ANN 2 signals

This trading strategy uses an artificial neural network (ANN) to predict entry and exit points for trades by comparing the change in the closing price average (ohlc4) between the current and previous periods on two different timeframes. It goes long or short when the ANN's entry and exit predictions are both beyond a user-defined threshold.

To trade this strategy manually on TradingView:

• Identify two timeframes: one larger (e.g., Daily) and one smaller (e.g., 1-hour).
• Calculate the average close price (ohlc4) for both timeframes over two periods and find their percentage change.
• If both predictions are below a negative threshold, enter a long position; if above a positive threshold, enter a short position.
• Exit long positions if the ANN predicts values equal to or exceeding the threshold, and vice versa for short positions.
• While this ANN-based strategy involves complex mathematical functions, traders can approximate this using the Rate of Change (ROC) indicator to gauge momentum and set thresholds for entries and exits.

Improving the "ANN 2 signals" strategy manually necessitates an analytical approach to enhance entry/exit signals, optimize thresholds, and conduct robust risk management. These enhancements strive to improve upon the existing neural network-based model, maximizing the trading strategy’s effectiveness in different market conditions.

• Refine the threshold value: Optimize the threshold parameters by back-testing the strategy across various market conditions to ascertain the most effective values for triggering entries and exits. This process involves methodical testing and adjustment to determine the threshold providing the highest win rate while minimizing drawdowns.
• Incorporate additional technical indicators: While the ANN predictions serve as the core of the strategy, integrating additional technical indicators such as Moving Averages, RSI, or MACD might filter out false signals. Employing these indicators can help confirm the trends predicted by the ANN and provide a more robust signal for trade entries or exits.
• Analyze price action and volume: Complement the ANN's outputs with price action analysis and trading volume to better understand market sentiment. Candlestick patterns and support/resistance levels may refine trade timing and improve signal accuracy.
• Adapt to varying market volatility: Adjust the strategy to account for high volatility periods by widening the thresholds or employing a scaling approach to position sizing. Conversely, during low volatility, the strategy could implement tighter thresholds or increase position size to exploit smaller market moves.
• Employ a multi-tier exit strategy: Instead of a single threshold for exits, implement a tiered approach where partial profits can be taken at different levels of prediction confidence. This could potentially lock in profits and mitigate risk while still allowing a portion of the position to capitalize on longer trends.
• Use economic and news analysis: Inform trading decisions with fundamental analysis. High-impact news events can drastically shift market sentiment and technical analysis patterns. Suspend trading or adjust strategies during significant news releases until the market stabilizes.
• Integrate market phase identification: Determine the market phase (trending, range-bound) using additional analysis techniques. Tailor the use of ANN predictions to phase-specific behaviors; for instance, restrict trade entries to the direction of the predominant trend during trending phases.

The "ANN 2 signals" strategy is suited for traders who are comfortable with artificial intelligence and quantitative analysis. Its reliance on neural network predictions aligns well with traders who have a penchant for data-driven decision making. This strategy is particularly tailored for:

• Technically Savvy Traders: Those with an understanding of ANN and algorithmic trading.
• Active Day Traders: It is ideal for those who can monitor trades and signals throughout the day due to the need for timely execution based on fast-changing predictions.
• Swing Traders: Traders looking for opportunities that span several days may also find the strategy’s use of dual timeframes beneficial in identifying medium-term trends.

Overall, the strategy aligns with a systematic, analytical trading style, demanding a degree of automation awareness and confidence in handling computational trading tools.

• Strategy Basics: Utilizes dual ANN predictions for trade entries and exits; action is taken only when both agree.
• Operation: Functions through a programmed script on TradingView, ideal for automation or manual oversight enhanced with alerts.
• Optimization: Threshold and indicators can be fine-tuned via back-testing to improve prediction accuracy and adapt to volatility.
• Manual Enhancement: To manually improve the strategy, integrate traditional technical analysis, adjust for market conditions, and incorporate tiered exit strategies.
• Risk Management: Apply varying position sizes and consider the impacts of market news for better risk control.
• Ideal Users: Best for data-centric, active day traders, and swing traders with knowledge of AI and quantitative models.
• Adaptability: Can be tailored to different trading styles with the inclusion of custom technical inputs and fundamental analysis.