naveenuppunda / kinetick

Framework for creating and running trading strategies. Blatantly stolen copy of qtpylib to make it work for Indian markets.

>_• Kinetick Trade Bot

.. image:: https://img.shields.io/github/checks-status/imvinaypatil/kinetick/main :target: https://github.com/imvinaypatil/kinetick :alt: Branch state

Kinetick is a framework for creating and running trading strategies without worrying about integration with broker and data streams (currently integrates with zerodha¹). 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

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Installation

Install using pip:

$ pip install kinetick

Quickstart

There are 5 main components in Kinetick:

1. Bot - sends alert and signals with actions to perform.
2. Blotter - handles market data retrieval and processing.
3. Broker - sends and process orders/positions (abstracted layer).
4. Algo - (sub-class of Broker) communicates with the Blotter to pass market data to your strategies, and process/positions orders via Broker.
5. 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
• /reset-rms - resets RiskAssessor parameters to its initial values.

Configuration

Can be specified either as env variable or cmdline arg

option	required?	example	default	note
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
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

Docker Instructions

1. Build blotter

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

2. Build strategy

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

3. 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

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.

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1. Kinetick is not affiliated to zerodha.↩