-reverting strategies can be profitable in specific market environments, particularly when combined with volatility filters like Bollinger Bands. However, the same studies caution that such strategies are highly sensitive to market conditions and often perform poorly during periods of prolonged trends.
Bollinger Bands themselves were popularized by John Bollinger and are widely used to assess price volatility and detect potential overbought and oversold conditions. The %b value is a critical part of this analysis, as it standardizes the position of price relative to the bands, making it easier to compare conditions across different securities and time frames.
Conclusion
Larry Connors’ %b Strategy is a well-known mean-reversion technique that leverages Bollinger Bands to identify buying opportunities in uptrending markets when prices are temporarily oversold. While the strategy can be effective under the right conditions, traders should be aware of its limitations and risks, particularly in trending or highly volatile markets. Incorporating risk management techniques, such as stop losses, could help mitigate some of these risks, making the strategy more robust against adverse market conditions.
Larry Connors RSI 3 StrategyThe Larry Connors RSI 3 Strategy is a short-term mean-reversion trading strategy. It combines a moving average filter and a modified version of the Relative Strength Index (RSI) to identify potential buying opportunities in an uptrend. The strategy assumes that a short-term pullback within a long-term uptrend is an opportunity to buy at a discount before the trend resumes.
Components of the Strategy:
200-Day Simple Moving Average (SMA): The price must be above the 200-day SMA, indicating a long-term uptrend.
2-Period RSI: This is a very short-term RSI, used to measure the speed and magnitude of recent price changes. The standard RSI is typically calculated over 14 periods, but Connors uses just 2 periods to capture extreme overbought and oversold conditions.
Three-Day RSI Drop: The RSI must decline for three consecutive days, with the first drop occurring from an RSI reading above 60.
RSI Below 10: After the three-day drop, the RSI must reach a level below 10, indicating a highly oversold condition.
Buy Condition: All the above conditions must be satisfied to trigger a buy order.
Sell Condition: The strategy closes the position when the RSI rises above 70, signaling that the asset is overbought.
Who Was Larry Connors?
Larry Connors is a trader, author, and founder of Connors Research, a firm specializing in quantitative trading research. He is best known for developing strategies that focus on short-term market movements. Connors co-authored several popular books, including "Street Smarts: High Probability Short-Term Trading Strategies" with Linda Raschke, which has become a staple among traders seeking reliable, rule-based strategies. His research often emphasizes simplicity and robust testing, which appeals to both retail and institutional traders.
Scientific Foundations
The Relative Strength Index (RSI), originally developed by J. Welles Wilder in 1978, is a momentum oscillator that measures the speed and change of price movements. It oscillates between 0 and 100 and is typically used to identify overbought or oversold conditions in an asset. However, the use of a 2-period RSI in Connors' strategy is unconventional, as most traders rely on longer periods, such as 14. Connors' research showed that using a shorter period like 2 can better capture short-term reversals, particularly when combined with a longer-term trend filter such as the 200-day SMA.
Connors' strategies, including this one, are built on empirical research using historical data. For example, in a study of over 1,000 signals generated by this strategy, Connors found that it performed consistently well across various markets, especially when trading ETFs and large-cap stocks (Connors & Alvarez, 2009).
Risks and Considerations
While the Larry Connors RSI 3 Strategy is backed by empirical research, it is not without risks:
Mean-Reversion Assumption: The strategy is based on the premise that markets revert to the mean. However, in strong trending markets, the strategy may underperform as prices can remain oversold or overbought for extended periods.
Short-Term Nature: The strategy focuses on very short-term movements, which can result in frequent trading. High trading frequency can lead to increased transaction costs, which may erode profits.
Market Conditions: The strategy performs best in certain market environments, particularly in stable uptrends. In highly volatile or strongly trending markets, the strategy's performance can deteriorate.
Data and Backtesting Limitations: While backtests may show positive results, they rely on historical data and do not account for future market conditions, slippage, or liquidity issues.
Scientific literature suggests that while technical analysis strategies like this can be effective in certain market conditions, they are not foolproof. According to Lo et al. (2000), technical strategies may show patterns that are statistically significant, but these patterns often diminish once they are widely adopted by traders.
References
Connors, L., & Alvarez, C. (2009). Short-Term Trading Strategies That Work. TradingMarkets Publishing Group.
Lo, A. W., Mamaysky, H., & Wang, J. (2000). Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation. The Journal of Finance, 55(4), 1705-1770.
Wilder, J. W. (1978). New Concepts in Technical Trading Systems. Trend Research
Larry Connors 3 Day High/Low StrategyThe Larry Connors 3 Day High/Low Strategy is a short-term mean-reversion trading strategy that is designed to identify potential buying opportunities when a security is oversold. This strategy is based on the principles developed by Larry Connors, a well-known trading system developer and author.
Key Strategy Elements:
1. Trend Confirmation: The strategy first confirms that the security is in a long-term uptrend by ensuring that the closing price is above the 200-day moving average (condition1). This rule helps filter trades to align with the longer-term trend.
2. Short-Term Pullback: The strategy looks for a short-term pullback by ensuring that the closing price is below the 5-day moving average (condition2). This identifies potential entry points when the price temporarily moves against the longer-term trend.
3. Three Consecutive Lower Highs and Lows:
• The high and low two days ago are lower than those of the day before (condition3).
• The high and low yesterday are lower than those of two days ago (condition4).
• Today’s high and low are lower than yesterday’s (condition5).
These conditions are used to identify a sequence of declining highs and lows, signaling a short-term pullback or oversold condition in the context of an overall uptrend.
4. Entry and Exit Signals:
• Buy Signal: A buy order is triggered when all the above conditions are met (buyCondition).
• Sell Signal: A sell order is executed when the closing price is above the 5-day moving average (sellCondition), indicating that the pullback might be ending.
Risks of the Strategy
1. Mean Reversion Failure: This strategy relies on the assumption that prices will revert to the mean after a short-term pullback. In strong downtrends or during market crashes, prices may continue to decline, leading to significant losses.
2. Whipsaws and False Signals: The strategy may generate false signals, especially in choppy or sideways markets where the price does not follow a clear trend. This can lead to frequent small losses that can add up over time.
3. Dependence on Historical Patterns: The strategy is based on historical price patterns, which do not always predict future price movements accurately. Sudden market news or economic changes can disrupt the pattern.
4. Lack of Risk Management: The strategy as written does not include stop losses or position sizing rules, which can expose traders to larger-than-expected losses if conditions change rapidly.
About Larry Connors
Larry Connors is a renowned trader, author, and founder of Connors Research and TradingMarkets.com. He is widely recognized for his development of quantitative trading strategies, especially those focusing on short-term mean reversion techniques. Connors has authored several books on trading, including “Short-Term Trading Strategies That Work” and “Street Smarts,” co-authored with Linda Raschke. His strategies are known for their systematic, rules-based approach and have been widely used by traders and investment professionals.
Connors’ research often emphasizes the importance of trading with the trend, managing risk, and using statistically validated techniques to improve trading outcomes. His work has been influential in the field of quantitative trading, providing accessible strategies for traders at various skill levels.
References
1. Connors, L., & Raschke, L. (1995). Street Smarts: High Probability Short-Term Trading Strategies.
2. Connors, L. (2009). Short-Term Trading Strategies That Work.
3. Fama, E. F., & French, K. R. (1988). Permanent and Temporary Components of Stock Prices. Journal of Political Economy, 96(2), 246-273.
This strategy and its variations are popular among traders looking to capitalize on short-term price movements while aligning with longer-term trends. However, like all trading strategies, it requires rigorous backtesting and risk management to ensure its effectiveness under different market conditions.
123 Reversal Trading StrategyThe 123 Reversal Trading Strategy is a technical analysis approach that seeks to identify potential reversal points in the market by analyzing price patterns. This Pine Script™ code implements a version of this strategy, and here’s a detailed description:
Strategy Overview
Objective: The strategy aims to identify bullish reversal patterns using the 123 pattern and manage trades with a specified holding period and a 20-day moving average as an additional exit condition.
Key Components:
Holding Period: The number of days to hold a trade is adjustable, with the default set to 7 days.
Moving Average: A 200-day simple moving average (SMA) is used to determine an exitcondition based on the price crossing this average.
Pattern Recognition:
Condition 1: The low of the current day must be lower than the low of the previous day.
Condition 2: The low of the previous day must be lower than the low from three days ago.
Condition 3: The low two days ago must be lower than the low from four days ago.
Condition 4: The high two days ago must be lower than the high three days ago.
Entry Condition: All four conditions must be met for a buy signal.
Exit Condition: The position is closed either after the specified holding period or when the price reaches or exceeds the 200-day moving average.
Relevant Literature
Graham, B., & Dodd, D. L. (1934). Security Analysis. This classic work introduces fundamental analysis and technical analysis principles which are foundational to understanding patterns like the 123 reversal.
Murphy, J. J. (1999). Technical Analysis of the Financial Markets. Murphy provides an extensive overview of technical indicators and chart patterns, including reversal patterns similar to the 123 pattern.
Elder, A. (1993). Trading for a Living. Elder discusses various trading strategies and technical analysis techniques that complement the understanding of reversal patterns and their application in trading.
Risks and Considerations
Pattern Reliability: The 123 reversal pattern, like many technical patterns, is not foolproof. It can generate false signals, especially in volatile or trending markets. This may lead to losses if the pattern does not play out as expected.
Market Conditions: The strategy may perform differently under various market conditions. In strongly trending markets, reversal patterns might not be as reliable.
Lagging Indicators: The use of the 200-day moving average as an exit condition can be considered a lagging indicator. This means it reacts to price movements with a delay, which might result in late exits and missed profit opportunities.
Holding Period: The fixed holding period of 7 days may not be optimal for all market conditions or stocks. It is essential to adjust the holding period based on market dynamics and individual stock behavior.
Overfitting: The parameters used (like the number of days and moving average length) are set based on historical data. Overfitting can occur if these parameters are tailored too specifically to past data, leading to reduced performance in future scenarios.
Conclusion
The 123 Reversal Trading Strategy is designed to identify potential market reversals using specific conditions related to price lows and highs. While it offers a structured approach to trading, it is essential to be aware of its limitations and potential risks. As with any trading strategy, it should be tested thoroughly in various market conditions and adjusted according to the individual trading style and risk tolerance.
TRIN (Arms Index) Trading StrategyThe TRIN (Arms Index), also known as the Short-Term Trading Index, is a technical indicator designed to gauge the internal strength or weakness of the market. It compares the number of advancing and declining stocks to the advancing and declining volume (AD Volume). A TRIN value above 1.0 generally indicates bearish market conditions, while a value below 1.0 suggests bullish market sentiment.
Strategy Rules:
Entry Condition (Long Position): When the TRIN value is above 1.0, the strategy enters a long position, indicating that the market may be oversold, and a potential reversal could occur.
Exit Condition: The strategy exits the long position when the closing price is higher than the previous day’s high, signaling a potential rebound in the market.
This strategy aims to capitalize on short-term market inefficiencies by entering trades during periods of potential market weakness and exiting when signs of recovery appear.
How the TRIN Index Works:
The TRIN is calculated as follows:
TRIN=Advancing Issues / Declining IssuesAdvancing Volume / Declining Volume
TRIN=Advancing Volume / Declining VolumeAdvancing Issues / Declining Issues
A TRIN value above 1.0 indicates that the market is potentially oversold (more declining stocks with higher volume), while a value below 1.0 suggests the market may be overbought (more advancing stocks with higher volume) .
Empirical Evidence:
Market Sentiment Indicator: The TRIN has been widely used as a sentiment indicator. Research by Zweig (1997) suggests that extreme TRIN values can serve as a contrarian signal, indicating potential turning points in the market. For instance, a TRIN above 2.0 is often considered a sign of panic selling, which can precede a market bottom .
Overbought/Oversold Conditions: Studies have shown that indicators like TRIN, which measure market breadth and volume, can be effective in identifying overbought and oversold conditions. According to Fama and French (1988), market sentiment indicators that consider both price and volume data can offer insights into future price movements .
Risks and Limitations:
False Signals:
One of the primary risks of using the TRIN-based strategy is the possibility of false signals. A TRIN value above 1.0 does not always guarantee a market rebound, especially in sustained bearish trends. In such cases, the strategy might enter long positions prematurely, leading to losses.
Research by Brock, Lakonishok, and LeBaron (1992) found that while market indicators like TRIN can be useful, they are not foolproof and can generate multiple false positives, particularly in volatile markets .
Market Regimes:
The effectiveness of the TRIN index can vary depending on the market regime. In strongly trending markets, either bullish or bearish, the TRIN may not provide reliable reversal signals, and relying on it could result in trades that go against the prevailing trend. For instance, during strong bear markets, the TRIN may frequently remain above 1.0, leading to multiple losing trades as the market continues to decline.
Short-Term Focus:
The TRIN strategy is inherently short-term focused, aiming to capture quick market reversals. This makes it sensitive to market noise and less effective for longer-term investors. Moreover, short-term trading strategies often require more frequent adjustments and can incur higher transaction costs, which may erode profitability over time.
Liquidity and Execution Risk:
Since the TRIN strategy requires entering and exiting trades based on short-term market movements, it is vulnerable to liquidity and execution risks. In fast-moving markets, the execution of trades may be delayed, leading to slippage and potentially unfavorable entry or exit points.
Conclusion:
The TRIN (Arms Index) Trading Strategy can be an effective tool for traders looking to capitalize on short-term market inefficiencies and potential reversals. However, it is important to recognize the risks associated with this strategy, including false signals, sensitivity to market regimes, and execution risks. Traders should employ proper risk management techniques and consider combining the TRIN with other indicators to improve the robustness of the strategy.
While the TRIN provides valuable insights into market sentiment, it is not a standalone solution and should be used in conjunction with a broader trading plan that takes into account both technical and fundamental analysis.
References:
Arms, Richard W. "Volume Adjusted Moving Averages." Technical Analysis of Stocks & Commodities, 1993.
Zweig, Martin. Winning on Wall Street. Warner Books, 1997.
Fama, Eugene F., and Kenneth R. French. "Permanent and Temporary Components of Stock Prices." Journal of Political Economy, 1988.
Brock, William, Josef Lakonishok, and Blake LeBaron. "Simple Technical Trading Rules and the Stochastic Properties of Stock Returns." Journal of Finance, 1992.
Averaging Down Strategy1. Averaging Down:
Definition: "Averaging Down" is a strategy in which an investor buys more shares of a declining asset, thus lowering the average purchase price. The main idea is that, by averaging down, the investor can recover faster when the price eventually rebounds.
Risk Considerations: This strategy assumes that the asset will recover in value. If the price continues to decline, however, the investor may suffer larger losses. Academic research highlights the psychological bias of loss aversion that often leads investors to engage in averaging down, despite the increased risk (Barberis & Huang, 2001).
2. RSI (Relative Strength Index):
Definition: The RSI is a momentum oscillator that measures the speed and change of price movements. It ranges from 0 to 100 and is commonly used to identify overbought or oversold conditions. A reading below 30 (or in this case, 35) typically indicates an oversold condition, which might suggest a potential buying opportunity (Wilder, 1978).
Risk Considerations: RSI-based strategies can produce many false signals in range-bound or choppy markets, where prices do not exhibit strong trends. This can lead to multiple losing trades and an overall negative performance (Gencay, 1998).
3. Combination of RSI and Price Movement:
Approach: The combination of RSI for entry signals and price movement (previous day's high) for exit signals aims to capture short-term market reversals. This hybrid approach attempts to balance momentum with price confirmation.
Risk Considerations: While this combination can work well in trending markets, it may struggle in volatile or sideways markets. Additionally, a significant risk of averaging down is that the trader may continue adding to a losing position, which can exacerbate losses if the price keeps falling.
Risk Warnings:
Increased Losses Through Averaging Down:
Averaging down involves buying more of a falling asset, which can increase exposure to downside risk. Studies have shown that this approach can lead to larger losses when markets continue to decline, especially during prolonged bear markets (Statman, 2004).
A key risk is that this strategy may lead to significant capital drawdowns if the price of the asset does not recover as expected. In the worst-case scenario, this can result in a total loss of the invested capital.
False Signals with RSI:
RSI-based strategies are prone to generating false signals, particularly in markets that do not exhibit strong trends. For example, Gencay (1998) found that while RSI can be effective in certain conditions, it often fails in choppy or range-bound markets, leading to frequent stop-outs and drawdowns.
Psychological Bias:
Behavioral finance research suggests that the "Averaging Down" strategy may be influenced by loss aversion, a bias where investors prefer to avoid losses rather than achieve gains (Kahneman & Tversky, 1979). This can lead to poor decision-making, as investors continue to add to losing positions in the hope of a recovery.
Empirical Studies:
Gencay (1998): The study "The Predictability of Security Returns with Simple Technical Trading Rules" found that technical indicators like RSI can provide predictive value in certain markets, particularly in volatile environments. However, they are less reliable in markets that lack clear trends.
Barberis & Huang (2001): Their research on behavioral biases, including loss aversion, explains why investors are often tempted to average down despite the risks, as they attempt to avoid realizing losses.
Statman (2004): In "The Diversification Puzzle," Statman discusses how strategies like averaging down can increase risk exposure without necessarily improving long-term returns, especially if the underlying asset continues to perform poorly.
Conclusion:
The "Averaging Down Strategy with RSI" combines elements of technical analysis with a psychologically-driven averaging down approach. While the strategy may offer opportunities in trending or oversold markets, it carries significant risks, particularly in volatile or declining markets. Traders should be cautious when using this strategy, ensuring they manage risk effectively and avoid overexposure to a losing position.
Nifty scalping 3 minutesOverview:
The "Nifty Scalping 3 Minutes" strategy is a uniquely tailored trading system for Nifty Futures traders, with a clear focus on capital preservation, dynamic risk management, and high-probability trade entries. This strategy uses unique combination of standard technical indicators like Jurik Moving Average (JMA), Exponential Moving Average (EMA), and Bollinger Bands, but it truly stands out through its Price-Volume Spike Detection system—a unique mechanism designed to trigger trades only during periods of high momentum and market participation. The strategy also incorporates robust risk management, ensuring that traders minimize losses while maximizing profits. in complete back test range max drawdown is less than 1%
Scalping Approach and Requirements:
The strategy focuses on quick in and out trades, aiming to capture small, quick profits during periods of heightened market activity. For optimal performance, traders should have ₹2,00,000 or more in capital available per trade. The dynamic lot calculation and risk controls require this level of capital to function effectively.
Small, frequent trades are the focus, and the strategy is ideal for traders comfortable with high-frequency executions. Traders with insufficient capital or those not comfortable with frequent trades may find this strategy unsuitable.
Default Properties for Publication:
Initial Capital: ₹2,000,000
Lot Size: 25 contracts (adjusted dynamically based on available margin)
Stop-Loss: Risk per trade capped at 1% of equity.
Slippage and Commission: Realistic values are factored into the backtesting.
Key Feature: Price-Volume Spike Detection
1. Condition: Trades are executed only when there is a significant price spike confirmed by a volume spike. The candle width is calculated by multiplying the price change (difference between the candle's open and close) by the volume, and this result is compared to a 126-period average of both price and volume.
A trade is triggered when the current price-volume spike exceeds this average by a preset volume multiplier (default set at 3). This ensures that both the price change and volume are unusually strong compared to normal market behavior.
2. Reasoning: Many traders fail to incorporate the relationship between price movement and volume effectively. By using this Price-Volume Spike Detection mechanism, the strategy ensures that it only enters trades during periods of strong market momentum when both price and volume confirm a real market move, not just noise or small fluctuations.
The 126-period moving average of volume is chosen specifically because it represents a complete trading session on the 3-minute chart. This ensures that the volume spike is compared against a realistic baseline of daily activity, making the detection more robust and reliable.
The volume multiplier allows flexibility in determining the threshold for a significant spike, enabling users to fine-tune the strategy according to their risk tolerance and market conditions.
Trade Placement Logic:
1. Trend Confirmation with JMA and EMA:
Condition: The strategy will only consider entering a trade when JMA crosses above EMA for a long trade or JMA crosses below EMA for a short trade.
Reasoning: The JMA is used for its low lag and responsiveness, allowing it to capture early trends, while the EMA adds a level of confirmation by weighing recent price action more heavily. This dual confirmation ensures that trades are entered only when a solid trend is in place.
2. Bollinger Bands for Volatility Breakouts:
Condition: In addition to the JMA-EMA crossover, the price must break outside the Bollinger Bands—above the upper band for long trades, or below the lower band for short trades.
Reasoning: Bollinger Bands are a volatility indicator. By requiring a price breakout beyond the bands, the strategy ensures that trades are placed during periods of high volatility, avoiding low-momentum, sideways markets.
3. Volume and Price Confirmation (Price-Volume Spike Detection):
Condition: A trade is only triggered if the price-volume spike condition is met. This ensures that the market move is backed by strong volume and that the price change is significant relative to the recent average activity.
Reasoning: This condition filters out low-volume environments where price movements are more likely to reverse or stall. By waiting for a spike in both price and volume, the strategy ensures that it enters trades during high-momentum periods, where follow-through is more likely.
Exit Logic and Risk Management:
1. Stop-Loss (SL) Placement:
Condition: Upon entering a trade, an initial stop-loss is placed below the candle low for long trades or above the candle high for short trades. This is adjusted if the risk exceeds 1% of total capital.
Reasoning: The stop-loss is placed at a logical level that accounts for recent price action, ensuring that the trade is given room to develop while protecting capital from unexpected market reversals.
2. Profit Target and Partial Profit Booking:
Condition: The first profit target is set at 2.1x the initial risk for long trades, and 2.5x the initial risk for short trades.
Reasoning: The 2.1x risk-reward ratio for long trades provides a solid return while maintaining a conservative risk profile. For short trades, the strategy uses a higher 2.5x risk-reward ratio because market falls tend to be sharper and quicker than rises, allowing for larger profit targets to be reached more reliably.
Partial Profit Booking: Once the first target is hit, 60% of the position is closed to lock in profits. The remaining 40% is left to run with a trailing stop.
3. ATR-Based Trailing Stop:
Condition: Once the first target is hit, the ATR (Average True Range) trailing stop is applied to the remaining position. This dynamically adjusts the stop-loss as the trade moves in a favorable direction.
Reasoning: The trailing stop allows the trade to capture further gains if the trend continues, while protecting profits if the momentum weakens. The ATR ensures that the stop adjusts according to the market's current volatility, providing flexibility and protection.
4. Time-Based Exit:
Condition: If a trade is still open by 3:20 PM, it is automatically closed to avoid end-of-day volatility.
Reasoning: The time-based exit ensures that trades are not held into the often-volatile closing minutes of the market, reducing the risk of unexpected price swings.
Capital and Risk Management:
1. Lot Size Calculation:
Condition: The strategy calculates the number of lots dynamically based on the available margin. It uses only 10% of total equity for each trade, and ensures that the maximum risk per trade does not exceed 1% of total capital.
Reasoning: This ensures that traders are not over-leveraged and that the risk is controlled for each trade. Capital protection is at the core of the strategy, ensuring that even during adverse market conditions, the trader’s capital is preserved.
2. Stop-Loss Protection:
Condition: The stop-loss is designed to ensure that no more than 1% of capital is at risk in any trade.
Reasoning: By limiting risk exposure, the strategy focuses on long-term capital preservation while still allowing for profitable trades in favorable market conditions.
STBT/BTST Facilitation:
1. Feature: The strategy allows traders the option to hold positions overnight, facilitating STBT (Sell Today Buy Tomorrow) and BTST (Buy Today Sell Tomorrow) trades.
Reasoning: Backtests show that holding positions overnight when all trade conditions are still valid can lead to beneficial outcomes. This feature allows traders to take advantage of overnight market movements, providing flexibility beyond intraday trades.
Why This Strategy Stands Out:
Price-Volume Spike Detection: Unlike traditional strategies, this one uniquely focuses on Price-Volume Spike Detection to filter out low-probability trades. By ensuring that both price and volume spikes are present, the strategy guarantees that trades are placed only when there is significant market momentum.
Risk Management with Capital Protection: The strategy strictly limits the risk per trade to 1% of capital, ensuring long-term capital preservation. This is especially important for traders who wish to avoid large drawdowns and prefer a sustainable approach to trading.
2.5x Risk-Reward for Short Trades: Recognizing the sharpness of market declines, the strategy employs a 2.5x risk-reward ratio for short trades, maximizing profits during bearish trends.
Dynamic Exit Strategy: With partial profit booking and ATR-based trailing stops, the strategy is designed to capture gains efficiently while protecting capital through dynamic exit conditions.
Summary of Execution:
Entry: Triggered when JMA crosses EMA, combined with Bollinger Band breakouts and Price-Volume Spike Detection.
Capital Management: Trades are executed with 10% of available capital, and the risk per trade is capped at 1%.
Exit: Trades exit when stop-loss, ATR trailing stop, or time-based exit conditions are met.
Profit Booking: 60% of the position is closed at the first target, with the remainder trailed using an ATR-based stop.
Price-Volume w Trendline - Strategy [presentTrading]█ Introduction and How it is Different
The Price-Volume with Trendline Strategy is an innovative strategy that combines volume profile analysis, price-based Z-scores, and dynamic trendline filtering to identify optimal entry and exit points in the market. What sets this strategy apart is the integration of volume concentration (Point of Control or PoC) with dynamic volatility thresholds. Additionally, this strategy introduces a multi-step take profit (TP) mechanism that adjusts based on predefined levels, allowing traders to exit trades progressively while capitalizing on market momentum.
BTCUSD 6hr LS Performance
█ Strategy, How it Works: Detailed Explanation
The combination of multiple indicators and methodologies serves to create a more robust and reliable trading system. Each element is carefully chosen for its complementary role in providing accurate signals while minimizing false entries and exits. Here’s why the different components were chosen and how they work together:
- PoC and Z-Scores: The volume profile identifies key price areas, while the Z-score measures deviations from the mean. Together, they highlight points where the market is likely to react. For example, when the Z-score indicates an oversold condition near a PoC support level, it increases the probability of a reversal, providing a clear entry signal.
- Trendlines and Z-Scores: Trendlines serve as a secondary filter to ensure that price deviations identified by Z-scores align with broader market trends. This ensures that trades are only entered when the price has both deviated from its average and broken through a significant trendline level, reducing the likelihood of false signals.
- Multi-Step TP and Risk Management: Finally, the multi-step take profit logic works in tandem with the entry signals generated by the PoC, Z-scores, and trendlines. As the price moves in favor of the trade, profits are gradually locked in, ensuring the trader captures gains while still leaving room for further upside.
🔶 Point of Control (PoC) and Volume Profile Analysis
The PoC identifies the price level with the highest volume concentration within a specified lookback period. This price level represents where the most trading activity has occurred, often acting as a strong support or resistance. By breaking down the range into several rows (bins), the strategy identifies how much volume was traded at each price level.
🔶 Z-Score Calculation
The Z-score is a statistical metric that measures how far the current price is from its mean, expressed in terms of standard deviations. This is calculated both for price deviation and PoC-based deviation.
🔶 Trendline Breakout Filtering
The trendline filtering is a crucial aspect that refines entry signals by confirming trend continuation or reversals. It calculates trendlines based on pivot highs and lows using the selected method (e.g., ATR or standard deviation).
🔶 Multi-Step Take Profit
The multi-step take profit mechanism allows the strategy to take partial profits at several predefined levels. For example, when the price reaches 3%, 8%, 14%, or 21% above (or below) the entry price, it exits portions of the position. This is a useful technique for locking in profits as the market moves favorably.
Local
█ Usage
The Price-Volume with Trendline Strategy can be applied to various asset classes, including stocks, cryptocurrencies, and commodities. It is particularly effective in volatile markets where price deviations and volume concentrations signal potential reversals or trend continuations. By adjusting the settings for volatility and the lookback period, this strategy can be tailored to both short-term intraday trades and longer-term swing trades.
█ Default Settings
The default settings in the strategy play a vital role in shaping its performance.
- POC_lookbackLength (144): This defines the number of bars used to calculate the PoC. A longer lookback captures more data, leading to a more stable PoC, but may result in delayed signals. A shorter lookback increases responsiveness but may introduce noise.
- priceDeviationLength (200): This determines the period for calculating the standard deviation of price. A higher length smooths out the volatility, reducing the likelihood of false signals. Shorter lengths make the strategy more sensitive to sudden price movements.
- TL_length (14): Controls the swing detection period for trendline calculation. A shorter length will generate more frequent trendline breakouts, while a longer length captures only significant moves.
- Stop Loss and Take Profit: The strategy offers both fixed and SuperTrend-based stop losses. SuperTrend is adaptive to volatility, while fixed stop losses provide simpler risk control. The multi-step take profit ensures that profits are secured progressively, which can improve performance in trending markets by reducing the risk of full reversals.
Each of these settings can significantly affect the strategy’s risk-reward balance. For instance, increasing the stop loss level or the take profit percentages allows the strategy to stay in trades longer, potentially increasing profit per trade but at the cost of larger drawdowns. Conversely, tighter stops and smaller profit targets result in more frequent trades with lower average profit per trade.
Black-Scholes option price model & delta hedge strategyBlack-Scholes Option Pricing Model Strategy
The strategy is based on the Black-Scholes option pricing model and allows the calculation of option prices, various option metrics (the Greeks), and the creation of synthetic positions through delta hedging.
ATTENTION!
Trading derivative financial instruments involves high risks. The author of the strategy is not responsible for your financial results! The strategy is not self-sufficient for generating profit! It is created exclusively for constructing a synthetic derivative financial instrument. Also, there might be errors in the script, so use it at your own risk! I would appreciate it if you point out any mistakes in the comments! I would be even more grateful if you send the corrected code!
Application Scope
This strategy can be used for delta hedging short positions in sold options. For example, suppose you sold a call option on Bitcoin on the Deribit exchange with a strike price of $60,000 and an expiration date of September 27, 2024. Using this script, you can create a delta hedge to protect against the risk of loss in the option position if the price of Bitcoin rises.
Another example: Suppose you use staking of altcoins in your strategies, for which options are not available. By using this strategy, you can hedge the risk of a price drop (Put option). In this case, you won't lose money if the underlying asset price increases, unlike with a short futures position.
Another example: You received an airdrop, but your tokens will not be fully unlocked soon. Using this script, you can fully hedge your position and preserve their dollar value by the time the tokens are fully unlocked. And you won't fear the underlying asset price increasing, as the loss in the event of a price rise is limited to the option premium you will pay if you rebalance the portfolio.
Of course, this script can also be used for simple directional trading of momentum and mean reversion strategies!
Key Features and Input Parameters
1. Option settings:
- Style of option: "European vanilla", "Binary", "Asian geometric".
- Type of option: "Call" (bet on the rise) or "Put" (bet on the fall).
- Strike price: the option contract price.
- Expiration: the expiry date and time of the option contract.
2. Market statistic settings:
- Type of price source: open, high, low, close, hl2, hlc3, ohlc4, hlcc4 (using hl2, hlc3, ohlc4, hlcc4 allows smoothing the price in more volatile series).
- Risk-free return symbol: the risk-free rate for the market where the underlying asset is traded. For the cryptocurrency market, the return on the funding rate arbitrage strategy is accepted (a special function is written for its calculation based on the Premium Price).
- Volatility calculation model: realized (standard deviation over a moving period), implied (e.g., DVOL or VIX), or custom (you can specify a specific number in the field below). For the cryptocurrency market, the calculation of implied volatility is implemented based on the product of the realized volatility ratio of the considered asset and Bitcoin to the Bitcoin implied volatility index.
- User implied volatility: fixed implied volatility (used if "Custom" is selected in the "Volatility Calculation Method").
3. Display settings:
- Choose metric: what to display on the indicator scale – the price of the underlying asset, the option price, volatility, or Greeks (all are available).
- Measure: bps (basis points), percent. This parameter allows choosing the unit of measurement for the displayed metric (for all except the Greeks).
4. Trading settings:
- Hedge model: None (do not trade, default), Simple (just open a position for the full volume when the strike price is crossed), Synthetic option (creating a synthetic option based on the Black-Scholes model).
- Position side: Long, Short.
- Position size: the number of units of the underlying asset needed to create the option.
- Strategy start time: the moment in time after which the strategy will start working to create a synthetic option.
- Delta hedge interval: the interval in minutes for rebalancing the portfolio. For example, a value of 5 corresponds to rebalancing the portfolio every 5 minutes.
Post scriptum
My strategy based on the SegaRKO model. Many thanks to the author! Unfortunately, I don't have enough reputation points to include a link to the author in the description. You can find the original model via the link in the code, as well as through the search indicators on the charts by entering the name: "Black-Scholes Option Pricing Model". I have significantly improved the model: the calculation of volatility, risk-free rate and time value of the option have been reworked. The code performance has also been significantly optimized. And the most significant change is the execution, with which you can now trade using this script.
Fractal Proximity MA Aligment Scalping StrategyFractal Analysis
Fractals in trading help identify potential reversal points by marking significant price changes. Our strategy calculates a "fractal value" by comparing the current price to recent high and low fractal points. This is done by evaluating the sum of distances from the current closing price to the recent highs and lows. A positive fractal value suggests proximity to recent lows, hinting at upward momentum. Conversely, a negative value indicates closeness to recent highs, signaling potential downward movement.
Moving Averages for Confirmation
We use a series of 20 moving averages ranging from 5 to 100 to confirm trend directions indicated by fractal analysis. An entry signal is considered bullish when shorter-term moving averages are all above a long-term moving average, aligning with a positive fractal value.
Exit Strategy
The strategy employs dynamic stop-loss levels set at various moving averages, allowing for partial exits when the price crosses below specific thresholds. This helps manage the trade by locking in profits gradually. A full exit might be triggered by strong reversal signals suggested by both fractal values and moving average trends.
This open-source strategy is available for the community to test, adapt, and utilize. Your feedback and modifications are welcome as we refine the approach based on collective user experiences.
Combo 2/20 EMA & CCI
This is another part of my research work, where I test a combination of two strategies, receiving a combined signal. In order to understand which indicator combinations work better, which work worse, as filters for trades. This is combo strategies for get a cumulative signal.
First strategy
This indicator plots 2/20 exponential moving average. For the Mov Avg X 2/20 Indicator, the EMA bar will be painted when the Alert criteria is met.
Second strategy
The Commodity Channel Index (CCI) is best used with markets that display cyclical or seasonal characteristics, and is formulated to detect the beginning and ending of the cycles by incorporating a moving average together with a divisor that reflects both possible and actual trading ranges. The final index measures the deviation from normal, which indicates major changes in market trend.
Strategy tester settings:
Initial capital: 1000
Order size: 0.5
Commission: 0.1%
Other as default.
Indicator settings:
EMA Length: 50
CCI Length: 10
Fast MA Length: 15
Slow MA Length: 20
Other as default.
WARNING:
- For purpose educate only
- This script to change bars colors.
RSI Trend Following StrategyOverview
The RSI Trend Following Strategy utilizes Relative Strength Index (RSI) to enter the trade for the potential trend continuation. It uses Stochastic indicator to check is the price is not in overbought territory and the MACD to measure the current price momentum. Moreover, it uses the 200-period EMA to filter the counter trend trades with the higher probability. The strategy opens only long trades.
Unique Features
Dynamic stop-loss system: Instead of fixed stop-loss level strategy utilizes average true range (ATR) multiplied by user given number subtracted from the position entry price as a dynamic stop loss level.
Configurable Trading Periods: Users can tailor the strategy to specific market windows, adapting to different market conditions.
Two layers trade filtering system: Strategy utilizes MACD and Stochastic indicators measure the current momentum and overbought condition and use 200-period EMA to filter trades against major trend.
Trailing take profit level: After reaching the trailing profit activation level script activates the trailing of long trade using EMA. More information in methodology.
Wide opportunities for strategy optimization: Flexible strategy settings allows users to optimize the strategy entries and exits for chosen trading pair and time frame.
Methodology
The strategy opens long trade when the following price met the conditions:
RSI is above 50 level.
MACD line shall be above the signal line
Both lines of Stochastic shall be not higher than 80 (overbought territory)
Candle’s low shall be above the 200 period EMA
When long trade is executed, strategy set the stop-loss level at the price ATR multiplied by user-given value below the entry price. This level is recalculated on every next candle close, adjusting to the current market volatility.
At the same time strategy set up the trailing stop validation level. When the price crosses the level equals entry price plus ATR multiplied by user-given value script starts to trail the price with trailing EMA(by default = 20 period). If price closes below EMA long trade is closed. When the trailing starts, script prints the label “Trailing Activated”.
Strategy settings
In the inputs window user can setup the following strategy settings:
ATR Stop Loss (by default = 1.75)
ATR Trailing Profit Activation Level (by default = 2.25)
MACD Fast Length (by default = 12, period of averaging fast MACD line)
MACD Fast Length (by default = 26, period of averaging slow MACD line)
MACD Signal Smoothing (by default = 9, period of smoothing MACD signal line)
Oscillator MA Type (by default = EMA, available options: SMA, EMA)
Signal Line MA Type (by default = EMA, available options: SMA, EMA)
RSI Length (by default = 14, period for RSI calculation)
Trailing EMA Length (by default = 20, period for EMA, which shall be broken close the trade after trailing profit activation)
Justification of Methodology
This trading strategy is designed to leverage a combination of technical indicators—Relative Strength Index (RSI), Moving Average Convergence Divergence (MACD), Stochastic Oscillator, and the 200-period Exponential Moving Average (EMA)—to determine optimal entry points for long trades. Additionally, the strategy uses the Average True Range (ATR) for dynamic risk management to adapt to varying market conditions. Let's look in details for which purpose each indicator is used for and why it is used in this combination.
Relative Strength Index (RSI) is a momentum indicator used in technical analysis to measure the speed and change of price movements in a financial market. It helps traders identify whether an asset is potentially overbought (overvalued) or oversold (undervalued), which can indicate a potential reversal or continuation of the current trend.
How RSI Works? RSI tracks the strength of recent price changes. It compares the average gains and losses over a specific period (usually 14 periods) to assess the momentum of an asset. Average gain is the average of all positive price changes over the chosen period. It reflects how much the price has typically increased during upward movements. Average loss is the average of all negative price changes over the same period. It reflects how much the price has typically decreased during downward movements.
RSI calculates these average gains and losses and compares them to create a value between 0 and 100. If the RSI value is above 70, the asset is generally considered overbought, meaning it might be due for a price correction or reversal downward. Conversely, if the RSI value is below 30, the asset is considered oversold, suggesting it could be poised for an upward reversal or recovery. RSI is a useful tool for traders to determine market conditions and make informed decisions about entering or exiting trades based on the perceived strength or weakness of an asset's price movements.
This strategy uses RSI as a short-term trend approximation. If RSI crosses over 50 it means that there is a high probability of short-term trend change from downtrend to uptrend. Therefore RSI above 50 is our first trend filter to look for a long position.
The MACD (Moving Average Convergence Divergence) is a popular momentum and trend-following indicator used in technical analysis. It helps traders identify changes in the strength, direction, momentum, and duration of a trend in an asset's price.
The MACD consists of three components:
MACD Line: This is the difference between a short-term Exponential Moving Average (EMA) and a long-term EMA, typically calculated as: MACD Line = 12 period EMA − 26 period EMA
Signal Line: This is a 9-period EMA of the MACD Line, which helps to identify buy or sell signals. When the MACD Line crosses above the Signal Line, it can be a bullish signal (suggesting a buy); when it crosses below, it can be a bearish signal (suggesting a sell).
Histogram: The histogram shows the difference between the MACD Line and the Signal Line, visually representing the momentum of the trend. Positive histogram values indicate increasing bullish momentum, while negative values indicate increasing bearish momentum.
This strategy uses MACD as a second short-term trend filter. When MACD line crossed over the signal line there is a high probability that uptrend has been started. Therefore MACD line above signal line is our additional short-term trend filter. In conjunction with RSI it decreases probability of following false trend change signals.
The Stochastic Indicator is a momentum oscillator that compares a security's closing price to its price range over a specific period. It's used to identify overbought and oversold conditions. The indicator ranges from 0 to 100, with readings above 80 indicating overbought conditions and readings below 20 indicating oversold conditions.
It consists of two lines:
%K: The main line, calculated using the formula (CurrentClose−LowestLow)/(HighestHigh−LowestLow)×100 . Highest and lowest price taken for 14 periods.
%D: A smoothed moving average of %K, often used as a signal line.
This strategy uses stochastic to define the overbought conditions. The logic here is the following: we want to avoid long trades in the overbought territory, because when indicator reaches it there is a high probability that the potential move is gonna be restricted.
The 200-period EMA is a widely recognized indicator for identifying the long-term trend direction. The strategy only trades in the direction of this primary trend to increase the probability of successful trades. For instance, when the price is above the 200 EMA, only long trades are considered, aligning with the overarching trend direction.
Therefore, strategy uses combination of RSI and MACD to increase the probability that price now is in short-term uptrend, Stochastic helps to avoid the trades in the overbought (>80) territory. To increase the probability of opening long trades in the direction of a main trend and avoid local bounces we use 200 period EMA.
ATR is used to adjust the strategy risk management to the current market volatility. If volatility is low, we don’t need the large stop loss to understand the there is a high probability that we made a mistake opening the trade. User can setup the settings ATR Stop Loss and ATR Trailing Profit Activation Level to realize his own risk to reward preferences, but the unique feature of a strategy is that after reaching trailing profit activation level strategy is trying to follow the trend until it is likely to be finished instead of using fixed risk management settings. It allows sometimes to be involved in the large movements.
Backtest Results
Operating window: Date range of backtests is 2023.01.01 - 2024.08.01. It is chosen to let the strategy to close all opened positions.
Commission and Slippage: Includes a standard Binance commission of 0.1% and accounts for possible slippage over 5 ticks.
Initial capital: 10000 USDT
Percent of capital used in every trade: 30%
Maximum Single Position Loss: -3.94%
Maximum Single Profit: +15.78%
Net Profit: +1359.21 USDT (+13.59%)
Total Trades: 111 (36.04% win rate)
Profit Factor: 1.413
Maximum Accumulated Loss: 625.02 USDT (-5.85%)
Average Profit per Trade: 12.25 USDT (+0.40%)
Average Trade Duration: 40 hours
These results are obtained with realistic parameters representing trading conditions observed at major exchanges such as Binance and with realistic trading portfolio usage parameters.
How to Use
Add the script to favorites for easy access.
Apply to the desired timeframe and chart (optimal performance observed on 2h BTC/USDT).
Configure settings using the dropdown choice list in the built-in menu.
Set up alerts to automate strategy positions through web hook with the text: {{strategy.order.alert_message}}
Disclaimer:
Educational and informational tool reflecting Skyrex commitment to informed trading. Past performance does not guarantee future results. Test strategies in a simulated environment before live implementation
Dow Theory based Strategy (Markttechnik)What makes this script unique?
calculates two trends at the same time: a big one for the overall strong trend - and a small one to trigger a trade after a small correction within the big trend
only if both trends (the small and the big trend) are in an uptrend, a buy signal is created: this prevents a buy signal from being generated in a falling market just because an upward movement begins in a small trend
the exit strategy can be configured very flexibly and individually: use the last low as stop loss and automatically switch to a trialing stop loss as soon as the take profit is reached (instead of finishing the trade)
the take profit strategy can also be configured - e.g. use the last high, a fixed percentage or a combination of it
plots each trade in detail on the chart - e.g. inner candles or the exact progression of the stop loss over the entire duration of the trade to allow you to analyze each trade precisely
What does the script do and how?
In this strategy an intact upward trend is characterized by higher highs and lower lows only if the big trend and the small trend are in an upward trend at the same time.
The following describes how the script calculates a buy signal. Every step is drawn to the chart immediately - see example chart above:
1. the stock rises in the big trend - i.e. in a longer time frame
2. a correction takes place (the share price falls) - but does not create a new low
3. the stock rises again in the big trend and creates a new high
From now on, the big trend is in an intact upward trend (until it falls below its last low).
This is drawn to the chart as 3 bold green zigzag lines.
But we do not buy right now! Instead, we want to wait for a correction in the big trend and for the start of a small upward trend.
4. a correction takes place (not below the low from 2.)
Now, the script also starts to calculate the small trend:
5. the stock rises in the small trend - i.e. in a shorter time frame
6. a small correction takes place (not below the low from 4.)
7. the stock rises above the high from 5.: a new high in the shorter time frame
Now, both trends are in an intact upward trend.
A buy signal is created and both the minor and major trend are colored green on the chart.
Now, the trade is active and:
the stop loss is calculated and drawn for each candle
the take profit is calculated and drawn to the chart
as soon as the price reaches the take profit or the stop loss, the trade is closed
Features and functionalities
Uptrend : An intact upward trend is characterized by higher highs and lower lows. Uptrends are shown in green on the chart.
The beginning of an uptrend is numbered 1, each subsequent high is numbered 2, and each low is numbered 3.
Downtrend: An intact downtrend is characterized by lower highs and lower lows. Downtrends are displayed in red on the chart.
Note that our indicator does not show the numbering of the points of the downtrend.
Trendless phases: If there is no intact trend, we are in a trendless phase. Trendless phases are shown in blue on the chart.
This occurs after an uptrend, when a lower low or a lower high is formed. Or after a downtrend, when a higher low or a higher high is formed.
Buy signals
A buy signal is generated as soon as a new upward trend has been formed or a new high has been established in an intact upward trend.
But even before a buy signal is generated, this strategy anticipates a possible emerging trend and draws the next possible trading opportunity to the chart.
In addition to the (not yet reached) buy price, the risk-reward ratio, the StopLoss and the TakeProfit price is shown.
With this information, you can already enter a StopBuy order, which is thus triggered directly with the then created buy signal.
You can configure, if a buy signal shall be created while the big trend is an uptrend, a downtrend and/or trendless.
Exit strategy
With this strategy, you have multiple possibilities to close your position. All of them can be configured within the settings. In general, you can combine a take profit strategy with a stop loss strategy.
The take profit price will be calculated once for each trade. It will be drawn to the chart for active trade.
Depending on your configuration, this can be the last high (which is often a resistance level), a fixed percentage added to the buy price or the maximum of both.
You can also configure that a trailing stop loss is used as soon as the take profit price is reached once.
The stop loss gets recalculated with each candle and is displayed and plotted for each active and finished trade. With this, you can easily check how the stop loss changed during your trades.
The stop loss can be configured flexibly:
Use the classic "trailing stop loss" that follows the price from below.
Set the stop loss to the last low and tighten it every time the small trend marks a new local low.
Confiure that the stop loss is tightened as soon as the break even is reached. Nothing is more annoying than a trade turning from a win to a loss.
Ignore inside candles (see description below) and relax the stop loss to use the outside candle for its calculation.
Inner candles
Inner candles are created when the candle body is within the maximum values of a previous candle (the outer candle). There can be any number of consecutive inner candles. As soon as you have activated the "Check inner candles" setting, all consecutive inner candles will be highlighted in yellow on the chart.
Prices during an inner candle scenario might be irrelevant for trading and can be interpreted as fluctuations within the outside candle. For this reason, the trailing stop loss should not be aligned with inner candles. Therefore, as soon as an inner candle occurs, the stop loss is reset and the low at the time of the outside candle is used as the calculation for the trailing stop loss. This will all be plotted for you on the chart.
Display of the trades:
All active and closed trades of the last 5 years are displayed in the chart with buy signal, sell, stop loss history, inside candles and statistics.
Backtesting:
The strategy can be simulated for each stock over the period of the last 5 years. Each individual trade is recorded and can be traced and analyzed in the chart including stop loss history. Detailed evaluations and statistics are available to evaluate the performance of the strategy.
Additional Statistics
This strategy immediately displays a statistic table to the chart area giving you an overview of its performance over the last years for the given chart.
This includes:
The total win/loss in $ and %
The win/loss per year in %
The active investment time in days and % (e.g. invested 10 of 100 trading days -> 10%)
The total win/loss in %, extrapolated to 100% equity usage: Only with this value can strategies really be compared. Because you are not invested between the trades and could invest in other stocks during this time. This value indicates how much profit you would have made if you had been invested 100% of the time - or to put it another way - if you had been invested 100% of the time in stocks with exactly the same performance. Let's say you had only one trade in the last 5 years that lasted, say, only one month and made 5% profit. This would be significantly better than a strategy with which you were invested for, say, 5 years and made 10% profit.
The total profit/loss per year in %, extrapolated to 100% equity usage
Notifications (alerts):
Get alerted before a new buy signal emerges to create an order if necessary and not miss a trade. You can also be notified when the stop loss needs to be adjusted. The notification can be done in different ways, e.g. by Mail, PopUp or App-Notification. This saves them the annoying, time-consuming and error-prone "click through" all the charts.
Settings: Display Settings
With these settings, you have the possibility to:
Show the small or the big trend as a background color
Configure if the numbers (1-2-3-2-3) shall be shown at all or only for the small, the big trend or both
Settings: Trend calculation - fine tuning
Drawing trend lines on a chart is not an exact science. Some highs and lows are not very clear or significant. And so it will always happen that 2 different people would draw different trendlines for the same chart. Unfortunately, there is no exact "right" or "wrong" here.
With the options under "Trend Calculation - Fine Tuning" you have the possibility to influence the drawing in of trends and to adapt it to your personal taste.
Small Trend, Big Trend : With these settings you can influence how significant a high or low has to be to recognize them as an independent high or low. The larger the values, the more significant a high or low must be to be recognized as such.
High and low recognition : With this setting you can influence when two adjacent, almost identical highs or lows should be recognized as independent highs or lows. The higher the value, the more different "similar" highs or lows must be in order to be recognized as such.
Which default settings were selected and why
Show Trades: true - its often useful to see all recent trades in the chart
Time Frame: 1 day - most common time frame (except for day traders)
Take Profit: combined 10% - the last high is taken as take profit because the trend often changes there, but only if there is at least 10% profit to ensure we do not risk money for a tiny profit
Stop Loss: combined - the last low is used as stop loss because the trend would break there and switch to a trailing stop loss as soon as our take profit is reached to let our profits run without risking them anymore
Stop Loss distance: 3% - we are giving the price 3% air (below the last low) to avoid being stopped out due to a short price drop
Trailing Stop Loss: 2% - we have to give the stop loss some room to avoid being stopped out prematurely; this is a value that is well balanced between a certain downside distance and the profit-taking ratio
Set Stop Loss to break even: true, 2% - once we reached the break even, it is a common practice to not risk our money anymore, the value is set to the same value as the trailing stop loss
Trade Filter: Uptrend - we only start trades if the big trend is an uptrend in the expectation that it will continue after a small correction
Display settings: those will not influence the trades, feel free to change them to your needs
Trend calculation - Fine Tuning: 1/1,5/0,05; influences the internal calculation for highs and lows and how significant they need to be to be considered a new high or low; the default values will provide you nicely calculated trends in the daily time frame; if there are too many or too few lows and highs according to your taste, feel free to play around and immediately see the result drawn to the chart; read the manual for a detailed description of this values
Note that you can (and should) configure the general trading properties like your initial capital, order size, slippage and commission.
Intramarket Difference Index StrategyHi Traders !!
The IDI Strategy:
In layman’s terms this strategy compares two indicators across markets and exploits their differences.
note: it is best the two markets are correlated as then we know we are trading a short to long term deviation from both markets' general trend with the assumption both markets will trend again sometime in the future thereby exhausting our trading opportunity.
📍 Import Notes:
This Strategy calculates trade position size independently (i.e. risk per trade is controlled in the user inputs tab), this means that the ‘Order size’ input in the ‘Properties’ tab will have no effect on the strategy. Why ? because this allows us to define custom position size algorithms which we can use to improve our risk management and equity growth over time. Here we have the option to have fixed quantity or fixed percentage of equity ATR (Average True Range) based stops in addition to the turtle trading position size algorithm.
‘Pyramiding’ does not work for this strategy’, similar to the order size input togeling this input will have no effect on the strategy as the strategy explicitly defines the maximum order size to be 1.
This strategy is not perfect, and as of writing of this post I have not traded this algo.
Always take your time to backtests and debug the strategy.
🔷 The IDI Strategy:
By default this strategy pulls data from your current TV chart and then compares it to the base market, be default BINANCE:BTCUSD . The strategy pulls SMA and RSI data from either market (we call this the difference data), standardizes the data (solving the different unit problem across markets) such that it is comparable and then differentiates the data, calling the result of this transformation and difference the Intramarket Difference (ID). The formula for the the ID is
ID = market1_diff_data - market2_diff_data (1)
Where
market(i)_diff_data = diff_data / ATR(j)_market(i)^0.5,
where i = {1, 2} and j = the natural numbers excluding 0
Formula (1) interpretation is the following
When ID > 0: this means the current market outperforms the base market
When ID = 0: Markets are at long run equilibrium
When ID < 0: this means the current market underperforms the base market
To form the strategy we define one of two strategy type’s which are Trend and Mean Revesion respectively.
🔸 Trend Case:
Given the ‘‘Strategy Type’’ is equal to TREND we define a threshold for which if the ID crosses over we go long and if the ID crosses under the negative of the threshold we go short.
The motivating idea is that the ID is an indicator of the two symbols being out of sync, and given we know volatility clustering, momentum and mean reversion of anomalies to be a stylised fact of financial data we can construct a trading premise. Let's first talk more about this premise.
For some markets (cryptocurrency markets - synthetic symbols in TV) the stylised fact of momentum is true, this means that higher momentum is followed by higher momentum, and given we know momentum to be a vector quantity (with magnitude and direction) this momentum can be both positive and negative i.e. when the ID crosses above some threshold we make an assumption it will continue in that direction for some time before executing back to its long run equilibrium of 0 which is a reasonable assumption to make if the market are correlated. For example for the BTCUSD - ETHUSD pair, if the ID > +threshold (inputs for MA and RSI based ID thresholds are found under the ‘‘INTRAMARKET DIFFERENCE INDEX’’ group’), ETHUSD outperforms BTCUSD, we assume the momentum to continue so we go long ETHUSD.
In the standard case we would exit the market when the IDI returns to its long run equilibrium of 0 (for the positive case the ID may return to 0 because ETH’s difference data may have decreased or BTC’s difference data may have increased). However in this strategy we will not define this as our exit condition, why ?
This is because we want to ‘‘let our winners run’’, to achieve this we define a trailing Donchian Channel stop loss (along with a fixed ATR based stop as our volatility proxy). If we were too use the 0 exit the strategy may print a buy signal (ID > +threshold in the simple case, market regimes may be used), return to 0 and then print another buy signal, and this process can loop may times, this high trade frequency means we fail capture the entire market move lowering our profit, furthermore on lower time frames this high trade frequencies mean we pay more transaction costs (due to price slippage, commission and big-ask spread) which means less profit.
By capturing the sum of many momentum moves we are essentially following the trend hence the trend following strategy type.
Here we also print the IDI (with default strategy settings with the MA difference type), we can see that by letting our winners run we may catch many valid momentum moves, that results in a larger final pnl that if we would otherwise exit based on the equilibrium condition(Valid trades are denoted by solid green and red arrows respectively and all other valid trades which occur within the original signal are light green and red small arrows).
another example...
Note: if you would like to plot the IDI separately copy and paste the following code in a new Pine Script indicator template.
indicator("IDI")
// INTRAMARKET INDEX
var string g_idi = "intramarket diffirence index"
ui_index_1 = input.symbol("BINANCE:BTCUSD", title = "Base market", group = g_idi)
// ui_index_2 = input.symbol("BINANCE:ETHUSD", title = "Quote Market", group = g_idi)
type = input.string("MA", title = "Differrencing Series", options = , group = g_idi)
ui_ma_lkb = input.int(24, title = "lookback of ma and volatility scaling constant", group = g_idi)
ui_rsi_lkb = input.int(14, title = "Lookback of RSI", group = g_idi)
ui_atr_lkb = input.int(300, title = "ATR lookback - Normalising value", group = g_idi)
ui_ma_threshold = input.float(5, title = "Threshold of Upward/Downward Trend (MA)", group = g_idi)
ui_rsi_threshold = input.float(20, title = "Threshold of Upward/Downward Trend (RSI)", group = g_idi)
//>>+----------------------------------------------------------------+}
// CUSTOM FUNCTIONS |
//<<+----------------------------------------------------------------+{
// construct UDT (User defined type) containing the IDI (Intramarket Difference Index) source values
// UDT will hold many variables / functions grouped under the UDT
type functions
float Close // close price
float ma // ma of symbol
float rsi // rsi of the asset
float atr // atr of the asset
// the security data
getUDTdata(symbol, malookback, rsilookback, atrlookback) =>
indexHighTF = barstate.isrealtime ? 1 : 0
= request.security(symbol, timeframe = timeframe.period,
expression = [close , // Instentiate UDT variables
ta.sma(close, malookback) ,
ta.rsi(close, rsilookback) ,
ta.atr(atrlookback) ])
data = functions.new(close_, ma_, rsi_, atr_)
data
// Intramerket Difference Index
idi(type, symbol1, malookback, rsilookback, atrlookback, mathreshold, rsithreshold) =>
threshold = float(na)
index1 = getUDTdata(symbol1, malookback, rsilookback, atrlookback)
index2 = getUDTdata(syminfo.tickerid, malookback, rsilookback, atrlookback)
// declare difference variables for both base and quote symbols, conditional on which difference type is selected
var diffindex1 = 0.0, var diffindex2 = 0.0,
// declare Intramarket Difference Index based on series type, note
// if > 0, index 2 outpreforms index 1, buy index 2 (momentum based) until equalibrium
// if < 0, index 2 underpreforms index 1, sell index 1 (momentum based) until equalibrium
// for idi to be valid both series must be stationary and normalised so both series hae he same scale
intramarket_difference = 0.0
if type == "MA"
threshold := mathreshold
diffindex1 := (index1.Close - index1.ma) / math.pow(index1.atr*malookback, 0.5)
diffindex2 := (index2.Close - index2.ma) / math.pow(index2.atr*malookback, 0.5)
intramarket_difference := diffindex2 - diffindex1
else if type == "RSI"
threshold := rsilookback
diffindex1 := index1.rsi
diffindex2 := index2.rsi
intramarket_difference := diffindex2 - diffindex1
//>>+----------------------------------------------------------------+}
// STRATEGY FUNCTIONS CALLS |
//<<+----------------------------------------------------------------+{
// plot the intramarket difference
= idi(type,
ui_index_1,
ui_ma_lkb,
ui_rsi_lkb,
ui_atr_lkb,
ui_ma_threshold,
ui_rsi_threshold)
//>>+----------------------------------------------------------------+}
plot(intramarket_difference, color = color.orange)
hline(type == "MA" ? ui_ma_threshold : ui_rsi_threshold, color = color.green)
hline(type == "MA" ? -ui_ma_threshold : -ui_rsi_threshold, color = color.red)
hline(0)
Note it is possible that after printing a buy the strategy then prints many sell signals before returning to a buy, which again has the same implication (less profit. Potentially because we exit early only for price to continue upwards hence missing the larger "trend"). The image below showcases this cenario and again, by allowing our winner to run we may capture more profit (theoretically).
This should be clear...
🔸 Mean Reversion Case:
We stated prior that mean reversion of anomalies is an standerdies fact of financial data, how can we exploit this ?
We exploit this by normalizing the ID by applying the Ehlers fisher transformation. The transformed data is then assumed to be approximately normally distributed. To form the strategy we employ the same logic as for the z score, if the FT normalized ID > 2.5 (< -2.5) we buy (short). Our exit conditions remain unchanged (fixed ATR stop and trailing Donchian Trailing stop)
🔷 Position Sizing:
If ‘‘Fixed Risk From Initial Balance’’ is toggled true this means we risk a fixed percentage of our initial balance, if false we risk a fixed percentage of our equity (current balance).
Note we also employ a volatility adjusted position sizing formula, the turtle training method which is defined as follows.
Turtle position size = (1/ r * ATR * DV) * C
Where,
r = risk factor coefficient (default is 20)
ATR(j) = risk proxy, over j times steps
DV = Dollar Volatility, where DV = (1/Asset Price) * Capital at Risk
🔷 Risk Management:
Correct money management means we can limit risk and increase reward (theoretically). Here we employ
Max loss and gain per day
Max loss per trade
Max number of consecutive losing trades until trade skip
To read more see the tooltips (info circle).
🔷 Take Profit:
By defualt the script uses a Donchain Channel as a trailing stop and take profit, In addition to this the script defines a fixed ATR stop losses (by defualt, this covers cases where the DC range may be to wide making a fixed ATR stop usefull), ATR take profits however are defined but optional.
ATR SL and TP defined for all trades
🔷 Hurst Regime (Regime Filter):
The Hurst Exponent (H) aims to segment the market into three different states, Trending (H > 0.5), Random Geometric Brownian Motion (H = 0.5) and Mean Reverting / Contrarian (H < 0.5). In my interpretation this can be used as a trend filter that eliminates market noise.
We utilize the trending and mean reverting based states, as extra conditions required for valid trades for both strategy types respectively, in the process increasing our trade entry quality.
🔷 Example model Architecture:
Here is an example of one configuration of this strategy, combining all aspects discussed in this post.
Future Updates
- Automation integration (next update)
[Shoshi] Better Than UCTS StrategyThe Strategy is a versatile trading strategy designed to work across various timeframes and assets, with customizable presets tailored for different market conditions. This strategy leverages a combination of Exponential Moving Averages (EMAs) to identify trend directions and entry points. It includes the following key features:
**EMA Crossovers:**
Utilizes short-term and long-term EMAs to generate buy and sell signals when a crossover occurs.
**Customizable Take Profits (TP1, TP2):**
Define up to two take-profit levels to lock in gains. These can be activated or deactivated as needed.
**Stop Loss and Break-Even:**
Implements a dynamic stop loss that can adjust to break-even after hitting the first take-profit level, providing additional risk management.
**Time and Volume Filters:**
Optional filters to avoid signals in low-volume or unfavorable time periods, enhancing the reliability of the signals.
**Risk Management Tools:**
Includes maximum drawdown, losing streak, and intraday loss limits to safeguard your capital.
This strategy is fully configurable, allowing traders to adjust settings to suit their specific needs and market conditions, making it a powerful tool for both beginner and experienced traders.
VRS (Vegas Reversal Strategy)It is based on the reversal of the price after an accentuated volatility of the previous day. It is tested only on BTC, TF Day, and has an activation value equal to a spike of minimum 2.4% amplitude, a value that I have left in the settings free to be modified if it is found valid for other assets.
In the settings you can change how many of the latest longs or shorts I want to view in the past, colors and various aesthetics.
When the system detects a spike at the end of the day from 2.4% onwards it will signal the direction of Reversal, generating the 3 TP, dotted lines.
Entry into the market must be done at the close of the candle day, unfortunately at night time if you want to enter on the tick.
Stop above/below the spike that generated the condition.
If the Day2 candle closes FULL inside the spike, immediate and early closing of the operation.
There cannot be two consecutive Day events: if you are Long or Short and have taken a stop on the next candle, even if the latter generates another entry, this must not be activated.
TP 1 and 2 are both mandatory at 33% of the position, TP3, based on the current movement, can be considered to be left to run to the bitter end or in any case to structuring confirmations of a slowdown in the price.
Upon reaching TP1 it is mandatory to move the STOP to even.
In the event of the presence of extremely strong directional movements, for example Long direction, an opposite activation, Short, must be done but with reduced capital, on the contrary an activation in the same direction as the trend movement can be done with a surcharge. Always pay attention to Money Management and Risk Management.
Always manage Risk and Money Management in an adequate, technical and sustainable manner in relation to your capital. A fair exposure per transaction is between 1% and 2% of the capital.
Signal Tester (v1.2)This is an automation test Strategy, which helps you to get Strategy Alerts quickly on the 1m chart.
This is useful when you want to start automating Strategies but first you want to see if the connection between TradingView and your automation tool works properly.
This Strategy sends LONG Buy/Sell signals every 1 minute so you don't have to wait for a long time to see if your integration with an automation tool works.
How it works:
It works on the 1m chart
Every 1 minute it will send a BUY or a SELL signal (alternating between them forever)
Trend Signals with TP & SL [UAlgo] StrategyThe "Trend Signals with TP & SL Strategy" is a trading strategy designed to capture trend continuation signals while incorporating sophisticated risk management techniques. This strategy is tailored for traders who wish to capitalize on trending market conditions with precise entry and exit points, automatically calculating Take Profit (TP) and Stop Loss (SL) levels based on either Average True Range (ATR) or percentage values. The strategy aims to enhance trade management by preventing multiple simultaneous positions and dynamically adapting to changing market conditions.
This strategy is highly configurable, allowing traders to adjust sensitivity, the ATR calculation method, and the cloud moving average length. Additionally, the strategy can display buy and sell signals directly on the chart, along with visual representation of entry points, stop losses, and take profits. It also features a cloud-based trend analysis using a MACD-driven color fill that indicates the strength and direction of the trend.
🔶 Key Features
Configurable Trend Continuation Signals:
Source Selection: The strategy uses the midpoint of the high-low range as the default source, but it is adjustable.
Sensitivity: The sensitivity of the trend signals can be adjusted using a multiplier, ranging from 0.5 to 5.
ATR Calculation: The strategy allows users to choose between two ATR calculation methods for better adaptability to different market conditions.
Cloud Moving Average: Traders can adjust the cloud moving average length, which is used in conjunction with MACD to provide a visual trend indication.
Take Profit & Stop Loss Management:
ATR-Based or Percent-Based: The strategy offers flexibility in setting TP and SL levels, allowing traders to choose between ATR-based multipliers or fixed percentage values.
Dynamic Adjustment: TP and SL levels are dynamically adjusted according to the selected method, ensuring trades are managed based on real-time market conditions.
Prevention of Multiple Positions:
Single Position Control: To reduce risk and enhance strategy reliability, the strategy includes an option to prevent multiple positions from being opened simultaneously.
Visual Trade Indicators:
Buy/Sell Signals: Clearly displays buy and sell signals on the chart for easy interpretation.
Entry, SL, and TP Lines: Draws lines for entry price, stop loss, and take profit directly on the chart, helping traders to monitor trades visually.
Trend Cloud: A color-filled cloud based on MACD and the cloud moving average provides a visual cue of the trend’s direction and strength.
Performance Summary Table:
In-Chart Statistics: A table in the top right of the chart displays key performance metrics, including total trades, wins, losses, and win rate percentage, offering a quick overview of the strategy’s effectiveness.
🔶 Interpreting the Indicator
Trend Signals: The strategy identifies trend continuation signals based on price action relative to an ATR-based threshold. A buy signal is generated when the price crosses above a key level, indicating an uptrend. Conversely, a sell signal occurs when the price crosses below a level, signaling a downtrend.
Cloud Visualization: The cloud, derived from MACD and moving averages, changes color to reflect the current trend. A positive cloud in aqua suggests an uptrend, while a red cloud indicates a downtrend. The transparency of the cloud offers further nuance, with more solid colors denoting stronger trends.
Entry and Exit Management: Once a trend signal is generated, the strategy automatically sets TP and SL levels based on your chosen method (ATR or percentage). The stop loss and take profit lines will appear on the chart, showing where the strategy will exit the trade. If the price reaches either the SL or TP, the trade is closed, and the respective line is deleted from the chart.
Performance Metrics: The strategy’s performance is tracked in real-time with an in-chart table. This table provides essential information about the number of trades executed, the win/loss ratio, and the overall win rate. This information helps traders assess the strategy's effectiveness and make necessary adjustments.
This strategy is designed for those who seek to engage with trending markets, offering robust tools for entry, exit, and overall trade management. By understanding and leveraging these features, traders can potentially improve their trading outcomes and risk management.
🔷 Related Script
🔶 Disclaimer
Use with Caution: This indicator is provided for educational and informational purposes only and should not be considered as financial advice. Users should exercise caution and perform their own analysis before making trading decisions based on the indicator's signals.
Not Financial Advice: The information provided by this indicator does not constitute financial advice, and the creator (UAlgo) shall not be held responsible for any trading losses incurred as a result of using this indicator.
Backtesting Recommended: Traders are encouraged to backtest the indicator thoroughly on historical data before using it in live trading to assess its performance and suitability for their trading strategies.
Risk Management: Trading involves inherent risks, and users should implement proper risk management strategies, including but not limited to stop-loss orders and position sizing, to mitigate potential losses.
No Guarantees: The accuracy and reliability of the indicator's signals cannot be guaranteed, as they are based on historical price data and past performance may not be indicative of future results.
PVT Crossover Strategy**Release Notes**
**Strategy Name**: PVT Crossover Strategy
**Purpose**: This strategy aims to capture entry and exit points in the market using the Price-Volume Trend (PVT) and its Exponential Moving Average (EMA). It specifically uses the crossover of PVT with its EMA as signals to identify changes in market trends.
**Uniqueness and Usefulness**
**Uniqueness**: This strategy is unique in its use of the PVT indicator, which combines price changes with trading volume to track trends. The filtering with EMA reduces noise and provides more accurate signals compared to other indicators.
**Usefulness**: This strategy is effective for traders looking to detect trend changes early. The signals based on PVT and its EMA crossover work particularly well in markets where volume fluctuations are significant.
**Entry Conditions**
**Long Entry**:
- **Condition**: A crossover occurs where PVT crosses above its EMA.
- **Signal**: A buy signal is generated, indicating a potential uptrend.
**Short Entry**:
- **Condition**: A crossunder occurs where PVT crosses below its EMA.
- **Signal**: A sell signal is generated, indicating a potential downtrend.
**Exit Conditions**
**Exit Strategy**:
- The strategy does not explicitly program exit conditions beyond the entry signals, but traders are encouraged to close positions manually based on signals or apply their own risk management strategy.
**Risk Management**
This strategy does not include default risk management rules, so traders should implement their own. Consider using trailing stops or fixed stop losses to manage risk.
**Account Size**: ¥100,000
**Commissions and Slippage**: 94 pips per trade for commissions and 1 pip for slippage
**Risk per Trade**: 10% of account equity
**Configurable Options**
**Configurable Options**:
- **EMA Length**: The length of the EMA used to calculate the EMA of PVT (default is 20).
- **Signal Display Control**: The option to turn the display of signals on or off.
**Adequate Sample Size**
To ensure the robustness and reliability of this strategy, it is recommended to backtest it with a sufficiently long period of historical data, especially across different market conditions.
**Credits**
**Acknowledgments**:
This strategy is based on the concept of the PVT indicator and its application in strategy design, drawing on contributions from technical analysis and the trading community.
**Clean Chart Description**
**Chart Appearance**:
This strategy is designed to maintain a clean and simple chart by turning off the plot of PVT, its EMA, and entry signals. This reduces clutter and allows for more effective trend analysis.
**Addressing the House Rule Violations**
**Omissions and Unrealistic Claims**
**Clarification**:
This strategy does not make unrealistic or unsupported claims about its performance, and all signals are for educational purposes only, not guaranteeing future results. It is important to understand that past performance does not guarantee future outcomes.
