How can I create trade algorithm from scratch?

<p id="isPasted">Creating a trading algorithm from scratch is a multi-stage process that blends quantitative thinking, programming skills, and a deep understanding of market dynamics. It involves a systematic approach to turn a trading idea into executable code.</p><p><strong>Here is a breakdown of the proper steps required:</strong></p><p><strong>Phase 1: Strategy Development &amp; Hypothesis</strong></p><p>The algorithm starts as an idea or a "hypothesis" about how the market behaves.</p><ul><li>Define a Trading Edge: Identify a non-random, repetitive pattern or inefficiency in the market. (e.g., "Stocks that gap up on high volume tend to revert to their previous day's close within the first hour.")</li><li>Establish Clear Rules: Translate your general idea into precise, objective, and testable rules.</li><li>Entry Rules: When exactly do you buy or sell? (e.g., "Buy when RSI (14) crosses above 30 AND price is within 1% of the 50-day MA").</li><li>Exit Rules: When do you take profit or cut losses? (e.g., "Sell when profit hits 2x risk, or stop-loss hits 0.5x risk").</li><li>Risk Management Rules: How much capital is allocated per trade?</li></ul><p><strong>Phase 2: Data Acquisition &amp; Programming</strong></p><p>This is where you bring your rules to life using code.</p><ul><li>Choose a Programming Language: Python is the industry standard due to its excellent data analysis libraries (Pandas, NumPy) and algorithmic trading platforms.</li><li>Acquire Clean Data: You need historical price data for the assets you want to trade. This data must be reliable, accurate, and clean (free of errors, gaps, or survivor bias). Brokers or data providers often offer APIs for this.</li><li>Code the Strategy: Write scripts that transform your objective rules into functions and conditional statements that can read the historical data.</li></ul><p><strong>Phase 3: Backtesting &amp; Validation</strong></p><p>Backtesting simulates how your algorithm would have performed on historical data.</p><ul><li>Run the Backtest: Use a backtesting library (like backtrader or vectorbt in Python) to run your algorithm against several years of historical data across different market conditions (bull, bear, volatile, calm).</li><li>Analyze Performance Metrics: Don't just look at total profit. Evaluate critical metrics like:</li><li>Sharpe Ratio: Measures risk-adjusted return (higher is better).</li><li>Max Drawdown: The largest peak-to-trough decline in your capital.</li><li>Win Rate and Profit Factor: How often you win and how much you make per win relative to losses.</li><li>Optimize and Refine: If the results are poor, return to Phase 1 and adjust your rules. If they are good, consider out-of-sample testing—testing the algorithm on data it hasn't "seen" before to ensure it works universally and is not overfit to the initial data set.</li></ul><p><strong>Phase 4: Forward Testing &amp; Deployment (Paper Trading to Live)</strong></p><p>Moving from simulation to real-world application.</p><ul><li>Paper Trading (Simulated Trading): Run your algorithm in real-time with fake money. This ensures the code functions correctly and interacts with the broker's API without risking capital. This stage reveals operational errors that backtesting misses.</li><li>Go Live (Small Scale): Once paper trading is consistently successful, deploy the algorithm with a small amount of real capital. This manages the psychological risk of live trading.</li><li>Monitoring and Maintenance: Continuously monitor the algorithm's performance. Markets change, and algorithms often need regular maintenance and re-evaluation to remain effective.</li></ul><p>Creating an algorithm is an iterative engineering process where success depends heavily on the quality of your original idea and rigorous testing.</p>