Quantitative Financial Research✎ Zero Hype • Mathematical Models

Crypto Strategy Research Center

An educational laboratory dedicated to understanding algorithmic market dynamics, backtesting pitfalls, order book liquidity, and capital preservation formulas.

Strict Educational Research Notice: None of the research, strategy backtests, or code repositories presented here constitute financial or investment recommendations. Cryptocurrency markets carry extreme volatility and substantial risk of partial or total capital loss. Hypothetical backtest results are subject to look-ahead bias, curve-fitting, and unrealistic slippage assumptions. Never risk capital you cannot afford to lose.

Major Market Reference Pairs

Source: CoinGecko Public Feed • USD Aggregation

BTCBitcoin

+2.34%

$67,420

24h Vol: $28.45BRange: $65,900 - $68,150

ETHEthereum

+1.82%

$2,640

24h Vol: $14.20BRange: $2,570 - $2,685

SOLSolana

+4.15%

$172.5

24h Vol: $4.10BRange: $164.2 - $176.8

Strategy Breakdown Library

Comparative evaluation of 5 systematic models: EMA Trend Following, RSI Mean Reversion, Donchian Channel Breakouts, Arithmetic Grid Trading, and Order Book Flow.

Explore Strategy Models

Interactive Risk Calculator

Calculate exact position sizing, dollar risk per trade, liquidation thresholds, and reward-to-risk ratios using mathematically verified formulas.

Launch Calculator

Open-Source Trading Tools

Discover vetted GitHub repositories for Python backtesting engines, CCXT exchange connectors, Freqtrade bot configs, and NautilusTrader quants.

Browse Quant Repos

The 4 Critical Biases That Invalidate Crypto Backtests

1. Look-Ahead Bias

Occurs when indicator logic uses data (such as the candle close price) that was not yet known at the hypothetical entry execution moment.

2. Survivorship Bias

Testing a strategy only on tokens that survived in the top 50, ignoring the hundreds of tokens that went to zero during the same multi-year testing window.

3. Overfitting / Curve-Fitting

Optimizing 15 different indicator parameters until historical returns appear astronomical. In live testing, overfitted models decay rapidly.

4. Unrealistic Fee & Slippage Drag

Assuming limit orders fill 100% at best price with 0% slippage. In reality, taker fees and market impact can turn a "winning" backtest into a losing live system.