The intelligence of VoltaEdge Algo is two proprietary indicators, ported to Python and validated against the author's live charts to within 0.5% across five asset classes and roughly 20,000 candles each. Here is what each actually does — and how they combine.
Most signal frameworks blend indicators by summing weighted scores. VoltaEdge treats them as answering two different questions and requires both to say yes. MASTER answers "is the technical structure aligned for a trade?" CDV PRO answers "is order flow actually confirming it?" A trade without agreement doesn't fire.
Non-repainting, always.
Signals compute only on closed candles. Once fired, a signal never changes retroactively. This is stricter than the TradingView source, which updates intrabar and can therefore repaint. We deliberately trade slightly more conservative timing for the guarantee that the system never acts on something that later vanishes.
A large multi-component system that combines many independent technical signals into a single, graded decision. On top of the components sits an adaptive market-condition engine that gates signals by an overall market-condition score. The engine emits tiered signals — from full-confluence high-conviction entries down to lighter setups — each with three staged take-profit levels.
Each signal comes with a tier and staged exits. A+ requires full confluence across trend, regime, and order flow. A drops one requirement. B is a lighter setup. The lowest "scalp" tier — which repaints by nature — is disabled in live trading. Every signal carries three take-profit levels (partials on the first two, a trailing final leg) plus a hard stop-loss.
The trade-approval rule, in one line:
MASTER = A+ ∧ CDV ≥ 10 ∧ Macro filter = pass → EXECUTE
A cumulative-delta-volume system that reads the balance of buying versus selling pressure bar by bar. It produces a weighted 0–20 order-flow score with graded tiers, an exhaustion measure that flags when a move is running out of participation, volatility bands, and several confirming oscillators.
For each bar, the engine attributes traded volume to the buying side or the selling side based on the direction of price change relative to the bar's open and volume distribution. The cumulative sum of these signed volumes over the session (or a rolling window) is the delta baseline. Divergences between price and delta — price up on falling delta, or vice versa — flag moves that lack participation and are candidates to fail.
The engine's session reset points and score thresholds adjust to the instrument being traded. Equities reset at cash open. Futures use their own session. Crypto runs 24/7 with a rolling window. The score bands that define "strong buying pressure" on BTCUSD are not the same as on HDFCBANK — they are calibrated per asset, in configuration.
A dedicated exhaustion score climbs as a directional move extends without fresh participation. High exhaustion in the direction of an A+ MASTER signal is a strong no — even a technically perfect setup will not fire if the order-flow engine says the move is out of gas.
Every component of every indicator has been ported to Python and cross-checked bar-for-bar against the author's live TradingView charts across five asset classes, roughly 20 000 candles each. Every residual difference is documented and explained (warm-up effects, data-source rounding, or intentional non-repainting divergences). There are zero genuine mismatches.
Tolerance ≤ 0.5%
Across the full validation set, no computed value deviates by more than 0.5% from the TradingView reference. The residual gap is almost entirely the non-repainting behaviour — the platform is deliberately using closed-candle values where TradingView is showing intrabar ones.
There is no machine-learning or AI model in the core system, and that is by design. The intelligence is the two deterministic technical indicators. What has been built is the framework to add AI later — a plugin system with sandboxing and shadow-mode evaluation — but the actual AI modules the original specification envisioned (a machine-learning signal classifier, news-sentiment analysis, an order-flow-imbalance model) are explicitly future scope. Hooks ready; models not built.