---
title: 'Built-in Strategy Library'
description: 'Leverage the prebuilt Neural strategies for momentum, mean reversion, arbitrage, and sentiment trading.'
---

Neural ships with a catalogue of ready-to-run strategies housed in `neural.analysis.strategies`. Each class inherits from the common `Strategy` base and focuses on a specific trading style.

## Presets

Use `create_strategy(preset, **overrides)` to spin up a strategy with sensible defaults:

```python
from neural.analysis.strategies import create_strategy

strategy = create_strategy("momentum", initial_capital=15_000)
```

Available presets: `conservative`, `momentum`, `arbitrage`, `news`, `aggressive`, `high_frequency`.

## Strategy Catalog

### MeanReversionStrategy

Targets price divergence between Kalshi markets and sportsbook consensus or historical averages.

- Tracks rolling price history per ticker and computes fair value.
- Configurable divergence threshold, reversion target, and confidence decay.
- Fits scenarios where you expect prices to revert to a mean (e.g., overreaction to news).

### SportsbookArbitrageStrategy

Specialized flavor of mean reversion that explicitly hedges Kalshi markets against sportsbook lines.

- Consumes external sportsbook data and flags mispricings.
- Produces dual-legged signals (buy YES / buy NO) for guaranteed spreads.

### MomentumStrategy & GameMomentumStrategy

Follows trends during live games.

- Calculates momentum over configurable lookbacks.
- Integrates RSI, volume confirmation, and trend-strength (R²) filters.
- `GameMomentumStrategy` adds ESPN event awareness to fade blowouts or ride momentum after pivotal plays.

### ArbitrageStrategy & HighSpeedArbitrageStrategy

Search for risk-free opportunities.

- Detects YES+NO price sums below $1.00, cross-market discrepancies, and sportsbook vs Kalshi edges.
- Produces composite signals with metadata instructing the order manager to execute both sides.
- `HighSpeedArbitrageStrategy` emphasizes low latency with fixed sizes and tight execution rules.

### NewsBasedStrategy & BreakingNewsStrategy

React to injuries, lineup changes, weather, and social chatter.

- Measures sentiment strength, news decay, and social volume.
- Applies configurable impact maps for different positions (QB, star, role player).
- `BreakingNewsStrategy` accelerates entries when viral spikes occur.

### SentimentTradingStrategy

Combines aggregated sentiment (Twitter + ESPN) to detect divergence, momentum shifts, viral moments, and contrarian setups.

- Maintains multiple rolling windows of sentiment scores.
- Applies position sizing curves based on sentiment strength and confidence thresholds.
- Ideal when running the `MultiSourceAggregator` alongside market data.

Explore the source files in `neural/analysis/strategies/` for deeper implementation details and extend them when you need bespoke logic.
