A Python Module That Uses ANN To Predict A Stocks Price And Also Provides Accurate Technical Analysis With Many High Potential Implementations!

Overview

Stox

A Module to predict the "close price" for the next day and give "technical analysis". It uses a Neural Network and the LSTM algorithm to predict the price. It uses a technical indicator algorithm developed by the Stox team for technical analysis. Check out how it works here.

Installation

Get it from PyPi:

pip3 install stox

Clone it from github:

git clone https://github.com/dopevog/stox.git
cd stox
python3 setup.py

Usage

Arguments:

    stock (str): stock ticker symbol
    output (str): 'list' or 'message' (Format Of Output)
    years (int or float): years of data to be considered
    chart (bool): generate performance plot

Returns:

List:

[company name, current price, predicted price, technical analysis, date (For)]

Message:

company name
current price
predicted price
technical analysis
data (for)

Examples:

Basic

import stox

script = input("Stock Ticker Symbol: ")
data = stox.stox.exec(script,'list')

print(data)
$ stox> python3 main.py
$ Stock Ticker Symbol: AAPL
$ ['Apple Inc.', 125.43000030517578, 124.91, 'Bearish (Already)', '2021-05-24']

Intermediate

= data[1] * 0.02: if data[3] == "Bullish (Starting)": df['Signal'] = "Buy" elif data[3] == "Bullish (Already)": df['Signal'] = "Up" elif data[2] - data[1] <= data[1] * -0.02: if data[3] == "Bearish (Starting)": df['Signal'] = "Sell" elif data[3] == "Bearish (Already)": df['Signal'] = "Down" else: df['Signal'] = "None" x = x+1 df.to_csv("output.csv") print("Done") ">
import stox
import pandas as pd

stock_list = pd.read_csv("SPX500.csv") 
df = stock_list 
number_of_stocks = 505 
x = 0
while x < number_of_stocks:
    ticker = stock_list.iloc[x]["Symbols"]
    data = stox.stox.exec(ticker,'list')
    df['Price'] = data[1] 
    df['Prediction'] = data[2]
    df['Analysis'] = data[3]
    df['DateFor'] = data[4]
    if data[2] - data[1]  >= data[1]  * 0.02:
        if data[3] == "Bullish (Starting)":
            df['Signal'] = "Buy"
        elif data[3] == "Bullish (Already)":
            df['Signal'] = "Up"
    elif data[2] - data[1]  <= data[1]  * -0.02:
        if data[3] == "Bearish (Starting)":
            df['Signal'] = "Sell"
        elif data[3] == "Bearish (Already)":
            df['Signal'] = "Down"
    else:
        df['Signal'] = "None"
    x = x+1
df.to_csv("output.csv") 
print("Done") 
$ stox> python3 main.py
$ Done

More Examples Including These Ones Can Be Found Here

Possible Implentations

  • Algorithmic Trading
  • Single Stock Analysis
  • Multistock Analysis
  • And Much More!

Credits

License

This Project Has Been MIT Licensed

You might also like...
 Warren - Stock Price Predictor
Warren - Stock Price Predictor

Web app to predict closing stock prices in real time using Facebook's Prophet time series algorithm with a multi-variate, single-step time series forecasting strategy.

A machine learning project that predicts the price of used cars in the UK
A machine learning project that predicts the price of used cars in the UK

Car Price Prediction Image Credit: AA Cars Project Overview Scraped 3000 used cars data from AA Cars website using Python and BeautifulSoup. Cleaned t

A linear regression model for house price prediction

Linear_Regression_Model A linear regression model for house price prediction. This code is using these packages, so please make sure your have install

learn python in 100 days, a simple step could be follow from beginner to master of every aspect of python programming and project also include side project which you can use as demo project for your personal portfolio
learn python in 100 days, a simple step could be follow from beginner to master of every aspect of python programming and project also include side project which you can use as demo project for your personal portfolio

learn python in 100 days, a simple step could be follow from beginner to master of every aspect of python programming and project also include side project which you can use as demo project for your personal portfolio

Python-based implementations of algorithms for learning on imbalanced data.

ND DIAL: Imbalanced Algorithms Minimalist Python-based implementations of algorithms for imbalanced learning. Includes deep and representational learn

Houseprices - Predict sales prices and practice feature engineering, RFs, and gradient boosting
Houseprices - Predict sales prices and practice feature engineering, RFs, and gradient boosting

House Prices - Advanced Regression Techniques Predicting House Prices with Machine Learning This project is build to enhance my knowledge about machin

Used Logistic Regression, Random Forest, and XGBoost to predict the outcome of Search & Destroy games from the Call of Duty World League for the 2018 and 2019 seasons.
Used Logistic Regression, Random Forest, and XGBoost to predict the outcome of Search & Destroy games from the Call of Duty World League for the 2018 and 2019 seasons.

Call of Duty World League: Search & Destroy Outcome Predictions Growing up as an avid Call of Duty player, I was always curious about what factors led

Implementations of Machine Learning models, Regularizers, Optimizers and different Cost functions.

Linear Models Implementations of LinearRegression, LassoRegression and RidgeRegression with appropriate Regularizers and Optimizers. Linear Regression

 pure-predict: Machine learning prediction in pure Python
pure-predict: Machine learning prediction in pure Python

pure-predict speeds up and slims down machine learning prediction applications. It is a foundational tool for serverless inference or small batch prediction with popular machine learning frameworks like scikit-learn and fasttext. It implements the predict methods of these frameworks in pure Python.

Comments
  • new

    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.

    opened by Leci37 0
Releases(0.5)
Owner
Stox
Making Apps & Modules For The Stockmarket & To Make Life Easier!
Stox
PyTorch extensions for high performance and large scale training.

Description FairScale is a PyTorch extension library for high performance and large scale training on one or multiple machines/nodes. This library ext

Facebook Research 2k Dec 28, 2022
Tutorial for Decision Threshold In Machine Learning.

Decision-Threshold-ML Tutorial for improve skills: 'Decision Threshold In Machine Learning' (from GeeksforGeeks) by Marcus Mariano For more informatio

0 Jan 20, 2022
100 Days of Machine and Deep Learning Code

💯 Days of Machine Learning and Deep Learning Code MACHINE LEARNING TOPICS COVERED - FROM SCRATCH Linear Regression Logistic Regression K Means Cluste

Tanishq Gautam 66 Nov 02, 2022
To-Be is a machine learning challenge on CodaLab Platform about Mortality Prediction

To-Be is a machine learning challenge on CodaLab Platform about Mortality Prediction. The challenge aims to adress the problems of medical imbalanced data classification.

Marwan Mashra 1 Jan 31, 2022
Penguins species predictor app is used to classify penguins species created using python's scikit-learn, fastapi, numpy and joblib packages.

Penguins Classification App Penguins species predictor app is used to classify penguins species using their island, sex, bill length (mm), bill depth

Siva Prakash 3 Apr 05, 2022
Timeseries analysis for neuroscience data

=================================================== Nitime: timeseries analysis for neuroscience data ===============================================

NIPY developers 212 Dec 09, 2022
Nixtla is an open-source time series forecasting library.

Nixtla Nixtla is an open-source time series forecasting library. We are helping data scientists and developers to have access to open source state-of-

Nixtla 401 Jan 08, 2023
Module for statistical learning, with a particular emphasis on time-dependent modelling

Operating system Build Status Linux/Mac Windows tick tick is a Python 3 module for statistical learning, with a particular emphasis on time-dependent

X - Data Science Initiative 410 Dec 14, 2022
Auto updating website that tracks closed & open issues/PRs on scikit-learn/scikit-learn.

Repository Status for Scikit-learn Live webpage Auto updating website that tracks closed & open issues/PRs on scikit-learn/scikit-learn. Running local

Thomas J. Fan 6 Dec 27, 2022
This machine learning model was developed for House Prices

This machine learning model was developed for House Prices - Advanced Regression Techniques competition in Kaggle by using several machine learning models such as Random Forest, XGBoost and LightGBM.

serhat_derya 1 Mar 02, 2022
Dive into Machine Learning

Dive into Machine Learning Hi there! You might find this guide helpful if: You know Python or you're learning it 🐍 You're new to Machine Learning You

Michael Floering 11.1k Jan 03, 2023
The code from the Machine Learning Bookcamp book and a free course based on the book

The code from the Machine Learning Bookcamp book and a free course based on the book

Alexey Grigorev 5.5k Jan 09, 2023
Land Cover Classification Random Forest

You can perform Land Cover Classification on Satellite Images using Random Forest and visualize the result using Earthpy package. Make sure to install the required packages and such as

Dr. Sander Ali Khowaja 1 Jan 21, 2022
Data Version Control or DVC is an open-source tool for data science and machine learning projects

Continuous Machine Learning project integration with DVC Data Version Control or DVC is an open-source tool for data science and machine learning proj

Azaria Gebremichael 2 Jul 29, 2021
李航《统计学习方法》复现

本项目复现李航《统计学习方法》每一章节的算法 特点: 笔记摘要:在每个文件开头都会有一些核心的摘要 pythonic:这里会用尽可能规范的方式来实现,包括编程风格几乎严格按照PEP8 循序渐进:前期的算法会更list的方式来做计算,可读性比较强,后期几乎完全为numpy.array的计算,并且辅助详

58 Oct 22, 2021
A toolkit for geo ML data processing and model evaluation (fork of solaris)

An open source ML toolkit for overhead imagery. This is a beta version of lunular which may continue to develop. Please report any bugs through issues

Ryan Avery 4 Nov 04, 2021
Fast Fourier Transform-accelerated Interpolation-based t-SNE (FIt-SNE)

FFT-accelerated Interpolation-based t-SNE (FIt-SNE) Introduction t-Stochastic Neighborhood Embedding (t-SNE) is a highly successful method for dimensi

Kluger Lab 547 Dec 21, 2022
🚪✊Knock Knock: Get notified when your training ends with only two additional lines of code

Knock Knock A small library to get a notification when your training is complete or when it crashes during the process with two additional lines of co

Hugging Face 2.5k Jan 07, 2023
Cohort Intelligence used to solve various mathematical functions

Cohort-Intelligence-for-Mathematical-Functions About Cohort Intelligence : Cohort Intelligence ( CI ) is an optimization technique. It attempts to mod

Aayush Khandekar 2 Oct 25, 2021
An MLOps framework to package, deploy, monitor and manage thousands of production machine learning models

Seldon Core: Blazing Fast, Industry-Ready ML An open source platform to deploy your machine learning models on Kubernetes at massive scale. Overview S

Seldon 3.5k Jan 01, 2023