regime

Regime — Markov regime detection for any asset

This skill answers one question for any asset: what regime are we in, how sticky is it, and what does that imply for risk and direction? It's built to slot into a trading agent the user already has — as a confirmation layer, a signal, or a risk gate — without them rewriting their strategy.

Framework: Roan (@RohOnChain). Refactored into this plugin by Lewis Jackson. Backtests are historical, not forward-looking.

How to invoke

One command. It takes EITHER a ticker OR the user's own CSV, so it drops into any pipeline regardless of asset:

# any ticker yfinance knows (stocks, ETFs, crypto, FX, futures):
uv run ${CLAUDE_PLUGIN_ROOT}/scripts/markov_regime.py --ticker BTC-USD --json

# the user's own price file (their data, their asset, their pipeline):
uv run ${CLAUDE_PLUGIN_ROOT}/scripts/markov_regime.py --csv ./my_prices.csv --json

• Drop --json for the on-camera pretty terminal output (matrix, persistence diagonal, stationary mix, walk-forward Sharpe + max DD, HMM line).
• --csv needs only a date column and a close column. It auto-detects common names (date/time/timestamp, close/adj close/price/last); if there's exactly one numeric column it uses that. No reformatting required.
• Defaults: --window 20, --threshold 0.05 (±5%), --years 10, --min-train 252. All overridable. --no-hmm skips the HMM.
• First uv run resolves dependencies once (~10–20s), then it's instant.

The JSON contract (every field)

--json prints exactly one JSON object to stdout and nothing else. On failure it prints {"error": "..."} and exits non-zero. Fields:

Field	Type	Meaning
source	str	the ticker or CSV path analysed
rows	int	number of price rows used
date_start, date_end	str	ISO dates of the window
params	obj	window, threshold, min_train actually used
states	list	["Bear","Sideways","Bull"] — fixed index order 0,1,2
current_regime	str	the regime as of the last bar
next_state_probabilities	obj	bear/sideways/bull — P(next | current)
signal	float	bull_prob − bear_prob in [-1, 1]. >0 long bias, <0 short bias, magnitude = conviction
transition_matrix	3×3 list	row = from-state, col = to-state, rows sum to 1
persistence_diagonal	obj	bear/sideways/bull — P(stay in same regime). High = sticky regime
stationary_distribution	obj	bear/sideways/bull — long-run fraction of time in each regime, sums to 1
walk_forward	obj	sharpe, max_drawdown, n_trades from a re-estimated-every-step, no-lookahead backtest. sharpe/max_drawdown may be NaN if history is too short
hmm	obj	available: true → regimes (label, latent_state, mean_daily_return) + caveat. available: false → reason (graceful degrade — everything else is still valid)
framework, disclaimer	str	attribution + "historical, not forward-looking"

signal and current_regime are the two fields most strategies consume. stationary_distribution is the one most risk layers consume.

Composition — slot it into what the user already has

The user already has a trading agent or strategy on some asset. This skill is a layer they add, not a system they adopt. Run it, read one or two fields, gate their existing logic. Three patterns:

(a) Regime confirmation on an existing momentum/strategy

The user has entry logic that already fires. Wrap it: only take longs when the regime agrees, only short when it disagrees.

import json, subprocess
r = json.loads(subprocess.check_output(
    ["uv","run",f"{PLUGIN}/scripts/markov_regime.py","--ticker","SPY","--json"]))

if my_strategy_says_long and r["signal"] > 0:
    enter_long()          # momentum + regime agree → take it
elif my_strategy_says_long and r["signal"] <= 0:
    skip()                # momentum says go, regime says don't → stand down

One line of gating. Their strategy is untouched; the regime just vetoes trades that fight the prevailing chain.

(b) Stationary distribution as a tail-risk / position-size filter

The stationary mix is the asset's long-run baseline. A high baseline Bear share means this asset structurally spends a lot of time in drawdown — size down.

bear_baseline = r["stationary_distribution"]["bear"]
size = base_size * (1.0 - bear_baseline)      # heavier bear regime → smaller bets
# or hard gate: if bear_baseline > 0.40: size = 0   # too tail-heavy to trade

No new model. The user keeps their sizing logic and scales it by a single number that reflects how regime-dangerous the asset actually is.

(c) Standalone signal

No existing strategy needed. The signal field is already a direction + conviction in [-1, 1]:

position = r["signal"]        # +0.6 → 60% long; -0.4 → 40% short; ~0 → flat

Sanity-check it first with the printed walk-forward Sharpe + max drawdown (run without --json to see them on screen) before sizing real capital.

Notes

• Asset-agnostic by design. --ticker for anything yfinance covers; --csv for the user's own data on any asset/timeframe their pipeline produces. The math is identical either way.
• No lookahead. The walk-forward refits the matrix using only past data at every step (incremental O(n), bit-identical to a from-scratch rebuild).
• HMM degrades gracefully. If hmmlearn can't compile (e.g. Windows without MSVC), hmm.available is false with a reason and every other field is still correct. HMM states are labelled by ascending mean return, so a positive "Bear" mean just means the worst latent state was still net-positive over that window.
• Defaults match the on-camera demo and the Pine Script bonus (window 20, ±5%). Use --threshold 0.02 to reproduce the tighter labelling from the original onboarding prompt.