Chande Momentum Oscillator StrategyThe Chande Momentum Oscillator (CMO) Trading Strategy is based on the momentum oscillator developed by Tushar Chande in 1994. The CMO measures the momentum of a security by calculating the difference between the sum of recent gains and losses over a defined period. The indicator offers a means to identify overbought and oversold conditions, making it suitable for developing mean-reversion trading strategies (Chande, 1997).
Strategy Overview:
Calculation of the Chande Momentum Oscillator (CMO):
The CMO formula considers both positive and negative price changes over a defined period (commonly set to 9 days) and computes the net momentum as a percentage.
The formula is as follows:
CMO=100×(Sum of Gains−Sum of Losses)(Sum of Gains+Sum of Losses)
CMO=100×(Sum of Gains+Sum of Losses)(Sum of Gains−Sum of Losses)
This approach distinguishes the CMO from other oscillators like the RSI by using both price gains and losses in the numerator, providing a more symmetrical measurement of momentum (Chande, 1997).
Entry Condition:
The strategy opens a long position when the CMO value falls below -50, signaling an oversold condition where the price may revert to the mean. Research in mean-reversion, such as by Poterba and Summers (1988), supports this approach, highlighting that prices often revert after sharp movements due to overreaction in the markets.
Exit Conditions:
The strategy closes the long position when:
The CMO rises above 50, indicating that the price may have become overbought and may not provide further upside potential.
Alternatively, the position is closed 5 days after the buy signal is triggered, regardless of the CMO value, to ensure a timely exit even if the momentum signal does not reach the predefined level.
This exit strategy aligns with the concept of time-based exits, reducing the risk of prolonged exposure to adverse price movements (Fama, 1970).
Scientific Basis and Rationale:
Momentum and Mean-Reversion:
The strategy leverages the well-known phenomenon of mean-reversion in financial markets. According to research by Jegadeesh and Titman (1993), prices tend to revert to their mean over short periods following strong movements, creating opportunities for traders to profit from temporary deviations.
The CMO captures this mean-reversion behavior by monitoring extreme price conditions. When the CMO reaches oversold levels (below -50), it signals potential buying opportunities, whereas crossing overbought levels (above 50) indicates conditions for selling.
Market Efficiency and Overreaction:
The strategy takes advantage of behavioral inefficiencies and overreactions, which are often the drivers behind sharp price movements (Shiller, 2003). By identifying these extreme conditions with the CMO, the strategy aims to capitalize on the market’s tendency to correct itself when price deviations become too large.
Optimization and Parameter Selection:
The 9-day period used for the CMO calculation is a widely accepted timeframe that balances responsiveness and noise reduction, making it suitable for capturing short-term price fluctuations. Studies in technical analysis suggest that oscillators optimized over such periods are effective in detecting reversals (Murphy, 1999).
Performance and Backtesting:
The strategy's effectiveness is confirmed through backtesting, which shows that using the CMO as a mean-reversion tool yields profitable opportunities. The use of time-based exits alongside momentum-based signals enhances the reliability of the strategy by ensuring that trades are closed even when the momentum signal alone does not materialize.
Conclusion:
The Chande Momentum Oscillator Trading Strategy combines the principles of momentum measurement and mean-reversion to identify and capitalize on short-term price fluctuations. By using a widely tested oscillator like the CMO and integrating a systematic exit approach, the strategy effectively addresses both entry and exit conditions, providing a robust method for trading in diverse market environments.
References:
Chande, T. S. (1997). The New Technical Trader: Boost Your Profit by Plugging into the Latest Indicators. John Wiley & Sons.
Fama, E. F. (1970). Efficient Capital Markets: A Review of Theory and Empirical Work. The Journal of Finance, 25(2), 383-417.
Jegadeesh, N., & Titman, S. (1993). Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency. The Journal of Finance, 48(1), 65-91.
Murphy, J. J. (1999). Technical Analysis of the Financial Markets: A Comprehensive Guide to Trading Methods and Applications. New York Institute of Finance.
Poterba, J. M., & Summers, L. H. (1988). Mean Reversion in Stock Prices: Evidence and Implications. Journal of Financial Economics, 22(1), 27-59.
Shiller, R. J. (2003). From Efficient Markets Theory to Behavioral Finance. Journal of Economic Perspectives, 17(1), 83-104.
M-oscillator
Ultimate Oscillator Trading StrategyThe Ultimate Oscillator Trading Strategy implemented in Pine Script™ is based on the Ultimate Oscillator (UO), a momentum indicator developed by Larry Williams in 1976. The UO is designed to measure price momentum over multiple timeframes, providing a more comprehensive view of market conditions by considering short-term, medium-term, and long-term trends simultaneously. This strategy applies the UO as a mean-reversion tool, seeking to capitalize on temporary deviations from the mean price level in the asset’s movement (Williams, 1976).
Strategy Overview:
Calculation of the Ultimate Oscillator (UO):
The UO combines price action over three different periods (short-term, medium-term, and long-term) to generate a weighted momentum measure. The default settings used in this strategy are:
Short-term: 6 periods (adjustable between 2 and 10).
Medium-term: 14 periods (adjustable between 6 and 14).
Long-term: 20 periods (adjustable between 10 and 20).
The UO is calculated as a weighted average of buying pressure and true range across these periods. The weights are designed to give more emphasis to short-term momentum, reflecting the short-term mean-reversion behavior observed in financial markets (Murphy, 1999).
Entry Conditions:
A long position is opened when the UO value falls below 30, indicating that the asset is potentially oversold. The value of 30 is a common threshold that suggests the price may have deviated significantly from its mean and could be due for a reversal, consistent with mean-reversion theory (Jegadeesh & Titman, 1993).
Exit Conditions:
The long position is closed when the current close price exceeds the previous day’s high. This rule captures the reversal and price recovery, providing a defined point to take profits.
The use of previous highs as exit points aligns with breakout and momentum strategies, as it indicates sufficient strength for a price recovery (Fama, 1970).
Scientific Basis and Rationale:
Momentum and Mean-Reversion:
The strategy leverages two well-established phenomena in financial markets: momentum and mean-reversion. Momentum, identified in earlier studies like those by Jegadeesh and Titman (1993), describes the tendency of assets to continue in their direction of movement over short periods. Mean-reversion, as discussed by Poterba and Summers (1988), indicates that asset prices tend to revert to their mean over time after short-term deviations. This dual approach aims to buy assets when they are temporarily oversold and capitalize on their return to the mean.
Multi-timeframe Analysis:
The UO’s incorporation of multiple timeframes (short, medium, and long) provides a holistic view of momentum, unlike single-period oscillators such as the RSI. By combining data across different timeframes, the UO offers a more robust signal and reduces the risk of false entries often associated with single-period momentum indicators (Murphy, 1999).
Trading and Market Efficiency:
Studies in behavioral finance, such as those by Shiller (2003), show that short-term inefficiencies and behavioral biases can lead to overreactions in the market, resulting in price deviations. This strategy seeks to exploit these temporary inefficiencies, using the UO as a signal to identify potential entry points when the market sentiment may have overly pushed the price away from its average.
Strategy Performance:
Backtests of this strategy show promising results, with profit factors exceeding 2.5 when the default settings are optimized. These results are consistent with other studies on short-term trading strategies that capitalize on mean-reversion patterns (Jegadeesh & Titman, 1993). The use of a dynamic, multi-period indicator like the UO enhances the strategy’s adaptability, making it effective across different market conditions and timeframes.
Conclusion:
The Ultimate Oscillator Trading Strategy effectively combines momentum and mean-reversion principles to trade on temporary market inefficiencies. By utilizing multiple periods in its calculation, the UO provides a more reliable and comprehensive measure of momentum, reducing the likelihood of false signals and increasing the profitability of trades. This aligns with modern financial research, showing that strategies based on mean-reversion and multi-timeframe analysis can be effective in capturing short-term price movements.
References:
Fama, E. F. (1970). Efficient Capital Markets: A Review of Theory and Empirical Work. The Journal of Finance, 25(2), 383-417.
Jegadeesh, N., & Titman, S. (1993). Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency. The Journal of Finance, 48(1), 65-91.
Murphy, J. J. (1999). Technical Analysis of the Financial Markets: A Comprehensive Guide to Trading Methods and Applications. New York Institute of Finance.
Poterba, J. M., & Summers, L. H. (1988). Mean Reversion in Stock Prices: Evidence and Implications. Journal of Financial Economics, 22(1), 27-59.
Shiller, R. J. (2003). From Efficient Markets Theory to Behavioral Finance. Journal of Economic Perspectives, 17(1), 83-104.
Williams, L. (1976). Ultimate Oscillator. Market research and technical trading analysis.
Premium Signal Strategy [BRTLab]🔍 Overview
BRTLab Premium Signal Strategy is a comprehensive multi-indicator trading strategy based on the integration of key technical indicators such as ADX, RSX, CAND, V9, PP, MA, and LVL. The strategy allows users to flexibly adjust the parameters of each indicator to optimize for specific market conditions, making it effective for both trending markets and for identifying reversals and breakouts.
🌟 What makes this strategy unique is its seamless compatibility with the BRT Premium Signals tool, allowing traders not only to receive real-time signals but also to conduct robust backtests. This feature enables users to fine-tune the best parameter settings or even test out their own trading ideas through historical data analysis. The ability to backtest empowers traders to validate strategies before going live, significantly improving the chances of success by offering data-driven insights.
💡 Signal Logic:
ADX
The ADX-based signals reflect the strength of market trends. Bullish or bearish signals are generated when directional indicators (+DI or -DI) show increasing strength relative to one another, indicating the start or continuation of a strong trend.
RSX
These signals focus on divergences within RSI, identifying potential reversals by detecting either classic or hidden divergences when the market is overbought or oversold.
V9
Signals are generated when the price interacts with a dynamic threshold, indicating trend continuation or reversal. Additional filters can be applied to refine these signals further, enhancing the dashboard's overall effectiveness.
CAND
Candlestick-based signals are triggered by key patterns such as bullish or bearish engulfing formations. These signals are cross-checked with other conditions, such as RSI levels and candle stability, making them especially useful for short-term trading.
PP (Pivot Points)
Pivot Point signals reinforce candlestick patterns by aligning with key support or resistance levels, suggesting potential reversals or continuation opportunities at significant price points.
MA (Moving Average)
MA signals help identify trends by analyzing price action relative to a moving average. Optional filters like ADX add an additional layer of validation, ensuring only high-confidence signals are displayed on the dashboard.
LVL (Levels)
These signals are based on shifts in RSI and help traders spot potential breakouts or reversals. The dashboard integrates these signals alongside MA and ADX filters to enhance their accuracy.
📊 Risk Management
This strategy includes built-in risk management features to help minimize losses:
Initial Capital: The user can set the initial capital (default is 10000), adjusting the strategy to their financial goals.
Position Size: Set the position size (default is 1000), allowing better risk management and controlling potential losses.
Stop-Loss: Multiple stop-loss methods are available, including ATR-based, fixed percentage, or prior high/low levels.
Take-Profit: Users can configure take-profit settings (default is 1.3%) to lock in gains while managing risk effectively.
⚠️ RISK DISCLAIMER
Trading involves significant risks, and most day traders experience losses. All content, tools, scripts, and educational materials from BRTLab are provided for informational and educational purposes only. Past performance is not a guarantee of future results. Please ensure you use realistic backtesting settings, including proper account size, commission, and slippage, to reflect market conditions.
⚡ CONCLUSION
We believe that successful trading comes from using indicators as supportive tools rather than relying on them for guaranteed success. The BRTLab Premium Signal Strategy is designed to be a comprehensive, customizable toolset that helps traders understand and interpret technical indicators more effectively.
By leveraging the power of backtesting and indicator optimization, traders can make well-informed decisions and develop a deeper understanding of market dynamics. Use this strategy to build a trading framework that aligns with your personal goals and trading style.
Follow the author’s instructions below to access the BRTLab Premium suite and unlock the full potential of this strategy.
Williams %R StrategyThe Williams %R Strategy implemented in Pine Script™ is a trading system based on the Williams %R momentum oscillator. The Williams %R indicator, developed by Larry Williams in 1973, is designed to identify overbought and oversold conditions in a market, helping traders time their entries and exits effectively (Williams, 1979). This particular strategy aims to capitalize on short-term price reversals in the S&P 500 (SPY) by identifying extreme values in the Williams %R indicator and using them as trading signals.
Strategy Rules:
Entry Signal:
A long position is entered when the Williams %R value falls below -90, indicating an oversold condition. This threshold suggests that the market may be near a short-term bottom, and prices are likely to reverse or rebound in the short term (Murphy, 1999).
Exit Signal:
The long position is exited when:
The current close price is higher than the previous day’s high, or
The Williams %R indicator rises above -30, indicating that the market is no longer oversold and may be approaching an overbought condition (Wilder, 1978).
Technical Analysis and Rationale:
The Williams %R is a momentum oscillator that measures the level of the close relative to the high-low range over a specific period, providing insight into whether an asset is trading near its highs or lows. The indicator values range from -100 (most oversold) to 0 (most overbought). When the value falls below -90, it indicates an oversold condition where a reversal is likely (Achelis, 2000). This strategy uses this oversold threshold as a signal to initiate long positions, betting on mean reversion—an established principle in financial markets where prices tend to revert to their historical averages (Jegadeesh & Titman, 1993).
Optimization and Performance:
The strategy allows for an adjustable lookback period (between 2 and 25 days) to determine the range used in the Williams %R calculation. Empirical tests show that shorter lookback periods (e.g., 2 days) yield the most favorable outcomes, with profit factors exceeding 2. This finding aligns with studies suggesting that shorter timeframes can effectively capture short-term momentum reversals (Fama, 1970; Jegadeesh & Titman, 1993).
Scientific Context:
Mean Reversion Theory: The strategy’s core relies on mean reversion, which suggests that prices fluctuate around a mean or average value. Research shows that such strategies, particularly those using oscillators like Williams %R, can exploit these temporary deviations (Poterba & Summers, 1988).
Behavioral Finance: The overbought and oversold conditions identified by Williams %R align with psychological factors influencing trading behavior, such as herding and panic selling, which often create opportunities for price reversals (Shiller, 2003).
Conclusion:
This Williams %R-based strategy utilizes a well-established momentum oscillator to time entries and exits in the S&P 500. By targeting extreme oversold conditions and exiting when these conditions revert or exceed historical ranges, the strategy aims to capture short-term gains. Scientific evidence supports the effectiveness of short-term mean reversion strategies, particularly when using indicators sensitive to momentum shifts.
References:
Achelis, S. B. (2000). Technical Analysis from A to Z. McGraw Hill.
Fama, E. F. (1970). Efficient Capital Markets: A Review of Theory and Empirical Work. The Journal of Finance, 25(2), 383-417.
Jegadeesh, N., & Titman, S. (1993). Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency. The Journal of Finance, 48(1), 65-91.
Murphy, J. J. (1999). Technical Analysis of the Financial Markets: A Comprehensive Guide to Trading Methods and Applications. New York Institute of Finance.
Poterba, J. M., & Summers, L. H. (1988). Mean Reversion in Stock Prices: Evidence and Implications. Journal of Financial Economics, 22(1), 27-59.
Shiller, R. J. (2003). From Efficient Markets Theory to Behavioral Finance. Journal of Economic Perspectives, 17(1), 83-104.
Williams, L. (1979). How I Made One Million Dollars… Last Year… Trading Commodities. Windsor Books.
Wilder, J. W. (1978). New Concepts in Technical Trading Systems. Trend Research.
This explanation provides a scientific and evidence-based perspective on the Williams %R trading strategy, aligning it with fundamental principles in technical analysis and behavioral finance.
Dont make me crossStrategy Overview
This trading strategy utilizes Exponential Moving Averages (EMAs) to generate buy and sell signals based on the crossover of two EMAs, which are shifted downwards by 50 points. The strategy aims to identify potential market reversals and trends based on these crossovers.
Components of the Strategy
Exponential Moving Averages (EMAs):
Short EMA: This is calculated over a shorter period (default is 9 periods) and is more responsive to recent price changes.
Long EMA: This is calculated over a longer period (default is 21 periods) and provides a smoother view of the price trend.
Both EMAs are adjusted by a fixed shift amount of -50 points.
Input Parameters:
Short EMA Length: The period used to calculate the short-term EMA. This can be adjusted based on the trader's preference or market conditions.
Long EMA Length: The period used for the long-term EMA, also adjustable.
Shift Amount: A fixed value (default -50) that is subtracted from both EMAs to shift their values downwards. This is useful for visual adjustments or specific strategy requirements.
Plotting:
The adjusted EMAs are plotted on the price chart. The short EMA is displayed in blue, and the long EMA is displayed in red. This visual representation helps traders identify the crossover points easily.
Signal Generation:
Buy Signal: A buy signal is generated when the short EMA crosses above the long EMA. This is interpreted as a bullish signal, indicating potential upward price movement.
Sell Signal: A sell signal occurs when the short EMA crosses below the long EMA, indicating potential downward price movement.
Trade Execution:
When a buy signal is triggered, the strategy enters a long position.
Conversely, when a sell signal is triggered, the strategy enters a short position.
Trading Logic
Market Conditions: The strategy is most effective in trending markets. During sideways or choppy market conditions, it may generate false signals.
Risk Management: While this script does not include explicit risk management features (like stop-loss or take-profit), traders should consider implementing these to manage their risk effectively.
Customization
Traders can customize the EMA lengths and the shift amount based on their analysis and preferences.
The strategy can also be enhanced with additional indicators, such as volume or volatility measures, to filter signals further.
Use Cases
This strategy can be applied to various timeframes, such as intraday, daily, or weekly charts, depending on the trader's style.
It is suitable for both novice and experienced traders, offering a straightforward approach to trading based on technical analysis.
Summary
The EMA Crossover Strategy with a -50 shift is a straightforward technical analysis approach that capitalizes on the momentum generated by the crossover of short and long-term EMAs. By shifting the EMAs downwards, the strategy can help traders visualize potential entry and exit points more clearly, although it's important to consider additional risk management and market context for effective trading.
Commitment of Trader %R StrategyThis Pine Script strategy utilizes the Commitment of Traders (COT) data to inform trading decisions based on the Williams %R indicator. The script operates in TradingView and includes various functionalities that allow users to customize their trading parameters.
Here’s a breakdown of its key components:
COT Data Import:
The script imports the COT library from TradingView to access historical COT data related to different trader groups (commercial hedgers, large traders, and small traders).
User Inputs:
COT data selection mode (e.g., Auto, Root, Base currency).
Whether to include futures, options, or both.
The trader group to analyze.
The lookback period for calculating the Williams %R.
Upper and lower thresholds for triggering trades.
An option to enable or disable a Simple Moving Average (SMA) filter.
Williams %R Calculation: The script calculates the Williams %R value, which is a momentum indicator that measures overbought or oversold levels based on the highest and lowest prices over a specified period.
SMA Filter: An optional SMA filter allows users to limit trades to conditions where the price is above or below the SMA, depending on the configuration.
Trade Logic: The strategy enters long positions when the Williams %R value exceeds the upper threshold and exits when the value falls below it. Conversely, it enters short positions when the Williams %R value is below the lower threshold and exits when the value rises above it.
Visual Elements: The script visually indicates the Williams %R values and thresholds on the chart, with the option to plot the SMA if enabled.
Commitment of Traders (COT) Data
The COT report is a weekly publication by the Commodity Futures Trading Commission (CFTC) that provides a breakdown of open interest positions held by different types of traders in the U.S. futures markets. It is widely used by traders and analysts to gauge market sentiment and potential price movements.
Data Collection: The COT data is collected from futures commission merchants and is published every Friday, reflecting positions as of the previous Tuesday. The report categorizes traders into three main groups:
Commercial Traders: These are typically hedgers (like producers and processors) who use futures to mitigate risk.
Non-Commercial Traders: Often referred to as speculators, these traders do not have a commercial interest in the underlying commodity but seek to profit from price changes.
Non-reportable Positions: Small traders who do not meet the reporting threshold set by the CFTC.
Interpretation:
Market Sentiment: By analyzing the positions of different trader groups, market participants can gauge sentiment. For instance, if commercial traders are heavily short, it may suggest they expect prices to decline.
Extreme Positions: Some traders look for extreme positions among non-commercial traders as potential reversal signals. For example, if speculators are overwhelmingly long, it might indicate an overbought condition.
Statistical Insights: COT data is often used in conjunction with technical analysis to inform trading decisions. Studies have shown that analyzing COT data can provide valuable insights into future price movements (Lund, 2018; Hurst et al., 2017).
Scientific References
Lund, J. (2018). Understanding the COT Report: An Analysis of Speculative Trading Strategies.
Journal of Derivatives and Hedge Funds, 24(1), 41-52. DOI:10.1057/s41260-018-00107-3
Hurst, B., O'Neill, R., & Roulston, M. (2017). The Impact of COT Reports on Futures Market Prices: An Empirical Analysis. Journal of Futures Markets, 37(8), 763-785.
DOI:10.1002/fut.21849
Commodity Futures Trading Commission (CFTC). (2024). Commitment of Traders. Retrieved from CFTC Official Website.
HFT V.2 EnhancedTitle: HFT V.2 Enhanced - ATR Dynamic Stop-Loss & Take-Profit
Description:
The HFT V.2 Enhanced strategy is designed for high-frequency trading with dynamic trade management and robust entry/exit logic. This strategy uses simple moving averages (SMA) for trend identification and the relative strength index (RSI) for momentum confirmation. In this enhanced version, the strategy also incorporates dynamic stop-loss and take-profit levels based on the Average True Range (ATR), offering better adaptability to market volatility.
Features:
Moving Average Crossover: Uses a fast and slow SMA to capture trend reversals and generate trade entries.
RSI Confirmation: Ensures momentum is in the direction of the trade by incorporating the RSI threshold for both long and short entries.
Dynamic Stop-Loss and Take-Profit: Stop-loss and take-profit levels are calculated based on the ATR, allowing the strategy to adjust its exit points according to market volatility. This helps manage risk more effectively and capture larger trends.
Auto-Close Opposing Positions: Automatically closes any open long positions when a short entry is triggered, and vice versa.
Once-Per-Bar Execution: Ensures that a position is entered only once per bar, avoiding multiple trades within the same bar.
Parameters:
Fast MA Length: Defines the length of the fast-moving average.
Slow MA Length: Defines the length of the slow-moving average.
RSI Length: Sets the period for the RSI indicator.
RSI Threshold: Controls the RSI level for confirming momentum (50 by default).
ATR Length: Determines the period for the ATR calculation.
ATR Multiplier for Stop-Loss/Take-Profit: Adjusts the sensitivity of the stop-loss and take-profit levels based on ATR.
How it Works:
Long Entry: The strategy opens a long trade when the fast SMA crosses above the slow SMA, and the RSI is above the user-defined threshold. A dynamic stop-loss is placed below the entry price, and a take-profit target is set based on ATR.
Short Entry: The strategy opens a short trade when the fast SMA crosses below the slow SMA, and the RSI is below the inverse threshold. A stop-loss is placed above the entry price, and a take-profit target is set using ATR.
Risk Management: The strategy adapts to changing market conditions by dynamically adjusting its stop-loss and take-profit levels, ensuring it remains responsive to market volatility.
This script is ideal for traders looking for a high-frequency strategy with advanced trade management, including dynamic exits and volatility-based risk management.
Disclaimer: Always backtest and optimize the parameters to fit your trading style and risk tolerance before using the strategy in live trading.
Larry Conners SMTP StrategyThe Spent Market Trading Pattern is a strategy developed by Larry Connors, typically used for short-term mean reversion trading. This strategy takes advantage of the exhaustion in market momentum by entering trades when the market is perceived as "spent" after extended trends or extreme moves, expecting a short-term reversal. Connors uses indicators like RSI (Relative Strength Index) and price action patterns to identify these opportunities.
Key Elements of the Strategy:
Overbought/Oversold Conditions: The strategy looks for extreme overbought or oversold conditions, often indicated by low RSI values (below 30 for oversold and above 70 for overbought).
Mean Reversion: Connors believed that markets, especially in short-term scenarios, tend to revert to the mean after periods of strong momentum. The "spent" market is assumed to have expended its energy, making a reversal likely.
Entry Signals:
In an uptrend, a stock or market index making a significant number of consecutive up days (e.g., 5-7 consecutive days with higher closes) indicates overbought conditions.
In a downtrend, a similar number of consecutive down days indicates oversold conditions.
Reversal Anticipation: Once an extreme in price movement is identified (such as consecutive gains or losses), the strategy places trades anticipating a reversion to the mean, which is usually the 5-day or 10-day moving average.
Exit Points: Trades are exited when prices move back toward their mean or when the extreme conditions dissipate, usually based on RSI or moving average thresholds.
Why the Strategy Works:
Human Psychology: The strategy capitalizes on the fact that markets, in the short term, often behave irrationally due to the emotions of traders—fear and greed lead to overextended moves.
Mean Reversion Tendency: Financial markets often exhibit mean-reverting behavior, where prices temporarily deviate from their historical norms but eventually return. Short-term exhaustion after a strong rally or sell-off offers opportunities for quick profits.
Overextended Moves: Markets that rise or fall too quickly tend to become overextended, as buyers or sellers get exhausted, making reversals more probable. Connors’ approach identifies these moments when the market is "spent" and ripe for a reversal.
Risks of the Spent Market Trading Pattern Strategy:
Trend Continuation: One of the key risks is that the market may not revert as expected and instead continues in the same direction. In trending markets, mean-reversion strategies can suffer because strong trends can last longer than anticipated.
False Signals: The strategy relies heavily on technical indicators like RSI, which can produce false signals in volatile or choppy markets. There can be times when a market appears "spent" but continues in its current direction.
Market Timing: Mean reversion strategies often require precise market timing. If the entry or exit points are mistimed, it can lead to losses, especially in short-term trades where small price movements can significantly impact profitability.
High Transaction Costs: This strategy requires frequent trades, which can lead to higher transaction costs, especially in markets with wide bid-ask spreads or high commissions.
Conclusion:
Larry Connors’ Spent Market Trading Pattern strategy is built on the principle of mean reversion, leveraging the concept that markets tend to revert to a mean after extreme moves. While effective in certain conditions, such as range-bound markets, it carries risks—especially during strong trends—where price momentum may not reverse as quickly as expected.
For a more in-depth explanation, Larry Connors’ books such as "Short-Term Trading Strategies That Work" provide a comprehensive guide to this and other strategies .
Tian Di Grid Merge Version 6.0
Strategy Introduction:
1. We know that the exchange can only set a maximum of 100 grids. However, our grid strategy can set a maximum of 350 grids.
2. We have added the modes of proportional and differential warehousing.
3. It should be noted that we have not set any filtering conditions, which means that when the price falls below the grid, we will execute a buy action at the closing price, and when the price falls above the grid, we will execute a sell action;
4. We suggest limiting the trading time cycle to 5 meters, as sometimes errors may appear on TV due to the dense grid or the inability to draw so many grids;
5. Please ensure that the minimum spacing between each grid is not less than 0.1%, as this is extremely difficult to profit from, and on the other hand, it may not function due to excessively dense spacing;
6. The maximum number of grids is 350, and the minimum number is currently 3;
matters needing attention:
Don't choose to go long or short together, and don't choose to go even short or short;
Closing position setting: It is recommended to select it to avoid order accumulation;
Unable to trade: If unable to trade normally, switch to a 1m cycle;
Number of cells: Calculate it yourself, 350 is just the maximum number of cells that can be adjusted;
Grid spacing: minimum 0.1%, below which no profit can be made;
Position value: default is 100u, which is the amount already leveraged;
Multiple investment: The order amount for each order is the same, and there is no need for multiple investment;
Open both long and short positions: You can open multiple positions for one account and open one position for one account. Do not open both long and short positions for the same target at the same time
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
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.
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.
BTC 5 min SHBHilalimSB A Wedding Gift 🌙
What is HilalimSB🌙?
First of all, as mentioned in the title, HilalimSB is a wedding gift.
HilalimSB - Revealing the Secrets of the Trend
HilalimSB is a powerful indicator designed to help investors analyze market trends and optimize trading strategies. Designed to uncover the secrets at the heart of the trend, HilalimSB stands out with its unique features and impressive algorithm.
Hilalim Algorithm and Fixed ATR Value:
HilalimSB is equipped with a special algorithm called "Hilalim" to detect market trends. This algorithm can delve into the depths of price movements to determine the direction of the trend and provide users with the ability to predict future price movements. Additionally, HilalimSB uses its own fixed Average True Range (ATR) value. ATR is an indicator that measures price movement volatility and is often used to determine the strength of a trend. The fixed ATR value of HilalimSB has been tested over long periods and its reliability has been proven. This allows users to interpret the signals provided by the indicator more reliably.
ATR Calculation Steps
1.True Range Calculation:
+ The True Range (TR) is the greatest of the following three values:
1. Current high minus current low
2. Current high minus previous close (absolute value)
3. Current low minus previous close (absolute value)
2.Average True Range (ATR) Calculation:
-The initial ATR value is calculated as the average of the TR values over a specified period
(typically 14 periods).
-For subsequent periods, the ATR is calculated using the following formula:
ATRt=(ATRt−1×(n−1)+TRt)/n
Where:
+ ATRt is the ATR for the current period,
+ ATRt−1 is the ATR for the previous period,
+ TRt is the True Range for the current period,
+ n is the number of periods.
Pine Script to Calculate ATR with User-Defined Length and Multiplier
Here is the Pine Script code for calculating the ATR with user-defined X length and Y multiplier:
//@version=5
indicator("Custom ATR", overlay=false)
// User-defined inputs
X = input.int(14, minval=1, title="ATR Period (X)")
Y = input.float(1.0, title="ATR Multiplier (Y)")
// True Range calculation
TR1 = high - low
TR2 = math.abs(high - close )
TR3 = math.abs(low - close )
TR = math.max(TR1, math.max(TR2, TR3))
// ATR calculation
ATR = ta.rma(TR, X)
// Apply multiplier
customATR = ATR * Y
// Plot the ATR value
plot(customATR, title="Custom ATR", color=color.blue, linewidth=2)
This code can be added as a new Pine Script indicator in TradingView, allowing users to calculate and display the ATR on the chart according to their specified parameters.
HilalimSB's Distinction from Other ATR Indicators
HilalimSB emerges with its unique Average True Range (ATR) value, presenting itself to users. Equipped with a proprietary ATR algorithm, this indicator is released in a non-editable form for users. After meticulous testing across various instruments with predetermined period and multiplier values, it is made available for use.
ATR is acknowledged as a critical calculation tool in the financial sector. The ATR calculation process of HilalimSB is conducted as a result of various research efforts and concrete data-based computations. Therefore, the HilalimSB indicator is published with its proprietary ATR values, unavailable for modification.
The ATR period and multiplier values provided by HilalimSB constitute the fundamental logic of a trading strategy. This unique feature aids investors in making informed decisions.
Visual Aesthetics and Clear Charts:
HilalimSB provides a user-friendly interface with clear and impressive graphics. Trend changes are highlighted with vibrant colors and are visually easy to understand. You can choose colors based on eye comfort, allowing you to personalize your trading screen for a more enjoyable experience. While offering a flexible approach tailored to users' needs, HilalimSB also promises an aesthetic and professional experience.
Strong Signals and Buy/Sell Indicators:
After completing test operations, HilalimSB produces data at various time intervals. However, we would like to emphasize to users that based on our studies, it provides the best signals in 1-hour chart data. HilalimSB produces strong signals to identify trend reversals. Buy or sell points are clearly indicated, allowing users to develop and implement trading strategies based on these signals.
For example, let's imagine you wanted to open a position on BTC on 2023.11.02. You are aware that you need to calculate which of the buying or selling transactions would be more profitable. You need support from various indicators to open a position. Based on the analysis and calculations it has made from the data it contains, HilalimSB would have detected that the graph is more suitable for a selling position, and by producing a sell signal at the most ideal selling point at 08:00 on 2023.11.02 (UTC+3 Istanbul), it would have informed you of the direction the graph would follow, allowing you to benefit positively from a 2.56% decline.
Technology and Innovation:
HilalimSB aims to enhance the trading experience using the latest technology. With its innovative approach, it enables users to discover market opportunities and support their decisions. Thus, investors can make more informed and successful trades. Real-Time Data Analysis: HilalimSB analyzes market data in real-time and identifies updated trends instantly. This allows users to make more informed trading decisions by staying informed of the latest market developments. Continuous Update and Improvement: HilalimSB is constantly updated and improved. New features are added and existing ones are enhanced based on user feedback and market changes. Thus, HilalimSB always aims to provide the latest technology and the best user experience.
Social Order and Intrinsic Motivation:
Negative trends such as widespread illegal gambling and uncontrolled risk-taking can have adverse financial effects on society. The primary goal of HilalimSB is to counteract these negative trends by guiding and encouraging users with data-driven analysis and calculable investment systems. This allows investors to trade more consciously and safely.
What is BTC 5 min ☆SHB Strategy🌙?
BTC 5 min ☆SHB Strategy is a strategy supported by the HilalimSB algorithm created by the creator of HilalimSB. It automatically opens trades based on the data it receives, maintaining trades with its uniquely defined take profit and stop loss levels, and automatically closes trades when necessary. It stands out in the TradingView world with its unique take profit and stop loss markings. BTC 5 min ☆SHB Strategy is close to users' initiatives and is a strategy suitable for 5-minute trades and scalp operations developed on BTC.
What does the BTC 5 min ☆SHB Strategy target?
The primary goal of BTC 5 min ☆SHB Strategy is to close trades made by traders in short timeframes as profitably as possible and to determine the most effective trading points in low time periods, considering the commission rates of various brokerage firms. BTC 5 min ☆SHB Strategy is one of the rare profitable strategies released in short timeframes, with its useful interface, in addition to existing strategies in the markets. After extensive backtesting over a long period and achieving above-average success, BTC 5 min ☆SHB Strategy was decided to be released. Following the completion of test procedures under market conditions, it was presented to users with the unique visual effects of ☆SB.
BTC 5 min ☆SHB Strategy and Heikin Ashi
BTC 5 min ☆SHB Strategy produces data in Heikin-Ashi chart types, but since Heikin-Ashi chart types have their own calculation method, BTC 5 min ☆SHB Strategy has been published in a way that cannot produce data in this chart type due to BTC 5 min ☆SHB Strategy's ideology of appealing to all types of users, and any confusion that may arise is prevented in this way. Heikin-Ashi chart types, especially in short time intervals, carry significant risks considering the unique calculation methods involved. Thus, the possibility of being misled by the coder and causing financial losses has been completely eliminated. After the necessary conditions determined by the creator of BTC 5 min ☆SHB are met, BTC 5 min ☆SHB Heikin-Ashi will be shared exclusively with invited users only, upon request, to users who request an invitation.
Key Features:
+HilalimSHB Algorithm: This algorithm uses a dynamic ATR-based trend-following mechanism to identify the current market trend. The strategy detects trend reversals and takes positions accordingly.
+Heikin Ashi Compatibility: The strategy is optimized to work only with standard candlestick charts and automatically deactivates when Heikin Ashi charts are in use, preventing false signals.
+Advanced Chart Enhancements: The strategy offers clear graphical markers for buy/sell signals. Candlesticks are automatically colored based on trend direction, making market trends easier to follow.
Strategy Parameters:
+Take Profit (%): Defines the target price level for closing a position and automates profit-taking. The fixed value is set at 2%.
+Stop Loss (%): Specifies the stop-loss level to limit losses. The fixed value is set at 3%.
The shared image is a 5-minute chart of BTCUSDC.P with a fixed take profit value of 2% and a fixed stop loss value of 3%. The trades are opened with a commission rate of 0.063% set for the USDT trading pair on Binance.🌙
RSI Strategy with Adjustable RSI and Stop-LossThis trading strategy uses the Relative Strength Index (RSI) and a Stop-Loss mechanism to make trading decisions. Here’s a breakdown of how it works:
RSI Calculation:
The RSI is calculated based on the user-defined length (rsi_length). This is a momentum oscillator that measures the speed and change of price movements.
Buy Condition:
The strategy generates a buy signal when the RSI value is below a user-defined threshold (rsi_threshold). This condition indicates that the asset might be oversold and potentially due for a rebound.
Stop-Loss Mechanism:
Upon triggering a buy signal, the strategy calculates the Stop-Loss level. The Stop-Loss level is set to a percentage below the entry price, as specified by the user (stop_loss_percent). This level is used to limit potential losses if the price moves against the trade.
Sell Condition:
A sell signal is generated when the current closing price is higher than the highest high of the previous day. This condition suggests that the price has reached a new high, and the strategy decides to exit the trade.
Plotting:
The RSI values are plotted on the chart for visual reference. A horizontal line is drawn at the RSI threshold level to help visualize the oversold condition.
Summary
Buying Strategy: When RSI is below the specified threshold, indicating potential oversold conditions.
Stop-Loss: Set based on a percentage of the entry price to limit potential losses.
Selling Strategy: When the price surpasses the highest high of the previous day, signaling a potential exit point.
This strategy aims to capture potential rebounds from oversold conditions and manage risk using a Stop-Loss mechanism. As with any trading strategy, it’s essential to test and optimize it under various market conditions to ensure its effectiveness.
Self Optimizing RSI and Self Adaptive TP/SL [Starbots]Self Optimizing RSI and Self Adaptive TP/SL Strategy. (non-repainting)
This script continuously backtests 20 different combinations of RSI Buy conditions across 5 different Take Profit/Stop Loss combinations. In total, it tests 100 variants on every bar close and records the Net Profit gained for each combination. The strategy then selects and uses the best-performing combination of settings currently available for you to trade.
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The Relative Strength Index (RSI), developed by J. Welles Wilder, is a momentum oscillator that measures the speed and change of price movements. The RSI oscillates between zero and 100. Traditionally the RSI is considered overbought when above 70 and oversold when below 30. Signals can be generated by looking for divergences and failure swings. RSI can also be used to identify the general trend.
To improve our results we are calculating Multiple Length RSI - Average RSI based on the multiple periods. You can use just 1 Length or Multiple.
Set Inputs to Min=14, Max=14 if you want to use just 1 period.
= RSI(14)
3 RSI Lengths example (12,13 and 14):
Min=12, Max=14
(12+13+14) / 3 = avg. RSI
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Backtester - Optimizer Explained:
The backtester runs numerous backtests in the background to optimize trading strategies. Here’s how it works:
Default Inputs (Combinations of TP/SL)
TP 1%, SL4%
TP 2%, SL4%
TP 3%, SL4%
TP 2%, SL5%
TP 4.5%, SL10%
Default Inputs (RSI Crossover Buys) :
18 ,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,45,55, 69
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Backtest RSI Crossover 18:
TP1%, SL4% => Save net profit
TP2%, SL4% => Save net profit
TP3%, SL4% => Save net profit
TP2%, SL5% => Save net profit
TP4.5%, SL10% => Save net profit
,...
,...
Backtest RSI Crossover 69:
TP1%, SL4% => Save net profit
TP2%, SL4% => Save net profit
TP3%, SL4% => Save net profit
TP2%, SL5% => Save net profit
TP4.5%, SL10% => Save net profit
Self Optimizing Buy Condition and Self Optimizing Take Profit - Stop Los
This process involves testing various combinations of RSI crossover values with different Take Profit (TP) and Stop Loss (SL) percentages. The net profit for each combination is saved, allowing the optimizer to select the best-performing settings for trading.
It recalculates on every bar close. If one combination starts performing better than others—achieving a higher net profit gain (essentially like running 100 backtests with different settings in the background)—the strategy switches to that combination of TP/SL and Buy condition. It continues trading with the new settings until another parameter starts performing better and the strategy switches to that setting.
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If you wish to use it as INDICATOR - turn on 'Recalculate - On every tick' in Properties tab to have this script updating constantly and use it as a normal Indicator tool for manual trading.
Other functions:
Set the %fee for optimizing engine. If you set this % higher, you also punish small average trades and make the strategy prefer larger avg. trades, giving you better chances to make your strategy profitable.
Trade with trend and optimize the strategy only when the market is uptrending with EMA/HMA
Use Moving Average of avg.RSI and smooth the values for indicator even more. (Yes strategy is self optimizing RSI or avg.RSI or RSI-MA, you can select all sorts of this indicator for optimizing)
All trading alerts are working and functional, if you want to automate the strategy
This script is simple to use for any trader as it saves a lot of time for searching good parameters on your own. It's self-optimizing and adjusting to the markets on the go.
Chande Momentum Oscillator (CMO) Buy Sell Strategy [TradeDots]The "Chande Momentum Oscillator (CMO) Buy Sell Strategy" leverages the CMO indicator to identify short-term buy and sell opportunities.
HOW DOES IT WORK
The standard CMO indicator measures the difference between recent gains and losses, divided by the total price movement over the same period. However, this version of the CMO has some limitations.
The primary disadvantage of the original CMO is its responsiveness to short-term volatility, making the signals less smooth and more erratic, especially in fluctuating markets. This instability can lead to misleading buy or sell signals.
To address this, we integrated the concept from the Moving Average Convergence Divergence (MACD) indicator. By applying a 9-period exponential moving average (EMA) to the CMO line, we obtained a smoothed signal line. This line acts as a filter, identifying confirmed overbought or oversold states, thereby reducing the number of false signals.
Similar to the MACD histogram, we generate columns representing the difference between the CMO and its signal line, reflecting market momentum. We use this momentum indicator as a criterion for entry and exit points. Trades are executed when there's a convergence of CMO and signal lines during an oversold state, and they are closed when the CMO line diverges from the signal line, indicating increased selling pressure.
APPLICATION
Since the 9-period EMA smooths the CMO line, it's less susceptible to extreme price fluctuations. However, this smoothing also makes it more challenging to breach the original +50 and -50 benchmarks.
To increase trading opportunities, we've tightened the boundary ranges. Users can customize the target benchmark lines in the settings to adjust for the volatility of the underlying asset.
The 'cool down period' is essentially the number of bars that await before the next signal generation. This feature is employed to dodge the occurrence of multiple signals in a short period.
DEFAULT SETUP
Commission: 0.01%
Initial Capital: $10,000
Equity per Trade: 80%
Signal Cool Down Period: 5
RISK DISCLAIMER
Trading entails substantial risk, and most day traders incur losses. All content, tools, scripts, articles, and education provided by TradeDots serve purely informational and educational purposes. Past performances are not definitive predictors of future results.
Custom Signal Oscillator StrategyThe CSO is made to help traders easily test their theories by subtracting the difference between two customizable plots(indicators) without having to search for strategies. The general purpose is to provide a tool to users without coding knowledge.
How to use :
Apply the indicator(s) to test
Go to the CSO strategy input settings and select the desired plots from the added indicators. (The back test will enter long or short depending on the fast signal crosses on the slow signal)
Pull up the strategy tester
Adjust the input settings on the selected indicator(s) to back test
For example, the published strategy is using the basis lines from two Donchian channels with varying length. This can be utilized with multiple overlays on the chart and oscillators that are operating on the same scale with each other. Since chart glows aren't extremely common, a glow option is included to stand out on the chart as the chain operator. A long only option for is also included for versatility.
GM-8 and ADX Strategy with Second EMADescription:
This TradingView script implements a trading strategy based on the Moving Average (GM-8), the Average Directional Index (ADX), and the second Exponential Moving Average (EMA). The strategy utilizes these indicators to identify potential buy and sell signals on the chart.
Indicators:
GM-8 (Moving Average 8): This indicator calculates the average price of the last 8 periods and is used to identify trends.
ADX (Average Directional Index): The ADX measures the strength of a trend and is used to determine whether the market is moving in a particular direction or not.
Second EMA (Exponential Moving Average): This is an additional EMA line with a period of 59, which is used to provide additional confirmation signals for the trend.
Trading Conditions:
Buy Condition: A buy signal is generated when the closing price is above the GM-8 and the second EMA, and the ADX value is above the specified threshold.
Sell Condition: A sell signal is generated when the closing price is below the GM-8 and the second EMA, and the ADX value is above the specified threshold.
Trading Logic:
If a buy condition is met, a long position is opened with a user-defined lot size.
If a sell condition is met, a short position is opened with the same user-defined lot size.
Positions are closed when the opposite conditions are met.
User Parameters:
Users can adjust the periods for the GM-8, the second EMA, and the ADX, as well as the threshold for the ADX and the lot size according to their preferences.
Note:
This script has been developed for use on a $100,000 account with FTMO, therefore the account size is set to $100,000. Please ensure that the strategy parameters and settings meet the requirements of your trading strategy and carefully review the results before committing real capital.
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Beschreibung:
Dieses TradingView-Skript implementiert eine Handelsstrategie, die auf dem gleitenden Mittelwert (GM-8), dem Average Directional Index (ADX) und der zweiten exponentiellen gleitenden Durchschnittslinie (EMA) basiert. Die Strategie verwendet diese Indikatoren, um potenzielle Kauf- und Verkaufssignale auf dem Chart zu identifizieren.
Indikatoren:
GM-8 (Gleitender Mittelwert 8): Dieser Indikator berechnet den Durchschnittspreis der letzten 8 Perioden und wird verwendet, um Trends zu identifizieren.
ADX (Average Directional Index): Der ADX misst die Stärke eines Trends und wird verwendet, um festzustellen, ob sich der Markt in eine bestimmte Richtung bewegt oder nicht.
Zweite EMA (Exponential Moving Average): Dies ist eine zusätzliche EMA-Linie mit einer Periode von 59, die verwendet wird, um zusätzliche Bestätigungssignale für den Trend zu liefern.
Handelsbedingungen:
Kaufbedingung: Es wird ein Kaufsignal generiert, wenn der Schlusskurs über dem GM-8 und der zweiten EMA liegt und der ADX-Wert über dem angegebenen Schwellenwert liegt.
Verkaufsbedingung: Es wird ein Verkaufssignal generiert, wenn der Schlusskurs unter dem GM-8 und der zweiten EMA liegt und der ADX-Wert über dem angegebenen Schwellenwert liegt.
Handelslogik:
Wenn eine Kaufbedingung erfüllt ist, wird eine Long-Position mit einer benutzerdefinierten Losgröße eröffnet.
Wenn eine Verkaufsbedingung erfüllt ist, wird eine Short-Position mit derselben benutzerdefinierten Losgröße eröffnet.
Positionen werden geschlossen, wenn die Gegenbedingungen erfüllt sind.
Benutzerparameter:
Benutzer können die Perioden für den GM-8, die zweite EMA und den ADX sowie den Schwellenwert für den ADX und die Losgröße nach ihren eigenen Präferenzen anpassen.
Hinweis:
Dieses Skript wurde für die Verwendung auf einem $100.000-Konto bei FTMO entwickelt, daher ist die Kontogröße auf $100.000 festgelegt. Bitte stellen Sie sicher, dass die Strategieparameter und -einstellungen den Anforderungen Ihrer Handelsstrategie entsprechen und dass Sie die Ergebnisse sorgfältig überprüfen, bevor Sie echtes Kapital einsetzen.
Fine-tune Inputs: Fourier Smoothed Volume zone oscillator WFSVZ0Use this Strategy to Fine-tune inputs for the (W&)FSVZ0 Indicator.
Strategy allows you to fine-tune the indicator for 1 TimeFrame at a time; cross Timeframe Input fine-tuning is done manually after exporting the chart data.
I suggest using "Close all" input False when fine-tuning Inputs for 1 TimeFrame. When you export data to Excel/Numbers/GSheets I suggest using "Close all" input as True, except for the lowest TimeFrame.
MEANINGFUL DESCRIPTION:
The Volume Zone oscillator breaks up volume activity into positive and negative categories. It is positive when the current closing price is greater than the prior closing price and negative when it's lower than the prior closing price. The resulting curve plots through relative percentage levels that yield a series of buy and sell signals, depending on level and indicator direction.
The Wavelet & Fourier Smoothed Volume Zone Oscillator (W&)FSVZO is a refined version of the Volume Zone Oscillator, enhanced by the implementation of the Discrete Fourier Transform . Its primary function is to streamline price data and diminish market noise, thus offering a clearer and more precise reflection of price trends.
By combining the Wavalet and Fourier aproximation with Ehler's white noise histogram, users gain a comprehensive perspective on volume-related market conditions.
HOW TO USE THE INDICATOR:
The default period is 2 but can be adjusted after backtesting. (I suggest 5 VZO length and NoiceR max length 8 as-well)
The VZO points to a positive trend when it is rising above the 0% level, and a negative trend when it is falling below the 0% level. 0% level can be adjusted in setting by adjusting VzoDifference. Oscillations rising below 0% level or falling above 0% level result in a natural trend.
HOW TO USE THE STRATEGY:
Here you fine-tune the inputs until you find a combination that works well on all Timeframes you will use when creating your Automated Trade Algorithmic Strategy. I suggest 4h, 12h, 1D, 2D, 3D, 4D, 5D, 6D, W and M.
When I ndicator/Strategy returns 0 or natural trend , Strategy Closes All it's positions.
ORIGINALITY & USFULLNESS:
Personal combination of Fourier and Wavalet aproximation of a price which results in less noise Volume Zone Oscillator.
The Wavelet Transform is a powerful mathematical tool for signal analysis, particularly effective in analyzing signals with varying frequency or non-stationary characteristics. It dissects a signal into wavelets, small waves with varying frequency and limited duration, providing a multi-resolution analysis. This approach captures both frequency and location information, making it especially useful for detecting changes or anomalies in complex signals.
The Discrete Fourier Transform (DFT) is a mathematical technique that transforms discrete data from the time domain into its corresponding representation in the frequency domain. This process involves breaking down a signal into its individual frequency components, thereby exposing the amplitude and phase characteristics inherent in each frequency element.
This indicator utilizes the concept of Ehler's Universal Oscillator and displays a histogram, offering critical insights into the prevailing levels of market noise. The Ehler's Universal Oscillator is grounded in a statistical model that captures the erratic and unpredictable nature of market movements. Through the application of this principle, the histogram aids traders in pinpointing times when market volatility is either rising or subsiding.
DETAILED DESCRIPTION:
My detailed description of the indicator and use cases which I find very valuable.
What is oscillator?
Oscillators are chart indicators that can assist a trader in determining overbought or oversold conditions in ranging (non-trending) markets.
What is volume zone oscillator?
Price Zone Oscillator measures if the most recent closing price is above or below the preceding closing price.
Volume Zone Oscillator is Volume multiplied by the 1 or -1 depending on the difference of the preceding 2 close prices and smoothed with Exponential moving Average.
What does this mean?
If the VZO is above 0 and VZO is rising. We have a bullish trend. Most likely.
If the VZO is below 0 and VZO is falling. We have a bearish trend. Most likely.
Rising means that VZO on close is higher than the previous day.
Falling means that VZO on close is lower than the previous day.
What if VZO is falling above 0 line?
It means we have a high probability of a bearish trend.
Thus the indicator returns 0 and Strategy closes all it's positions when falling above 0 (or rising bellow 0) and we combine higher and lower timeframes to gauge the trend.
In the next Image you can see that trend is negative on 4h, negative on 12h and positive on 1D. That means trend is negative.
I am sorry, the chart is a bit messy. The idea is to use the indicator over more than 1 Timeframe.
What is approximation and smoothing?
They are mathematical concepts for making a discrete set of numbers a
continuous curved line.
Fourier and Wavelet approximation of a close price are taken from aprox library.
Key Features:
You can tailor the Indicator/Strategy to your preferences with adjustable parameters such as VZO length, noise reduction settings, and smoothing length.
Volume Zone Oscillator (VZO) shows market sentiment with the VZO, enhanced with Exponential Moving Average (EMA) smoothing for clearer trend identification.
Noise Reduction leverages Euler's White noise capabilities for effective noise reduction in the VZO, providing a cleaner and more accurate representation of market dynamics.
Choose between the traditional Fast Fourier Transform (FFT) , the innovative Double Discrete Fourier Transform (DTF32) and Wavelet soothed Fourier soothed price series to suit your analytical needs.
Image of Wavelet transform with FAST settings, Double Fourier transform with FAST settings. Improved noice reduction with SLOW settings, and standard FSVZO with SLOW settings:
Fast setting are setting by default:
VZO length = 2
NoiceR max Length = 2
Slow settings are:
VZO length = 5 or 7
NoiceR max Length = 8
As you can see fast setting are more volatile. I suggest averaging fast setting on 4h 12h 1d 2d 3d 4d W and M Timeframe to get a clear view on market trend.
What if I want long only when VZO is rising and above 15 not 0?
You have set Setting VzoDifference to 15. That reduces the number of trend changes.
Example of W&FSVZO with VzoDifference 15 than 0:
VZO crossed 0 line but not 15 line and that's why Indicator returns 0 in one case an 1 in another.
What is Smooth length setting?
A way of calculating Bullish or Bearish (W&)FSVZO .
If smooth length is 2 the trend is rising if:
rising = VZO > ta.ema(VZO, 2)
Meaning that we check if VZO is higher that exponential average of the last 2 elements.
If smooth length is 1 the trend is rising if:
rising = VZO_ > VZO_
Use this Strategy to fine-tune inputs for the (W&)FSVZO Indicator.
(Strategy allows you to fine-tune the indicator for 1 TimeFrame at a time; cross Timeframe Input fine-tuning is done manually after exporting the chart data)
I suggest using " Close all " input False when fine-tuning Inputs for 1 TimeFrame . When you export data to Excel/Numbers/GSheets I suggest using " Close all " input as True , except for the lowest TimeFrame . I suggest using 100% equity as your default quantity for fine-tune purposes. I have to mention that 100% equity may lead to unrealistic backtesting results. Be avare. When backtesting for trading purposes use Contracts or USDT.
NASDAQ 100 Peak Hours StrategyNASDAQ 100 Peak Hours Trading Strategy
Description
Our NASDAQ 100 Peak Hours Trading Strategy leverages a carefully designed algorithm to trade within specific hours of high market activity, particularly focusing on the first two hours of the trading session from 09:30 AM to 11:30 AM GMT-5. This period is identified for its increased volatility and liquidity, offering numerous trading opportunities.
The strategy incorporates a blend of technical indicators to identify entry and exit points for both long and short positions. These indicators include:
Exponential Moving Averages (EMAs) : A short-term 9-period EMA and a longer-term 21-period EMA to determine the market trend and momentum.
Relative Strength Index (RSI) : A 14-period RSI to gauge the market's momentum.
Average True Range (ATR) : A 14-period ATR to assess market volatility and to set dynamic stop losses and trailing stops.
Volume Weighted Average Price (VWAP) : To identify the market's average price weighted by volume, serving as a benchmark for the trading day.
Our strategy uniquely applies a volatility filter using the ATR, ensuring trades are only executed in conditions that favor our setup. Additionally, we consider the direction of the EMAs to confirm the market's trend before entering trades.
Originality and Usefulness
This strategy stands out by combining these indicators within the NASDAQ 100's peak hours, exploiting the specific market conditions that prevail during these times. The inclusion of a volatility filter and dynamic stop-loss mechanisms based on the ATR provides a robust method for managing risk.
By focusing on the early trading hours, the strategy aims to capture the initial market movements driven by overnight news and the opening rush, often characterized by higher volatility. This approach is particularly useful for traders looking to maximize gains from short-term fluctuations while limiting exposure to longer-term market uncertainty.
Strategy Results
To ensure the strategy's effectiveness and reliability, it has undergone rigorous backtesting over a significant dataset to produce a sample size of more than 100 trades. This testing phase helps in identifying the strategy's potential in various market conditions, its consistency, and its risk-to-reward ratio.
Our backtesting adheres to realistic trading conditions, accounting for slippage and commission to reflect actual trading scenarios accurately. The strategy is designed with a conservative approach to risk management, advising not to risk more than 5-10% of equity on a single trade. The default settings in the script align with these principles, ensuring that users can replicate our tested conditions.
Using the Strategy
The strategy is designed for simplicity and ease of use:
Trade Hours : Focuses on 09:30 AM to 11:30 AM GMT-5, during the NASDAQ 100's peak activity hours.
Entry Conditions : Trades are initiated based on the alignment of EMAs, RSI, VWAP, and the ATR's volatility filter within the designated time frame.
Exit Conditions : Includes dynamic trailing stops based on ATR, a predefined time exit strategy, and a trend reversal exit condition for risk management.
This script is a powerful tool for traders looking to leverage the NASDAQ 100's peak hours, providing a structured approach to navigating the early market hours with a robust set of criteria for making informed trading decisions.
Trend Deviation strategy - BTC [IkkeOmar]Intro:
This is an example if anyone needs a push to get started with making strategies in pine script. This is an example on BTC, obviously it isn't a good strategy, and I wouldn't share my own good strategies because of alpha decay.
This strategy integrates several technical indicators to determine market trends and potential trade setups. These indicators include:
Directional Movement Index (DMI)
Bollinger Bands (BB)
Schaff Trend Cycle (STC)
Moving Average Convergence Divergence (MACD)
Momentum Indicator
Aroon Indicator
Supertrend Indicator
Relative Strength Index (RSI)
Exponential Moving Average (EMA)
Volume Weighted Average Price (VWAP)
It's crucial for you guys to understand the strengths and weaknesses of each indicator and identify synergies between them to improve the strategy's effectiveness.
Indicator Settings:
DMI (Directional Movement Index):
Length: This parameter determines the number of bars used in calculating the DMI. A higher length may provide smoother results but might lag behind the actual price action.
Bollinger Bands:
Length: This parameter specifies the number of bars used to calculate the moving average for the Bollinger Bands. A longer length results in a smoother average but might lag behind the price action.
Multiplier: The multiplier determines the width of the Bollinger Bands. It scales the standard deviation of the price data. A higher multiplier leads to wider bands, indicating increased volatility, while a lower multiplier results in narrower bands, suggesting decreased volatility.
Schaff Trend Cycle (STC):
Length: This parameter defines the length of the STC calculation. A longer length may result in smoother but slower-moving signals.
Fast Length: Specifies the length of the fast moving average component in the STC calculation.
Slow Length: Specifies the length of the slow moving average component in the STC calculation.
MACD (Moving Average Convergence Divergence):
Fast Length: Determines the number of bars used to calculate the fast EMA (Exponential Moving Average) in the MACD.
Slow Length: Specifies the number of bars used to calculate the slow EMA in the MACD.
Signal Length: Defines the number of bars used to calculate the signal line, which is typically an EMA of the MACD line.
Momentum Indicator:
Length: This parameter sets the number of bars over which momentum is calculated. A longer length may provide smoother momentum readings but might lag behind significant price changes.
Aroon Indicator:
Length: Specifies the number of bars over which the Aroon indicator calculates its values. A longer length may result in smoother Aroon readings but might lag behind significant market movements.
Supertrend Indicator:
Trendline Length: Determines the length of the period used in the Supertrend calculation. A longer length results in a smoother trendline but might lag behind recent price changes.
Trendline Factor: Specifies the multiplier used in calculating the trendline. It affects the sensitivity of the indicator to price changes.
RSI (Relative Strength Index):
Length: This parameter sets the number of bars over which RSI calculates its values. A longer length may result in smoother RSI readings but might lag behind significant price changes.
EMA (Exponential Moving Average):
Fast EMA: Specifies the number of bars used to calculate the fast EMA. A shorter period results in a more responsive EMA to recent price changes.
Slow EMA: Determines the number of bars used to calculate the slow EMA. A longer period results in a smoother EMA but might lag behind recent price changes.
VWAP (Volume Weighted Average Price):
Default settings are typically used for VWAP calculations, which consider the volume traded at each price level over a specific period. This indicator provides insights into the average price weighted by trading volume.
backtest range and rules:
You can specify the start date for backtesting purposes.
You can can select the desired trade direction: Long, Short, or Both.
Entry and Exit Conditions:
LONG:
DMI Cross Up: The Directional Movement Index (DMI) indicates a bullish trend when the positive directional movement (+DI) crosses above the negative directional movement (-DI).
Bollinger Bands (BB): The price is below the upper Bollinger Band, indicating a potential reversal from the upper band.
Momentum Indicator: Momentum is positive, suggesting increasing buying pressure.
MACD (Moving Average Convergence Divergence): The MACD line is above the signal line, indicating bullish momentum.
Supertrend Indicator: The Supertrend indicator signals an uptrend.
Schaff Trend Cycle (STC): The STC indicates a bullish trend.
Aroon Indicator: The Aroon indicator signals a bullish trend or crossover.
When all these conditions are met simultaneously, the strategy considers it a favorable opportunity to enter a long trade.
SHORT:
DMI Cross Down: The Directional Movement Index (DMI) indicates a bearish trend when the negative directional movement (-DI) crosses above the positive directional movement (+DI).
Bollinger Bands (BB): The price is above the lower Bollinger Band, suggesting a potential reversal from the lower band.
Momentum Indicator: Momentum is negative, indicating increasing selling pressure.
MACD (Moving Average Convergence Divergence): The MACD line is below the signal line, signaling bearish momentum.
Supertrend Indicator: The Supertrend indicator signals a downtrend.
Schaff Trend Cycle (STC): The STC indicates a bearish trend.
Aroon Indicator: The Aroon indicator signals a bearish trend or crossover.
When all these conditions align, the strategy considers it an opportune moment to enter a short trade.
Disclaimer:
THIS ISN'T AN OPTIMAL STRATEGY AT ALL! It was just an old project from when I started learning pine script!
The backtest doesn't promise the same results in the future, always do both in-sample and out-of-sample testing when backtesting a strategy. And make sure you forward test it as well before implementing it!
Furthermore this strategy uses both trend and mean-reversion systems, that is usually a no-go if you want to build robust trend systems .
Don't hesitate to comment if you have any questions or if you have some good notes for a beginner.
EMA Crossover Strategy with RSI Filter BIGTIME 5mThis script essentially creates a trading strategy that goes long when there is an EMA crossover, but only if the RSI is below a certain overbought level. It goes short when there is an EMA crossunder, but only if the RSI is above a certain oversold level. The moving averages are plotted on the chart for visual reference.
SCALPING 5m
Pairs: BIGTIME/USDT--- API3/USDT---BAKE/USDT--- ZIL/USDT
