rscdev7 / stock_advisor

Standard & Poor's 500 Financial Analysis

Stock Advisor

This project aim to analyze Standard & Poor's 500 historical data in order to:

1. Extract meaningful insights from data
2. Forecast future S&P 500 Indexes

S&P 500 (Standard and Poor's 500) is a free-float, capitalization-weighted index of the top 500 publicly listed stocks in the US (top 500 by market cap).

Analyzed Dataset is a multi-variate Time Series one that contains records from 1871-01 to 2018-04.

PROJECT CUES

[DATASET]

DATASET SIZE: 1,769 record

FEATURES

Date	SP500	Dividend	Earnings	Consumer Price Index	Long Interest Rate	Real Price	Real Dividend	Real Earnings	PE10
Date	Number	Number	Number	Number	Number	Number	Number	Number	Number

DATASET YEAR BUILD: 2018

[ALGORITHMS]

1. SARIMA
2. Holt-Winters
3. Facebook Prophet

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STACK SOFTWARE

Component
Python
Jupyter Notebook
Pandas
SciPy
StatsModels
Matplotlib
NumPy
Kats
FilterPy
PyTorch

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REFERENCES

• [1] Bisgaard, Kulahci - Time Series Analysis and Forecasting by Example (First Edition)
• [2] Brockwell, Davis - Introduction to Time Series and Forecasting (Third Edition)
• [3] Jason Brownlee - Introduction to Time Series Forecasting With Python
• [4] Taylor et. al. (Facebook Research) - Forecasting at Scale
• [5] Tashman - Out-of sample tests of forecasting accuracy: an analysis and review
• [6] Gooijer et. al. - 25 Years of Time Series Forecasting
• [7] Local Regression
• [8] Cleveland et. al. - STL: A Seasonal-Trend Decomposition Procedure Based on Loess
• [9] Standard and Poor's (S&P) 500 Index Data
• [10] SciPy, python-based ecosystem for mathematics, science, and engineering
• [11] Kats, one stop shop for time series analysis in Python

AUTHOR

• @r-scalia Rosario Scalia

stock_advisor's Issues

new

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.

If tou want, Please read the readme , and in case of any problem you can contact me ,
If you are convinced try to install it with the documentation.
https://github.com/Leci37/LecTrade/tree/develop I appreciate the feedback