Improved frequency support for data.history() in backtest, standardized class names, improved unit tests and working on new sample algo.

conatus_strategies/neo_rsi.py

@@ -1,197 +0,0 @@
-import talib
-import pandas as pd
-from logbook import Logger
-
-from catalyst.api import (
-    order,
-    order_target_percent,
-    symbol,
-    record,
-    get_open_orders,
-)
-from catalyst.exchange.stats_utils import get_pretty_stats
-from catalyst.utils.run_algo import run_algorithm
-
-algo_namespace = 'neo_rsi'
-log = Logger(algo_namespace)
-
-
-def initialize(context):
-    log.info('initializing algo')
-    context.asset = symbol('neo_usd', 'bitfinex')
-
-    context.BUY_SIGNAL = 30
-    context.SELL_SIGNAL = 70
-    context.SLIPPAGE_ALLOWED = 0.02
-
-    context.errors = []
-    pass
-
-
-def _handle_data(context, data):
-    dt = data.current_dt
-    log.info('BAR {}'.format(dt))
-
-    price = data.current(context.asset, 'close')
-    log.info('got price {price}'.format(price=price))
-
-    if price is None:
-        log.warn('no pricing data')
-        return
-
-    try:
-        prices = data.history(
-            context.asset,
-            fields='price',
-            bar_count=15,
-            frequency='15m'
-        )
-    except Exception as e:
-        log.warn('historical data not available: '.format(e))
-        return
-
-    rsi = talib.RSI(prices.values, timeperiod=14)[-1]
-    log.info('got rsi: {}'.format(rsi))
-
-    cash = context.portfolio.cash
-    log.info('base currency available: {cash}'.format(cash=cash))
-
-
-    orders = get_open_orders(context.asset)
-    # if len(orders) > 0:
-    #     log.info('skipping bar until all open orders execute')
-    #     return
-
-    if context.asset in context.portfolio.positions:
-        if rsi >= context.SELL_SIGNAL:
-            position = context.portfolio.positions[context.asset]
-            log.info('closing position')
-            amount = -position.amount
-            expected_proceeds = -(amount * price)
-            order(
-                asset=context.asset,
-                amount=amount,
-                limit_price=price * (1 - context.SLIPPAGE_ALLOWED),
-            )
-    else:
-        if rsi <= context.BUY_SIGNAL:
-            log.info('opening position')
-            order(
-                asset=context.asset,
-                amount=cash / price,
-                limit_price=price * (1 + context.SLIPPAGE_ALLOWED),
-            )
-
-    volume = data.current(context.asset, 'volume')
-    record(
-        price=price,
-        volume=volume,
-        cash=cash,
-        starting_cash=context.portfolio.starting_cash,
-        leverage=context.account.leverage,
-        rsi=rsi
-    )
-
-
-def handle_data(context, data):
-    try:
-        _handle_data(context, data)
-    except Exception as e:
-        log.warn('aborting the bar on error {}'.format(e))
-        context.errors.append(e)
-
-    log.debug('completed bar {}, total execution errors {}'.format(
-        data.current_dt,
-        len(context.errors)
-    ))
-
-    if len(context.errors) > 0:
-        log.info('the errors:\n{}'.format(context.errors))
-
-
-def analyze(context=None, results=None):
-    import matplotlib.pyplot as plt
-
-    # 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.symbol))
-    (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.plot(
-        buys.index,
-        results.price[buys.index],
-        '^',
-        markersize=10,
-        color='g',
-    )
-
-    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()
-    pass
-
-
-# run_algorithm(
-#     initialize=initialize,
-#     handle_data=handle_data,
-#     analyze=analyze,
-#     exchange_name='bitfinex',
-#     live=True,
-#     algo_namespace=algo_namespace,
-#     base_currency='btc',
-#     live_graph=False
-# )
-
-# Backtest
-run_algorithm(
-    capital_base=10000,
-    data_frequency='minute',
-    initialize=initialize,
-    handle_data=handle_data,
-    analyze=analyze,
-    exchange_name='bitfinex',
-    algo_namespace=algo_namespace,
-    base_currency='usd',
-    start=pd.to_datetime('2017-9-10', utc=True),
-    end=pd.to_datetime('2017-10-22', utc=True),
-)