Core Concepts
Understanding natural language processing signals requires familiarity with market microstructure, order flow dynamics, and execution latency. The following sections detail each component.
Production Notes
In production, natural language processing signals requires monitoring for API rate limits, database connection pooling, log rotation, alert thresholds, and automated restart procedures. Document runbooks for incident response.
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 Natural Language Processing Signals
strategy:
name: natural-language-processing-signals
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 natural language processing signals addresses position limits, stop loss placement, correlation exposure, and worst-case drawdown scenarios. Regular monitoring prevents drift from intended behavior.
Common Issues
Common issues with natural language processing signals include look-ahead bias in backtesting, overfitting to historical regimes, inadequate handling of exchange downtime, and insufficient logging for debugging.