Merge branch 'master' into develop

catalyst/examples/buy_and_hodl.py

@@ -15,15 +15,11 @@
 # 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 import run_algorithm
-from catalyst.api import (
-    order_target_value,
-    symbol,
-    record,
-    cancel_order,
-    get_open_orders,
-)
+from catalyst.api import (order_target_value, symbol, record,
+    cancel_order, get_open_orders, )
 
 
 def initialize(context):
@@ -78,15 +74,14 @@ def handle_data(context, data):
 
 
 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)')
+    ax1.set_ylabel('Portfolio\nValue\n(USD)')
 
     ax2 = plt.subplot(612, sharex=ax1)
-    ax2.set_ylabel('{asset} (USD)'.format(asset=context.ASSET_NAME))
+    ax2.set_ylabel('{asset}\n(USD)'.format(asset=context.ASSET_NAME))
     results[['price']].plot(ax=ax2)
 
     trans = results.ix[[t != [] for t in results.transactions]]
@@ -126,11 +121,11 @@ def analyze(context=None, results=None):
         'algorithm',
         'benchmark',
     ]].plot(ax=ax5)
-    ax5.set_ylabel('Percent Change')
+    ax5.set_ylabel('Percent\nChange')
 
     ax6 = plt.subplot(616, sharex=ax1)
     results[['volume']].plot(ax=ax6)
-    ax6.set_ylabel('Volume (mCoins/5min)')
+    ax6.set_ylabel('Volume')
 
     plt.legend(loc=3)
 
@@ -142,13 +137,13 @@ def analyze(context=None, results=None):
 if __name__ == '__main__':
     run_algorithm(
         capital_base=10000,
-        data_frequency='minute',
+        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('2017-11-01', utc=True),
-        end=pd.to_datetime('2017-11-10', utc=True),
+        start=pd.to_datetime('2015-03-01', utc=True),
+        end=pd.to_datetime('2017-10-31', utc=True),
     )

catalyst/examples/buy_btc_simple.py

@@ -3,7 +3,8 @@
 	https://enigmampc.github.io/catalyst/beginner-tutorial.html
 
 	Run this example, by executing the following from your terminal:
-	catalyst run -f buy_btc_simple.py -x bitfinex --start 2016-1-1 --end 2017-9-30 -o buy_btc_simple_out.pickle
+	  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 
@@ -12,7 +13,7 @@
 		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:

catalyst/examples/dual_moving_average.py

@@ -0,0 +1,153 @@
+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),
+        )

catalyst/examples/mean_reversion_simple.py

@@ -248,7 +248,7 @@ if __name__ == '__main__':
         # 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',
+            data_frequency='daily',
             initialize=initialize,
             handle_data=handle_data,
             analyze=analyze,

docs/source/releases.rst

@@ -85,7 +85,7 @@ Bug Fixes
 - Fixed issue with sell orders in backtesting
 - Fixed data frequency issues with data.history() in backtesting
 - Fixed an issue with can_trade()
-- Reduced the commission and slippage values to account for lower volume 
+- Reduced the commission and slippage values to account for lower volume
   transactions
 
 Build
@@ -97,17 +97,17 @@ Documentation
 ~~~~~~~~~~~~~
 
 - Improved installation notes for Windows C++ compiler and Conda
-- Addition of 
+- Addition of
   `Jupyter Notebook guide <https://enigmampc.github.io/catalyst/jupyter.html>`_
-- Addition of 
+- Addition of
   `Live Trading page <https://enigmampc.github.io/catalyst/live-trading.html>`_
-- Addition of 
+- Addition of
   `Videos page <https://enigmampc.github.io/catalyst/videos.html>`_
-- Addition of 
+- Addition of
   `Resources page <https://enigmampc.github.io/catalyst/resources.html>`_
-- Addition of `Development Guidelines 
+- Addition of `Development Guidelines
   <https://enigmampc.github.io/catalyst/development-guidelines.html>`_
-- Addition of 
+- Addition of
   `Release Notes <https://enigmampc.github.io/catalyst/releases.html>`_
 - Updated code docstrings
 
@@ -158,10 +158,10 @@ Bug Fixes
 ~~~~~~~~~
 
 - Fixed OS-dependent path issue in data bundle
-- Changed handling of empty ``auth.json``, instead of throwing an error for 
+- Changed handling of empty ``auth.json``, instead of throwing an error for
   missing file
 - Updated ``etc/python2.7-environment.yml`` to work with Catalyst version 0.3
-- Updated ``catalyst/examples/buy_and_hodl.py``  and 
+- Updated ``catalyst/examples/buy_and_hodl.py``  and
   ``catalyst/examples/buy_low_sell_high.py`` to work with Catalyst version 0.3
 
 
@@ -181,18 +181,18 @@ Version 0.2.dev5
 ^^^^^^^^^^^^^^^^
 **Release Date**: 2017-10-03
 
-- Fixes bug in data.history function that was formatting 'volume' data as 
-  integers, now they are returned as floats with up to 9 decimals of precision. 
+- Fixes bug in data.history function that was formatting 'volume' data as
+  integers, now they are returned as floats with up to 9 decimals of precision.
   Data bundles redone.
 
-Version 0.2.dev4 
+Version 0.2.dev4
 ^^^^^^^^^^^^^^^^
 
 **Release Date**: 2017-09-20
 
-- Fixes bug in the pricing resolution of 1-minute data, now set to 8 decimal 
+- Fixes bug in the pricing resolution of 1-minute data, now set to 8 decimal
   places. Pricing resolution of daily data remains set to 9 decimal places.
-- The current data bundle takes 340MB compressed for download, and 460MB 
+- The current data bundle takes 340MB compressed for download, and 460MB
   uncompressed on disk for Catalyst to use.
 
 Version 0.2.dev3
@@ -202,14 +202,14 @@ Version 0.2.dev3
 
 - 1-minute resolution OHLCV data bundle for backtesting from Poloniex exchange
 - Implementation of trading of fractional crypto assets (i.e. 0.01 BTC)
-- Minimum trade size of a coin can be configured on a per-coin basis, defaults 
-  to 0.00000001 in backtesting (most exchanges set the minimum trade to larger 
+- Minimum trade size of a coin can be configured on a per-coin basis, defaults
+  to 0.00000001 in backtesting (most exchanges set the minimum trade to larger
   amounts, which will impact live trading)
 - Increased pricing resolution from 3 to 9 decimal places
-- The current data bundle takes 40MB compressed for download, and 99MB 
+- The current data bundle takes 40MB compressed for download, and 99MB
   uncompressed on disk for Catalyst to use.
 
-Version 0.2.dev2 
+Version 0.2.dev2
 ^^^^^^^^^^^^^^^^
 
 **Release Date**: 2017-09-07
@@ -225,15 +225,15 @@ Version 0.2.dev1
 
   - Comprehensive trading functionality against exchanges Bitfinex and Bittrex.
   - Support for all trading pairs available on each exchange.
-  - Multiple algorithms can trade simultaneously against a single exchange 
+  - Multiple algorithms can trade simultaneously against a single exchange
     using the same account.
-  - Each algorithm has a persisted state (i.e. algorithm can be stopped and 
-    restarted preserving the state without data loss) that tracks all open 
+  - Each algorithm has a persisted state (i.e. algorithm can be stopped and
+    restarted preserving the state without data loss) that tracks all open
     orders, executed transactions and portfolio positions.
 
 - Minute by minute portfolio performance metrics.
 
-  - Daily summary performance statistics compatible with pyfolio, a Python 
+  - Daily summary performance statistics compatible with pyfolio, a Python
     library for performance and risk analysis of financial portfolios
 
 Version 0.1.dev9
@@ -241,13 +241,13 @@ Version 0.1.dev9
 
 **Release Date**: 2017-08-28
 
-- Retrieval of crypto benchmark from bundle, instead of hitting Poloniex 
+- Retrieval of crypto benchmark from bundle, instead of hitting Poloniex
   exchange directly
 - Change of bundle storage provider from Dropbox to AWS
 - Fix issue with 1/1000 scaling issue of prices in bundle
 
 Version 0.1.dev8
-^^^^^^^^^^^^^^^^ 
+^^^^^^^^^^^^^^^^
 
 **Release Date**: 2017-08-18
 
@@ -267,4 +267,3 @@ Version 0.1.dev6
 **Release Date**: 2017-07-13
 
 - Initial public release
-