TradeYodha synthesizes options flow, dark pool, gamma, volume, and ML predictions into a single AI-powered thesis — so you see what institutions see, in real time, on one screen.
3 of 8 panels bullish: institutional call sweep ($2.4M), dark pool accumulation at $188-190, and ML model confirms. Entry above VWAP ($189.82), targeting call wall at $195.
Scattered tools, delayed data, gut decisions. Sound familiar?
Every panel feeds into Yodha's AI engine. Options flow, dark pool, gamma, volume, ML predictions, relative strength, news, and strike breakdown — all synthesized into a clear directional thesis, updated every 90 seconds during market hours.
Yodha synthesizes all 8 data streams into a clear directional thesis with entry, targets, and stop levels — updated every 90 seconds.
Real-time sweeps, blocks, and unusual activity. See where institutions are placing their bets — call/put ratio, net delta, premium flow.
Block prints from institutional desks. Accumulation and distribution detection with price-level concentration analysis.
Call walls, put walls, GEX flip, max pain. Know exactly where market makers are positioned and where they'll defend.
Cumulative volume delta reveals hidden buying or selling pressure beneath the surface. Session, micro, and tactical windows.
LightGBM model analyzing 57 features — price action, flow, positioning, volatility, macro context. Predicts move probability and direction.
Ask anything about any ticker. Yodha pulls live data using MCP tools — checks accuracy, cross-references signals, and reasons over structured data.
Real-time news sentiment with ticker-specific scoring. Macro headlines filtered for relevance — so you know what's moving the market.
From scattered data to a clear thesis in 2 minutes. Here's how it works on a real setup.
Heavy institutional sweep at the 195 strike — $2.4M in premium, above-ask fills. Options Flow panel flags it instantly.
3 large block prints totaling $12.4M, all at or above the midpoint. Smart money is buying while the tape looks quiet.
The prediction engine sees the pattern across 57 features — flow, positioning, volatility context, and macro regime all align.
All signals synthesized: institutional call buying + dark pool accumulation + positive gamma + ML confirmation. Entry above VWAP with targets at call wall.
Ask Yodha pulls structured dark pool data and signal accuracy — "This combination on NVDA has preceded a 15-min move 68% of the time. Dark pool prints are above VWAP — this is accumulation, not hedging."
The move played out. Yodha flagged it 43 minutes before the breakout — not with a "buy now" alert, but with the full institutional picture.
Options flow, dark pool, gamma exposure, volume pressure, relative strength, news sentiment, ML predictions, and strike breakdown — all updating in sync.
A LightGBM model analyzing 57 features plus 12 rule-based detectors: confluence, sweep clusters, CVD divergence, VWAP reclaim, gamma squeeze, and more.
Not just a chatbot. Yodha calls live tools to pull signal accuracy, cross-reference dark pool data, and check ML predictions. It reasons over structured data — like having a quant desk analyst on demand.
Other platforms show you data. Yodha shows you what it means.
Most platforms send you "NVDA call sweep $2M" and leave you to figure it out. Yodha synthesizes all 8 data streams into a clear thesis with entry, targets, and stop — updated every 90 seconds.
"Is the dark pool accumulation on SPY real or hedging?" "What's the historical accuracy of sweep clusters on TSLA?" Yodha pulls live data and gives you a real answer — not a canned response.
Every signal is tracked to outcome. Yodha knows that confluence + sweep + dark pool on NVDA at the 15-minute horizon has preceded a move 68% of the time. This data compounds — no one else has it.
Multi-window scoring (5m, 15m, session) matched to your timeframe. Phase-aware filtering suppresses noise during midday chop. ATR-relative thresholds adapt to each ticker's volatility.
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TradeYodha is not a registered investment advisor. All analysis, predictions, and trade levels are for informational and educational purposes only.
AI models can and will make mistakes. Our ML model is wrong approximately 40% of the time. Predictions are probabilistic, not guarantees.
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One screen. Eight signals. One AI thesis.