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BLD: created a simpler mean-reversion algo for the video
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fredfortier
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# For this example, we're going to write a simple momentum script. When the
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# stock goes up quickly, we're going to buy; when it goes down quickly, we're
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# going to sell. Hopefully we'll ride the waves.
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from datetime import timedelta
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import pandas as pd
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import talib
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# To run an algorithm in Catalyst, you need two functions: initialize and
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# handle_data.
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from logbook import Logger
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from talib.common import MA_Type
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from catalyst import run_algorithm
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from catalyst.api import symbol, record, order_target_percent, \
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get_open_orders
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# We give a name to the algorithm which Catalyst will use to persist its state.
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# In this example, Catalyst will create the `.catalyst/data/live_algos`
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# directory. If we stop and start the algorithm, Catalyst will resume its
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# state using the files included in the folder.
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from catalyst.exchange.stats_utils import extract_transactions, trend_direction
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algo_namespace = 'momentum'
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log = Logger(algo_namespace)
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def initialize(context):
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# This initialize function sets any data or variables that you'll use in
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# your algorithm. For instance, you'll want to define the trading pair (or
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# trading pairs) you want to backtest. You'll also want to define any
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# parameters or values you're going to use.
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# In our example, we're looking at Ether in USD Tether.
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context.eth_btc = symbol('etc_usdt')
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context.base_price = None
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context.current_day = None
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def handle_data(context, data):
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# This handle_data function is where the real work is done. Our data is
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# minute-level tick data, and each minute is called a frame. This function
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# runs on each frame of the data.
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# We flag the first period of each day.
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# Since cryptocurrencies trade 24/7 the `before_trading_starts` handle
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# would only execute once. This method works with minute and daily
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# frequencies.
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today = data.current_dt.floor('1D')
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if today != context.current_day:
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context.traded_today = False
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context.current_day = today
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# We're computing the volume-weighted-average-price of the security
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# defined above, in the context.eth_btc variable. For this example, we're
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# using three bars on the 15 min bars.
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# The frequency attribute determine the bar size. We use this convention
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# for the frequency alias:
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# http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases
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prices = data.history(
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context.eth_btc,
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fields='close',
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bar_count=50,
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frequency='15T'
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)
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# Ta-lib calculates various technical indicator based on price and
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# volume arrays.
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# In this example, we are comp
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rsi = talib.RSI(prices.values, timeperiod=14)
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# We need a variable for the current price of the security to compare to
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# the average. Since we are requesting two fields, data.current()
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# returns a DataFrame with
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current = data.current(context.eth_btc, fields=['close', 'volume'])
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price = current['close']
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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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cash = context.portfolio.cash
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# Now that we've collected all current data for this frame, we use
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# the record() method to save it. This data will be available as
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# a parameter of the analyze() function for further analysis.
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record(
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price=price,
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volume=current['volume'],
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price_change=price_change,
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rsi=rsi[-1],
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cash=cash
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)
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# We are trying to avoid over-trading by limiting our trades to
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# one per day.
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if context.traded_today:
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return
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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.eth_btc)
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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.eth_btc):
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return
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# Another powerful built-in feature of the Catalyst backtester is the
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# portfolio object. The portfolio object tracks your positions, cash,
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# cost basis of specific holdings, and more. In this line, we calculate
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# how long or short our position is at this minute.
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pos_amount = context.portfolio.positions[context.eth_btc].amount
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if rsi[-1] <= 30 and pos_amount == 0:
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log.info(
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'{}: buying - price: {}, rsi: {}'.format(
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data.current_dt, price, rsi[-1]
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)
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)
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order_target_percent(context.eth_btc, 1)
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context.traded_today = True
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elif rsi[-1] >= 80 and pos_amount > 0:
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log.info(
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'{}: selling - price: {}, rsi: {}'.format(
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data.current_dt, price, rsi[-1]
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)
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)
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order_target_percent(context.eth_btc, 0)
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context.traded_today = True
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def analyze(context=None, perf=None):
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import matplotlib.pyplot as plt
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# The base currency of the algo exchange
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base_currency = context.exchanges.values()[0].base_currency.upper()
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# Plot the portfolio value over time.
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ax1 = plt.subplot(611)
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perf.loc[:, 'portfolio_value'].plot(ax=ax1)
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ax1.set_ylabel('Portfolio Value ({})'.format(base_currency))
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# Plot the price increase or decrease over time.
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ax2 = plt.subplot(612, sharex=ax1)
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perf.loc[:, 'price'].plot(ax=ax2, label='Price')
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ax2.set_ylabel('{asset} ({base})'.format(
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asset=context.eth_btc.symbol, base=base_currency
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))
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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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ax4 = plt.subplot(613, sharex=ax1)
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perf.loc[:, 'cash'].plot(
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ax=ax4, label='Base Currency ({})'.format(base_currency)
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)
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ax4.set_ylabel('Cash ({})'.format(base_currency))
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perf['algorithm'] = perf.loc[:, 'algorithm_period_return']
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ax5 = plt.subplot(614, sharex=ax1)
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perf.loc[:, ['algorithm', 'price_change']].plot(ax=ax5)
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ax5.set_ylabel('Percent Change')
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ax6 = plt.subplot(615, sharex=ax1)
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perf.loc[:, 'rsi'].plot(ax=ax6, label='RSI')
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ax6.axhline(70, color='darkgoldenrod')
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ax6.axhline(30, color='darkgoldenrod')
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if not transaction_df.empty:
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ax6.scatter(
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buy_df.index.to_pydatetime(),
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perf.loc[buy_df.index, 'rsi'],
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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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ax6.scatter(
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sell_df.index.to_pydatetime(),
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perf.loc[sell_df.index, 'rsi'],
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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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plt.legend(loc=3)
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# Show the plot.
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plt.gcf().set_size_inches(18, 8)
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plt.show()
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pass
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if __name__ == '__main__':
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# The execution mode: backtest or live
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MODE = 'backtest'
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if MODE == 'backtest':
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run_algorithm(
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capital_base=10000,
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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='poloniex',
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algo_namespace=algo_namespace,
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base_currency='usdt',
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start=pd.to_datetime('2017-7-1', utc=True),
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end=pd.to_datetime('2017-7-31', utc=True),
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)
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elif MODE == 'live':
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run_algorithm(
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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='poloniex',
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live=True,
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algo_namespace=algo_namespace,
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base_currency='usdt',
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live_graph=True
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)
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