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DOC: linking example_algo to their sources
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Victor Grau Serrat
@@ -580,162 +580,8 @@ which you can skim through for now. A copy of this algorithm is available in
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the ``examples`` directory:
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`dual_moving_average.py <https://github.com/enigmampc/catalyst/blob/master/catalyst/examples/dual_moving_average.py>`_.
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.. code-block:: python
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import numpy as np
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import pandas as pd
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from logbook import Logger
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import matplotlib.pyplot as plt
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from catalyst import run_algorithm
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from catalyst.api import (order, record, symbol, order_target_percent,
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get_open_orders)
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from catalyst.exchange.utils.stats_utils import extract_transactions
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NAMESPACE = 'dual_moving_average'
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log = Logger(NAMESPACE)
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def initialize(context):
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context.i = 0
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context.asset = symbol('ltc_usd')
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context.base_price = None
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def handle_data(context, data):
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# define the windows for the moving averages
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short_window = 50
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long_window = 200
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# Skip as many bars as long_window to properly compute the average
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context.i += 1
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if context.i < long_window:
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return
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# Compute moving averages calling data.history() for each
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# moving average with the appropriate parameters. We choose to use
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# minute bars for this simulation -> freq="1m"
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# Returns a pandas dataframe.
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short_mavg = data.history(context.asset, 'price',
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bar_count=short_window, frequency="1m").mean()
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long_mavg = data.history(context.asset, 'price',
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bar_count=long_window, frequency="1m").mean()
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# Let's keep the price of our asset in a more handy variable
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price = data.current(context.asset, 'price')
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# If base_price is not set, we use the current value. This is the
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# price at the first bar which we reference to calculate price_change.
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if context.base_price is None:
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context.base_price = price
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price_change = (price - context.base_price) / context.base_price
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# Save values for later inspection
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record(price=price,
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cash=context.portfolio.cash,
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price_change=price_change,
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short_mavg=short_mavg,
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long_mavg=long_mavg)
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# Since we are using limit orders, some orders may not execute immediately
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# we wait until all orders are executed before considering more trades.
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orders = get_open_orders(context.asset)
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if len(orders) > 0:
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return
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# Exit if we cannot trade
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if not data.can_trade(context.asset):
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return
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# We check what's our position on our portfolio and trade accordingly
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pos_amount = context.portfolio.positions[context.asset].amount
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# Trading logic
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if short_mavg > long_mavg and pos_amount == 0:
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# we buy 100% of our portfolio for this asset
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order_target_percent(context.asset, 1)
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elif short_mavg < long_mavg and pos_amount > 0:
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# we sell all our positions for this asset
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order_target_percent(context.asset, 0)
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def analyze(context, perf):
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# Get the base_currency that was passed as a parameter to the simulation
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exchange = list(context.exchanges.values())[0]
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base_currency = exchange.base_currency.upper()
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# First chart: Plot portfolio value using base_currency
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ax1 = plt.subplot(411)
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perf.loc[:, ['portfolio_value']].plot(ax=ax1)
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ax1.legend_.remove()
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ax1.set_ylabel('Portfolio Value\n({})'.format(base_currency))
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start, end = ax1.get_ylim()
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ax1.yaxis.set_ticks(np.arange(start, end, (end-start)/5))
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# Second chart: Plot asset price, moving averages and buys/sells
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ax2 = plt.subplot(412, sharex=ax1)
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perf.loc[:, ['price','short_mavg','long_mavg']].plot(ax=ax2, label='Price')
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ax2.legend_.remove()
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ax2.set_ylabel('{asset}\n({base})'.format(
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asset = context.asset.symbol,
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base = base_currency
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))
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start, end = ax2.get_ylim()
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ax2.yaxis.set_ticks(np.arange(start, end, (end-start)/5))
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transaction_df = extract_transactions(perf)
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if not transaction_df.empty:
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buy_df = transaction_df[transaction_df['amount'] > 0]
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sell_df = transaction_df[transaction_df['amount'] < 0]
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ax2.scatter(
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buy_df.index.to_pydatetime(),
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perf.loc[buy_df.index, 'price'],
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marker='^',
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s=100,
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c='green',
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label=''
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)
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ax2.scatter(
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sell_df.index.to_pydatetime(),
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perf.loc[sell_df.index, 'price'],
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marker='v',
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s=100,
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c='red',
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label=''
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)
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# Third chart: Compare percentage change between our portfolio
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# and the price of the asset
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ax3 = plt.subplot(413, sharex=ax1)
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perf.loc[:, ['algorithm_period_return', 'price_change']].plot(ax=ax3)
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ax3.legend_.remove()
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ax3.set_ylabel('Percent Change')
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start, end = ax3.get_ylim()
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ax3.yaxis.set_ticks(np.arange(start, end, (end-start)/5))
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# Fourth chart: Plot our cash
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ax4 = plt.subplot(414, sharex=ax1)
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perf.cash.plot(ax=ax4)
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ax4.set_ylabel('Cash\n({})'.format(base_currency))
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start, end = ax4.get_ylim()
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ax4.yaxis.set_ticks(np.arange(0, end, end/5))
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plt.show()
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if __name__ == '__main__':
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run_algorithm(
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capital_base=1000,
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data_frequency='minute',
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initialize=initialize,
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handle_data=handle_data,
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analyze=analyze,
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exchange_name='bitfinex',
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algo_namespace=NAMESPACE,
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base_currency='usd',
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start=pd.to_datetime('2017-9-22', utc=True),
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end=pd.to_datetime('2017-9-23', utc=True),
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)
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.. literalinclude:: ../../catalyst/examples/dual_moving_average.py
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:language: python
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In order to run the code above, you have to ingest the needed data first:
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