marlonsc / stolgo

Price Action Trading APIs, Algorithmic approach, Dealing with securities. Get APIs to detect candlestick patterns, identify trends, support resistance, and price breakout.

Stolgo is Price Action Trading Analysis Library. Whenever the price reaches resistance during an upward trend, more sellers will enter the market and enter their sell trades. This is a simple price action rule. But How to automate this rule? How to write backtest for this? Stolgo provides APIs for Price Action Trading.

Why Stolgo?

There are many libraries to backtest technical indicators (such as moving average crossover, MACD, RSI, etc.) base strategies, But What about the Price Action Trading? A Price Action Trader uses support/resistance, candlestick pattern, trend, breakout, and other parameters based on price. You can use Stolgo to backtest your price action trading rules.

Installation

Use the package manager pip to install stolgo.

pip install stolgo

For data feed, Stolgo uses bandl.io , Where by just calling get_data API, You can get data from your favourite broker, directly from exchange website or yahoo finance.

Usage

Get the data, for example using yahoo finance module form bandl

pip install bandl

Example: Get Indian (NSE/BSE) stock data using Yahoo finance

from bandl.yfinance import Yfinance
testObj = Yfinance() # returns 'Yfinance class object'.
dfs = testObj.get_data("SBIN",start="21-Jan-2020") #retruns data from 21Jan 2020 to till today

Example: Get the data of Apple (US Stock) from Nasdaq

from bandl.nasdaq import Nasdaq
testObj = Nasdaq() # returns 'Nasdaq class object'.
dfs = testObj.get_data("AAPL",periods=90) # returns last 90 days data

check for bullish engulfing pattern

from stolgo.candlestick import CandleStick
candle_test = CandleStick()
is_be = candle_test.is_bullish_engulfing(dfs)

check for an inverted hammer candle pattern

from stolgo.candlestick import CandleStick
candle_test = CandleStick()
is_it = candle_test.is_inverse_hammer_candle(dfs)

check for breakout

from stolgo.breakout import Breakout
breakout_test = Breakout()
is_be = breakout_test.is_breaking_out(dfs,periods=None,percentage=None) #periods:Number of candles,percentage: range of consolidation in percentage

Todo

• Add unittest
• Add more features such as Support Resistance, momemtum, etc.
• Add Event-Driven Backtester

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Kindly follow PEP 8 Coding Style guidelines. Refer: https://www.python.org/dev/peps/pep-0008/

Please make sure to update tests as appropriate.

License

MIT