Design and backtest trading strategies using technical indicators, fundamental analysis, and statistical models. Use when designing and backtesting trading strategies.
Design and backtest trading strategies using technical indicators, fundamental analysis, and statistical models. Use when designing and backtesting trading strategies.
A validated strategy with Sharpe >1.5 and profit factor >1.3 compounds capital at 20-50%+/year. One good strategy can generate $500-5K/month on a $25K account. Multiple uncorrelated strategies scale exponentially.
Revenue Streams
Personal Trading — run your own capital
Strategy Subscriptions ($97-497/mo) — sell signal access
Strategy Development ($2K-10K) — build for prop firms/hedge funds
Backtesting Service ($500-2K) — validate client strategies
First Action in 60 Minutes
#!/usr/bin/env bash# Quick strategy screen: test a simple moving average crossovermkdir -p ~/trading-strategies/{backtests,reports,optimization}
echo"=== Quick Strategy Screen ==="echo"Strategy: SMA crossover (50/200)"echo"Pair: SPY (daily)"echo"Timeframe: 5 years"echo""echo"Calculate:"echo" - Total return vs buy-hold"echo" - Max drawdown"echo" - Sharpe ratio"echo" - Win rate"echo" - Profit factor"echo""echo"Gate: Pass only if Sharpe >1.3 AND profit factor >1.5"echo"If passes → add to optimization queue"echo"If fails → discard or modify"
Trading Strategist
Overview
Build and optimize trading strategies with clear entry/exit rules and risk parameters. The Strategist serves as the design arm of the trading team, responsible for creating systematic trading strategies that can be consistently replicated and optimized. It focuses on turning market insights into executable strategies with defined rules, parameters, and risk controls that can be backtested and deployed.
When to Use
Trigger phrases:
"trading strategist"
"Design and backtest trading strategies using technical indicators, fundamental a"
Creating new trading strategies based on market analysis or hypotheses
Defining precise entry and exit conditions for strategy trading
Optimizing strategy parameters using historical backtesting
Testing strategies across different market regimes (bull, bear, sideways)
Documenting strategy rules and parameters in Notion
Generating strategy performance metrics and Sharpe ratios
Maintaining strategy version history and change tracking
Strategy Design Pipeline
The strategy design pipeline defines entry/exit rules, runs scenario analysis, optimizes parameters, and documents strategies in Notion.
Task is about portfolio management, not trading (use portfolio skills)
Task is about financial analysis (use analysis skills)
You need to analyze trade results (use analytics skills)
Task is about risk management (use risk skills)
You don't have trading capital
Task requires financial advice (consult advisors)
Risk Controls & Verification
Red Flags
Strategy parameters produce overfitting: Strategy too closely tuned to specific historical data; test on out-of-sample data and reduce parameter complexity
Entry rules too complex or contradictory: Strategy may be too complicated to execute reliably; simplify to essential rules
Backtest results inconsistent across scenarios: Strategy may not be robust; identify scenario-specific weaknesses
Sharpe ratio negative or below 0.5: Strategy not compensating for risk; reject or significantly modify before deployment
Profit factor below 1.3: Strategy not generating sufficient return relative to losses; review risk/reward ratios
Drawdown exceeds acceptable threshold (>20%): Position sizing or risk management may need adjustment; reduce position sizes or tighten stop losses
Strategy parameters frequently need adjustment: Market regime dependency too high; implement dynamic parameter adaptation
Verification Checklist
Strategy Design Verification
Entry rules clearly defined and executable
Exit rules properly implemented with stop loss and take profit
Parameters quantified with specific values
Risk controls documented and configured
Backtest Verification
Historical data quality validated for backtest period
Backtest includes realistic slippage and commission
All trades in backtest execute as expected
Metrics match expected performance ranges
Optimization Verification
Grid search covers reasonable parameter range
Optimization tested on out-of-sample data
Best parameters validated with walk-forward analysis
No data snooping bias in optimization process
Scenario Testing Verification
Strategy tested across bull, bear, and sideways markets
Scenario results documented and compared
Strategy weaknesses identified for specific scenarios
Adjustments made for scenario-specific performance
Documentation Verification
Notion strategy page includes all rules and parameters
Backtest results documented with full metrics
Risk controls clearly specified
Documentation reviewed and approved by trading team
Referenced Strategies
The following strategy-level skills are maintained alongside this skill as sub-references for specific strategy implementations:
