I Analyzed 500+ Crypto Trades From My Bot — Here Are the 5 Risk Management Rules

<p>Overfitting is the silent killer of algorithmic trading strategies. Your backtest shows incredible results, but the moment you go live, everything falls apart.</p> <p>After

<p>I've been working on crypto exchange trading bot (python) for a while and wanted to share what I learned.</p> <h2> The problem </h2> <p>Production-ready Python trading bot for

<p>I didn't design this strategy on paper. I found it by trading, losing, adjusting, and trading again until the win rate stabilized and stopped moving around.</p> <p>11,717

<h1> How to Avoid Losses in a Bear Market: A Crypto Trader's Survival Guide </h1> <p>Here's a number most traders don't want to hear: over the last 51 days of continuous market

<p>When I started building my Polymarket TWAP End-Cycle Sniper, the original idea was simple:</p> <p>Find a high-probability opportunity near the end of a short-duration market,

<p>I built a custom monitoring script, <code>daily_check.py</code>, to watch my bots while I slept. I wanted to see if they were actually following the rules I set or if they were

<h1> Gray-Scale Risk Control: Dynamic Scaling Instead of Hard Veto for High-Score Signals </h1> <p>Imagine your AI trading engine screams a massive 90/100 confidence score for a

<!-- SC_OFF --><div class="md"><p>I am building an AI trading system called Tradie. </p> <p>It is still Futures Testnet / paper-only. No live money. </p> <p>The idea is to build a