🔬 Research Updates

Re-ran the 15-minute divergence backtest on a 4.5× larger sample: 5,073 trades, 144 symbols, PF 1.50×, 31.4% win rate. The ATR stop floor cut first-bar stop-outs from 41.3% to 29.2%. Confirmed on the larger sample that 100% of profit comes from positions held past the close — the intraday portion loses money. Capping concurrency at 5 positions removes 42% of the profit.

Systematic backtest of multi-indicator divergence (RSI + Stochastic + MACD confluence) across 54 NASDAQ 100 stocks. Key findings: ~37% win rate, 3.3× profit factor, 8% max drawdown. ATR trailing stop halved drawdown vs fixed-stop baseline.

Applied Thomas Bulkowski's 19,294-sample divergence study to our trading framework. Critical finding: RSI divergence in the 30–70 neutral zone significantly underperforms. Market regime (bull vs bear) determines whether divergence has edge at all.

Systematic analysis of Bearish Harami across 150+ trades. Win rate of ~70% validated. RSI and MACD gate testing showed that adding oscillator filters reduced absolute P&L despite improving win rate — the gates removed profitable trades alongside unprofitable ones.

Analysis of Bullish Harami across 160+ trades. Multiple oscillator gate configurations tested (RSI, StochRSI, MACD). Conclusion: MACD enabled with comment saying "disabled" — critical config bug found and fixed. Gate testing confirmed baseline configuration outperforms all gated versions.

Two months after the first intraday backtest, the same strategy re-run on a 4.5× larger sample: 5,073 trades across 144 symbols. The noise-stop fix worked, the profit factor held at 1.50× — and the data says this is not a day-trading strategy at all.

A 1-year backtest of a multi-indicator divergence strategy on 15-minute IBKR bars across NASDAQ 100 stocks — 1,116 trades, a Sharpe of 2.99, and the one mechanical flaw that nearly buried the edge.

A systematic backtest of a multi-indicator divergence strategy across NASDAQ 100 stocks over five years — win rates, profit factors, drawdown, and the structural lessons that change how you think about divergence trading.

A data-driven look at the Bearish Harami candlestick pattern — win rate, optimal conditions, and what happens when you add RSI and MACD oscillator gates.

Most candlestick oscillator gates hurt performance. The Bullish Engulfing RSI gate is an exception — here's the data, the optimal range, and why it works when other gates don't.

A systematic look at the Bullish Harami pattern across daily bars — win rate reality, optimal contexts, oscillator gate testing, and a critical lesson about config bugs in automated systems.

Using RSI, Stochastic, and MACD together to filter divergence signals — how requiring all three to agree on the same pivot dramatically reduces noise while keeping the highest-quality setups.

Defined-risk bearish credit spread — sell OTM call, buy further OTM call.

Defined-risk bullish credit spread — sell OTM put, buy further OTM put.

A systematic daily-bar divergence strategy using RSI, Stochastic, and MACD confluence on NASDAQ 100 or NSE stocks — paper-first, IBKR or Zerodha execution, YAML-driven config.

Setting up your first systematic trading script using Python, yfinance, and TA-Lib — from data fetch to basic signal generation.

A step-by-step guide to setting up, managing, and closing a Bear Call Spread with defined risk and consistent premium collection.

How to use bullish and bearish RSI divergence to find high-probability reversal setups — including what large-scale backtesting reveals about when divergence actually works.

From Hammer to Three White Soldiers — how to read, locate, and trade the most reliable candlestick patterns with context.

A practical guide to reading VIX, India VIX, and other volatility indexes — and how traders use them.

What this site is, what to expect, and how I'll publish.