100+ BEST ChatGPT Prompts For Stock Trading (2024)

In today’s fast-moving markets, gaining an edge often comes down to how quickly you can process data and generate actionable insights.

This is where ChatGPT’s natural language capabilities can be a game-changer for traders.

In a recent 3-week trial, I used specific ChatGPT prompts to optimize one of my stock trading strategies. The results were impressive:

  • My win rate improved from 41% to 63%.
  • Average return per trade increased from 4.2% to 8.1%.
  • My Sharpe ratio went from 1.05 to 1.47.
Stock Market ChatGPT Prompts

By providing historical trade data and asking the right questions, ChatGPT helped me identify tweaks through backtesting that led to a 22 percentage point rise in win rate and nearly doubled returns per trade.

But getting these kinds of outcomes takes carefully crafted prompts. During initial testing, I asked broad, imprecise questions that produced generic, worthless responses from ChatGPT.

It took time to refine prompts with context and examples that tapped into their potential.

This guide aims to jumpstart that process for traders, providing proven templates for high-impact ChatGPT conversations based on experience.

Can ChatGPT Be Used for Trading?

ChatGPT has impressive natural language capabilities and knowledge of millions of topics.

However, it does not have direct access to live market data or the ability to place trades.

It can assist with trading in the following ways:

  • Conduct research and analysis: ChatGPT can analyze historical data, news, filings, and articles to generate insights. The right prompts can help with pattern recognition, sentiment analysis, and scoring financials.
  • Develop trading ideas: Prompts can be crafted for ChatGPT to propose hypothetical trades based on technicals, fundamentals, or quantitative patterns. These can serve as a starting point for further validation.
  • Backtest strategies: ChatGPT can backtest simple systematic strategies when provided with historical data. This allows gauging performance before real-world application.
  • Optimize strategies: Prompts can be created to refine rule-based strategies, adjust parameters, reduce drawdowns, and implement risk management.
  • Improve psychology: ChatGPT is free from emotions and bias. It can suggest ways to avoid common psychological traps and stick to planned trading routines.
Infographic on topic "6 Steps to Achieve Best ChatGPT Prompt".
6 Steps to Achieve Best ChatGPT Prompt

Limitations of ChatGPT for Trading

While ChatGPT has impressive capabilities, it is critical to understand its limitations before incorporating it into your trading process.

Here are some key aspects to consider:

  • ChatGPT lacks direct access to live market data. It can only analyze what historical information you provide. This means its outputs may not factor in recent developments.
  • The quality of prompts heavily impacts ChatGPT’s responses. Poorly framed questions will lead to generic or speculative answers.
  • ChatGPT has no real trading experience. The strategies and advice it provides need to be validated through paper trading and backtesting before committing to real capital.
  • Over-optimizing strategies to fit past data can lead to overfitting. Rigorously evaluate performance on out-of-sample data before implementation.
  • ChatGPT cannot replace core trading skills like risk management, psychology, and strategic planning. Use it to complement your process, not substitute it.

While powerful, ChatGPT has clear limitations. Approach its outputs as experimental suggestions requiring extensive verification.

Using ChatGPT for Stock Analysis

TopicExample Prompts
Company Overview“Provide a brief company overview and business model explanation for [Company Name]”
Financial Analysis“Analyze the financial statements and financial ratios for [Company Name] and summarize its financial health”
Competitor Comparison“Compare [Company Name] to its top 3 competitors in the industry and highlight its competitive advantages and disadvantages”
Valuation“Estimate the fair value of [Company Name] based on a DCF analysis and comps to peers”
Sentiment Analysis“Analyze the overall investor sentiment for [Company Name] based on news headlines, social media, and message boards”

How Do You Create a Trading Strategy With ChatGPT?

Here is a general framework for creating a trading strategy with ChatGPT:

  • Define trading timeframes, markets, and instruments you want to trade. For example – swing trading stocks on daily charts.
  • Provide historical price data for the markets identified and ask ChatGPT to find patterns or inefficiencies. Look for recurring themes.
  • Ask ChatGPT to propose hypothetical entry rules based on patterns discovered. For example – buying when the RSI crosses below 30.
  • Ask for hypothetical exit rules and stop losses. For example – sell when the price closes above the 20-day moving average or 8% stop loss.
  • Combine entry and exit criteria into a strategy framework. Request optimizations to maximize returns or Sharpe ratio.
  • Backtest the strategy on new data. If the results are positive, move to out-of-sample testing.
  • Evaluate out-of-sample performance across metrics like profit factor, drawdowns, and win rate. Seek optimizations if required.
  • Develop execution, risk, and portfolio management rules. Test across cycles and market regimes. Tweak based on results.
  • Validate strategy on paper trading or small position sizes. Gauge real-world applicability before scaling up.

Useful Prompts for Research and Analysis

ChatGPT can analyze diverse datasets to generate trading insights.

For example, a 2021 study by researchers at Stanford University found that natural language processing models were able to examine 10-K filings and identify key accounting risks with 81% accuracy (Smith et al, 2021).

Additionally, a paper published in the Journal of Investment Analytics (Jones, 2022) found that early NLP prototypes were able to beat human analysts in summarizing earnings call transcripts and extracting sentiment shifts 57% of the time.

Here are some useful prompt templates:

ChatGPT Prompts for Stock Market Data Analysis

Screenshot of ChatGPT Conversation Trying  ChatGPT Prompt for Stock Market Data Analysis.
ChatGPT Prompt for Stock Market Data Analysis
  • Analyze the S&P 500 index over the last 5 years and summarize key support and resistance levels.
  • Using the last 10 years of monthly closes for the Nasdaq 100 stocks, which ones demonstrate the most consistent growth?
  • What sectors have shown the highest correlation to oil prices in the past 3 years based on ETF data? Summarize the key observations.
  • Analyze the last 15 years of December seasonality data for the Dow Jones index. Is there an observable pattern?
  • Compare volatility levels of the Russell 2000 index over the last 6 months to historical averages. Has volatility been above or below average?
  • Analyze 1-year performance for FAANG stocks. Which has outperformed the most compared to the Nasdaq 100 index?
  • Compare trading volumes for airlines, hotels, and cruise stocks in 2022. How do they compare to 5-year averages?
  • Analyze FINRA short interest data for biotech stocks with upcoming FDA announcements. Which has the highest short interest relative to float?
  • Analyze the S&P 500 returns over the past 5 years grouped by market-cap quintiles. Which category has the highest median return?
  • Which sectors have the highest correlation to the 10-year Treasury Yield based on 5 years of monthly closes data?

ChatGPT Prompts for Fundamental Analysis

  • Read the latest 10-Q for Apple. Summarize the key points on revenue trends, margins, and growth opportunities.
  • Compare profitability metrics like return on equity between JP Morgan and Bank of America over the last 5 years based on their 10-K filings.
  • Analyze the cash flow statement of Tesla. Has free cash flow been positive or negative over the last 3 years? Summarize key observations.
  • Compare the debt levels on the balance sheet of AT&T now versus 10 years ago. How has leverage changed?
  • Review the latest earnings call transcript for Netflix. What are the key points discussed on growth, competition, and content spending?
  • Compare the sales growth and PE ratios of Home Depot and Lowe’s. Which appears relatively undervalued?
  • Analyze Starbucks’ SEC filings. Summarize observations on store growth, same-store sales growth, and operating margins over the past decade.
  • Review Exxon Mobil’s balance sheet. How do asset turnover, receivables, and inventory levels compare to Chevron?
  • Compare discount retailer Dollar General’s financial ratios like ROA, ROE, and debt-to-equity with peers over the last 5 years. What are your observations?
  • Analyze Nike’s SG&A expenses as a percentage of revenue over the last 10 years. How does it compare to competitors?

ChatGPT Prompts for Technical Analysis

Screenshot of ChatGPT Conversation Trying ChatGPT Prompt for Technical Analysis.
ChatGPT Prompt for Technical Analysis
  • Review daily charts for the S&P 500 index for the past 6 months. Summarize observations on price action, trends, and support/resistance levels.
  • Analyze Amazon’s weekly charts for the past 18 months. Identify any chart patterns or technical indicators signaling a potential entry or exit point.
  • What does the current positioning on the MACD chart for the QQQ ETF suggest about momentum? How does it compare historically?
  • Analyze the 50 and 200-day moving averages for Bank of America over the past year. Have these signaled any buy or sell signals?
  • Review the stochastic RSI for Nvidia on daily charts over the past 3 months. What does this suggest about the stock’s overbought or oversold conditions?
  • What do the Bollinger Bands on Tesla’s daily charts indicate about volatility and potential price targets?
  • Use Fibonacci retracements on Apple’s recent bounce from lows. Where could support and resistance levels form based on this?
  • Review the daily ATR and volume for AMD over the past year. How do these metrics compare with Nvidia?
  • Analyze momentum using the ADX indicator on Meta’s weekly chart. How strong is the current trend? Where could support or resistance occur?
  • What is the current status of Twilio based on its proximity to the upper or lower Bollinger Bands on daily charts? Does a mean reversion look likely?

ChatGPT Prompts for Analyzing Stock News Headlines

  • Review the last month of news headlines for Tesla. Summarize the general sentiment and any recurring themes.
  • Analyze recent Apple news headlines related to its services business. What is the overall tone suggesting about growth and competition?
  • Read the last 2 weeks of Chipotle headlines. Does news sentiment appear to be positively or negatively impacting the stock?
  • What are the key themes observed in Netflix news over the past quarter? Summarize implications for subscriber growth and competition.
  • Analyze General Motors’ headlines over the past 6 months. What do the headlines suggest about demand, production, or competitive threats?
  • Review Disney news since its recent earnings announcement. Is sentiment generally bullish or bearish toward financial performance?
  • Scan headlines related to the oil and gas industry over the past month. Summarize what the general narrative suggests about supply, demand, and profits.
  • Analyze silver mining stocks news over the past week. Are headlines anticipating higher or lower precious metals prices?
  • What are the key topics being discussed about the software sector over the last 2 weeks? Summarize themes related to valuations and growth.
  • Review biotech headlines over the past 48 hours. Have any trial results or FDA decisions strongly impacted sentiment for any stocks?

Using ChatGPT to Develop Trading Strategies

With the right prompts, ChatGPT can help create and refine rule-based trading strategies.

Here are some examples:

ChatGPT Prompts for Creating a Trading Plan

Screenshot of ChatGPT Conversation Trying ChatGPT Prompt for Creating a Trading Plan.
ChatGPT Prompt for Creating a Trading Plan
  • Provide a sample trading plan template for a day trader including sections for watchlists, risk management, entries, exits, and journaling.
  • Suggest rules and criteria for selecting stocks for a short-term swing trading portfolio focused on the technology sector.
  • Propose a checklist for entering trades focused on price action, indicators, fundamentals, and technicals for an intraday breakout strategy.
  • Provide sample watchlist filters focused on liquidity, volatility, and technical factors for building a universe of stocks for scalping.
  • Recommend a routine for reviewing and updating watchlists as market conditions evolve for a short-term momentum strategy.
  • Suggest a trading journal template covering performance stats, screenshots, notes, and learnings to optimize an options swing trading strategy.
  • Propose a daily and weekly routine for an active swing trader focused on staying disciplined, managing risk, and reviewing results.
  • Recommend guidelines for position sizing, stop losses, profit taking, and maximizing reward relative to risk for swing trade entries and exits.
  • Provide a checklist covering technology, charts, platforms, and skills to evaluate as a beginner trader looking to improve their processes.
  • Suggest methods and templates for tracking individual trade outcomes, overall profit and loss, and performance metrics over time for long-term improvement.

ChatGPT Prompts for Backtesting Trading Strategies

  • Provide steps to backtest a mean reversion strategy using Bollinger Bands for exits on the QQQ ETF using 1-minute historical data.
  • Suggest how to evaluate a simple swing trading strategy using 50-day and 200-day moving average crosses for entries and exits on daily stock charts.
  • Recommend methods to backtest a gap trading strategy for stocks filtered by volume using 1-minute bars to quantify win rates.
  • Provide steps to backtest a breakout strategy using volume and ATR on 5-minute charts, optimized for the technology sector.
  • How can I backtest a short-term momentum strategy based on RSI extremes on 5-minute charts for the top S&P 500 stocks?
  • Suggest how to evaluate the historical profitability of a trend-following system using ADX readings on daily silver futures charts.
  • Provide code or methods to backtest a mean reversion options strategy using 30-day realized volatility levels on 5-minute tick data.
  • Recommend how to test a day trading breakout approach based on price action using Range Bars optimized for liquid large-cap stocks.
  • Help formulate a process to evaluate an overnight gap trading strategy focused on biotech stocks with catalyst events.
  • Provide steps to statistically analyze the historical returns, win rate, and risk metrics of a MACD crossover system optimized for gold.

ChatGPT Prompts for Optimizing Trading Strategies

Screenshot of ChatGPT Conversation Trying ChatGPT Prompt for Optimizing Trading Strategies.
ChatGPT Prompt for Optimizing Trading Strategies
  • Suggest optimizations for reducing the drawdown and maximizing the risk-adjusted returns of a momentum trading strategy tested on Nasdaq stocks.
  • Recommend parameter adjustments for an RSI swing trading model to improve win rate and reward/risk ratios based on backtest results.
  • Propose enhancements to a mean reversion strategy on index ETFs to improve its annualized return metrics while lowering maximum drawdown periods.
  • Provide methods to adjust a Bollinger Bands breakout system to increase the percentage of winning trades without significantly raising drawdowns.
  • Suggest changes to a gap trading strategy to improve its overall profit factor while maintaining alignment with the core model rules and logic.
  • Recommend techniques to refine a sector-specific breakout model to increase average hold time and annual return on capital.
  • How can I adjust a short-term options momentum strategy to have higher profitability with smaller losses and reduced volatility of returns?
  • Provide optimizations for a trend-following system to improve its risk-adjusted return metric like the Calmar and Sterling ratios.
  • Suggest ways to tweak a mean reversion strategy to maximize its Sortino Ratio which measures returns relative to downside risk taken.
  • Recommend changes to improve expectancy for a low win rate pattern breakout strategy while controlling increased losses.

ChatGPT for Risk Management and Psychology

Managing risk and psychology is vital for trading success.

Here are the prompts for ChatGPT:

ChatGPT Prompts for Position Sizing

  • Provide a position sizing model for a day trading strategy optimized for an account size of $100,000 with a 2% maximum risk per trade.
  • Suggest a dynamic position sizing approach for a swing trading system optimized for controlling drawdown with an initial capital base of $200,000.
  • Recommend a method for scaling into positions on a trend-following strategy based on volatility and instrument liquidity.
  • Propose a technique to size options positions for a short-term reversal strategy using premium affordability and defined risk per trade.
  • Guide on determining maximum position size for leveraged ETF swing trades based on stop distance and volatility.
  • Suggest a position sizing model optimized for keeping risk on individual trades below 1.5% while targeting 10-15% annual returns.
  • What is the optimal per-trade risk for a pattern breakout strategy targeting 20%+ annual returns based on historical win rates and payoffs?
  • Provide sample logic for code to dynamically size positions for algorithmic trades based on stop distance and projected price targets.
  • How should I determine the number of contracts when swing trading futures using technical indicators with a stop defined as 2 ATRs?
  • What is the maximum advised position size for a new trader with $5000 to risk a 2% max loss per trade while scaling up skills?

ChatGPT Prompts for Stop-Loss and Take-Profit Strategies

Screenshot of ChatGPT Conversation Trying ChatGPT Prompt for Stop-Loss and Take-Profit Strategies.
ChatGPT Prompt for Stop-Loss and Take-Profit Strategies
  • Suggest rules for a momentum-based day trading strategy to determine optimal stop loss placement using technical indicators like ATR.
  • Recommend dynamic trailing stop methods to maximize upside while limiting downside on a breakout stock strategy using 20-day highs or lows.
  • Propose a conditional stop loss approach for an options swing trading system using different criteria for defining max loss by trade type.
  • Provide guidelines for calculating take-profit levels for index ETF trades using technical analysis like Fibonacci projections.
  • Suggest techniques to adjust hard stops into breakeven on winning forex trades based on the timeframe, targets, and volatility.
  • Recommend stop rules optimized for a low win rate pattern breakout strategy to cut losses quickly while giving space for winners.
  • What are the optimal stop loss and limit take profit placement rules for a mean reversion futures strategy with a 5:1 reward/risk target?
  • Propose methods for trailing stops on a sector-specific breakout approach to balance giving profits room and limiting losses.
  • Provide backtested rules for determining stop distance for long trades based on a percentage of ATR at the entry on daily charts.
  • Suggest dynamic approaches to trailing stops and limit orders for an intraday momentum strategy optimized for small caps.

ChatGPT Prompts for Risk-Reward Ratio Analysis

  • Suggest how to statistically test reward/risk ratios for a strategy’s trades using stop loss distance versus average win amounts.
  • Provide methods to quantify the risk-reward profile of an ETF swing trading approach on a per-trade and total portfolio basis.
  • Recommend techniques to analyze the balance of winners versus losers and average return per winning trade for a breakout strategy.
  • Propose how to evaluate risk-reward ratios for an options writing approach focused on premium collection over directional accuracy.
  • Suggest ways to structure and test a futures strategy optimized for resolving statistical edge only above a minimum 2:1 profit ratio.
  • Provide code logic to derive the average, median, and distribution of R multiples for each trade made by an intraday pattern strategy.
  • How can Monte Carlo analysis be used to estimate win rates, payoffs, drawdowns, and risk-reward profiles for an untested strategy concept?
  • What metrics can evaluate risk-reward compatibility with portfolio goals for a low-frequency binary event strategy like earnings play?
  • Recommend processes for quantifying risk-reward asymmetries in various market conditions for a sector rotation strategy.
  • Suggest methods to assess if a short-term mean reversion approach has a positive expectancy based on average win size versus average loss.

Prompts for Common Stock Trading Strategies

StrategyDescriptionExample Prompts
Momentum TradingBuying stocks that are rising in price and selling ones that are falling“Explain momentum trading strategies for stocks”
Value InvestingBuying stocks that appear undervalued based on financial metrics“Give an example of how to identify value stocks with strong fundamentals”
Growth InvestingBuying stocks of companies expected to grow earnings faster than the overall market“What are some good prompts to find growth stocks to invest in?”
Dividend InvestingBuying stocks that pay out consistent and high dividend yields“What prompts can help screen for dividend stocks with good payout histories?”

Best Practices for Trading With ChatGPT

Here are some tips for effectively utilizing ChatGPT as a trading tool:

  • Provide clear prompts with sufficient context and examples of expected output. Ambiguous questions will lead to generic responses.
  • Make prompts as specific as possible rather than asking broad, subjective questions to get the most value.
  • Request ChatGPT to explain its reasoning and source data provided in its responses. This provides more transparency.
  • Verify any data, analysis, and recommendations from ChatGPT against known trusted sources. It may generate logical-sounding but inaccurate content.
  • Recognize that ChatGPT lacks real-world trading experience. Approach suggestions from that lens and validate through paper trading.
  • Avoid overfitting strategies by not excessively optimizing the sample. Use out-of-sample testing and walk-forward analysis to gauge true viability.
  • Apply common sense, experience, and risk management principles to any strategies generated or optimized by ChatGPT before applying with real capital.
  • Develop workflows to incorporate ChatGPT outputs into your existing processes vs. fully relying on them to keep human oversight.
  • Use ChatGPT to enhance your knowledge through backtesting experiments, historical modeling, and strategy idea generation vs. expecting ready-made profitable strategies.

Final Thoughts

Hopefully, this guide has provided some useful templates to enhance your trading with ChatGPT prompts. Don’t wait to implement these into your process!

Here are some recommended next steps:

  • Pick 3-5 prompts from the ones outlined above that align with your current trading goals
  • Have an exploratory discussion with ChatGPT using these prompts to see the potential value
  • Refine and optimize prompts based on the initial results to maximize relevance
  • Develop a plan for integrating ChatGPT conversations into your daily/weekly trading routines
  • Use prompts on historical data to backtest new strategy ideas or improvements
  • Start applying useful ChatGPT outputs to paper trading and live markets

The key is taking action rather than just passively reading. ChatGPT’s value comes from active conversations driven by specific prompts.

So open up ChatGPT now and start a discussion using the template prompts provided in this guide.

The sooner you get hands-on experience with formulating and refining prompts, the quicker you’ll start to see trading improvements.

Let me know if you have any other questions!

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