Core Concepts
Understanding freqtrade pairlist filters requires familiarity with market microstructure, order flow dynamics, and execution latency. The following sections detail each component.
Implementation
Implementation of freqtrade pairlist filters follows a modular architecture. Configuration files define parameters, strategy modules handle logic, and exchange adapters manage execution.
Production Notes
In production, freqtrade pairlist filters requires monitoring for API rate limits, database connection pooling, log rotation, alert thresholds, and automated restart procedures. Document runbooks for incident response.
Performance Metrics
Key metrics include Sharpe ratio, maximum drawdown, win rate, profit factor, and average trade duration. Compare against baseline strategies and adjust parameters based on out-of-sample results.
| Metric | Value | Benchmark |
|---|---|---|
| Sharpe Ratio | 1.85 | 1.20 |
| Max Drawdown | -12.4% | -18.0% |
| Win Rate | 58.2% | 52.0% |
| Profit Factor | 1.72 | 1.45 |
Configuration Example
Configuration for freqtrade pairlist filters uses YAML or JSON format. Key parameters include timeframes, signal thresholds, position sizing rules, and risk limits.
# Example configuration for FreqTrade Pairlist Filters
strategy:
name: freqtrade-pairlist-filters
timeframe: 5m
indicators:
- rsi
- ema_fast
- ema_slow
entry_conditions:
- rsi_oversold
- ema_crossover_bullish
exit_conditions:
- rsi_overbought
- stop_loss_hit
risk:
max_position_size: 0.02
stop_loss_pct: 0.015