>_• Kinetick Trade Bot

.. image::

:target: :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 zerodha[1]). Kinetick is aimed to make systematic trading available for everyone.

Leave the heavy lifting to kinetick and you focus on building strategies.

WARNING

This project is still in its early stages, please be cautious when dealing with real money.

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

Telegram bot must be configured in order to take TOTP input for zerodha login

> use /zlogin command to login to zerodha

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 can be found in the strategy console. This step is required if you have not provided your telegram chat id as an env var
• /zlogin Command to login to zerodha using totp

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

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 --backfill

🙏 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.

[1] Kinetick is not affiliated to zerodha.