Time Based Performance

Code Structure

The code structure separates concerns: data ingestion, signal generation, risk checks, and order submission. This modularity enables testing and reuse across strategies.

# Example configuration for Time Based Performance
strategy:
  name: time-based-performance
  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

Configuration Example

Configuration for time based performance uses YAML or JSON format. Key parameters include timeframes, signal thresholds, position sizing rules, and risk limits.

# Example configuration for Time Based Performance
strategy:
  name: time-based-performance
  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

Risk Considerations

Risk management for time based performance addresses position limits, stop loss placement, correlation exposure, and worst-case drawdown scenarios. Regular monitoring prevents drift from intended behavior.

Production Notes

In production, time based performance requires monitoring for API rate limits, database connection pooling, log rotation, alert thresholds, and automated restart procedures. Document runbooks for incident response.

Implementation

Implementation of time based performance follows a modular architecture. Configuration files define parameters, strategy modules handle logic, and exchange adapters manage execution.