kinetick: A Python repository from raonyguimaraes

>_• Kinetick Trade Bot

Kinetick is a framework for creating and running trading strategies without worrying about integration with broker and data streams (currently integrates with zerodha [*]). Kinetick is aimed to make systematic trading available for everyone.

Leave the heavy lifting to kinetick so that you can focus on building strategies.

Changelog »

📱 Screenshots

Features

A continuously-running Blotter that lets you capture market data even when your algos aren't running.
Tick, Bar and Trade data is stored in MongoDB for later analysis and backtesting.
Using pub/sub architecture using ØMQ (ZeroMQ) for communicating between the Algo and the Blotter allows for a single Blotter/multiple Algos running on the same machine.
Support for Order Book, Quote, Time, Tick or Volume based strategy resolutions.
Includes many common indicators that you can seamlessly use in your algorithm.
Market data events use asynchronous, non-blocking architecture.
Realtime alerts and order confirmation delivered to your mobile via Telegram bot (requires a Telegram bot token).
Full integration with TA-Lib via dedicated module (see example).
Ability to import any Python library (such as scikit-learn or TensorFlow) to use them in your algorithms.
Live charts powered by TradingView
RiskAssessor to manage and limit the risk even if strategy goes unexpected
Power packed batteries included
Deploy wherever Docker lives

Installation

Install using pip:

$ pip install kinetick

Quickstart

There are 5 main components in Kinetick:

Bot - sends alert and signals with actions to perform.
Blotter - handles market data retrieval and processing.
Broker - sends and process orders/positions (abstracted layer).
Algo - (sub-class of Broker) communicates with the Blotter to pass market data to your strategies, and process/positions orders via Broker.
Lastly, Your Strategies, which are sub-classes of Algo, handle the trading logic/rules. This is where you'll write most of your code.

1. Get Market Data

To get started, you need to first create a Blotter script:

# blotter.py
from kinetick.blotter import Blotter

class MainBlotter(Blotter):
    pass # we just need the name

if __name__ == "__main__":
    blotter = MainBlotter()
    blotter.run()

Then run the Blotter from the command line:

$ python -m blotter

If your strategy needs order book / market depth data, add the --orderbook flag to the command:

$ python -m blotter --orderbook

2. Write your Algorithm

While the Blotter running in the background, write and execute your algorithm:

# strategy.py
from kinetick.algo import Algo

class CrossOver(Algo):

    def on_start(self):
        pass

    def on_fill(self, instrument, order):
        pass

    def on_quote(self, instrument):
        pass

    def on_orderbook(self, instrument):
        pass

    def on_tick(self, instrument):
        pass

    def on_bar(self, instrument):
        # get instrument history
        bars = instrument.get_bars(window=100)

        # or get all instruments history
        # bars = self.bars[-20:]

        # skip first 20 days to get full windows
        if len(bars) < 20:
            return

        # compute averages using internal rolling_mean
        bars['short_ma'] = bars['close'].rolling(window=10).mean()
        bars['long_ma']  = bars['close'].rolling(window=20).mean()

        # get current position data
        positions = instrument.get_positions()

        # trading logic - entry signal
        if bars['short_ma'].crossed_above(bars['long_ma'])[-1]:
            if not instrument.pending_orders and positions["position"] == 0:

                """ buy one contract.
                 WARNING: buy or order instrument methods will bypass bot and risk assessor.
                 Instead, It is advised to use create_position, open_position and close_position instrument methods
                 to route the order via bot and risk assessor. """
                instrument.buy(1)

                # record values for later analysis
                self.record(ma_cross=1)

        # trading logic - exit signal
        elif bars['short_ma'].crossed_below(bars['long_ma'])[-1]:
            if positions["position"] != 0:

                # exit / flatten position
                instrument.exit()

                # record values for later analysis
                self.record(ma_cross=-1)


if __name__ == "__main__":
    strategy = CrossOver(
        instruments = ['ACC', 'SBIN'], # scrip symbols
        resolution  = "1T", # Pandas resolution (use "K" for tick bars)
        tick_window = 20, # no. of ticks to keep
        bar_window  = 5, # no. of bars to keep
        preload     = "1D", # preload 1 day history when starting
        timezone    = "Asia/Calcutta" # convert all ticks/bars to this timezone
    )
    strategy.run()

To run your algo in a live environment, from the command line, type:

$ python -m strategy --logpath ~/orders

The resulting trades be saved in ~/orders/STRATEGY_YYYYMMDD.csv for later analysis.

3. Login to bot

While the Strategy running in the background:

Assuming you have added the telegram bot to your chat

/login <password> - password can be found in the strategy console.

commands

/report - get overview about trades
/help - get help
/resetrms - resets RiskAssessor parameters to its initial values.

Configuration

Can be specified either as env variable or cmdline arg

Parameter	Required?	Example	Default	Description
`symbols`		symbols=./symbols.csv
`LOGLEVEL`		LOGLEVEL=DEBUG	INFO
`zerodha_user`	yes - if live trading	zerodha_user=ABCD
`zerodha_password`	yes - if live trading	zerodha_password=abcd
`zerodha_pin`	yes - if live trading	zerodha_pin=1234
`BOT_TOKEN`	optional	BOT_TOKEN=12323:asdcldf..		IF not provided then orders will bypass
`initial_capital`	yes	initial_capital=10000	1000	Max capital deployed
`initial_margin`	yes	initial_margin=1000	100	Not to be mistaken with broker margin. This is the max amount you can afford to loose
`risk2reward`	yes	risk2reward=1.2	1	Set risk2reward for your strategy. This will be used in determining qty to trade
`risk_per_trade`	yes	risk_per_trade=200	100	Risk you can afford with each trade
`max_trades`	yes	max_trades=2	1	Max allowed concurrent positions
`dbport`		dbport=27017	27017
`dbhost`		dbhost=localhost	localhost
`dbuser`		dbuser=user
`dbpassword`		dbpassword=pass
`dbname`		dbname=kinetick	kinetick
`orderbook`		orderbook=true	false	Enable orderbook stream
`resolution`		resolution=1m	1	Min Bar interval
`preload_positions`	No	preload_positions=30D		Loads only overnight positions.Available options: 1D - 1 Day, 1W - 1 Week, 1H - 1 Hour
`CHAT_ID`	No	CHAT_ID=12345		default chat user id to which trade notifications are sent requiring no login

Docker Instructions

Build blotter

$ docker build -t kinetick:blotter -f blotter.Dockerfile .
Build strategy

$ docker build -t kinetick:strategy -f strategy.Dockerfile .
Run with docker-compose

$ docker compose up

Backtesting

$ python -m strategy --start "2021-03-06 00:15:00" --end "2021-03-10 00:15:00" --backtest --backfill

Note

To get started checkout the patented BuyLowSellHigh strategy in strategies/ directory.

🙏 Credits

Thanks to @ran aroussi for all his initial work with Qtpylib. Most of work here is derived from his library

Disclaimer

Kinetick is licensed under the Apache License, Version 2.0. A copy of which is included in LICENSE.txt.

All trademarks belong to the respective company and owners. Kinetick is not affiliated to any entity.

[*]	Kinetick is not affiliated to zerodha.

raonyguimaraes/kinetick