Stox
⚡
A Python Module For The Stock Market
⚡
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.
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 install
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
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
- Dopevog
- Gerard López - Logo
License
This Project Has Been MIT Licensed
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