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A framework for creating and running trading strategies without worrying about integration


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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 »


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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:

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:

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:

<div class=”highlight highlight-source-python position-relative” data-snippet-clipboard-copy-content=”# 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)

# 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()

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

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

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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