349starchild-strategies

starchild strategies v2

Nine curated trading strategies for perpetual futures markets.

workflow

1. User describes intent → match to category or specific strategy from catalog.json
2. Load decision tree from references/ for full strategy logic
3. Configure interactively using scripts/configure.py
4. Validate with backtest using scripts/backtest.py
5. Execute through platform's strategy engine

strategy selection guide

User says...	Category	Strategy
"buy dips, sell rips"	mean reversion	rsi reversal
"oversold/overbought signals"	mean reversion	rsi reversal
"trade the spread between BTC/ETH"	mean reversion	convergence trade
"pairs trading"	mean reversion	zscore reversion
"follow the trend"	momentum	volatility breakout
"breakout after quiet periods"	momentum	volatility breakout
"gap continuation"	momentum	gap continuation
"rank and rotate assets"	momentum	rsi rotation
"set up a grid"	market neutral	grid bot
"earn funding without risk"	market neutral	funding arb
"overnight trading"	market neutral	overnight drift

automation scripts

• scripts/configure.py - Interactive strategy configurator with risk calculations
• scripts/backtest.py - Quick validation against recent market data
• scripts/risk_calc.py - Capital requirements and fee analysis
• scripts/market_scan.py - Current market regime assessment for strategy selection

progressive disclosure

1. Browse catalog.json for overview (complexity, capital, holding period)
2. Load decision tree from references/{strategy-name}.md only when selected
3. Configure using scripts for interactive setup with validation
4. Deploy with monitoring using generated JSON configs

⚠️ All strategies enforce pre-trade liquidity checks and fill verification.

Use scripts/configure.py {strategy-id} to generate ready-to-deploy configs.