GemAI

Analysis and forecasting stock prices using different machine learning strategies and parameters: In this project I have collected closing prices from all the main stocks in the USA for the last 25 years. To each stock values I have added related markets indexes and indicators. Than I have run pattern recognitions on this data.

I have checked the following classifiers:

• RandomForestClassifier
• KNeighborsClassifier
• GradientBoostingClassifier
• SVC – support vector machine
• GradientBoostingClassifier

Best classifier was the random forest predictor with the 14 days predictor and sliding window of 3 days.

It predicts buying opportunity.

The results are around 33 percent for true positive and true negative. The label is 3 options label (buy/sell/hold) – which means that randomly deciding will result also in 33 percent.

Main.py – main console to run

Import Module

The Import Modlue is in charge of importing the raw data like daily stocks values and daily indexes values

• Scrapper – scrap stocks data
• ScrapperIndex – scrap indexes values

Strategy Module

This module is in charge of implementing different strategies to calculate the labels from the raw data

AbstractStrategy – abstract class – implements:

• ProcessSpecificTicker – extract label, creates confusionmatrix and prediction for the next day
• ProcessComposedTicker – collects raw data from different stocks to one data table and run ProcessSpecificTickeron it. Under the assumption that all stocks behave the same, I create more raw data.

AbstractPercentForPeriodStrategy – abstract class – inherits from AbstractStrategy, implements strategy for change in percentage of the value of the stock for specified period

PercentForPeriodIndexCorrRollingStrategy – concrete class – inherits from AbstractPercentForPeriodStrategy, implements a rolling window to average an already divided by the index values

PercentForPeriodIndexCorrStrategy - concrete class – inherits from AbstractPercentForPeriodStrategy, values are divided by the index values

PercentForPeriodIndexLogCorrStrategy – concrete class – inherits from AbstractPercentForPeriodStrategy, implements a log on the already divided by the index values

AbstractSpikeForPeriodStrategy – abstract class – inherits from AbstractStrategy, implements strategy for sharp change in percentage of the value of the stock for specified period, once the level is crossed – the label is set

SpikeForPeriodStrategy – concrete class – inherits from AbstractSpikeForPeriodStrategy – implements looking for a rise in the percentage for a specific period

GemAIModule

machine learning module: FeaturesLab – a lab, searches for the covariance values between the different features DataFrameUtils – utils for handling the raw data ModelBaseRankingLab – utils for searching for the best predictor

Predict - the predictor

• PredictTicker – predicts specific stock and returns also confusion matraix
• PredictAll – runs predict on all the stocks from Nasdaq(ndx) PredictSpikes – predictors that uses the spike strategy PredictCompose – predictors that runs throw all stocks in the Nasdaq index

Future improvement

1. adding text analysis (NLTK) to news about stocks to help predict future values can help create a more accurate predictor
2. using 1 minute real time raw data