HOW-TO: Forecast Next-Bar Odds with Markov ProbCast

🎯 Goal
In 5 minutes, you’ll add Markov ProbCast to a chart, calibrate the “big-move” threshold θ for your instrument/timeframe, and learn how to read the next-bar probabilities and regime signals
(🟩 Calm | 🟧 Neutral | 🟥 Volatile).

🧩 Add & basic setup

Open any chart and timeframe you trade.

Add Markov ProbCast — P(next-bar) Forecast Panel from the Public Library (search “Markov ProbCast”).

Inputs (recommended starting point):
• Returns: Log
• Include Volume (z-score): On (Lookback = 60)
• Include Range (HL/PrevClose): On
• Rolling window N (transitions): 90
• θ as percent: start at 0.5% (we’ll calibrate next)
• Freeze forecast at last close: On (stable readings)
• Display: leave plots/partition/samples On

📏 Calibrate θ (2-minute method)
Pick θ so the “>+θ” bucket truly flags meaningful bars for your market & timeframe. Try:
• If intraday majors / large caps: θ ≈ 0.2%–0.6% on 1–5m; 0.3%–0.8% on 15–60m.
• If high-vol crypto / small caps: θ ≈ 0.5%–1.5% on 1–5m; 0.8%–2.0% on 15–60m.
Then watch the Partition row for a day: if the “>+θ” bucket is almost never triggered, lower θ a bit; if it’s firing constantly, raise θ. Aim so “>+θ” captures move sizes you actually care about.

📖 Read the panel (what the numbers mean)
• P(next r > 0): Directional tilt for the very next candle.
• P(next r > +θ): Odds of a “big” upside move beyond your θ.
• P(next r < −θ): Odds of a “big” downside move.
• Partition (>+θ | 0..+θ | −θ..0 | <−θ): Four buckets that ≈ sum to 100%.
• Next Regime Probs: Chance the market flips to 🟩 Calm / 🟧 Neutral / 🟥 Volatile next bar.
• Samples: How many historical next-bar examples fed each next-state estimate (confidence cue).
Note: Heavy calculations update on confirmed bars; with “Freeze” on, values won’t flicker intrabar.

📚 Two practical playbooks

Breakout prep
• Watch P(next r > +θ) trending up and staying elevated (e.g., > 25–35%).
• A rising Next Regime: Volatile probability supports expansion context.
• Combine with your trigger (structure break, session open, liquidity sweep).

Mean-reversion defense
• If already long and P(next r < −θ) lifts while Volatile odds rise, consider trimming size, widening stops, or waiting for a better setup.
• Mirror the logic for shorts when P(next r > +θ) lifts.

⚙️ Tuning & tips
• N=90 balances adaptivity and stability. For very fast regimes, try 60; for slower instruments, 120.
• Keep Freeze at close on for cleaner alerts/decisions.
• If Samples are small and values look jumpy, give it time (more bars) or increase N slightly.

🧠 Why this works (the math, briefly)
We learn a 3-state regime and its transition matrix A (A[i,j] = P(Sₜ₊₁=j | Sₜ=i)), estimate next-bar event odds conditioned on the next state (e.g., q_gt(j)=P(rₜ₊₁>+θ | Sₜ₊₁=j)), then forecast by mixing:
P(event) = Σⱼ A[current,j] · q(event | next=j).
Laplace/Beta smoothing, per-state sample gating, and unconditional fallbacks keep estimates robust.

❓FAQ
• Why do probabilities change across instruments/timeframes? Different volatility structure → different transitions and conditional odds.
• Why do I sometimes see “…” or NA? Not enough recent samples for a next-state; the tool falls back until data accumulate.
• Can I use it standalone? It’s a context/forecast panel—pair it with your entry/exit rules and risk management.

📣 Want more?
If you’d like an edition with alerts, σ-based θ, quantile regime cutoffs, and a compact ribbon—or a full strategy that uses these probabilities for entries, filters, and sizing—please Like this post and comment “Pro” or “Strategy”. Your feedback decides what we release next.