Metadata-Version: 2.1
Name: StockTrainer
Version: 0.0.5
Summary: Stock environment for training machine learning agents
Home-page: UNKNOWN
Author: Daniel Prakah-Asante
Author-email: doprakah@gmail.com
License: UNKNOWN
Description: # StockTrainer: Stock Environment for Human
        ## StockTrainer: Stocks Made Easy
        
        StockTrainer is high level API data generator for training python machine learning models on stock/cryptocurrency data and is capable of running with Keras, Tensorflow, sklearn, and many other machine learning APIs
        
        Capabilities:
        
        - Predict day to day stock prices
        - Use multiple days to predict next stock price
        - Predict succeeding stock prices over multiple days
        - Train a reinforcement learning agent to simulate stock trades
        
        
        Documentation available soon ;)
        
        StockTrainer is compatible with: Python 3.6+
        
        ## Getting Started
        The core of algorithm is the model, here is a simple LSTM model to based on 5 days of stock data to predict the next day
        
        	import keras
        	import numpy as np
        	from keras.models import Sequential
        	from keras.layers import Dropout ,BatchNormalization, LSTM, Dense 
        
          
        	model = Sequential()
        	#input shape 5 days of data 
        	#each day has 6 data points (open, close, high , low volums, adj CLose)
        	model.add(BatchNormalization(input_shape=(5, 6)))#batchnorm bc high values
            model.add(LSTM(512, return_sequences=True, activation='relu'))
            model.add(Dropout(0.2))
            model.add(Dense(512, activation='relu'))
        	model.add(Dense(128, activation='relu'))	
            model.add(Dense(1, activation='relu'))
        
            model.compile(loss='mse', optimizer='adam')
        
        Next import StockTrainer and create your environment
         
            from StockTrainer import Env
            environment = Env("Standard", "AAPL")
        
        Time to collect your data to train!!!
        
        	test_percent =.30
        	shuffle =True
        	start_date ='2003-01-01'
        	end_date='now'
        	agent_memory = 5
        	seed = 42
        	trainx,testx,trainy, testy = environment.train_test(
              test_percent= test_percent, shuffle = shuffle, start_date=start_date, 
              end_date=end_date, agent_memory=agent_memory, seed=seed)
        
        Futher information on parameters in Documentation 
        
        
        That's it now train and test your model
        	
        	#fit model
            model.fit(trainx, trainy, epochs=10, batch_size=128, verbose=2)
            model.save('model.h5')
        
            #evaluate model
            model.evaluate(testx,testy )
            #use model to predict
            model.predict(testx)
        
        More examples on samples folder in github
        
        ## Installation
        
        Using pip
        	
        	pip install StockTrainer
        
        or download directly: [https://pypi.org/project/StockTrainer/](https://pypi.org/project/StockTrainer/ "install") 
        
Keywords: USEFULL,STOCKS,MACHINE LEARNING,AI,ARTIFICAL INTELLIGENCE
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Topic :: Software Development :: Build Tools
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Description-Content-Type: text/markdown
