Dual moving average for USDT_BTC

catalyst/examples/dual_moving_average_btc.py

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+#!/usr/bin/env python
+#
+# Copyright 2014 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 numpy as np
+
+from catalyst.api import (
+    order_target_percent,
+    record,
+    symbol,
+    get_open_orders,
+    set_commission,
+    set_slippage,
+    set_max_leverage,
+    schedule_function,
+    date_rules,
+    time_rules,
+    attach_pipeline,
+    pipeline_output,
+)
+
+from catalyst.pipeline import Pipeline
+from catalyst.pipeline.data import CryptoPricing
+from catalyst.pipeline.factors.crypto import (
+    VWAP,
+    SimpleMovingAverage,
+)
+
+from catalyst.finance.commission import PerDollar
+from catalyst.finance.slippage import VolumeShareSlippage
+
+TARGET_INVESTMENT_RATIO = 0.1
+SHORT_WINDOW = 30
+LONG_WINDOW = 100
+LIMIT_RATIO = 1.5
+STOP_RATIO = 0.5
+
+def initialize(context):
+    context.asset = symbol('USDT_BTC')
+    context.i = 0
+
+    set_commission(PerDollar(cost=0.001))
+    set_slippage(VolumeShareSlippage())
+    set_max_leverage(1.0)
+
+    attach_pipeline(make_pipeline(), 'my_pipeline')
+
+    schedule_function(
+        rebalance,
+        date_rules.every_day(),
+        time_rules.market_open(),
+    )
+
+
+def before_trading_start(context, data):
+    context.pipeline_data = pipeline_output('my_pipeline')
+
+def make_pipeline():
+    return Pipeline(
+        columns={
+            'price': CryptoPricing.open.latest,
+            'short_mavg': VWAP(window_length=SHORT_WINDOW),
+            'long_mavg': VWAP(window_length=LONG_WINDOW),
+        },
+    )
+
+
+def handle_data(context, data):
+    pass
+
+def rebalance(context, data):
+    context.i += 1
+
+    # skip first LONG_WINDOW bars to fill windows
+    if context.i < LONG_WINDOW:
+        return
+
+    # get pipeline data for asset of interest
+    pipeline_data = context.pipeline_data
+    pipeline_data = pipeline_data[pipeline_data.index == context.asset].iloc[0]
+
+    # retrieve long and short moving averages from pipeline
+    short_mavg = pipeline_data.short_mavg
+    long_mavg = pipeline_data.long_mavg
+    price = pipeline_data.price
+
+    # check that order has not already been placed
+    open_orders = get_open_orders()
+    if context.asset not in open_orders:
+        # check that the asset of interest can currently be traded
+        if data.can_trade(context.asset):
+            # adjust portfolio based on moving averages
+            if short_mavg > long_mavg:
+                order_target_percent(
+                    context.asset,
+                    TARGET_INVESTMENT_RATIO,
+                    #limit_price=LIMIT_RATIO * price,
+                )
+            elif short_mavg < long_mavg:
+                order_target_percent(
+                    context.asset, 
+                    0.0,
+                )
+
+    record(
+        USDT_BTC=data[context.asset].price,
+        cash=context.portfolio.cash,
+        leverage=context.account.leverage,
+        short_mavg=short_mavg,
+        long_mavg=long_mavg,
+    )
+    
+
+
+# Note: this function can be removed if running
+# this algorithm on quantopian.com
+def analyze(context=None, results=None):
+    import matplotlib.pyplot as plt
+    # Plot the portfolio and asset data.
+    ax1 = plt.subplot(511)
+    results[['portfolio_value']].plot(ax=ax1)
+    ax1.set_ylabel('Portfolio value (USD)')
+
+    ax2 = plt.subplot(512, sharex=ax1)
+    ax2.set_ylabel('USDT_BTC (USD)')
+
+    ax3 = plt.subplot(513, sharex=ax1)
+    results[['leverage', 'alpha', 'beta']].plot(ax=ax3)
+    ax3.set_ylabel('Leverage (USD)')
+
+    ax4 = plt.subplot(514, sharex=ax1)
+    results[['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(515, sharex=ax1)
+    results[[
+        'treasury',
+        'algorithm',
+        'benchmark',
+    ]].plot(ax=ax5)
+    ax5.set_ylabel('Dollars (USD)')
+
+    results[['USDT_BTC', 'short_mavg', 'long_mavg']].plot(ax=ax2)
+
+    trans = results.ix[[t != [] for t in results.transactions]]
+    buys = trans.ix[
+        [t[0]['amount'] > 0 for t in trans.transactions]
+    ]
+    sells = trans.ix[
+        [t[0]['amount'] < 0 for t in trans.transactions]
+    ]
+    print 'buys:', buys.head()
+    ax2.plot(
+        buys.index, results.USDT_BTC[buys.index],
+        '^',
+        markersize=10,
+        color='m',
+    )
+    ax2.plot(
+        sells.index, results.USDT_BTC[sells.index],
+        'v',
+        markersize=10,
+        color='k',
+    )
+
+    plt.legend(loc=3)
+
+    # Show the plot.
+    plt.gcf().set_size_inches(18, 8)
+    plt.show()
+
+
+def _test_args():
+    """Extra arguments to use when catalyst's automated tests run this example.
+    """
+    import pandas as pd
+
+    return {
+        'start': pd.Timestamp('2014-01-01', tz='utc'),
+        'end': pd.Timestamp('2014-11-01', tz='utc'),
+    }