Multi-Model AI Stock Prediction & Trading Signals Platform
Stock prediction platform that runs five independent ML models in parallel and shows their outputs side-by-side. Buy/sell/hold signals issue at 1-day, 5-day, and 20-day horizons so day traders, swing traders, and position traders each get a view matched to their strategy.
Five models, three horizons, no black box.
Pick a ticker, five independent ML models inference in parallel on the same five-year window, and the platform shows buy/sell/hold signals at three trading horizons. When models disagree, the UI surfaces it — and points at per-model accuracy so traders pick the view that fits their strategy.
This is an animated mockup of the multi-model signals capability — not a live product. Tickers, prices, signals, and accuracy figures are illustrative. Not investment advice — illustrative.
Multi-model inference pipeline
Each trading day, the pipeline runs all five models on the same five-year window of historical price, volume, and indicator data — fully independent inferences, no shared layer.
Five ML models in parallel
LSTM, Transformer, Hierarchical Clustering, RF + Gradient Boosting ensemble, and Hidden Markov Model — each architecture sees the market through a different lens.
Three signal horizons (1d / 5d / 20d)
Buy/sell/hold signals at 1-day for day traders, 5-day for swing traders, and 20-day for position traders — same models, different time windows, different strategies served.
Five-year sliding window
Models train on five years of historical data. The window slides forward as new data arrives, so predictions stay calibrated to current market microstructure rather than yesterday's regime.
Per-model accuracy tracking
Historical accuracy is recorded per model and per horizon. Traders see which model has the best recent record on the time window they care about — not the model's overall reputation.
Disagreement-first UI
When models split, the UI surfaces it. A trader looking at five views and their per-horizon accuracy makes a better call than one looking at a fused black-box number.
Stock prediction platform that runs five independent ML models in parallel and shows their outputs side-by-side. Buy/sell/hold signals issue at 1-day, 5-day, and 20-day horizons so day traders, swing traders, and position traders each get a view matched to their strategy.
Five distinct ML models — LSTM, Transformer, Hierarchical Clustering, Random Forest + Gradient Boosting ensemble, and Hidden Markov Model — run on the same five-year historical data and produce independent predictions. The signal layer converts each model's output to buy/sell/hold per horizon. Users see disagreement between models, not a fused black-box consensus.
Each trading day, all five models produce predictions across the three horizons. Signals are exposed via a dashboard where traders can compare model agreement, see historical accuracy per model, and pick signals that match their style. The training window slides forward as new data arrives so the models stay calibrated to current market microstructure. Showing disagreement (rather than a fused 'one number' answer) is the deliberate design choice — traders make better decisions when they can see where the models split.
How a request flows through it
Each request enters at the top of the diagram, flows through every box, and lands at the bottom — exactly the way the production system behaves. The scan-line traces where a live request would be right now.
What it's built with
The interesting parts
Five models in parallel — not a fused ensemble
LSTM, Transformer, Hierarchical Clustering, RF+GBM ensemble, and HMM run independently and the platform shows where they disagree. A trader looking at five views makes a better call than a trader looking at one black-box number.
Three signal horizons
Buy/sell/hold signals issue at 1-day (day traders), 5-day (swing traders), and 20-day (position traders) horizons. Same models, different time windows, different trading strategies served.
Five-year training window, sliding forward
All models train on five years of historical data including price, volume, and market indicators. The window slides forward as new data arrives so models stay calibrated to current market microstructure.
Per-model accuracy tracking
Historical accuracy is tracked per model and per horizon, so users know which model has the better recent record on which time window — not just the model's overall reputation.
The calls that did most of the work
A handful of engineering choices shape how a system feels. Here are the ones we'd still defend — alongside what each one cost.
Five models in parallel — not a single ensemble
Showing disagreement is part of the product. A trader deciding between five model outputs gets information that a fused 'one number' answer hides.
Tradeoff: The UI carries the cognitive load of comparing five views, and onboarding has to explain what each model is good at.
Three prediction horizons (1-day, 5-day, 20-day)
Different trading styles need different horizons; a single answer would suit none of them well.
Tradeoff: Each model now produces three outputs, and accuracy has to be tracked separately at each horizon.
Five years of training data
Five years covers multiple market regimes — bull, bear, sideways — which a shorter window wouldn't capture.
Tradeoff: Older data is less representative of current market microstructure; the model has to be re-evaluated as the window slides forward.
Tell us what you're building.
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