QuantMotion

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QuantMotion is a Python 3 library assisting quantitative analysis of financial assets.

Price Data

QuantMotion supports price formats common in the financial industry, for e.g. OHLC data from candlestick charts, OHLC bar charts, etc.

from quantmotion.ohlcvdata import OHLCVData

price = OHLCVData(open=1.00, high=2.00, low=0.50, close=1.33)

Time Series

QuantMotion provides code for manipulating time series data, for instance:

from datetime import datetime
from quantmotion.timeseries import TimeSeries

ts = TimeSeries()
ts.insert(datetime(2018, 1, 1), OHLCVData(open=1.00, high=2.00, low=0.50, close=1.33))
ts.insert(datetime(2018, 1, 1), OHLCVData(open=1.40, high=2.03, low=1.40, close=1.23))
len(ts) # 2
ts.remove(datetime(2018, 1, 1))
len(ts) # 1

File Format Support

QuantMotion supports common financial file formats. CSV is likely the most common:

from quantmotion.timeseries import TimeSeries
from quantmotion.convert import *

with open("MSFT.csv", "r") as f:
	ts = convert_csv_to_time_series(f.read())

len(ts) # 100

Portfolio Backtesting

QuantMotion also supports rich portfolio management and backtesting facilities. These allow portfolios to be backtested on entirely custom data and allow for custom trading strategies to be backtested across different asset classes.

from datetime import datetime
from quantmotion.asset import Asset
from quantmotion.portfolio import Portfolio

# list of `Asset` objects
assets = [MSFT, GOOG, APPL, WOW]

# portfolio with initial cash balance of $50,000
p = Portfolio(balance=50000)

# trade on the portfolio
p.buy(MSFT, 100, datetime(2018, 1, 1))
p.buy(GOOG, 20, datetime(2017, 4, 5))
p.sell(MSFT, 50, datetime(2018, 3, 5))

# value the portfolio
current_value = p.value(datetime(2018, 4, 28))
growth = p.growth()