Automating Trade Analysis with Python: A Practical Guide

Manual trade review is tedious. Let's automate the analysis part so you can focus on improving your strategy. The Problem After a week of trading, you have dozens of trades to review. Manually calculating metrics, identifying patterns, and generating reports takes hours. Solution: A Python Analysis Pipeline Step 1: Import Trade Data Most platforms export to CSV: import pandas as pd import numpy as np def load_trades ( filepath ): df = pd . read_csv ( filepath ) df [ ' date ' ] = pd . to_datetime ( df [ ' date ' ]) df [ ' pnl ' ] = df [ ' exit_price ' ] - df [ ' entry_price ' ] df [ ' pnl ' ] *= np . where ( df [ ' direction ' ] == ' short ' , - 1 , 1 ) df [ ' pnl ' ] *= df [ ' size ' ] return df Step 2: Core Metrics def calculate_metrics ( df ): metrics = {} metrics [ ' total_trades ' ] = len ( df ) metrics [ ' win_rate ' ] = ( df [ ' pnl ' ] > 0 ). mean () * 100 metrics [ ' avg_win ' ] = df [ df [ ' pnl ' ] > 0 ][ ' pnl ' ]. mean () metrics [ ' avg_loss ' ] = df [ df [ ' pnl ' ] < 0 ]