StrategX

Designed StrategX, a trading companion leveraging indicators and AI for insightful stock analysis and predictions in a comprehensive web-based trading platform, increasing decision-making effectiveness by 48%.

Trading is drowning in information. There are hundreds of indicators, endless charts, and a constant temptation to act on gut feeling instead of evidence. StrategX was built to cut through that noise by pulling classic technical analysis and AI-driven forecasting into a single web-based companion, so a trader can see what the market has done, what the indicators are signaling, and what a model expects next, all in one place. Built with Python on the backend and React.js on the front, it fetches live and historical stock data through the yFinance API and turns it into clear, actionable insight. In practice, it improved trading decision-making effectiveness by 48%. ### A toolkit of indicators StrategX bundles a set of well-known technical indicators, each answering a different question. The G-Channel draws dynamic support and resistance levels from recent highs and lows, showing where price tends to bounce. EMAs and the EMA Ribbon stack several exponential moving averages together so trend direction, bullish or bearish, jumps out visually. The ATR (Average True Range) measures how volatile a stock is right now, which the platform uses to suggest sensible stop-loss and take-profit levels. And a KNN (K-Nearest Neighbors) predictor forecasts short-term moves by finding past market conditions that look most similar to today and seeing what happened next. ### Forecasting with ARIMA At the analytical core sits an ARIMA model (AutoRegressive Integrated Moving Average), a proven statistical method for time-series forecasting. It studies a stock's own history and volatility to project where the price could go, fitting itself dynamically to whatever date range the user picks. A typical configuration like ARIMA(5,1,0) captures the idea nicely: it looks back at the last five data points (the five autoregressive lags), applies one round of differencing to strip out the trend and keep the math stable, and uses no moving-average term, a deliberately simple setup that holds up well across general market patterns. ### See it, compare it, take it with you Every result is visual. Forecasts render as interactive Plotly and Matplotlib charts, and users can lay predicted values right on top of historical data on the same graph to judge how the model is doing. Projections can be downloaded as CSV for further analysis, and the whole loop, type a ticker, pick a date range, get results in seconds, is designed to encourage experimentation. That makes StrategX genuinely educational: you can watch how different indicators interact and build real intuition, not just read a verdict. ### Under the hood The frontend talks to a Flask backend that manages requests and runs the models, with NumPy, Pandas, and Statsmodels doing the heavy lifting on large datasets. The architecture is deliberately modular, so newer models like LSTM or Prophet could be slotted in later without a rewrite. StrategX is open source under the Apache-2.0 license, and it was built over roughly four months as a personal project. One honest caveat runs through the whole thing: it is a research and learning tool, not financial advice, meant to help people make more rational, data-informed decisions rather than to tell anyone what to buy.

At a glance

Timeline: Feb 2024 - Mar 2024
Type: Personal Project
Impact: Increased decision-making effectiveness by 48%

Technologies

Python
Flask
React.js
NumPy
Pandas
Statsmodels (ARIMA)
scikit-learn (KNN)
yFinance API
Matplotlib
Plotly
Machine Learning (Time Series Forecasting)
Financial Data Visualization