Open-source Python algorithmic trading: signals, strategies, and real paper trading results. https://rmbell09-lang.github.io/tradesight/ 2026-04-03T18:00:00Z TradeSight Project https://github.com/rmbell09-lang/tradesight https://rmbell09-lang.github.io/tradesight/logo.png MIT License — open source https://rmbell09-lang.github.io/tradesight/blog/python-backtesting-libraries-2026.html 2026-04-03T18:00:00Z

An honest comparison of Backtrader, Zipline, VectorBT, and TradeSight in 2026. Each has real tradeoffs worth knowing before you commit to one.

TradeSight Project https://rmbell09-lang.github.io/tradesight/blog/macd-momentum-strategy.html 2026-04-03T12:00:00Z 2026-04-03T12:00:00Z

Deep dive into MACD crossover strategy in Python: the adjust=False gotcha in pandas EWM, histogram divergence signals, SMA trend filter that cut false signals by 40%, and live paper trading results: 82% win rate with trend filter vs 63% without.

lt;p gt;MACD momentum strategy in Python — built into lt;a href="https://rmbell09-lang.github.io/tradesight/" gt;TradeSight lt;/a gt;. Covers the 12/26/9 EMA implementation with adjust=False, crossover vs histogram divergence signals, and an SMA trend filter that cut false signals by 40% in backtesting. lt;/p gt; lt;p gt;Paper trading results: lt;strong gt;11 trades, 7 wins lt;/strong gt;. 82% win rate with trend filter vs 63% without. Best: JPM +0.51% on clean bullish crossover. lt;/p gt; lt;p gt; lt;a href="https://rmbell09-lang.github.io/tradesight/blog/macd-momentum-strategy.html" gt;Read the full post → lt;/a gt; lt;/p gt; https://rmbell09-lang.github.io/tradesight/blog/trading-strategy-performance-metrics.html 2026-04-03T01:00:00Z 2026-04-03T01:00:00Z

Sharpe ratio, max drawdown, win rate, profit factor, and Calmar ratio — the five Python implementations you need to evaluate whether a trading strategy is worth running live. Includes real TradeSight tournament benchmarks and a composite scoring system.

<p>A strategy that made 20% last year sounds great — until you learn it had a 40% max drawdown and got lucky on one spike. This post covers the five metrics I use in <a href="https://rmbell09-lang.github.io/tradesight/">TradeSight</a> to evaluate every strategy before it goes live.</p> <p>All implementations are pure Python + pandas + numpy — no special libraries needed. Includes a composite scorecard that TradeSight uses in its overnight tournament to rank 200+ strategy variants.</p> <p><a href="https://rmbell09-lang.github.io/tradesight/blog/trading-strategy-performance-metrics.html">Read the full post →</a></p> https://rmbell09-lang.github.io/tradesight/blog/paper-trading-bot.html 2026-04-02T19:00:00Z 2026-04-02T19:00:00Z

How TradeSight runs 4 concurrent strategies (MACD, RSI, VWAP, Bollinger) against Alpaca paper trading API. Live results: +6.43% portfolio return. MACD leading, RSI struggling in high-VIX regime. Full code included.

&lt;p&gt;TradeSight is an open-source Python paper trading bot that runs 4 concurrent strategies against Alpaca paper API. This post covers the architecture, the MACD implementation, and live results from the past week.&lt;/p&gt; &lt;p&gt;Portfolio: &lt;strong&gt;+6.43%&lt;/strong&gt; over ~32 trades. MACD Crossover +.89 on JPM. RSI struggling at -.77 net (high VIX kills mean reversion). Bollinger Breakout solid at +.44.&lt;/p&gt; &lt;p&gt;&lt;a href="https://rmbell09-lang.github.io/tradesight/blog/paper-trading-bot.html"&gt;Read the full post →&lt;/a&gt;&lt;/p&gt; https://rmbell09-lang.github.io/tradesight/blog/macd-momentum-strategy.html 2026-04-03T12:00:00Z 2026-04-03T12:00:00Z

Deep dive into MACD crossover strategy in Python: the adjust=False gotcha in pandas EWM, histogram divergence signals, SMA trend filter that cut false signals by 40%, and live paper trading results: 82% win rate with trend filter vs 63% without.

lt;p gt;MACD momentum strategy in Python — built into lt;a href="https://rmbell09-lang.github.io/tradesight/" gt;TradeSight lt;/a gt;. Covers the 12/26/9 EMA implementation with adjust=False, crossover vs histogram divergence signals, and an SMA trend filter that cut false signals by 40% in backtesting. lt;/p gt; lt;p gt;Paper trading results: lt;strong gt;11 trades, 7 wins lt;/strong gt;. 82% win rate with trend filter vs 63% without. Best: JPM +0.51% on clean bullish crossover. lt;/p gt; lt;p gt; lt;a href="https://rmbell09-lang.github.io/tradesight/blog/macd-momentum-strategy.html" gt;Read the full post → lt;/a gt; lt;/p gt; https://rmbell09-lang.github.io/tradesight/blog/python-rsi-stock-scanner.html 2026-04-02T12:00:00Z 2026-04-02T12:00:00Z

How I built a stock scanner using RSI, MACD, and relative volume signals in Python — with yfinance, pandas, and a circuit breaker that eliminated drawdown spirals. Includes live paper trading results (+6.43% return over 3 weeks on a $532 virtual portfolio).

<p>I've been building <a href="https://rmbell09-lang.github.io/tradesight/">TradeSight</a> — an open-source algorithmic trading system written in Python. At its core is a stock scanner that uses momentum signals (RSI, MACD, volume) to surface trade setups across 50+ symbols in real time.</p> <p>Three weeks of paper trading: <strong>+6.43% return</strong> on a $532 virtual portfolio. 3 active strategies. 0 crashes since adding a circuit breaker.</p> <p>This post covers the RSI calculation (the ewm vs rolling gotcha), the multi-signal scoring system, and the simple circuit breaker pattern that stopped the drawdown spiral.</p> <p><a href="https://rmbell09-lang.github.io/tradesight/blog/python-rsi-stock-scanner.html">Read the full post →</a></p>