# AI Quant Market Radar & The PhD Mathematics Behind It Most "AI trading tools" are just chat wrappers that send a text prompt to an external server and guess direction. **TM Pro's AI Quant Regime & Portfolio Risk Engine is fundamentally different:** * Every 5 seconds (and on every candle close), the TM Pro EA streams **120 closed OHLCV bars** from your live MetaTrader chart directly into TM Pro's on-device quantitative engine. * It runs **100% locally on your CPU in under 2 milliseconds**—requiring **zero cloud API keys, zero monthly token costs, and zero network latency**. ![TM Pro AI tab showing the PhD Quant Regime & Portfolio Risk Engine](/img/tm-pro/live-3-ai-quant-engine.png) Live MT5 Quant telemetry on the AI tab — Regime badge, Hurst Exponent, Yang-Zhang Volatility, Efficiency Ratio, Z-Score, Quarter-Kelly recommendation, and 95% 1-Day Parametric VaR. *** ## Part 1: The Trader's 3-Second Playbook (Plain English)[​](#part-1-the-traders-3-second-playbook-plain-english "Direct link to Part 1: The Trader's 3-Second Playbook (Plain English)") You do not need a PhD in quantitative finance to trade with the AI Quant Radar. Every calculation is distilled into **one color-coded Regime Badge** and **one "Apply Setup" button** on your **Trade** tab: ### How to Trade Each of the 4 Regime Badges[​](#how-to-trade-each-of-the-4-regime-badges "Direct link to How to Trade Each of the 4 Regime Badges") | Regime Badge | When It Appears | How to Trade It Effectively | | ----------------------------------------------- | -------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | **`TREND BULL · BUY`** *(Green)* | **Hurst `H > 0.55`** and positive directional efficiency | Institutional order-flow is persistent upward. Focus on **long pullbacks and breakouts**. Use a wider `3.2x ATR` Take Profit and enable **Trailing Stop** so you don't cap big trend runners. | | **`TREND BEAR · SELL`** *(Red)* | **Hurst `H > 0.55`** and negative directional efficiency | Institutional order-flow is persistent downward. Focus on **short pullbacks and breakdowns**. Let the EA trail your stop behind lower highs. | | **`MEAN REVERT`** *(Amber)* | **Hurst `H < 0.45`** or price is stretched (\` | Z | | **`HIGH VOL CHOP · NEUTRAL`** *(Muted/Warning)* | **Hurst `H ~ 0.50`** (random walk) or extreme volatility spike | Price action has no statistical memory (50/50 coin-flip noise). **Best action: sit on your hands** or let **Apply Setup** cut your risk down to a defensive `0.25%`. | ### What Clicking "Apply Setup" Does Automatically[​](#what-clicking-apply-setup-does-automatically "Direct link to What Clicking \"Apply Setup\" Does Automatically") Inside the **AI Quant Regime & Alpha Radar** card on the **Trade** tab, clicking **Apply Setup** transfers five volatility-calibrated parameters into your Order Ticket in a single click: 1. **Risk `%`**: Set to the **Quarter-Kelly Optimal** fraction (`0.25%` to `2.00%`) based on current regime edge quality. 2. **Stop Loss (`pips`)**: Calibrated to `1.5x ATR` in trends (or `1.8x ATR` in mean-reversion) so normal candle noise doesn't prematurely stop you out. 3. **Take Profit (`pips`)**: Calibrated to `3.2x ATR` in persistent trends (or `2.0x ATR` in ranges). 4. **Break-Even Trigger (`pips`)**: Enabled at `1.0x ATR` in profit. 5. **Trailing Stop (`pips`)**: Enabled at `1.2x ATR` behind peak price. As soon as you click **Apply Setup**, look right above your cursor at the top **SELL / BUY** buttons—your new lot size, `SL / TP` prices, and exact dollar risk are already updated and ready to arm! *** ## Part 2: The Institutional PhD Mathematics Under the Hood[​](#part-2-the-institutional-phd-mathematics-under-the-hood "Direct link to Part 2: The Institutional PhD Mathematics Under the Hood") For systematic traders, quants, and fund managers who want to inspect the exact stochastic calculus driving TM Pro's telemetry, here are the five estimators running on every 120-bar window (`N = 120`): ### 1. Rescaled-Range (`R/S`) Hurst Exponent (`H`)[​](#1-rescaled-range-rs-hurst-exponent-h "Direct link to 1-rescaled-range-rs-hurst-exponent-h") Proposed by Harold Edwin Hurst and popularized in financial fractals by Benoit Mandelbrot, the Hurst Exponent measures the **long-term memory of log-returns** across multiple lag windows `n in {8, 16, 32, 64}`: ``` Log-Returns: r_t = ln(Close_t / Close_{t-1}) Mean-Adjusted Series: Y_k = sum_{i=1..k} (r_i - mean(r)) Cumulative Range: R(n) = max_{1<=k<=n}(Y_k) - min_{1<=k<=n}(Y_k) Sample Std Deviation: S(n) = sqrt( (1/n) * sum_{i=1..n} (r_i - mean(r))^2 ) Scaling Power Law: E[ R(n) / S(n) ] = C * n^H OLS Log-Log Slope: H = Cov( ln(n), ln(R(n)/S(n)) ) / Var( ln(n) ) ``` * **`H = 0.50`**: Geometric Brownian Motion (memoryless random walk). * **`H in (0.55, 1.00)`**: **Persistent Fractal Process**—positive autocorrelation where trending moves statistically follow trending moves. * **`H in (0.00, 0.45)`**: **Anti-Persistent (Mean-Reverting) Process**—negative autocorrelation where up-bars are statistically followed by down-bars. *** ### 2. Yang-Zhang Minimum-Variance OHLC Volatility (`sigma_YZ`)[​](#2-yang-zhang-minimum-variance-ohlc-volatility-sigma_yz "Direct link to 2-yang-zhang-minimum-variance-ohlc-volatility-sigma_yz") Standard close-to-close volatility ignores intrabar highs/lows and overnight opening gaps. TM Pro implements **Yang & Zhang's (2000)** unbiased, minimum-variance estimator, which combines **overnight jump variance (`sigma_o^2`)**, **open-to-close drift variance (`sigma_c^2`)**, and **Rogers-Satchell intrabar extreme variance (`sigma_RS^2`)**: ``` Overnight Log-Jump: o_i = ln(Open_i / Close_{i-1}) Open-to-Close Drift: c_i = ln(Close_i / Open_i) Rogers-Satchell Term: rs_i = ln(High_i / Close_i)*ln(High_i / Open_i) + ln(Low_i / Close_i)*ln(Low_i / Open_i) Optimal Weighting: k = 0.34 / ( 1.34 + (N + 1) / (N - 1) ) Per-Bar YZ Variance: sigma_YZ^2 = Var(o) + k * Var(c) + (1 - k) * Mean(rs) Annualized Vol (%): Vol_Annual = sqrt( sigma_YZ^2 * BarsPerYear ) * 100 ``` This makes TM Pro's volatility reading up to **14x more statistically efficient** than standard close-to-close standard deviation on small sample windows. *** ### 3. Kaufman Efficiency Ratio (`ER`), Wilder ATR & Z-Score Stretch (`Z`)[​](#3-kaufman-efficiency-ratio-er-wilder-atr--z-score-stretch-z "Direct link to 3-kaufman-efficiency-ratio-er-wilder-atr--z-score-stretch-z") To measure directional signal-to-noise alongside mean-reversion stretch over a 20-bar lookback (`L = 20`): ``` True Range (TR_t): max( High_t - Low_t, |High_t - Close_{t-1}|, |Low_t - Close_{t-1}| ) ATR (14-bar): (1 / 14) * sum_{t=N-13..N} TR_t (converted to Pips) Efficiency Ratio (ER): (Close_N - Close_{N-20}) / sum_{t=N-19..N} |Close_t - Close_{t-1}| in [-1, +1] Z-Score Stretch (Z): (Close_N - SMA_20) / StdDev_20 ``` * When `|ER| > 0.35` and `H > 0.55`, directional trend velocity is clean and low-noise. * When `|Z| > 2.0`, price is stretched more than 2 standard deviations from its 20-bar equilibrium—warning trend traders not to chase late entries. *** ### 4. Fractional Quarter-Kelly Optimal Sizing (`f*`)[​](#4-fractional-quarter-kelly-optimal-sizing-f "Direct link to 4-fractional-quarter-kelly-optimal-sizing-f") The classic **Kelly Criterion** (John L. Kelly Jr., 1956; Edward O. Thorp) maximizes the expected geometric growth rate of capital `E[ln(1 + f * R)]`. Because full Kelly exhibits high drawdown variance under parameter estimation error, institutional quant desks use **Fractional Quarter-Kelly (`0.25 * f_full`)**: ``` Estimated Win Prob: p = clamp( 0.50 + 0.18 * Confidence * |ER|, 0.42, 0.68 ) Loss Probability: q = 1 - p Payoff Odds Ratio: b = Suggested_TP_Pips / Suggested_SL_Pips Full Kelly Fraction: f_full = (p * b - q) / b Quarter-Kelly Risk %: f* = clamp( 0.25 * f_full * 100, 0.25%, 2.00% ) ``` *** ### 5. Portfolio 95% 1-Day Parametric Value-at-Risk (`VaR_95`) & Stop-Locked Risk Audit[​](#5-portfolio-95-1-day-parametric-value-at-risk-var_95--stop-locked-risk-audit "Direct link to 5-portfolio-95-1-day-parametric-value-at-risk-var_95--stop-locked-risk-audit") In the **AI** tab under **Portfolio Risk Audit**, TM Pro continuously evaluates all open positions on your account against two institutional risk metrics: ``` 1. Stop-Locked Cash Risk ($): Sum across all open positions of: |OpenPrice - StopLoss| * TickValue * Lots (Flags any unprotected position with StopLoss == 0 immediately in amber/red) 2. 95% 1-Day Parametric VaR ($): DailySigma = (Vol_Annual / 100) / sqrt(252) VaR_95_1Day = 1.64485 * DailySigma * GrossNotionalExposureUSD ``` This tells you—in real dollars—the maximum 1-day statistical loss expected on 95% of trading days given current market volatility, alongside your hard stop-loss floor.