diff --git a/_sources/beginner-tutorial.txt b/_sources/beginner-tutorial.txt index 6743f82d..38603888 100644 --- a/_sources/beginner-tutorial.txt +++ b/_sources/beginner-tutorial.txt @@ -546,6 +546,7 @@ only bought bitcoin every chance it got. sudo apt install python-tk +.. _history: Access to previous prices using ``history`` ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ diff --git a/_sources/example-algos.txt b/_sources/example-algos.txt index e341f331..002dc46b 100644 --- a/_sources/example-algos.txt +++ b/_sources/example-algos.txt @@ -2,28 +2,102 @@ Example Algorithms ================== -This section documents a small number of example algorithms to complement the +This section documents a number of example algorithms to complement the beginner tutorial, and show how other trading algorithms can be implemented -using Catalyst: +using Catalyst. + +Overview +~~~~~~~~ + +- :ref:`Buy BTC Simple`: The simplest algorithm that introduces + the ``initialize()`` and ``handle_data()`` functions, and is used in the + :doc:`beginner tutorial` to show how to run catalyst + for the first time. + +- :ref:`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`: A classic momentum + strategy used in the second part of the + `beginner tutorial `_ 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 `: Another simple momentum + strategy that is used in our + `two-part video tutorial `_ to show how + to get started in backtesting and live trading with Catalyst. + + +.. _buy_btc_simple: + +Buy BTC Simple Algorithm +~~~~~~~~~~~~~~~~~~~~~~~~ + +Source code: `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: `examples/buy_and_hodl.py `_ +Source code: `examples/buy_and_hodl.py `_ First ingest the historical pricing data needed to run this algorithm: .. code-block:: bash - catalyst ingest-exchange -x poloniex -f daily -i btc_usdt + 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 poloniex -c btc -o bah.pickle + 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 @@ -35,156 +109,340 @@ 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 + #!/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.api import ( - order_target_value, - symbol, - record, - cancel_order, - get_open_orders, - ) + 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_usdt' - context.TARGET_HODL_RATIO = 0.8 - context.RESERVE_RATIO = 1.0 - context.TARGET_HODL_RATIO + 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.is_buying = True + context.asset = symbol(context.ASSET_NAME) - context.i = 0 + context.i = 0 - def handle_data(context, data): - context.i += 1 + 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 + 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) + # 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 + # 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') + # 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: - # 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, - stop_price=price * 0.9, - ) + # 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, + stop_price=price * 0.9, + ) - record( - price=price, - volume=data.current(context.asset, 'volume'), - cash=cash, - starting_cash=context.portfolio.starting_cash, - leverage=context.account.leverage, - ) + 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): - import matplotlib.pyplot as plt + 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)') + # 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) + 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.plot( - buys.index, - results.price[buys.index], - '^', - markersize=10, - color='g', - ) + 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 ') + 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)') + 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', - ]] + 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') + 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)') + ax6 = plt.subplot(616, sharex=ax1) + results[['volume']].plot(ax=ax6) + ax6.set_ylabel('Volume (mCoins/5min)') - plt.legend(loc=3) + 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 `_ + +This strategy is covered in detail in the last part of +`this tutorial `_. + +.. 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 - # Show the plot. - plt.gcf().set_size_inches(18, 8) - plt.show() .. _mean_reversion: Mean Reversion Algorithm ~~~~~~~~~~~~~~~~~~~~~~~~ -source: `examples/mean_reversion_simple.py `_ +Source code: `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 run this trading algorithm with the ``neo_usd`` currency pair -on the ``Bitfinex`` exchange. Thus, first ingest the historical pricing data +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 @@ -201,244 +459,291 @@ lines 218-245, so in order to run the algorithm we just type: .. code-block:: python - import pandas as pd - import talib - from logbook import Logger + import os + import tempfile + import time - 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 + import numpy as np + import pandas as pd + import talib + from logbook import Logger - # 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. - NAMESPACE = 'mean_reversion_simple' - log = Logger(NAMESPACE) + 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 - # 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 Ether in USD Tether. - context.neo_usd = symbol('neo_usd') - context.base_price = None - context.current_day = None + NAMESPACE = 'mean_reversion_simple' + log = Logger(NAMESPACE) - 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. + # To run an algorithm in Catalyst, you need two functions: initialize and + # handle_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 + 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. - # We're computing the volume-weighted-average-price of the security - # defined above, in the context.neo_usd variable. For this example, we're - # using three bars on the 15 min bars. + # In our example, we're looking at Neo in USD. + context.neo_eth = symbol('neo_usd') + context.base_price = None + context.current_day = None - # 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_usd, - fields='close', - bar_count=50, - frequency='15T' - ) + context.RSI_OVERSOLD = 30 + context.RSI_OVERBOUGHT = 80 + context.CANDLE_SIZE = '15T' - # 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_usd, 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_usd) - if len(orders) > 0: - return - - # Exit if we cannot trade - if not data.can_trade(context.neo_usd): - 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_usd].amount - - if rsi[-1] <= 30 and pos_amount == 0: - log.info( - '{}: buying - price: {}, rsi: {}'.format( - data.current_dt, price, rsi[-1] - ) - ) - order_target_percent(context.neo_usd, 1) - context.traded_today = True - - elif rsi[-1] >= 80 and pos_amount > 0: - log.info( - '{}: selling - price: {}, rsi: {}'.format( - data.current_dt, price, rsi[-1] - ) - ) - order_target_percent(context.neo_usd, 0) - context.traded_today = True + context.start_time = time.time() - def analyze(context=None, perf=None): - import matplotlib.pyplot as plt + 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. - # The base currency of the algo exchange - base_currency = context.exchanges.values()[0].base_currency.upper() + # 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 - # Plot the portfolio value over time. - ax1 = plt.subplot(611) - perf.loc[:, 'portfolio_value'].plot(ax=ax1) - ax1.set_ylabel('Portfolio Value ({})'.format(base_currency)) + # 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. - # Plot the price increase or decrease over time. - ax2 = plt.subplot(612, sharex=ax1) - perf.loc[:, 'price'].plot(ax=ax2, label='Price') + # 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 + ) - ax2.set_ylabel('{asset} ({base})'.format( - asset=context.neo_usd.symbol, base=base_currency - )) + # Ta-lib calculates various technical indicator based on price and + # volume arrays. - 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='' - ) + # In this example, we are comp + rsi = talib.RSI(prices.values, timeperiod=14) - ax4 = plt.subplot(613, sharex=ax1) - perf.loc[:, 'cash'].plot( - ax=ax4, label='Base Currency ({})'.format(base_currency) - ) - ax4.set_ylabel('Cash ({})'.format(base_currency)) + # 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'] - perf['algorithm'] = perf.loc[:, 'algorithm_period_return'] + # 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 - ax5 = plt.subplot(614, sharex=ax1) - perf.loc[:, ['algorithm', 'price_change']].plot(ax=ax5) - ax5.set_ylabel('Percent Change') + price_change = (price - context.base_price) / context.base_price + cash = context.portfolio.cash - ax6 = plt.subplot(615, sharex=ax1) - perf.loc[:, 'rsi'].plot(ax=ax6, label='RSI') - ax6.axhline(70, color='darkgoldenrod') - ax6.axhline(30, color='darkgoldenrod') + # 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 + ) - if not transaction_df.empty: - ax6.scatter( - buy_df.index.to_pydatetime(), - perf.loc[buy_df.index, 'rsi'], - marker='^', - s=100, - c='green', - label='' - ) - ax6.scatter( - sell_df.index.to_pydatetime(), - perf.loc[sell_df.index, 'rsi'], - marker='v', - s=100, - c='red', - label='' - ) - plt.legend(loc=3) + # We are trying to avoid over-trading by limiting our trades to + # one per day. + if context.traded_today: + return - # Show the plot. - plt.gcf().set_size_inches(18, 8) - plt.show() - pass + # 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 - if __name__ == '__main__': - # The execution mode: backtest or live - MODE = 'backtest' + 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 `_ 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. - if MODE == 'backtest': - # catalyst run -f catalyst/examples/mean_reversion_simple.py -x poloniex -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('2017-10-1', utc=True), - end=pd.to_datetime('2017-11-10', utc=True), - ) - elif MODE == 'live': - run_algorithm( - initialize=initialize, - handle_data=handle_data, - analyze=analyze, - exchange_name='bitfinex', - live=True, - algo_namespace=NAMESPACE, - base_currency='usd', - live_graph=True - ) diff --git a/appendix.html b/appendix.html index b3b8b437..6455bbe7 100644 --- a/appendix.html +++ b/appendix.html @@ -120,7 +120,10 @@
  • Naming Convention
  • Example Algorithms
    • diff --git a/beginner-tutorial.html b/beginner-tutorial.html index fef54173..89783713 100644 --- a/beginner-tutorial.html +++ b/beginner-tutorial.html @@ -122,7 +122,10 @@
  • Naming Convention
  • Example Algorithms
    • @@ -725,7 +728,7 @@ in a Linux environment:

    -

    Access to previous prices using history

    +

    Access to previous prices using history

    Working example: Dual Moving Average Cross-Over

    The Dual Moving Average (DMA) is a classic momentum strategy. It’s diff --git a/example-algos.html b/example-algos.html index 50b00e09..395a5779 100644 --- a/example-algos.html +++ b/example-algos.html @@ -122,7 +122,10 @@

  • Naming Convention
  • Example Algorithms
    • @@ -250,18 +253,80 @@

    Example Algorithms

    -

    This section documents a small number of example algorithms to complement the +

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

    +using Catalyst.

    +
    +

    Overview

    +
      +
    • Buy BTC Simple: The simplest algorithm that introduces +the initialize() and handle_data() functions, and is used in the +beginner tutorial to show how to run catalyst +for the first time.
      • +
    • 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.
      • +
    • Dual Moving Average Crossover: A classic momentum +strategy used in the second part of the +beginner tutorial 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.
      • +
    • Mean Reversion Algorithm: Another simple momentum +strategy that is used in our +two-part video tutorial to show how +to get started in backtesting and live trading with Catalyst.
      • +
    +
    +
    +

    Buy BTC Simple Algorithm

    +

    Source code: examples/buy_btc_simple.py

    +
    '''
+  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 Algorithm

    -

    source: examples/buy_and_hodl.py

    +

    Source code: examples/buy_and_hodl.py

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

    -
    catalyst ingest-exchange -x poloniex -f daily -i btc_usdt
+
    catalyst ingest-exchange -x bitfinex -f daily -i btc_usd
 
    
 
    
 
    Then, you can run the code below with the following command:
    
-
    catalyst run -f buy_and_hodl.py --start 2015-3-1 --end 2017-10-31 --capital-base 100000 -x poloniex -c btc -o bah.pickle
+
    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:
    
+
    python buy_and_hodl.py
 
    
 
    
 
    This command will run the trading algorithm in the specified time range and
@@ -287,18 +352,15 @@ one day prior to the current date.
    
 # 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.api import (
-    order_target_value,
-    symbol,
-    record,
-    cancel_order,
-    get_open_orders,
-)
+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_usdt'
+    context.ASSET_NAME = 'btc_usd'
     context.TARGET_HODL_RATIO = 0.8
     context.RESERVE_RATIO = 1.0 - context.TARGET_HODL_RATIO
 
@@ -330,6 +392,7 @@ one day prior to the current date.
    
 
     # 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,
@@ -348,7 +411,6 @@ one day prior to the current date.
    
 
 
 def analyze(context=None, results=None):
-    import matplotlib.pyplot as plt
 
     # Plot the portfolio and asset data.
     ax1 = plt.subplot(611)
@@ -363,12 +425,13 @@ one day prior to the current date.
    
     buys = trans.ix[
         [t[0]['amount'] > 0 for t in trans.transactions]
     ]
-    ax2.plot(
-        buys.index,
+    ax2.scatter(
+        buys.index.to_pydatetime(),
         results.price[buys.index],
-        '^',
-        markersize=10,
-        color='g',
+        marker='^',
+        s=100,
+        c='g',
+        label=''
     )
 
     ax3 = plt.subplot(613, sharex=ax1)
@@ -406,17 +469,195 @@ one day prior to the current date.
    
     # 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),
+    )
 
    
 
    
+https://s3.amazonaws.com/enigmaco-docs/github.io/example_buy_and_hodl.png
+
    +
    +

    Dual Moving Average Crossover

    +

    Source Code: examples/dual_moving_average.py

    +

    This strategy is covered in detail in the last part of +this tutorial.

    +
    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),
+        )
+
    +
    +https://s3.amazonaws.com/enigmaco-docs/github.io/tutorial_dual_moving_average.png

    Mean Reversion Algorithm

    -

    source: examples/mean_reversion_simple.py

    +

    Source code: 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 run this trading algorithm with the neo_usd currency pair -on the Bitfinex exchange. Thus, first ingest the historical pricing data +

    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:

    catalyst ingest-exchange -x bitfinex -f minute -i neo_usd
    @@ -427,21 +668,28 @@ lines 218-245, so in order to run the algorithm we just type:

    python mean_reversion_simple.py
    -
    import pandas as pd
+
    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.
 
@@ -451,11 +699,17 @@ lines 218-245, so in order to run the algorithm we just type:
    
     # 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 Ether in USD Tether.
-    context.neo_usd = symbol('neo_usd')
+    # 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
@@ -472,17 +726,17 @@ lines 218-245, so in order to run the algorithm we just type:
    
         context.current_day = today
 
     # We're computing the volume-weighted-average-price of the security
-    # defined above, in the context.neo_usd variable.  For this example, we're
+    # 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_usd,
+        context.neo_eth,
         fields='close',
         bar_count=50,
-        frequency='15T'
+        frequency=context.CANDLE_SIZE
     )
 
     # Ta-lib calculates various technical indicator based on price and
@@ -494,7 +748,7 @@ lines 218-245, so in order to run the algorithm we just type:
    
     # 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_usd, fields=['close', 'volume'])
+    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
@@ -523,56 +777,65 @@ lines 218-245, so in order to run the algorithm we just type:
    
 
     # 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_usd)
+    orders = get_open_orders(context.neo_eth)
     if len(orders) > 0:
         return
 
     # Exit if we cannot trade
-    if not data.can_trade(context.neo_usd):
+    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_usd].amount
+    pos_amount = context.portfolio.positions[context.neo_eth].amount
 
-    if rsi[-1] <= 30 and pos_amount == 0:
+    if rsi[-1] <= context.RSI_OVERSOLD and pos_amount == 0:
         log.info(
             '{}: buying - price: {}, rsi: {}'.format(
                 data.current_dt, price, rsi[-1]
             )
         )
-        order_target_percent(context.neo_usd, 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] >= 80 and pos_amount > 0:
+    elif rsi[-1] >= context.RSI_OVERBOUGHT and pos_amount > 0:
         log.info(
             '{}: selling - price: {}, rsi: {}'.format(
                 data.current_dt, price, rsi[-1]
             )
         )
-        order_target_percent(context.neo_usd, 0)
+        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):
-    import matplotlib.pyplot as plt
+    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 Value ({})'.format(base_currency))
+    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} ({base})'.format(
-        asset=context.neo_usd.symbol, base=base_currency
+    ax2.set_ylabel('{asset}\n({base})'.format(
+        asset=context.neo_eth.symbol, base=base_currency
     ))
 
     transaction_df = extract_transactions(perf)
@@ -581,7 +844,7 @@ lines 218-245, so in order to run the algorithm we just type:
    
         sell_df = transaction_df[transaction_df['amount'] < 0]
         ax2.scatter(
             buy_df.index.to_pydatetime(),
-            perf.loc[buy_df.index, 'price'],
+            perf.loc[buy_df.index.floor('1 min'), 'price'],
             marker='^',
             s=100,
             c='green',
@@ -589,7 +852,7 @@ lines 218-245, so in order to run the algorithm we just type:
    
         )
         ax2.scatter(
             sell_df.index.to_pydatetime(),
-            perf.loc[sell_df.index, 'price'],
+            perf.loc[sell_df.index.floor('1 min'), 'price'],
             marker='v',
             s=100,
             c='red',
@@ -600,23 +863,24 @@ lines 218-245, so in order to run the algorithm we just type:
    
     perf.loc[:, 'cash'].plot(
         ax=ax4, label='Base Currency ({})'.format(base_currency)
     )
-    ax4.set_ylabel('Cash ({})'.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 Change')
+    ax5.set_ylabel('Percent\nChange')
 
     ax6 = plt.subplot(615, sharex=ax1)
     perf.loc[:, 'rsi'].plot(ax=ax6, label='RSI')
-    ax6.axhline(70, color='darkgoldenrod')
-    ax6.axhline(30, color='darkgoldenrod')
+    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, 'rsi'],
+            perf.loc[buy_df.index.floor('1 min'), 'rsi'],
             marker='^',
             s=100,
             c='green',
@@ -624,13 +888,15 @@ lines 218-245, so in order to run the algorithm we just type:
    
         )
         ax6.scatter(
             sell_df.index.to_pydatetime(),
-            perf.loc[sell_df.index, 'rsi'],
+            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)
@@ -643,7 +909,14 @@ lines 218-245, so in order to run the algorithm we just type:
    
     MODE = 'backtest'
 
     if MODE == 'backtest':
-        # catalyst run -f catalyst/examples/mean_reversion_simple.py -x poloniex -s 2017-10-1 -e 2017-11-10 -c usdt -n mean-reversion --data-frequency minute --capital-base 10000
+        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',
@@ -653,23 +926,34 @@ lines 218-245, so in order to run the algorithm we just type:
    
             exchange_name='bitfinex',
             algo_namespace=NAMESPACE,
             base_currency='usd',
-            start=pd.to_datetime('2017-10-1', utc=True),
+            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='bitfinex',
+            exchange_name='bittrex',
             live=True,
             algo_namespace=NAMESPACE,
             base_currency='usd',
-            live_graph=True
+            live_graph=False
         )
 
    
 
    
+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 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.
    diff --git a/genindex.html b/genindex.html index 1e8ea3d3..9158892a 100644 --- a/genindex.html +++ b/genindex.html @@ -121,7 +121,10 @@
  • Naming Convention
  • Example Algorithms
    • @@ -231,7 +234,7 @@
    • Docs »
      • -
    • +
    • Index
    • diff --git a/index.html b/index.html index 8c63f67b..1b92f02a 100644 --- a/index.html +++ b/index.html @@ -121,7 +121,10 @@
    • Naming Convention
    • Example Algorithms
      • diff --git a/objects.inv b/objects.inv index ded63fcd..4848afe7 100644 Binary files a/objects.inv and b/objects.inv differ diff --git a/releases.html b/releases.html index 846a974f..966379dc 100644 --- a/releases.html +++ b/releases.html @@ -121,7 +121,10 @@
    • Naming Convention
    • Example Algorithms
      • diff --git a/search.html b/search.html index 6b0cf20c..8da6d5d9 100644 --- a/search.html +++ b/search.html @@ -120,7 +120,10 @@
    • Naming Convention
    • Example Algorithms
      • @@ -230,7 +233,7 @@
      • Docs »
        • -
      • +
      • Search
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