The first step is to select the stock or stocks you want to predict prices for. Some good options to start with are large, liquid stocks from major companies that have a long history of daily price data available. Example stocks could include Apple, Microsoft, Amazon, Google, etc. Narrowing down to 1-5 stocks will help keep the initial scope of the project manageable.
Next, you will need to collect historical daily price data for the stocks you selected. This data can be downloaded for free from finance websites like Yahoo Finance which provides closing prices and volumes going back many years for most stocks. Make sure to download data that includes the date, open, high, low, close and volume for each trading day. Having at least a few years of historical data will allow for proper testing and validation of your predictive models.
With the price data collected, you can now start exploring and analyzing the data to gain insights. Create visualizations to examine trends, volatility and relationships over time. Calculate key metrics like simple and exponential moving averages, MACD, RSI and Bollinger Bands to identify signals. Explore correlations between prices and external factors like economic reports, company news and sector performances. Examining the data from different angles will help inform feature selection for your models.
Feature engineering is an important step to transform the raw price data into parameters that can be used to build predictive models. Some common features include lagged price values (e.g. prior day close), moving averages, technical indicators, seasonality patterns and external regressors. You may also want to difference/normalize features and stocks to account for heterogeneity. Carefully selecting relevant, mutually exclusive features will optimize model performance.
Now with your historical data parsed into training features and target prices, it’s time to implement and test predictive models. A good starting approach is linear regression to serve as a simple baseline. More advanced techniques like random forest, gradient boosted trees and recurrent neural networks often work well for time series forecasting problems. Experiment with different model configurations, hyperparameters and ensemble techniques to maximize out-of-sample predictive power.
Evaluate each model using statistical measures like mean absolute error, mean squared error and correlation between predicted and actual prices on a validation set. Optimize models by adjusting parameters, adding/removing features, varying window sizes and adopting techniques like differencing, normalization, lags, etc. Visualize results to qualitatively assess residuals, fit and ability to capture trends/volatility.
Fine-tune top models by performing rolling forecast origin evaluations. For example, use data from 2015-2017 for training and sequentially predict 2018 prices on a daily basis. This simulates real-time forecasting more accurately than one-off origin tests. Monitor forecasting skill dynamically over time to identify model strengths/weaknesses.
Consider incorporating model output as signals/factors into algorithms and portfolio optimizers to test if predictive quality translates into meaningful investment benefits. For example, blend predicted prices to develop trading strategies, calculate portfolio returns with different holding periods or use forecasts to time market entry/exits. Quantitatively evaluating financial outcomes provides a clear, practical evaluation of model usefulness.
Document all steps thoroughly so the process could be replicated using consistent data and configurations. Save model objects and code for future reference, enhancement and to allow for re-training on new incoming data. Automating forecast generation and evaluation leads to a continually evolving system that adapts to changing market dynamics over long periods.
Some additional advanced techniques that can help improve predictive power include feature engineering techniques like decomposition, interaction effects and deep feature synthesis. Modeling techniques such as neural networks, kernel methods, topic modeling and hierarchical approaches also show promise for capturing complex price dynamics. Experimenting with big structural/combinatorial approaches allows squeezing more signal out of time series problems.
Consider open sourcing models, code and analyses to enable independent review, validation of results and fostering collaborative research. The financial forecasting problem involves many inter-related factors and pooling data/insights across different contributors accelerates collective progress towards building more sophisticated and useful solutions over time. Distribution of prediction data also allows downstream applications of forecasts to uncover new use cases.
A stock price prediction project requires systematically analyzing historical data from multiple perspectives to select optimal inputs for predictive models, carefully implementing and evaluating different techniques, rigorously optimizing model performance, blending results for practical applications and continually enhancing methods as new market behaviors emerge over extended periods. Adopting a scientific process that emphasizes experimentation, replication and sharing enables significant, impactful advances in financial market forecasting.