Financial trading analyzing app

Overview I’m looking for an experienced Python + web developer to build a production-quality trading analytics web app called Deulo. Deulo is a statistical pair trading scanner that identifies mean-reversion opportunities using cointegration, hedge ratios, and z-score signals. The MVP already exists in Python and needs to be converted into a polished, scalable Streamlit-based SaaS application. This is not a toy dashboard — it’s a trading decision tool intended for serious retail traders. Build a Pair Trading Analytics Web App (Streamlit) — “Deulo” We are building a lightweight quant trading analytics web app called Deulo that identifies statistically significant stock pairs using cointegration + z-score mean reversion signals. We already have a working Python prototype. We now need an experienced developer to turn it into a clean, production-quality Streamlit web application. This is a focused MVP build (not enterprise fintech infrastructure). What Deulo Does Users input a stock watchlist and the app: * Finds all valid stock pairs * Runs cointegration tests (Engle-Granger) * Calculates hedge ratios (OLS regression) * Builds spread + z-score signals * Ranks the best pair trading opportunities * Displays clear trade signals: * Long spread * Short spread * No trade zone ⸻ Core Features (Must Have) 1. Pair Scanning Engine (Python) * Pull historical price data (Yahoo Finance or similar) * Generate all pair combinations from watchlist * Run: * Cointegration test (p-value) * OLS regression (hedge ratio) * Compute: * Spread series * Z-score normalization * Rank pairs by strongest statistical relationship ⸻ 2. Streamlit Dashboard (UI) Clean, simple UI (trader-focused, not overdesigned): Inputs: * Watchlist text input (tickers) * Date range selector * “Run Scan” button Outputs: * Ranked table of best pairs: * Pair name * Cointegration p-value * Hedge ratio (beta) * Current z-score * Signal (Long / Short / Neutral) ⸻ 3. Visualization (Required) For selected pair: * Spread over time chart * Z-score chart * ±1 and ±2 standard deviation bands ⸻ 4. Trade Signal Logic Simple rule-based system: * Z-score ≥ +2 → Short spread * Z-score ≤ -2 → Long spread * Between -1 and +1 → No trade Displayed clearly in UI. ⸻ Performance Requirements * Must handle 50–150 tickers efficiently * Scan should complete in under ~30 seconds (target) * Clean handling of missing or incomplete market data ⸻ Code Expectations We want maintainable, modular code: Suggested structure: * app.py (Streamlit UI) * data.py (data loading) * analysis.py (cointegration + stats) * scanner.py (pair engine) * Clean, readable Python * Well-commented functions * Easy to extend later ⸻ 💡 Nice-to-Have (If You Have Experience) * Backtesting module for pair strategy performance * Save/load watchlists * Basic alert system (email/webhook) * Portfolio tracking layer * Deployment support (Streamlit Cloud / Render / AWS) ⸻ 🧰 Tech Stack * Python (required) * Streamlit (required) * Pandas, NumPy * Statsmodels * Matplotlib or Plotly ⸻ 📦 Deliverables * Fully working Streamlit app * Clean GitHub-style project structure * Requirements.txt * Setup + run instructions (README) * Basic deployment guide ⸻ 💰 Budget Flexible depending on experience: * MVP Build: $500 * Higher polish / scalable version: negotiable ⸻ ⏱ Timeline * MVP target: 1–2 weeks preferred * Faster delivery is a plus ⸻ 🧠 Ideal Freelancer You should have experience with: * Python data analysis or quant tools * Financial data APIs (Yahoo Finance, Alpaca, Polygon, etc.) * Streamlit or Dash apps * Statistical modeling (regression, time series, etc.) ⸻ 📩 When applying, please include: 1. Examples of dashboards or data apps you’ve built 2. Your experience with financial data or trading tools 3. Estimated timeline 4. Any suggestions to improve architecture ⸻ 🚀 Vision (Context) This MVP is the first version of a larger quantitative trading platform. If successful, this project will expand into: * live market scanning * backtesting engine * multi-strategy dashboard * signal subscription SaaS