Mean reversion

This trading strategy seeks to execute trades on the assumption of mean reversion after instances of large price moves or series of down or up bars, combining these factors with a Simple Moving Average (SMA) for confirmation.

To trade this strategy manually on TradingView:

• Determine a moving average length (maLength) and an extreme move threshold (moveLimit), for example, 200 and 70 respectively.
• Calculate the 200-period SMA of the closing prices to determine the mean.
• Identify three consecutive down bars (candles) where the close is lower than the open to confirm bearishness, or three consecutive up bars for bullishness.
• Determine if there is a significant move, either down or up, by dividing the difference between open and close by the range (high to low) and checking if this is greater than the moveLimit percentage threshold.
• Enter a long position when you detect three down bars followed by a big move down, and exit when the close is greater than the previous high.
• Enter a short position when the price closes below the moving average after three up bars and a big move up, and exit when the close is lower than the previous low.

The entry conditions for long positions are three consecutive down bars and a large downward move. For short positions, the entry conditions include a close below the SMA after three consecutive up bars and a large upward move. Long positions are closed when the closing price is higher than the high of the previous bar, and short positions are closed when the closing price is below the low of the previous bar.

Improving a mean reversion strategy for manual trading on TradingView involves refining the trade entry, management, and exit criteria to optimize performance. Here is a concise plan detailing steps for enhancing the strategy:

• Expand Trend Assessment: Incorporate multi-timeframe analysis to understand the broader market trend. Avoid mean reversion trades against a strong trend on higher timeframes to reduce the risk of being caught in continuous price movement.
• Optimize Moving Average Parameters: Experiment with different lengths and types of moving averages, such as exponential or weighted, to better fit the asset's volatility and price pattern.
• Refine Entry Signal: Add confirmation signals using additional indicators such as RSI or Stochastic Oscillator to identify oversold or overbought conditions before taking the trade. Consider entering trades after a reversal candlestick pattern for improved precision.
• Apply Volume Analysis: Confirm entry signals with above-average volume to ensure there is significant market interest in the price movement, enhancing the reliability of the trade.
• Adjust Move Limit: Tweak the threshold of what constitutes a 'big move' dynamically based on the asset's recent volatility history using Average True Range (ATR) instead of a static percentage, helping to adapt to changing market conditions.
• Implement Risk Control: Calculate position size based on a predetermined risk percentage per trade and use stop losses to protect against adverse price movements. Consider a trailing stop loss to capture profits while allowing room for the price to fluctuate.
• Set Profit Targets: Instead of waiting for a crossover above previous highs or lows, employ a more structured exit plan by setting take-profit targets at support/resistance levels or using a risk-reward ratio to secure profits.
• Include Divergence Analysis: Look for divergences between the price and momentum indicators to spot weakening trends and potential reversals, which can bolster trade entries and exits.
• Assess Trade Performance: Keep a detailed trade journal and regularly review the strategy's performance. Consider both winning and losing trades and identify patterns or recurrent issues that can be systematically addressed.
• Backtest and Forward Test: Before applying the adjustments, test the improved strategy on historical data and in a real-time environment to confirm its effectiveness and fine-tune it further.

The key to refining the mean reversion strategy is creating a resilient framework that adapts to the specific characteristics of the traded securities and current market conditions, leading to improved decision-making and potentially enhanced returns on trades.

This strategy is tailored for traders who are comfortable with short-term market fluctuations and who possess the agility to act swiftly on price movements. Particularly suitable for:

• Intraday Traders: Those who capitalize on daily price movements within a shorter time frame—minutes to hours.
• Swing Traders: Individuals looking for overnight holds or trades spanning several days to capture more significant price reversals.

The trading style is reactive, thriving on volatility and requiring meticulous attention to price actions. It calls for traders who are versed in reading candlestick patterns and possess an analytical mindset to understand and act on these patterns in conjunction with moving average signals. Given the strategy’s focus on reversion to the mean, it's best suited for traders who are patient and disciplined in waiting for the market to meet precise conditions before entering or exiting positions.

• Strategy Essence: Targets mean reversion after a price extreme with consecutive bearish or bullish bars, closing beyond previous highs or lows.
• Optimization Methods: Involve multi-timeframe analysis, tailored moving averages, volume confirmation, and oscillators to confirm overextended conditions.
• Manual Trading Approach: Regularly adjust move limits based on volatility, use pattern recognition and confirmatory indicators for entry and exit decisions.
• Automation: Can be set up using TradingView's alert functions to notify traders of potential entry and exit points based on predefined conditions.
• Risk Management: Implement ATR-adjusted stop-losses, determine position size by set risk parameters, and apply structured take-profit strategies.
• Adaptability: Suitable for intraday to swing trading styles, with the flexibility to adjust to the traded asset's specific characteristics.
• Backtesting Necessity: Test strategy improvements against historic data and in real-time to validate effectiveness before live deployment.