Computes the expected return of an altcoin given BTC's 1-hour return using linear regression over 200 data points. When the alt's actual return diverges significantly from what BTC predicts, the strategy trades the reversion. Goes long when the alt underperforms BTC by more than 8%, and shorts when it overperforms by more than 6.5%. Avoids historically low-alpha coins via a 10-symbol blacklist.

Compares prices of the same asset on Binance Spot vs Binance Futures. When a significant price dislocation appears (z-score > 2.5), the strategy trades the convergence. These dislocations arise from leverage imbalances in the futures market and typically close within hours.

Uses real Binance Futures listing dates (onboardDate from exchangeInfo API) to detect when a token first becomes available. Enters within 6 hours of listing with volume confirmation (2x+ normal). Rides the initial price surge that accompanies new exchange listings.

Monitors perpetual futures funding rates from Binance. When funding becomes extremely positive (longs paying shorts heavily), the strategy shorts overleveraged longs. Vice versa for extreme negative funding. Uses real funding rate data, but lacks historical open interest for proper quality filtering.

Same linear regression divergence approach as the BTC version, but uses ETH as the reference asset. Only takes short positions based on research showing longs lose money on ETH correlation.

Takes contrarian positions when the Crypto Fear & Greed Index hits extremes. Goes long at FGI <= 15 (extreme fear) and shorts at FGI >= 90 (extreme greed). Research from Nasdaq shows this strategy beats buy-and-hold over 7 years.

Buys when price shows strong upward momentum on 1-hour candles with volume confirmation. The problem: by the time momentum is confirmed on hourly candles, the move is largely over.

Classic EMA crossover — long when EMA(12) crosses above EMA(50), with MACD histogram confirmation and ADX > 25 trend filter. Long only, avoiding crypto's structural short bias.

Detects when Bollinger Bands contract inside Keltner Channels (John Carter's TTM Squeeze). Trades the breakout when the squeeze releases, using MACD histogram for direction.

Detects divergence between price and RSI — when price makes a new low but RSI makes a higher low (bullish), or vice versa. Requires confirmation candle and ADX filter.

Follows trends using EMA(9)/EMA(21) crossovers. Enters long when fast EMA crosses above slow, exits on reverse cross.

Trades price breakouts accompanied by above-average volume. The idea is that volume validates the breakout. Uses raw volume comparison.

Backtest uses Bollinger Band extremes, production uses VWAP deviation — a fundamental parity mismatch that invalidates results.

Detects cascading liquidation events and trades the post-cascade bounce. Uses price drop + volume spike as proxy for real liquidation data.

Identifies crypto sector rotations by tracking social media buzz velocity. When a narrative (AI tokens, RWA, DePIN) gains rapid attention, buys lagging tokens in that sector before the rotation reaches them.

Monitors on-chain protocol revenue and fee generation via DefiLlama. When a protocol shows sustained revenue surge, buys the token — revenue is a fundamental indicator that cuts through hype.

Analyzes Level 2 order book depth to detect significant bid/ask imbalances. Heavy bid wall with thin asks suggests upward pressure. Trades in direction of the imbalance.

Tracks large wallet flows between exchanges and cold storage. When whales move significant amounts off exchanges (accumulation), goes long. Exchange inflows (distribution) trigger shorts.

High-frequency 5-minute scalping that captures quick momentum bursts. Unlike hourly strategies that lag, 5-minute timeframe catches moves early. Requires sub-hourly candle data.