Welcome to fast_trader

Fast Trader is a simple yet fast deeplearning and statistics ready library for trading RND projects. The layout is simple clean and understandable. This is the first release. Models will be improved over time.

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Getting started (Docs coming soon)

Clone this repo git clone https://github.com/eddymina/fast_trader.git This will eventually be pip installable.

Use this quick start notebook repo.

import numpy as np 
import fast_trader as ft

# interval arguments must be in '1m','2m','5m','15m','60m','90m','1d','5d','1wk','1mo','3mo'

# date indexing for all date function is quick 
### note that all dates strings are interpreted in real time. Input can be a datetime as well
# '01-10-2020'=='2020-01-10'=='01/10/2020'=='01.10.2020'

msft= ft.YahooStock('MSFT',start=None,end=None,interval='1d').history(adjusted=False)
msft

Easy modular design for statistical analysis

msft.scatter(s=10,color='violet',zorder=1) #full support for matplotlub args 

msft.close.plot()

msft.close.date_range('01-10-2010')[::1]\
		.fourier_decomp(ncomps=23)\
		.mvg_avg(window=19)\
		.greater_than(10)\
		.less_than(10000) * 2\
		.buy_hold(nshares=100,plot=True)
		# .dropna(inplace=0,newarray=True)\# will not return new array if no nans found					# and many many more features 

Uses numpy backend for 5x indexing than pandas, but numpy and pandas friendly.

#numpy ops work here and preseve data information if they match 
close=msft.close

print(close>0)
print(close[0:100]/close[100:200])
print(2/close[100:200])
print(close[0:100]+close[100:200])
print(close[0:100]-close[100:200])


#split on date, index, or %
close.split_on('01-10-2020'),close.split_on(1000),close.split_on(.6)

#simple stats 
close.mean() #mean 
close.std() #standard deviation 
close.var() #variance 

#easily go to numpy 
close.to_numpy()
np.array(close)
close.data

#multiple ways to convert to df as well 
pd.DataFrame(close) 
close.to_df()