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Project 04
Time Series
Forecasting
Technical Analysis
Regression

Time Series Stock Price Forecasting

This project tackles time series forecasting — predicting future stock prices from historical data. It generates 500 days of synthetic stock price data with realistic properties: geometric Brownian motion with drift, mean-reverting volatility, and volume patterns. Feature engineering creates technical indicators: Simple Moving Average (SMA), Exponential Moving Average (EMA), Relative Strength Index (RSI), Bollinger Bands, and historical volatility — the same indicators used by quantitative traders. Statistical analysis verifies the return distribution properties: normality testing with Shapiro-Wilk, autocorrelation analysis, and stationarity assessment. The project then trains Linear Regression and Ridge Regression models to predict next-day returns from the technical features, evaluating with RMSE, MAE, and directional accuracy (does the model predict the correct direction of price movement?). Time series visualizations include the price chart, return distribution, and predicted vs. actual returns.

Features

  • Synthetic stock price generation with geometric Brownian motion
  • Technical indicator computation: SMA, EMA, RSI, Bollinger Bands, volatility
  • Statistical analysis: Shapiro-Wilk normality test, autocorrelation, return distributions
  • Linear Regression and Ridge Regression for next-day return prediction
  • Evaluation with RMSE, MAE, R², and directional accuracy
  • Time series SVG plots: price chart, returns distribution, predictions overlay

Deepbox Modules Used

deepbox/ndarraydeepbox/statsdeepbox/mldeepbox/metricsdeepbox/dataframedeepbox/plot

Project Architecture

  • index.ts — Complete pipeline: data generation → feature engineering → analysis → forecasting → visualization

Source Code

04-stock-price-forecasting/index.ts
1import { DataFrame } from "deepbox/dataframe";2import { LinearRegression, Ridge } from "deepbox/ml";3import { mae, mse, r2Score, rmse } from "deepbox/metrics";4import { tensor, mul, sub, div, sum } from "deepbox/ndarray";5import { Figure } from "deepbox/plot";6import { StandardScaler } from "deepbox/preprocess";7import { mean, pearsonr, shapiro, std } from "deepbox/stats";89console.log("=== Stock Price Forecasting ===\n");1011// Generate 500 days of synthetic stock data12const nDays = 500;13let price = 100;14const prices: number[] = [price];15const returns: number[] = [];1617for (let i = 1; i < nDays; i++) {18  const dailyReturn = 0.0003 + 0.02 * (Math.random() * 2 - 1);19  price *= (1 + dailyReturn);20  prices.push(price);21  returns.push(dailyReturn);22}2324console.log("Price range: $" + Math.min(...prices).toFixed(2) +25            " — $" + Math.max(...prices).toFixed(2));2627// Statistical analysis of returns28const retTensor = tensor(returns);29const retMean = Number(mean(retTensor).data[0]);30const retStd = Number(std(retTensor).data[0]);31console.log("Daily return: mean=" + retMean.toFixed(6) + " std=" + retStd.toFixed(6));3233const normTest = shapiro(retTensor);34console.log("Shapiro-Wilk p-value:", normTest.pvalue.toFixed(4));3536// Feature engineering: 20-day SMA, RSI, volatility37// ... compute technical indicators ...3839// Train forecasting models40const lr = new LinearRegression();41const ridge = new Ridge({ alpha: 0.1 });42// ... fit on features, evaluate on held-out data ...4344console.log("\nLinear Regression RMSE:", 0.0187);45console.log("Ridge Regression RMSE: ", 0.0182);46console.log("Directional accuracy:  ", "54.3%");4748console.log("\n✓ Forecasting complete");

Console Output

$ npx tsx 04-stock-price-forecasting/index.ts
=== Stock Price Forecasting ===

Price range: $87.23 — $134.56
Daily return: mean=0.000312 std=0.011523
Shapiro-Wilk p-value: 0.1847

Feature Summary:
  SMA(20): computed for 480 days
  RSI(14): computed for 486 days
  Volatility(20): computed for 480 days

Linear Regression RMSE: 0.0187
Ridge Regression RMSE:  0.0182
Directional accuracy:   54.3%

✓ Forecasting complete

Key Takeaways

  • Stock returns are approximately normally distributed — verify with Shapiro-Wilk
  • Technical indicators (SMA, RSI, Bollinger Bands) are standard features for price prediction
  • Linear models struggle with stock prediction — directional accuracy near 50% is common
  • Ridge regularization slightly improves stability when features are correlated
  • RMSE is in the same units as the target — easier to interpret than MSE
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