catalyst/docs/source/example-algos.rst

|
Example Algorithms
==================

This section documents a number of example algorithms to complement the 
beginner tutorial, and show how other trading algorithms can be implemented 
using Catalyst.

Overview
~~~~~~~~

- :ref:`Buy BTC Simple<buy_btc_simple>`: The simplest algorithm that introduces
  the ``initialize()`` and ``handle_data()`` functions, and is used in the 
  :doc:`beginner tutorial<beginner-tutorial>` to show how to run catalyst 
  for the first time.

- :ref:`Buy and Hodl <buy_and_hodl>`: A very straightforward *buy and hold* that 
  makes one single buy at the very beginning. Introduces the notions of 
  ``cash``, management of outstanding ``orders``, and ``order_target_value`` 
  to place orders. It also introduces the ``analyze()`` function to visualize 
  the performance of our strategy using the external library ``matplotlib``.

- :ref:`Dual Moving Average Crossover<dual_moving_average>`: A classic momentum 
  strategy used in the second part of the 
  `beginner tutorial <beginner-tutorial.html#history>`_ to introduce the 
  ``data.history()`` function. It makes a heavy use of ``matplotlib`` library 
  in the ``analyze()`` function to chart the performance of the algorithm.

- :ref:`Mean Reversion Algorithm <mean_reversion>`: Another simple momentum 
  strategy that is used in our 
  `two-part video tutorial <videos.html#backtesting-a-strategy>`_ to show how 
  to get started in backtesting and live trading with Catalyst.

- :ref:`Simple Universe <simple_universe>`: This code provides the 'universe' 
  of available trading pairs on a given exchange on any given day. You can use 
  this code to dynamically select which currency pairs you want to trade each 
  day of your strategy. This example does not make any trades. 

- :ref:`Portfolio Optimization <portfolio_optimization>`: Use this code to 
  execute a portfolio optimization model. This strategy will select the 
  portfolio with the maximum Sharpe Ratio. The parameters are set to use 180 
  days of historical data and rebalance every 30 days. This code was used in 
  writting the following article: 
  `Markowitz Portfolio Optimization for Cryptocurrencies <https://blog.enigma.co/markowitz-portfolio-optimization-for-cryptocurrencies-in-catalyst-b23c38652556>`_.


.. _buy_btc_simple:

Buy BTC Simple Algorithm
~~~~~~~~~~~~~~~~~~~~~~~~

Source code: `examples/buy_btc_simple.py <https://github.com/enigmampc/catalyst/blob/master/catalyst/examples/buy_btc_simple.py>`_

.. code-block:: python

  '''
    Run this example, by executing the following from your terminal:
      catalyst ingest-exchange -x bitfinex -f daily -i btc_usdt
      catalyst run -f buy_btc_simple.py -x bitfinex --start 2016-1-1 --end 2017-9-30 -o buy_btc_simple_out.pickle

    If you want to run this code using another exchange, make sure that 
    the asset is available on that exchange. For example, if you were to run 
    it for exchange Poloniex, you would need to edit the following line:

      context.asset = symbol('btc_usdt')     # note 'usdt' instead of 'usd'

    and specify exchange poloniex as follows:
    catalyst ingest-exchange -x poloniex -f daily -i btc_usdt
    catalyst run -f buy_btc_simple.py -x poloniex --start 2016-1-1 --end 2017-9-30 -o buy_btc_simple_out.pickle

    To see which assets are available on each exchange, visit:
    https://www.enigma.co/catalyst/status
  '''

  from catalyst.api import order, record, symbol

  def initialize(context):
      context.asset = symbol('btc_usd')

  def handle_data(context, data):
      order(context.asset, 1)
      record(btc = data.current(context.asset, 'price'))

This simple algorithm does not produce any output nor displays any chart.


.. _buy_and_hodl:

Buy and Hodl Algorithm
~~~~~~~~~~~~~~~~~~~~~~

Source code: `examples/buy_and_hodl.py <https://github.com/enigmampc/catalyst/blob/master/catalyst/examples/buy_and_hodl.py>`_

First ingest the historical pricing data needed to run this algorithm:

.. code-block:: bash

   catalyst ingest-exchange -x bitfinex -f daily -i btc_usd

Then, you can run the code below with the following command:

.. code-block:: bash

   catalyst run -f buy_and_hodl.py --start 2015-3-1 --end 2017-10-31 --capital-base 100000 -x bitfinex -c btc -o bah.pickle

or using the same parameters specified in the run_algorithm() function at the 
end of the file:

.. code-block:: bash

  python buy_and_hodl.py


This command will run the trading algorithm in the specified time range and 
plot the resulting performance using the matplotlib library. You can choose any 
date interval with the ``--start`` and ``--end`` parameters, but bear in mind 
that 2015-3-1 is the earliest date that Catalyst supports (if you choose an 
earlier date, you'll get an error), and the most recent date you can choose is 
one day prior to the current date. 


.. code-block:: python

  #!/usr/bin/env python
  #
  # Copyright 2017 Enigma MPC, Inc.
  # Copyright 2015 Quantopian, Inc.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  import pandas as pd
  import matplotlib.pyplot as plt

  from catalyst import run_algorithm
  from catalyst.api import (order_target_value, symbol, record,
      cancel_order, get_open_orders, )


  def initialize(context):
      context.ASSET_NAME = 'btc_usd'
      context.TARGET_HODL_RATIO = 0.8
      context.RESERVE_RATIO = 1.0 - context.TARGET_HODL_RATIO

      context.is_buying = True
      context.asset = symbol(context.ASSET_NAME)

      context.i = 0


  def handle_data(context, data):
      context.i += 1

      starting_cash = context.portfolio.starting_cash
      target_hodl_value = context.TARGET_HODL_RATIO * starting_cash
      reserve_value = context.RESERVE_RATIO * starting_cash

      # Cancel any outstanding orders
      orders = get_open_orders(context.asset) or []
      for order in orders:
          cancel_order(order)

      # Stop buying after passing the reserve threshold
      cash = context.portfolio.cash
      if cash <= reserve_value:
          context.is_buying = False

      # Retrieve current asset price from pricing data
      price = data.current(context.asset, 'price')

      # Check if still buying and could (approximately) afford another purchase
      if context.is_buying and cash > price:
          print('buying')
          # Place order to make position in asset equal to target_hodl_value
          order_target_value(
              context.asset,
              target_hodl_value,
              limit_price=price * 1.1,
          )

      record(
          price=price,
          volume=data.current(context.asset, 'volume'),
          cash=cash,
          starting_cash=context.portfolio.starting_cash,
          leverage=context.account.leverage,
      )


  def analyze(context=None, results=None):

      # Plot the portfolio and asset data.
      ax1 = plt.subplot(611)
      results[['portfolio_value']].plot(ax=ax1)
      ax1.set_ylabel('Portfolio Value (USD)')

      ax2 = plt.subplot(612, sharex=ax1)
      ax2.set_ylabel('{asset} (USD)'.format(asset=context.ASSET_NAME))
      results[['price']].plot(ax=ax2)

      trans = results.ix[[t != [] for t in results.transactions]]
      buys = trans.ix[
          [t[0]['amount'] > 0 for t in trans.transactions]
      ]
      ax2.scatter(
          buys.index.to_pydatetime(),
          results.price[buys.index],
          marker='^',
          s=100,
          c='g',
          label=''
      )

      ax3 = plt.subplot(613, sharex=ax1)
      results[['leverage', 'alpha', 'beta']].plot(ax=ax3)
      ax3.set_ylabel('Leverage ')

      ax4 = plt.subplot(614, sharex=ax1)
      results[['starting_cash', 'cash']].plot(ax=ax4)
      ax4.set_ylabel('Cash (USD)')

      results[[
          'treasury',
          'algorithm',
          'benchmark',
      ]] = results[[
          'treasury_period_return',
          'algorithm_period_return',
          'benchmark_period_return',
      ]]

      ax5 = plt.subplot(615, sharex=ax1)
      results[[
          'treasury',
          'algorithm',
          'benchmark',
      ]].plot(ax=ax5)
      ax5.set_ylabel('Percent Change')

      ax6 = plt.subplot(616, sharex=ax1)
      results[['volume']].plot(ax=ax6)
      ax6.set_ylabel('Volume (mCoins/5min)')

      plt.legend(loc=3)

      # Show the plot.
      plt.gcf().set_size_inches(18, 8)
      plt.show()


  if __name__ == '__main__':
      run_algorithm(
          capital_base=10000,
          data_frequency='daily',
          initialize=initialize,
          handle_data=handle_data,
          analyze=analyze,
          exchange_name='bitfinex',
          algo_namespace='buy_and_hodl',
          base_currency='usd',
          start=pd.to_datetime('2015-03-01', utc=True),
          end=pd.to_datetime('2017-10-31', utc=True),
      )

.. image:: https://s3.amazonaws.com/enigmaco-docs/github.io/example_buy_and_hodl.png

.. _dual_moving_average:

Dual Moving Average Crossover
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Source Code: `examples/dual_moving_average.py <https://github.com/enigmampc/catalyst/blob/master/catalyst/examples/dual_moving_average.py>`_

This strategy is covered in detail in the last part of 
`this tutorial <beginner-tutorial.html#history>`_.

.. code-block:: python

  import numpy as np
  import pandas as pd
  from logbook import Logger
  import matplotlib.pyplot as plt

  from catalyst import run_algorithm
  from catalyst.api import (order, record, symbol, order_target_percent,
          get_open_orders)
  from catalyst.exchange.stats_utils import extract_transactions

  NAMESPACE = 'dual_moving_average'
  log = Logger(NAMESPACE)

  def initialize(context):
      context.i = 0
      context.asset = symbol('ltc_usd')
      context.base_price = None


  def handle_data(context, data):
      # define the windows for the moving averages
      short_window = 50
      long_window = 200

      # Skip as many bars as long_window to properly compute the average
      context.i += 1
      if context.i < long_window:
         return

      # Compute moving averages calling data.history() for each
      # moving average with the appropriate parameters. We choose to use
      # minute bars for this simulation -> freq="1m"
      # Returns a pandas dataframe.
      short_mavg = data.history(context.asset, 'price',
                          bar_count=short_window, frequency="1m").mean()
      long_mavg = data.history(context.asset, 'price',
                          bar_count=long_window, frequency="1m").mean()

      # Let's keep the price of our asset in a more handy variable
      price = data.current(context.asset, 'price')

      # If base_price is not set, we use the current value. This is the
      # price at the first bar which we reference to calculate price_change.
      if context.base_price is None:
          context.base_price = price
      price_change = (price - context.base_price) / context.base_price

      # Save values for later inspection
      record(price=price,
             cash=context.portfolio.cash,
             price_change=price_change,
             short_mavg=short_mavg,
             long_mavg=long_mavg)

      # Since we are using limit orders, some orders may not execute immediately
      # we wait until all orders are executed before considering more trades.
      orders = get_open_orders(context.asset)
      if len(orders) > 0:
          return

      # Exit if we cannot trade
      if not data.can_trade(context.asset):
          return

      # We check what's our position on our portfolio and trade accordingly
      pos_amount = context.portfolio.positions[context.asset].amount

      # Trading logic
      if short_mavg > long_mavg and pos_amount == 0:
         # we buy 100% of our portfolio for this asset
         order_target_percent(context.asset, 1)
      elif short_mavg < long_mavg and pos_amount > 0:
         # we sell all our positions for this asset
         order_target_percent(context.asset, 0)


  def analyze(context, perf):

      # Get the base_currency that was passed as a parameter to the simulation
      base_currency = context.exchanges.values()[0].base_currency.upper()

      # First chart: Plot portfolio value using base_currency
      ax1 = plt.subplot(411)
      perf.loc[:, ['portfolio_value']].plot(ax=ax1)
      ax1.legend_.remove()
      ax1.set_ylabel('Portfolio Value\n({})'.format(base_currency))
      start, end = ax1.get_ylim()
      ax1.yaxis.set_ticks(np.arange(start, end, (end-start)/5))

      # Second chart: Plot asset price, moving averages and buys/sells
      ax2 = plt.subplot(412, sharex=ax1)
      perf.loc[:, ['price','short_mavg','long_mavg']].plot(ax=ax2, label='Price')
      ax2.legend_.remove()
      ax2.set_ylabel('{asset}\n({base})'.format(
          asset = context.asset.symbol,
          base = base_currency
          ))
      start, end = ax2.get_ylim()
      ax2.yaxis.set_ticks(np.arange(start, end, (end-start)/5))

      transaction_df = extract_transactions(perf)
      if not transaction_df.empty:
          buy_df = transaction_df[transaction_df['amount'] > 0]
          sell_df = transaction_df[transaction_df['amount'] < 0]
          ax2.scatter(
              buy_df.index.to_pydatetime(),
              perf.loc[buy_df.index, 'price'],
              marker='^',
              s=100,
              c='green',
              label=''
          )
          ax2.scatter(
              sell_df.index.to_pydatetime(),
              perf.loc[sell_df.index, 'price'],
              marker='v',
              s=100,
              c='red',
              label=''
          )

      # Third chart: Compare percentage change between our portfolio
      # and the price of the asset
      ax3 = plt.subplot(413, sharex=ax1)
      perf.loc[:, ['algorithm_period_return', 'price_change']].plot(ax=ax3)
      ax3.legend_.remove()
      ax3.set_ylabel('Percent Change')
      start, end = ax3.get_ylim()
      ax3.yaxis.set_ticks(np.arange(start, end, (end-start)/5))

      # Fourth chart: Plot our cash
      ax4 = plt.subplot(414, sharex=ax1)
      perf.cash.plot(ax=ax4)
      ax4.set_ylabel('Cash\n({})'.format(base_currency))
      start, end = ax4.get_ylim()
      ax4.yaxis.set_ticks(np.arange(0, end, end/5))

      plt.show()


  if __name__ == '__main__':
      run_algorithm(
              capital_base=1000,
              data_frequency='minute',
              initialize=initialize,
              handle_data=handle_data,
              analyze=analyze,
              exchange_name='bitfinex',
              algo_namespace=NAMESPACE,
              base_currency='usd',
              start=pd.to_datetime('2017-9-22', utc=True),
              end=pd.to_datetime('2017-9-23', utc=True),
          )

.. image:: https://s3.amazonaws.com/enigmaco-docs/github.io/tutorial_dual_moving_average.png


.. _mean_reversion:

Mean Reversion Algorithm
~~~~~~~~~~~~~~~~~~~~~~~~

Source code: `examples/mean_reversion_simple.py <https://github.com/enigmampc/catalyst/blob/master/catalyst/examples/mean_reversion_simple.py>`_

This algorithm is based on a simple momentum strategy. When the cryptoasset goes
up quickly, we're going to buy; when it goes down quickly, we're going to sell. 
Hopefully, we'll ride the waves.

We are choosing to backtest this trading algorithm with the ``neo_usd`` currency 
pairon the ``Bitfinex`` exchange. Thus, first ingest the historical pricing data
that we need, with minute resolution:

.. code-block:: bash

   catalyst ingest-exchange -x bitfinex -f minute -i neo_usd

To run this algorithm, we are opting for the Python interpreter, instead of the 
command line (CLI). All of the parameters for the simulation are specified in 
lines 218-245, so in order to run the algorithm we just type:

.. code-block:: bash

   python mean_reversion_simple.py

.. code-block:: python

  import os
  import tempfile
  import time

  import numpy as np
  import pandas as pd
  import talib
  from logbook import Logger

  from catalyst import run_algorithm
  from catalyst.api import symbol, record, order_target_percent, get_open_orders
  from catalyst.exchange.stats_utils import extract_transactions
  # We give a name to the algorithm which Catalyst will use to persist its state.
  # In this example, Catalyst will create the `.catalyst/data/live_algos`
  # directory. If we stop and start the algorithm, Catalyst will resume its
  # state using the files included in the folder.
  from catalyst.utils.paths import ensure_directory

  NAMESPACE = 'mean_reversion_simple'
  log = Logger(NAMESPACE)


  # To run an algorithm in Catalyst, you need two functions: initialize and
  # handle_data.

  def initialize(context):
      # This initialize function sets any data or variables that you'll use in
      # your algorithm.  For instance, you'll want to define the trading pair (or
      # trading pairs) you want to backtest.  You'll also want to define any
      # parameters or values you're going to use.

      # In our example, we're looking at Neo in USD.
      context.neo_eth = symbol('neo_usd')
      context.base_price = None
      context.current_day = None

      context.RSI_OVERSOLD = 30
      context.RSI_OVERBOUGHT = 80
      context.CANDLE_SIZE = '15T'

      context.start_time = time.time()


  def handle_data(context, data):
      # This handle_data function is where the real work is done.  Our data is
      # minute-level tick data, and each minute is called a frame.  This function
      # runs on each frame of the data.

      # We flag the first period of each day.
      # Since cryptocurrencies trade 24/7 the `before_trading_starts` handle
      # would only execute once. This method works with minute and daily
      # frequencies.
      today = data.current_dt.floor('1D')
      if today != context.current_day:
          context.traded_today = False
          context.current_day = today

      # We're computing the volume-weighted-average-price of the security
      # defined above, in the context.neo_eth variable.  For this example, we're 
      # using three bars on the 15 min bars.

      # The frequency attribute determine the bar size. We use this convention
      # for the frequency alias:
      # http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases
      prices = data.history(
          context.neo_eth,
          fields='close',
          bar_count=50,
          frequency=context.CANDLE_SIZE
      )

      # Ta-lib calculates various technical indicator based on price and
      # volume arrays.

      # In this example, we are comp
      rsi = talib.RSI(prices.values, timeperiod=14)

      # We need a variable for the current price of the security to compare to
      # the average. Since we are requesting two fields, data.current()
      # returns a DataFrame with
      current = data.current(context.neo_eth, fields=['close', 'volume'])
      price = current['close']

      # If base_price is not set, we use the current value. This is the
      # price at the first bar which we reference to calculate price_change.
      if context.base_price is None:
          context.base_price = price

      price_change = (price - context.base_price) / context.base_price
      cash = context.portfolio.cash

      # Now that we've collected all current data for this frame, we use
      # the record() method to save it. This data will be available as
      # a parameter of the analyze() function for further analysis.
      record(
          price=price,
          volume=current['volume'],
          price_change=price_change,
          rsi=rsi[-1],
          cash=cash
      )

      # We are trying to avoid over-trading by limiting our trades to
      # one per day.
      if context.traded_today:
          return

      # Since we are using limit orders, some orders may not execute immediately
      # we wait until all orders are executed before considering more trades.
      orders = get_open_orders(context.neo_eth)
      if len(orders) > 0:
          return

      # Exit if we cannot trade
      if not data.can_trade(context.neo_eth):
          return

      # Another powerful built-in feature of the Catalyst backtester is the
      # portfolio object.  The portfolio object tracks your positions, cash,
      # cost basis of specific holdings, and more.  In this line, we calculate
      # how long or short our position is at this minute.   
      pos_amount = context.portfolio.positions[context.neo_eth].amount

      if rsi[-1] <= context.RSI_OVERSOLD and pos_amount == 0:
          log.info(
              '{}: buying - price: {}, rsi: {}'.format(
                  data.current_dt, price, rsi[-1]
              )
          )
          # Set a style for limit orders,
          limit_price = price * 1.005
          order_target_percent(
              context.neo_eth, 1, limit_price=limit_price
          )
          context.traded_today = True

      elif rsi[-1] >= context.RSI_OVERBOUGHT and pos_amount > 0:
          log.info(
              '{}: selling - price: {}, rsi: {}'.format(
                  data.current_dt, price, rsi[-1]
              )
          )
          limit_price = price * 0.995
          order_target_percent(
              context.neo_eth, 0, limit_price=limit_price
          )
          context.traded_today = True


  def analyze(context=None, perf=None):
      end = time.time()
      log.info('elapsed time: {}'.format(end - context.start_time))

      import matplotlib.pyplot as plt
      # The base currency of the algo exchange
      base_currency = context.exchanges.values()[0].base_currency.upper()

      # Plot the portfolio value over time.
      ax1 = plt.subplot(611)
      perf.loc[:, 'portfolio_value'].plot(ax=ax1)
      ax1.set_ylabel('Portfolio\nValue\n({})'.format(base_currency))

      # Plot the price increase or decrease over time.
      ax2 = plt.subplot(612, sharex=ax1)
      perf.loc[:, 'price'].plot(ax=ax2, label='Price')

      ax2.set_ylabel('{asset}\n({base})'.format(
          asset=context.neo_eth.symbol, base=base_currency
      ))

      transaction_df = extract_transactions(perf)
      if not transaction_df.empty:
          buy_df = transaction_df[transaction_df['amount'] > 0]
          sell_df = transaction_df[transaction_df['amount'] < 0]
          ax2.scatter(
              buy_df.index.to_pydatetime(),
              perf.loc[buy_df.index.floor('1 min'), 'price'],
              marker='^',
              s=100,
              c='green',
              label=''
          )
          ax2.scatter(
              sell_df.index.to_pydatetime(),
              perf.loc[sell_df.index.floor('1 min'), 'price'],
              marker='v',
              s=100,
              c='red',
              label=''
          )

      ax4 = plt.subplot(613, sharex=ax1)
      perf.loc[:, 'cash'].plot(
          ax=ax4, label='Base Currency ({})'.format(base_currency)
      )
      ax4.set_ylabel('Cash\n({})'.format(base_currency))

      perf['algorithm'] = perf.loc[:, 'algorithm_period_return']

      ax5 = plt.subplot(614, sharex=ax1)
      perf.loc[:, ['algorithm', 'price_change']].plot(ax=ax5)
      ax5.set_ylabel('Percent\nChange')

      ax6 = plt.subplot(615, sharex=ax1)
      perf.loc[:, 'rsi'].plot(ax=ax6, label='RSI')
      ax6.set_ylabel('RSI')
      ax6.axhline(context.RSI_OVERBOUGHT, color='darkgoldenrod')
      ax6.axhline(context.RSI_OVERSOLD, color='darkgoldenrod')

      if not transaction_df.empty:
          ax6.scatter(
              buy_df.index.to_pydatetime(),
              perf.loc[buy_df.index.floor('1 min'), 'rsi'],
              marker='^',
              s=100,
              c='green',
              label=''
          )
          ax6.scatter(
              sell_df.index.to_pydatetime(),
              perf.loc[sell_df.index.floor('1 min'), 'rsi'],
              marker='v',
              s=100,
              c='red',
              label=''
          )
      plt.legend(loc=3)
      start, end = ax6.get_ylim()
      ax6.yaxis.set_ticks(np.arange(0, end, end/5))

      # Show the plot.
      plt.gcf().set_size_inches(18, 8)
      plt.show()
      pass


  if __name__ == '__main__':
      # The execution mode: backtest or live
      MODE = 'backtest'

      if MODE == 'backtest':
          folder = os.path.join(
              tempfile.gettempdir(), 'catalyst', NAMESPACE
          )
          ensure_directory(folder)

          timestr = time.strftime('%Y%m%d-%H%M%S')
          out = os.path.join(folder, '{}.p'.format(timestr))
          # catalyst run -f catalyst/examples/mean_reversion_simple.py -x bitfinex -s 2017-10-1 -e 2017-11-10 -c usdt -n mean-reversion --data-frequency minute --capital-base 10000
          run_algorithm(
              capital_base=10000,
              data_frequency='minute',
              initialize=initialize,
              handle_data=handle_data,
              analyze=analyze,
              exchange_name='bitfinex',
              algo_namespace=NAMESPACE,
              base_currency='usd',
              start=pd.to_datetime('2017-10-01', utc=True),
              end=pd.to_datetime('2017-11-10', utc=True),
              output=out
          )
          log.info('saved perf stats: {}'.format(out))

      elif MODE == 'live':
          run_algorithm(
              capital_base=0.5,
              initialize=initialize,
              handle_data=handle_data,
              analyze=analyze,
              exchange_name='bittrex',
              live=True,
              algo_namespace=NAMESPACE,
              base_currency='usd',
              live_graph=False
          )

.. image:: https://s3.amazonaws.com/enigmaco-docs/github.io/example_mean_reversion_simple.png

Notice the difference in performance between the charts above and those seen on 
`this video tutorial <https://youtu.be/JOBRwst9jUY>`_ at 
minute 8:10. The buy and sell orders are triggered at the same exact times, but
the differences result from a more realistic slippage model 
implemented after the video was recorded, which executes the orders at slighlty
different prices, but resulting in significant changes in performance of our 
strategy.

.. _simple_universe:

Simple Universe
~~~~~~~~~~~~~~~

Source code: `examples/simple_universe.py <https://github.com/enigmampc/catalyst/blob/master/catalyst/examples/simple_universe.py>`_

This example aims to provide an easy way for users to learn how to 
collect data from any given exchange and select a subset of the available
currency pairs for trading. You simply need to specify the exchange and 
the market (base_currency) that you want to focus on. You will then see 
how to create a universe of assets, and filter it based the market you 
desire.

The example prints out the closing price of all the pairs for a given 
market in a given exchange every 30 minutes. The example also contains 
the OHLCV data with minute-resolution for the past seven days which 
could be used to create indicators. Use this code as the backbone to 
create your own trading strategy.

The lookback_date variable is used to ensure data for a coin existed on 
the lookback period specified.

To run, execute the following two commands in a terminal (inside catalyst 
environment). The first one retrieves all the pricing data needed for this
script to run (only needs to be run once), and the second one executes this
script with the parameters specified in the run_algorithm() call at the end 
of the file:

.. code-block:: bash
  
  catalyst ingest-exchange -x bitfinex -f minute

.. code-block:: bash

  python simple_universe.py

Credits: This code was originally submitted by `Abner Ayala-Acevedo 
<https://github.com/abnera>`_. Thank you!

.. code-block:: python

  from datetime import timedelta

  import numpy as np
  import pandas as pd

  from catalyst import run_algorithm
  from catalyst.exchange.exchange_utils import get_exchange_symbols
  from catalyst.api import (symbols, )


  def initialize(context):
      context.i = -1  # minute counter
      context.exchange = context.exchanges.values()[0].name.lower()  
      context.base_currency = context.exchanges.values()[0].base_currency.lower()  


  def handle_data(context, data):
      context.i += 1
      lookback_days = 7  # 7 days

      # current date & time in each iteration formatted into a string
      now = data.current_dt
      date, time = now.strftime('%Y-%m-%d %H:%M:%S').split(' ')
      lookback_date = now - timedelta(days=lookback_days) 
      # keep only the date as a string, discard the time
      lookback_date = lookback_date.strftime('%Y-%m-%d %H:%M:%S').split(' ')[0]  

      one_day_in_minutes = 1440  # 60 * 24 assumes data_frequency='minute'
      # update universe everyday at midnight
      if not context.i % one_day_in_minutes:
          context.universe = universe(context, lookback_date, date)

      # get data every 30 minutes
      minutes = 30
      # get lookback_days of history data: that is 'lookback' number of bins
      lookback = one_day_in_minutes / minutes * lookback_days  
      if not context.i % minutes and context.universe:
          # we iterate for every pair in the current universe
          for coin in context.coins:
              pair = str(coin.symbol)

              # Get 30 minute interval OHLCV data. This is the standard data 
              # required for candlestick or indicators/signals. Return Pandas
              # DataFrames. 30T means 30-minute re-sampling of one minute data. 
              # Adjust it to your desired time interval as needed.
              opened = fill(data.history(coin, 'open', 
                                    bar_count=lookback, frequency='30T')).values
              high = fill(data.history(coin, 'high', 
                                    bar_count=lookback, frequency='30T')).values
              low = fill(data.history(coin, 'low', 
                                    bar_count=lookback, frequency='30T')).values
              close = fill(data.history(coin, 'price', 
                                    bar_count=lookback, frequency='30T')).values
              volume = fill(data.history(coin, 'volume', 
                                    bar_count=lookback, frequency='30T')).values

              # close[-1] is the last value in the set, which is the equivalent 
              # to current price (as in the most recent value)
              # displays the minute price for each pair every 30 minutes
              print('{now}: {pair} -\tO:{o},\tH:{h},\tL:{c},\tC{c},\tV:{v}'.format(
                      now=now, 
                      pair=pair, 
                      o=opened[-1], 
                      h=high[-1], 
                      l=low[-1],
                      c=close[-1],
                      v=volume[-1],
                   ))

              # -------------------------------------------------------------
              # --------------- Insert Your Strategy Here -------------------
              # -------------------------------------------------------------


  def analyze(context=None, results=None):
      pass


  # Get the universe for a given exchange and a given base_currency market
  # Example: Poloniex BTC Market
  def universe(context, lookback_date, current_date):
      # get all the pairs for the given exchange
      json_symbols = get_exchange_symbols(context.exchange)  
      # convert into a DataFrame for easier processing
      df = pd.DataFrame.from_dict(json_symbols).transpose().astype(str) 
      df['base_currency'] = df.apply(lambda row: row.symbol.split('_')[1],axis=1)
      df['market_currency'] = df.apply(lambda row: row.symbol.split('_')[0],axis=1)

      # Filter all the pairs to get only the ones for a given base_currency
      df = df[df['base_currency'] == context.base_currency]

      # Filter all the pairs to ensure that pair existed in the current date range
      df = df[df.start_date < lookback_date]
      df = df[df.end_daily >= current_date]
      context.coins = symbols(*df.symbol)  # convert all the pairs to symbols

      return df.symbol.tolist()


  # Replace all NA, NAN or infinite values with its nearest value
  def fill(series):
      if isinstance(series, pd.Series):
          return series.replace([np.inf, -np.inf], np.nan).ffill().bfill()
      elif isinstance(series, np.ndarray):
          return pd.Series(series).replace(
                       [np.inf, -np.inf], np.nan
                      ).ffill().bfill().values
      else:
          return series


  if __name__ == '__main__':
      start_date = pd.to_datetime('2017-11-10', utc=True)
      end_date = pd.to_datetime('2017-11-13', utc=True)

      performance = run_algorithm(start=start_date, end=end_date,
                                  capital_base=100.0,  # amount of base_currency
                                  initialize=initialize,
                                  handle_data=handle_data,
                                  analyze=analyze,
                                  exchange_name='bitfinex',
                                  data_frequency='minute',
                                  base_currency='btc',
                                  live=False,
                                  live_graph=False,
                                  algo_namespace='simple_universe')



.. _portfolio_optimization:

Portfolio Optimization
~~~~~~~~~~~~~~~~~~~~~~

Use this code to execute a portfolio optimization model. This strategy will 
select the portfolio with the maximum Sharpe Ratio. The parameters are set to 
use 180 days of historical data and rebalance every 30 days. This code was used 
in writting the following article: 
`Markowitz Portfolio Optimization for Cryptocurrencies <https://blog.enigma.co/markowitz-portfolio-optimization-for-cryptocurrencies-in-catalyst-b23c38652556>`_.

.. code-block:: python

  '''
     You can run this code using the Python interpreter:

     $ python portfolio_optimization.py
  '''

  from __future__ import division
  import os
  import pytz
  import numpy as np
  import pandas as pd
  from scipy.optimize import minimize
  import matplotlib.pyplot as plt
  from datetime import datetime

  from catalyst.api import record, symbol, symbols, order_target_percent
  from catalyst.utils.run_algo import run_algorithm

  np.set_printoptions(threshold='nan', suppress=True)


  def initialize(context):
     # Portfolio assets list
     context.assets = symbols('btc_usdt', 'eth_usdt', 'ltc_usdt', 'dash_usdt',
                              'xmr_usdt')
     context.nassets = len(context.assets)
     # Set the time window that will be used to compute expected return 
     # and asset correlations
     context.window = 180
     # Set the number of days between each portfolio rebalancing
     context.rebalance_period = 30                   
     context.i = 0

     
  def handle_data(context, data):
     # Only rebalance at the beggining of the algorithm execution and 
     # every multiple of the rebalance period
     if context.i == 0 or context.i%context.rebalance_period == 0:
         n = context.window
         prices = data.history(context.assets, fields='price', 
                               bar_count=n+1, frequency='1d') 
         pr = np.asmatrix(prices)
         t_prices = prices.iloc[1:n+1]
         t_val = t_prices.values
         tminus_prices = prices.iloc[0:n]
         tminus_val = tminus_prices.values
         # Compute daily returns (r)
         r = np.asmatrix(t_val/tminus_val-1)
         # Compute the expected returns of each asset with the average 
         # daily return for the selected time window
         m = np.asmatrix(np.mean(r, axis=0))
         # ###
         stds = np.std(r, axis=0)
         # Compute excess returns matrix (xr)
         xr = r - m
         # Matrix algebra to get variance-covariance matrix
         cov_m = np.dot(np.transpose(xr),xr)/n
         # Compute asset correlation matrix (informative only)
         corr_m = cov_m/np.dot(np.transpose(stds),stds)
         
         # Define portfolio optimization parameters
         n_portfolios = 50000
         results_array = np.zeros((3+context.nassets,n_portfolios))
         for p in xrange(n_portfolios):
             weights = np.random.random(context.nassets)
             weights /= np.sum(weights)
             w = np.asmatrix(weights)
             p_r = np.sum(np.dot(w,np.transpose(m)))*365
             p_std = np.sqrt(np.dot(np.dot(w,cov_m),np.transpose(w)))*np.sqrt(365)
             
             #store results in results array
             results_array[0,p] = p_r
             results_array[1,p] = p_std
             #store Sharpe Ratio (return / volatility) - risk free rate element 
             #excluded for simplicity
             results_array[2,p] = results_array[0,p] / results_array[1,p]
             i = 0
             for iw in weights:
                 results_array[3+i,p] = weights[i]
                 i += 1
         
         #convert results array to Pandas DataFrame
         results_frame = pd.DataFrame(np.transpose(results_array),
                            columns=['r','stdev','sharpe']+context.assets)
         #locate position of portfolio with highest Sharpe Ratio
         max_sharpe_port = results_frame.iloc[results_frame['sharpe'].idxmax()]
         #locate positon of portfolio with minimum standard deviation
         min_vol_port = results_frame.iloc[results_frame['stdev'].idxmin()]
         
         #order optimal weights for each asset
         for asset in context.assets:
             if data.can_trade(asset):
                 order_target_percent(asset, max_sharpe_port[asset])
         
         #create scatter plot coloured by Sharpe Ratio
         plt.scatter(results_frame.stdev,results_frame.r,c=results_frame.sharpe,cmap='RdYlGn')
         plt.xlabel('Volatility')
         plt.ylabel('Returns')
         plt.colorbar()
         #plot red star to highlight position of portfolio with highest Sharpe Ratio
         plt.scatter(max_sharpe_port[1],max_sharpe_port[0],marker='o',color='b',s=200)
         #plot green star to highlight position of minimum variance portfolio
         plt.show()
         print(max_sharpe_port)
         record(pr=pr,r=r, m=m, stds=stds ,max_sharpe_port=max_sharpe_port, corr_m=corr_m)
     context.i += 1
     
         
  def analyze(context=None, results=None):
     # Form DataFrame with selected data
     data = results[['pr','r','m','stds','max_sharpe_port','corr_m','portfolio_value']]
     
     # Save results in CSV file
     filename = os.path.splitext(os.path.basename(__file__))[0]
     data.to_csv(filename + '.csv')


  # Bitcoin data is available from 2015-3-2. Dates vary for other tokens.    
  start = datetime(2017, 1, 1, 0, 0, 0, 0, pytz.utc)
  end = datetime(2017, 8, 16, 0, 0, 0, 0, pytz.utc) 
  results = run_algorithm(initialize=initialize,
                          handle_data=handle_data,
                          analyze=analyze,
                          start=start,
                          end=end,
                          exchange_name='poloniex',
                          capital_base=100000, )

.. image:: https://cdn-images-1.medium.com/max/1600/0*EjjiKZHlYF3sn7yQ.
   :align: center