Re-Creating a Hedge Fund Quant Strategy Using AI
An interactive breakthrough breakdown of the Markov Regime Model—recalculated, backtested, and optimized using Claude Code.
- Inspired by elite hedge fund architecture.
- Ditches retail trendlines for mathematical probabilities.
- Uses code automation for continuous cross-market tracking.
💡 Use Left/Right Arrow keys or click Next below to navigate.
The Core Shift: Quantifying Vibes
Traditional retail trading relies heavily on indicators and subjective chart visuals. True quants operate strictly on empirical, mathematical probability distributions.
- Objective Sizing: Decisions are numerical, removing emotion.
- Continuous State Monitoring: Shifting focus from "what is the historical chart pattern" to "what condition is the market in right now".
1 & 2: Defining & Labeling States
Every asset's lifetime data is systematically processed backwards looking at a 20-day returns window. Every single historic day is pinned into one of three rigid market categories:
Bull State
Rolling 20-day cumulative return is +5% or higher.
Bear State
Rolling 20-day cumulative return is -5% or lower.
Sideways State
Any rolling value oscillating safely in-between thresholds.
3: The Markov Property
A core mathematical pillar: The market's next move depends exclusively on where it sits today. The historical route taken to get to the current state holds zero mathematical relevance for tomorrow's baseline path.
- The Geography Analogy: If you are driving to New York City and have reached Ohio, your mapping strategy depends purely on your position in Ohio. It does not matter if your original departure point was Arkansas or Nebraska.
- Weight is completely concentrated on the present live state.
4 & 5: Transition Matrix & Stickiness
Every historical state switch is tallied into a live 3x3 Hedge Fund Grid. Rows depict today's environment; columns dictate tomorrow's path. Each row must sum perfectly to 100% certainty.
80%
15%
5%
20%
60%
20%
5%
15%
80%
The highlighted diagonal path uncovers Persistence / Stickiness. Bull and Bear regimes are inherently sticky.
6 & 7: Squaring & Stationary Distribution
To view metrics past a one-day forecast window, quants utilize basic matrix exponential scaling:
- 2-Day Forecast: Square the matrix against itself (e.g., 80% stickiness × 80% = 64% probability to remain after 48 hours).
- 3-Day Forecast: Cube the matrix matrix.
- Stationary Distribution: If extended deep out (e.g., to the 28th power), the predictive signal decays into uniform, flat, and non-actionable slivers.
8: Sizing & Signal Generation
Behind the deep computing lies a beautifully simple final computation. The raw trading alpha is pulled directly using a clear logic gap:
Trading Signal % = (Bull Probability) - (Bear Probability)
- Positive Outcome: Generates an explicit Long trade signal.
- Negative Outcome: Generates an explicit Short trade signal.
- Risk Sizing: The absolute distance of the result determines precisely how heavily to capital-weight the trade.
9: Walk Forward Backtesting
Standard static backtesting leaks future market context into historic setups, inducing structural flaws known as lookahead bias.
- Models mistakenly optimize based on historical parameters that hadn't technically evolved yet.
- The Quant Solution: Walk-forward architecture forces a complete data recalculation of the entire transition matrix for *every independent step forward* in the asset's history line.
10: The Hidden Markov Model (HMM)
Hardcoded state barriers (like static 5% cutoffs) introduce human error. The system bypasses this using unsupervised Hidden Markov Models.
- The asset data is stripped of all arbitrary human labels.
- An unsupervised algorithm scrubs purely for patterns, discovering latent regimes independently.
- The Babysitter Analogy: A new sitter discovers the specific core personalities of children organically over a few days through silent tracking, rather than raw text profiles.
- Execution: Trades execute when human-labeled states match HMM states.
Automation Architecture
This institutional framework is implemented natively using dual tools:
- Claude Code Integration: Deployed as a direct background environment skill via terminal prompts to parse asset datasets automatically.
- TradingView Pine Script: Generates a clean 3x3 live probability scorecard floating inside asset charts for quick execution.
✔ Deck Complete. Clear to Trade.