Top 20 Trading Strategy Prompts for ChatGPT – Build Profitable Trading Plans with AI

Top 20 Trading Strategy Prompts — Ready-to-Use AI Prompts for Traders

Top 20 Trading Strategy Prompts (Ready-to-Use for ChatGPT & Trading Assistants)

Published: • Category: Trading • Tags: trading prompts, trading strategies, algo trading

Below are 20 high-quality, plug-and-play trading strategy prompts you can paste into ChatGPT (or other AI assistants) to generate rules, backtests, and code templates. Each prompt is tuned for clarity so an AI will return actionable guidance.

Contents
  1. Price Action Strategy
  2. Moving Average Crossover
  3. RSI + MACD Combo
  4. Breakout Trading
  5. Mean Reversion
  6. Fibonacci Retracement
  7. Scalping Strategy
  8. Volume Profile Strategy
  9. Trend-Following
  10. Swing Trading
  11. AI Predictive Model
  12. Options Greeks Strategy
  13. News-Based Trading
  14. Crypto Momentum
  15. Seasonal Pattern Trading
  16. Risk Parity Portfolio
  17. Volatility Breakout
  18. Supply & Demand Zones
  19. Statistical Arbitrage
  20. Backtesting & Optimization

1. Price Action Strategy

Act as a professional trader. Create a complete price action intraday strategy using candlestick patterns, support/resistance zones, and volume confirmation. Include entry/exit criteria and stop-loss placement.

2. Moving Average Crossover

Design a moving average crossover trading strategy for stocks using 9 EMA and 21 EMA. Explain how to confirm signals and filter false trades.

3. RSI + MACD Combo

Build a momentum trading system combining RSI (14) and MACD (12,26,9). Show entry, exit, and backtesting steps with Python or TradingView script.

4. Breakout Trading

Create a breakout strategy that uses volatility filters (ATR) to confirm strong breakouts. Include rules to avoid false breakouts.

5. Mean Reversion

Develop a mean reversion strategy for NASDAQ stocks using Bollinger Bands and RSI. Include logic for overextended moves and reversal confirmation.

6. Fibonacci Retracement

Explain a Fibonacci retracement pullback strategy for trending stocks. Define entry levels (38.2%, 50%, 61.8%) and stop-loss methods.

7. Scalping Strategy

Design a 1-minute scalping system for EUR/USD using EMA crossover, VWAP, and volume spikes. Include trade management and risk limits.

8. Volume Profile Strategy

Generate a volume profile-based trading plan for futures. Describe how to identify value areas, high-volume nodes, and Point of Control zones.

9. Trend-Following

Develop a trend-following strategy using ADX, SuperTrend, and EMA filters to trade only strong trends.

10. Swing Trading

Write a swing trading strategy using daily chart analysis (EMA, RSI, trendline confluence). Include ideal hold time and exit criteria.

11. AI Predictive Model

Build a machine learning trading model that predicts next-day returns from historical OHLCV data. Recommend best ML algorithms and evaluation metrics.

12. Options Greeks Strategy

Design a delta-neutral options strategy using call/put spreads and gamma scalping to profit from volatility changes.

13. News-Based Trading

Develop a news sentiment trading system that reacts to real-time headlines using sentiment analysis APIs. Include risk control and cooldown logic.

14. Crypto Momentum

Create a crypto momentum rotation strategy ranking the top 10 altcoins by 30-day performance. Rebalance weekly and include stop-loss logic.

15. Seasonal Pattern Trading

Build a seasonal pattern strategy for commodities (e.g., gold, oil) using historical seasonal tendencies to time entries and exits.

16. Risk Parity Portfolio

Construct a risk parity portfolio allocation model balancing volatility risk across stocks, bonds, and crypto. Include rebalancing frequency.

17. Volatility Breakout

Design a volatility breakout system using ATR range expansion on the NIFTY index. Show how to set entry levels and trailing stops.

18. Supply & Demand Zones

Create a supply and demand trading plan showing how to mark institutional zones and identify valid reversals with candle confirmations.

19. Statistical Arbitrage

Develop a pair trading strategy using cointegration and z-score to trade mean-reverting stock pairs. Include hedge ratio and stop logic.

20. Backtesting & Optimization

Provide Python code using Backtrader or vectorbt to backtest a trading strategy. Display Sharpe ratio, win rate, and drawdown metrics.