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

Related tags

Machine Learningpythonstock-marketstock-price-predictiontechnical-analysispython-modulestockmarket-ai
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.

Kumar Nityan Suman 153 Jan 3, 2023
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

Victor Umunna 7 Oct 13, 2022
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

ShawnWang 1 Nov 29, 2021
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

BDFD 6 Nov 5, 2022
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

DIAL | Notre Dame 220 Dec 13, 2022
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

null 1 Jan 1, 2022
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

Brett Vogelsang 2 Jan 18, 2022
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

Keivan Ipchi Hagh 1 Nov 22, 2021
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.

Ibotta 84 Dec 29, 2022
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
GitHub Repository
easyNeuron is a simple way to create powerful machine learning models, analyze data and research cutting-edge AI.

easyNeuron is a simple way to create powerful machine learning models, analyze data and research cutting-edge AI.

Neuron AI 5 Jun 18, 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
Kubeflow is a machine learning (ML) toolkit that is dedicated to making deployments of ML workflows on Kubernetes simple, portable, and scalable.

SDK: Overview of the Kubeflow pipelines service Kubeflow is a machine learning (ML) toolkit that is dedicated to making deployments of ML workflows on

Kubeflow 3.1k Jan 06, 2023
Customers Segmentation with RFM Scores and K-means

Customer Segmentation with RFM Scores and K-means RFM Segmentation table: K-Means Clustering: Business Problem Rule-based customer segmentation machin

5 Aug 10, 2022
In this Repo a simple Sklearn Model will be trained and pushed to MLFlow

SKlearn_to_MLFLow In this Repo a simple Sklearn Model will be trained and pushed to MLFlow Install This Repo is based on poetry python3 -m venv .venv

1 Dec 13, 2021
Dual Adaptive Sampling for Machine Learning Interatomic potential.

DAS Dual Adaptive Sampling for Machine Learning Interatomic potential. How to cite If you use this code in your research, please cite this using: Hong

6 Jul 06, 2022
A library of sklearn compatible categorical variable encoders

Categorical Encoding Methods A set of scikit-learn-style transformers for encoding categorical variables into numeric by means of different techniques

2.1k Jan 07, 2023
Using Logistic Regression and classifiers of the dataset to produce an accurate recall, f-1 and precision score

Using Logistic Regression and classifiers of the dataset to produce an accurate recall, f-1 and precision score

Thines Kumar 1 Jan 31, 2022
distfit - Probability density fitting

Python package for probability density function fitting of univariate distributions of non-censored data

Erdogan Taskesen 187 Dec 30, 2022
ETNA – time series forecasting framework

ETNA Time Series Library Predict your time series the easiest way Homepage | Documentation | Tutorials | Contribution Guide | Release Notes ETNA is an

Tinkoff.AI 675 Jan 08, 2023
Falken provides developers with a service that allows them to train AI that can play their games

Falken provides developers with a service that allows them to train AI that can play their games. Unlike traditional RL frameworks that learn through rewards or batches of offline training, Falken is

Google Research 223 Jan 03, 2023
🌲 Implementation of the Robust Random Cut Forest algorithm for anomaly detection on streams

🌲 Implementation of the Robust Random Cut Forest algorithm for anomaly detection on streams

Real-time water systems lab 416 Jan 06, 2023
Machine Learning approach for quantifying detector distortion fields

DistortionML Machine Learning approach for quantifying detector distortion fields. This project is a feasibility study for training a surrogate model

Joel Bernier 1 Nov 05, 2021
A machine learning web application for binary classification using streamlit

Machine Learning web App This is a machine learning web application for binary classification using streamlit options this application contains 3 clas

abdelhak mokri 1 Dec 20, 2021
使用数学和计算机知识投机倒把

偷鸡不成项目集锦 坦率地讲，涉及金融市场的好策略如果公开，必然导致使用的人多，最后策略变差。所以这个仓库只收集我目前失败了的案例。 加密货币组合套利 中国体育彩票预测 我赚不上钱的项目，也许可以帮助更有能力的人去赚钱。

Roy 28 Dec 29, 2022
Interactive Web App with Streamlit and Scikit-learn that applies different Classification algorithms to popular datasets

Interactive Web App with Streamlit and Scikit-learn that applies different Classification algorithms to popular datasets Datasets Used: Iris dataset,

Samrat Mitra 2 Nov 18, 2021
MICOM is a Python package for metabolic modeling of microbial communities

Welcome MICOM is a Python package for metabolic modeling of microbial communities currently developed in the Gibbons Lab at the Institute for Systems

57 Dec 21, 2022
Implementation of K-Nearest Neighbors Algorithm Using PySpark

KNN With Spark Implementation of KNN using PySpark. The KNN was used on two separate datasets (https://archive.ics.uci.edu/ml/datasets/iris and https:

Zachary Petroff 4 Dec 30, 2022
A python library for Bayesian time series modeling

PyDLM Welcome to pydlm, a flexible time series modeling library for python. This library is based on the Bayesian dynamic linear model (Harrison and W

Sam 438 Dec 17, 2022
Book Item Based Collaborative Filtering

Book-Item-Based-Collaborative-Filtering Collaborative filtering methods are used

Şebnem 3 Jan 06, 2022