Contact person: Business Client

Phone:Show

Email:Show

Location: Chandigarh, India

Budget: Recommended by industry experts

Time to start: As soon as possible

Project description:
"# Project: AI-Driven Stock Market System — Signals, **Auto-Trade**, **Scalping**, & **Future Movers**

## Summary

Build an end-to-end, **AI-controlled trading stack**: real-time data → AI signals → **auto-execution** with strict **no-loss guardrails** (risk caps & hard stops), plus predictive **stock recommendations** (future high-probability movers). Exchange/broker-agnostic; initial focus **[NSE/BSE + Zerodha/Upstox]** or **[US equities + IBKR/Alpaca]**.

> For research/automation enablement. Not financial advice. No performance guarantees; **hard risk limits** and **capital protection rules** are mandatory.

## Core Objectives

1. **AI Algo Suite (with “No-Loss Guardrails”)**

* Multi-model ensemble (GBM / Transformer / RL) producing **entry/exit** + **confidence**.
* **Capital protection**: hard per-trade stop, trailing stop, per-day max loss, portfolio drawdown cap, **kill-switch**.
* **Position sizing** via Kelly-capped or volatility-scaled risk.

2. **Auto-Trade & Execution**

* Paper + live modes, **pre-trade checks**, bracket orders (SL + TP), OCO management.
* **Latency-aware** routing suitable for **scalping** (see targets below).

3. **Predictive Insights & Recommendations**

* **Future risers**: daily & intraday ranking of symbols likely to go up (next session / next 15–60 min).
* **Top picks list** with rationale (signals, factors, news/NLP optional).

4. **Risk & Governance**

* Exposure caps, circuit filters, time gates, compliance logging, immutable audit trail.

5. **Monitoring & Explainability**

* Model drift, feature health, PnL attribution, alerting, dashboards (web).

## Key Features (MVP)

* **Data**

* Live + historical OHLCV, corporate actions; optional news/NLP and fundamentals.
* Resampling (tick/1m/5m/15m/EOD); survivorship-bias-aware universes.

* **Modeling**

* Signals for: **trend-follow**, **mean-revert**, **breakout-scalp**, **volatility-compression**, **RL policy** (buy/sell/hold/size).
* Walk-forward & **purged K-fold** validation; MLflow model registry.

* **Scalping Automation**

* Micro-targets with **tight SL**, position auto-scale, partial take-profit, time-stop, spread/impact filters.
* Venue/broker adapters with **latency budget**; fallback to passive/limit if slippage high.

* **Recommendations & Watchlists**

* Pre-open and intraday **“Future Movers”** list (top-N symbols with confidence score).
* “Why this stock?” tooltips: factors triggered, momentum/volume sweeps, regime tag.

* **Execution**

* Broker adapters: **[Zerodha/Upstox]** or **[IBKR/Alpaca]** (plug-in architecture).
* Order types: market, limit, stop, trailing, bracket (OCO).

* **Risk (“No-Loss Guardrails”)**

* **Mandatory**: per-trade SL, per-day loss cap, portfolio drawdown cap, cooldowns.
* Auto **kill-switch** on breach; manual override; full audit trail.

* **Observability**

* React/[login to view URL] dashboard: live PnL, positions, heatmaps, drawdowns, order/fill logs.
* Alerts to email/Telegram/Slack.

## Nice-to-Have (Phase 2)

* Options greeks & spreads; portfolio optimizer.
* Alternative data (order book depth, options chain, social sentiment).
* Strategy marketplace, multi-account orchestration.

## Tech Stack (Suggested)

* **ML/Backend:** Python (FastAPI), Pandas/NumPy, scikit-learn, PyTorch/LightGBM, MLflow.
* **Pipelines/MLOps:** Airflow/Prefect, Feast (feature store), Redis, Kafka (optional).
* **DB/Storage:** PostgreSQL + TimescaleDB; object storage for artifacts.
* **Frontend:** React/Next.js.
* **Infra:** Docker, CI/CD, IaC (Terraform), AWS/GCP/Azure.

## Deliverables

1. Architecture + data/contracts + broker adapters.
2. **AI Algo Suite** (signals, scalping module, future-mover ranker) with notebooks.
3. **Execution Engine** (paper + live) with risk guardrails & approvals.
4. **Recommendations UI** (top picks, explanations, confidence).
5. **Risk Policy Configs** (caps, SL templates, cooldown rules).
6. Backtesting + walk-forward reports; paper-trade harness with slippage model.
7. Web dashboard + alerts; complete documentation & runbooks.

## Acceptance Criteria

* **Risk guardrails**: SL, max daily loss, and drawdown cap **enforced 100%** (unit & integration tests).
* **Scalping latency**: signal→order **≤ 250 ms** target (same-region cloud → broker), or vendor-stated budget.
* **Paper vs backtest**: performance within defined tolerance bands (per strategy).
* **Recommendations**: daily top-N list with confidence & rationale; offline backtest precision/recall report.
* **Reproducibility**: pinned deps, MLflow versions, one-click deploy; immutable audit logs.

## Security & Compliance

* Secret vaults, RBAC, encrypted at rest/in transit; broker/regulatory compliance handled broker-side.
* Disclaimers throughout; no promises of profit; user-set risk limits required.

## What to Include in Your Bid

* Links to 2–3 quant/trading builds (especially with **auto-trade** or **scalping**).
* Your **broker/data** plan (APIs, rate limits, fallbacks).
* Modeling plan for **future-mover prediction** and **scalping** (features, labels, leakage guards).
* Latency profile & infra footprint; monitoring and drift strategy.
* Post-deployment support approach.

## Screening Questions

1. Show a **strategy JSON/DSL** expressing: entry from AI signal, SL/TP, time-stop, and size rules.
2. Explain your **walk-forward** & **purged CV** to avoid look-ahead.
3. How do you model **slippage**/fees for **scalping** and trigger **kill-switch** on anomaly?
4. What’s your plan for **future-mover recommendations** (labels, horizon, evaluation metrics)?
5. Provide a **latency budget** and measures to keep signal→order within target.

## Start Your Proposal With

`AI-TRADER MVP` — bids without this will be skipped.

## Tags/Skills

`Algorithmic Trading` `Auto-Trade` `Scalping` `Machine Learning` `Reinforcement Learning` `Python` `FastAPI` `PyTorch` `Backtesting` `Paper Trading` `Broker API` `Zerodha` `Upstox` `IBKR` `Alpaca` `TimescaleDB` `MLflow` `Airflow` `Docker` `React`" (client-provided description)