Price charts tell you what happened. Sentiment tells you what's about to happen. We built a trading bot that does both — and the results are interesting.
The Thesis
Social media sentiment is a leading indicator for crypto markets. When Reddit's r/cryptocurrency turns overwhelmingly bullish on an asset, the price move often follows within 4-12 hours. When sentiment peaks and starts declining while price is still rising, that's a distribution signal.
The problem: reading sentiment manually is slow, subjective, and emotionally compromising. So we automated it.
The Architecture
Three strategies running in parallel, each combining sentiment with different technical signals:
- Sentiment Momentum — Enters when both sentiment and technical momentum agree. Requires 55%+ sentiment conviction (measured by bullish/bearish post ratio) AND an RSI above 50 with price above VWAP. High-probability trend continuation trades.
- Sentiment Contrarian — Fades extreme sentiment. When agreement exceeds 80% and RSI is overextended (>75 or <25), the crowd is usually wrong. This strategy takes the other side.
- Sentiment Adaptive — Switches between momentum and contrarian based on which regime the market is in. Uses a 20-period volatility regime detector to decide.
The Conviction Gate
The most important design decision: the conviction gate. We don't trade on weak sentiment signals. The system requires at least 55% agreement across sources (Reddit posts, news articles) before generating a signal. Below that threshold, sentiment is noise.
We also require a divergence gate — sentiment and technicals must agree. If Reddit is screaming "buy" but RSI is at 80 and price is extended 3% above VWAP, we don't enter. The gates filter out about 60% of potential signals, but the remaining 40% have significantly higher win rates.
Avoiding API Overload
Reddit and news APIs have rate limits. Hammering them every minute is both wasteful and will get you banned. We implemented a 5-minute sentiment cache — the system pulls fresh sentiment data once every 5 minutes and uses the cached values for all signal calculations in between. At 5-minute candle resolution, this means sentiment data is never more than one candle stale.
Early Results
On paper trading over 48 hours:
- Sentiment Momentum — 3 trades, 2 winners (66% hit rate), +1.8% net
- Sentiment Contrarian — 1 trade, 1 winner (100% but small sample), +0.9% net
- Sentiment Adaptive — 2 trades, 1 winner (50% hit rate), +0.4% net
Too early to draw conclusions from these numbers. The real test is 2-4 weeks of continuous paper trading across different market regimes. But the signal quality feels right — the trades that trigger have clear logic behind them, not just indicator crossovers.
What's Next
This is Module 8 of the MindSparkStack course. We're documenting every decision, every line of code, every trade log. The sentiment bot is one of the most technically interesting things we've built — and students in the Elite tier get hands-on access to build their own version.