Retail traders building algos — a hard truth

Most strategies don’t fail because the idea is bad.

They fail because:

• No slippage modeling
• No position sizing framework
• No walk-forward validation
• No Monte Carlo stress testing
• Over-optimization on historical data
• Poor execution architecture
• No real-time monitoring layer

A 40% CAGR backtest means nothing if:

– The strategy collapses out-of-sample
– It can’t handle volatility regime shifts
– It breaks during API disconnects
– It isn’t deployed reliably
– You stop trading after a normal drawdown

Before deploying capital, you should know:

• Expected drawdown distribution
• 95% worst-case scenario
• Risk of ruin
• Capital required for survivability
• Sensitivity to slippage and costs

And you should have:

• A stable cloud deployment (VPS/AWS-style setup)
• Automated execution via broker APIs
• Kill switches & risk caps
• Real-time logging
• Discord/alert-based monitoring for trade + risk events

If you’re:

– Building rule-based strategies
– Looking to automate on NSE
– Unsure whether your backtest is overfit
– Wanting production-grade deployment instead of running scripts on your laptop

I offer:

• Strategy validation & robustness testing
• Walk-forward + Monte Carlo analysis
• Slippage and execution modeling
• Risk-layer engineering
• Cloud deployment setup
• Discord alerting & monitoring integration
• Production-ready execution architecture

No signals.
No guaranteed returns.
No copy trading.

Only systematic, risk-managed infrastructure.

If you're serious about validating and deploying a strategy properly before risking meaningful capital, feel free to DM me.