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Stock-Market-Predictor

Stock Market Predictor with LSTM network. Web scraping and analyzing tools (ohlc, mean)

With this stock market predictor you will be able to analyze almost 500 companies and their future. This project includes a WebScraping tool to get the stock data from different companies.

Also you will be able to plot different finance graphs of the OHLC or the mean of the AdjClose

Requirements

The program uses Python 3.7.3

There are python files and jupyter notebooks

Modules used: numpy, pandas, mpl_finance, matplotlib, keras, pickle...

Scraping the data

The file of web scraping is StockScraper.py

-Execute the function save_sp500_tickers() to get the 500 companies tickers and save on a file format .pickle

-Execute get_data() to use the yahoo_finance API and get the data with Pandas from the different companies symbols (required first step) and save it on a folder called stock_dfs

-Finally execute compile_data() to create the csv of all the companies data for the correlation

This steps are esential for the next steps

Optional

-Use visualize_data() to see the plot of the correlation between companies (it will take some time)

Training the netwok

The file of neural networkm predictor is is AIFinance.py

-To train and test a company you should use the function predictFuture('AAPL') You can use whatever company symbol you want

#Analyze data Use the jupyter notebook StockMarket.ipynb

You will be able to see step by step the different analyze tools and also the training of a company

The company can be change at the bottom of the notebook

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stock-market-predictor's Issues

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My name is Luis, I'm a big-data machine-learning developer, I'm a fan of your work, and I usually check your updates.

I was afraid that my savings would be eaten by inflation. I have created a powerful tool that based on past technical patterns (volatility, moving averages, statistics, trends, candlesticks, support and resistance, stock index indicators).
All the ones you know (RSI, MACD, STOCH, Bolinger Bands, SMA, DEMARK, Japanese candlesticks, ichimoku, fibonacci, williansR, balance of power, murrey math, etc) and more than 200 others.

The tool creates prediction models of correct trading points (buy signal and sell signal, every stock is good traded in time and direction).
For this I have used big data tools like pandas python, stock market libraries like: tablib, TAcharts ,pandas_ta... For data collection and calculation.
And powerful machine-learning libraries such as: Sklearn.RandomForest , Sklearn.GradientBoosting, XGBoost, Google TensorFlow and Google TensorFlow LSTM.

With the models trained with the selection of the best technical indicators, the tool is able to predict trading points (where to buy, where to sell) and send real-time alerts to Telegram or Mail. The points are calculated based on the learning of the correct trading points of the last 2 years (including the change to bear market after the rate hike).

I think it could be useful to you, to improve, I would like to share it with you, and if you are interested in improving and collaborating I am also willing, and if not file it in the box.

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