analytics Executive Summary & Strategy Metrics
Retail indicators fail primarily due to curve-fitting and lag. PragmAlgo MarkovEdge V3 models price moves as a first-order discrete-time stochastic process. By calculating conditional probabilities across 5 directional states, the engine filters out market noise before momentum shifts become visible on standard RSI or MACD tools.
Audited Backtest Proof Matrix
Audited across multiple liquid timeframes (BTC/USD 4H, NQ Futures 15M, ETH/USD 1H) using 100% bar-close non-repainting logic.
| Market Asset | Timeframe | Total Trades | Win Rate | Profit Factor | Net Return |
|---|---|---|---|---|---|
| ₿ BTC/USD Spot | 4 Hours | 412 | 68.4% | 2.31 | +342.8% |
| 📊 NQ Futures | 15 Mins | 684 | 64.2% | 2.14 | +289.4% |
| ⬡ ETH/USD Spot | 1 Hour | 520 | 66.1% | 2.22 | +314.5% |
How the 5-State Markov Matrix Works in PineScript
A Markov Chain is a stochastic model describing a sequence of possible events in which the probability of each event depends only on the state attained in the previous event. In PragmAlgo MarkovEdge V3, we discretize continuous price returns into 5 distinct states:
functions State Transition Probability Formula
Let P_ij = P(X_{t+1} = j | X_t = i) represent the transition probability from state i to state j. The PineScript engine builds an online 5x5 empirical transition matrix updated dynamically at every bar close:
When P(S4 | S2) exceeds threshold and ATR Hit Decay Probability exceeds 75%, the algorithm triggers an institutional entry signal with pre-calculated Risk/Reward structural targets.
Visual Indicator Showcase
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