AI Price Action Base Entry

1. Base Entry:
Price Action Setup: The base entry can be defined using common price action patterns like candlestick formations (e.g., engulfing candles, pin bars, doji, etc.), support/resistance levels, or trend analysis.
Machine Learning: A trained model (e.g., a regression model or neural network) can be used to predict the likelihood of price action following a certain pattern (bullish/bearish).
Example: If a bullish pattern (like an engulfing pattern) occurs at a significant support level, the model gives a "buy" signal.
2. Target:
Fixed Target: A fixed target can be set as a multiple of the entry price (e.g., 1.5 times the risk distance from entry).
Dynamic Target: The target can also be adjusted dynamically based on volatility (e.g., ATR – Average True Range) or market conditions. This could involve setting the target based on historical price movement statistics.
3. Trailing Profit:
Fixed Trailing Profit: A fixed trail (e.g., move the stop loss to break-even after 10 pips) could be used once the price moves in the favorable direction.
ATR-Based Trailing: A more advanced trailing stop could be based on ATR, where the trailing stop follows the price movement at a set distance (e.g., 2x ATR).
AI Adjustment: The AI could adjust the trailing stop dynamically based on ongoing market analysis, for example, if the price has been trending strongly, it could widen the trail to lock in profits.
4. Stop-Loss (SL):
Fixed Stop-Loss: A standard fixed stop-loss could be placed based on a predetermined percentage of the price move (e.g., 2% or based on support/resistance levels).
AI Optimization: The AI model can predict the optimal level for stop-loss based on historical price action, volatility, and other factors.
ATR-Based SL: A more adaptive approach might involve using ATR to set stop-loss dynamically, making it wider when volatility is higher.