leionion

leionion / ai-trading-journal-audit-tool

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Drop in your Binance or Bybit CSV export. An AI agent identifies revenge trading, overleverage, FOMO entries, and other psychological errors — with dollar attribution per error class. Runs locally. No API keys. No account linking.

ai-agent algorithmic-trading behavioral-finance binance bybit
13
2
100% credibility
Found Mar 18, 2026 at 13 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

A local analyzer for Binance and Bybit trade history CSV files that detects behavioral errors like revenge trading and overleveraging, then generates reports with error costs and session insights.

How It Works

1
🔍 Discover the Tool

You find a free tool that helps traders spot emotional mistakes in their trading history from Binance or Bybit.

2
📥 Save Your Trade Records

Log into your trading account and download a file of your past trades as a simple list.

3
💻 Set Up on Your Computer

Download the tool to your own computer so it can safely review your private trade file without sending it anywhere.

4
🔍 Start the Analysis

Point the tool at your trade file and let it scan for patterns like revenge trading or risky overbetting.

5
📄 Get Your Personal Report

A clear report appears showing flagged mistakes, their dollar costs, best trading times, and tips to improve.

📈 Trade Better Now

You learn your biggest pitfalls and rules to follow, turning losses into smarter wins over time.

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Star Growth

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AI-Generated Review

What is ai-trading-journal-audit-tool?

This Python tool analyzes CSV exports from Binance or Bybit accounts, spotting psychological trading pitfalls like revenge trading, overleverage, FOMO entries on bitcoin drop binance or pepe drop binance, and loss averaging, complete with dollar costs per error. Drop your file into the CLI—`python audit.py --csv trades.csv --exchange binance`—and get a local text report breaking down flagged trades, session performance, and fix-it rules, all without API keys or cloud uploads. It solves the gap in basic journals by quantifying emotional leaks in crypto futures trading.

Why is it gaining traction?

Unlike paid SaaS like TraderSync or manual spreadsheets, it runs offline with zero cost, auto-detects exchanges, and delivers behavioral taxonomy tuned for high-leverage crypto chaos—think attributing losses to chasing airdrop binance or binance drop usdt FOMO. Developers dig the clean CLI, sample CSVs for quick tests, and extensible config for custom thresholds, plus a roadmap hinting at local LLMs. Privacy-focused traders skip account linking, making it a low-risk entry over glitchy ChatGPT prompts.

Who should use this?

Retail crypto traders on Binance or Bybit auditing personal journals after drawdowns from holder drop binance or sec drop binance case volatility. Trading coaches reviewing student CSVs for patterns like midnight glacier drop binance revenge loops. Devs prototyping trading psych tools or prop firm dashboards, integrating its structured output without github drop repository hassles.

Verdict

Worth a spin for Binance/Bybit users—solid docs, tests, and samples make setup trivial despite 13 stars and 1.0% credibility signaling early maturity. Fork or contribute if you need OKX support; otherwise, it's a free behavioral edge over generic stats.

(198 words)

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