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 Learning python stock-market stock-price-prediction technical-analysis python-module stockmarket-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

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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
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Owner
Stox
Making Apps & Modules For The Stockmarket & To Make Life Easier!
Stox
GitHub
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