$ zipline bundles
-my-custom-bundle 2016-05-05 20:35:19.809398
-my-custom-bundle 2016-05-05 20:34:53.654082
-my-custom-bundle 2016-05-05 20:34:48.401767
+2016 -05-05 20 :35:19.809398
+my-custom-bundle 2016 -05-05 20 :34:53.654082
+my-custom-bundle 2016 -05-05 20 :34:48.401767
quandl <no ingestions>
-quantopian-quandl 2016-05-05 20:06:40.894956
+quantopian-quandl 2016 -05-05 20 :06:40.894956
The output here shows that there are 3 bundles available:
@@ -339,11 +196,11 @@ standard location that zipline can find. By default the location where ingested
data will be written is $ZIPLINE_ROOT/data/<bundle> ZIPLINE_ROOT=~/.zipline
Old Data
@@ -354,21 +211,21 @@ copies. Running a backtest with an old ingestion makes it easier to reproduce
backtest results later.
One drawback of saving all of the data by default is that the data directory
may grow quite large even if you do not want to use the data. As shown earlier,
-we can list all of the ingestions with the bundles command bundles command clean
For example:
-
# clean everything older than <date>
-$ zipline clean [ -b <bundle>] --before <date>
+# clean everything older than <date>
+$ zipline clean [ -b <bundle>] --before <date>
-# clean everything newer than <date>
-$ zipline clean [ -b <bundle>] --after <date>
+# clean everything newer than <date>
+$ zipline clean [ -b <bundle>] --after <date>
-# keep everything in the range of [before, after] and delete the rest
-$ zipline clean [ -b <bundle>] --before <date> --after <after>
+# keep everything in the range of [before, after] and delete the rest
+$ zipline clean [ -b <bundle>] --before <date> --after <after>
-# clean all but the last <int> runs
-$ zipline clean [ -b <bundle>] --keep-last <int>
+# clean all but the last <int> runs
+$ zipline clean [ -b <bundle>] --keep-last <int>
@@ -377,7 +234,7 @@ constraints.
Now that the data has been ingested we can use it to run backtests with the
run --bundle
-
$ zipline run --bundle <bundle> --algofile algo.py ...
+We may also specify the date to use to look up the bundle data with the
@@ -393,13 +250,13 @@ current day to use the most recent data.
Default Data Bundles
Quandl WIKI Bundle
-
By default zipline comes with the quandl
By default zipline comes with the quandl WIKI dataset . The quandl data bundle
includes daily pricing data, splits, cash dividends, and asset metadata. To
ingest the quandl
-
$ QUANDL_API_KEY = <api-key> zipline ingest -b quandl
+QUANDL_API_KEY = <api-key> zipline ingest -b quandl
though we may still run ingest ~/.zipline/extensions.py
-from zipline.data.bundles import register , yahoo_equities
+from zipline.data.bundles import register , yahoo_equities
-# these are the tickers you would like data for
+# these are the tickers you would like data for
equities = {
- 'AAPL' ,
- 'MSFT' ,
- 'GOOG' ,
+ 'AAPL' ,
+ 'MSFT' ,
+ 'GOOG' ,
}
register (
- 'my-yahoo-equities-bundle' , # name this whatever you like
+ 'my-yahoo-equities-bundle' , # name this whatever you like
yahoo_equities ( equities ),
)
This may now be used like:
-$ zipline ingest -b my-yahoo-equities-bundle
-$ zipline run -f algo.py --bundle my-yahoo-equities-bundle
+More than one yahoo equities bundle may be registered as long as they use
@@ -459,14 +316,14 @@ different names.
zipline. To add a new bundle, one must implement an ingest The ingest quandl
The signature of the ingest function should be:
-ingest ( environ ,
+ingest ( environ ,
asset_db_writer ,
minute_bar_writer ,
daily_bar_writer ,
@@ -500,7 +357,7 @@ docs for write.
minute_bar_writer minute_bar_writer BcolzMinuteBarWriter BcolzMinuteBarReader write()
daily_bar_writer daily_bar_writer BcolzDailyBarWriter BcolzDailyBarReader write()
cache dataframe_cache
show_progress show_progress show_progress minute_bar_writer.
written. output_dir $ZIPLINE_ROOT quantopian:quandl quantopian:quandl output_dir
+
+
+
- Built with
Sphinx using a
theme provided by
Read the Docs .
+ Built with
Sphinx using a
theme provided by
Read the Docs .
@@ -634,7 +496,8 @@ bundle uses this to directly untar the bundle into the
diff --git a/development-guidelines.html b/development-guidelines.html
index 1051b30a..0194487b 100644
--- a/development-guidelines.html
+++ b/development-guidelines.html
@@ -13,6 +13,8 @@
+
+
@@ -30,6 +32,9 @@
+
-
-
+
-
-
- Catalyst
+
+ Catalyst
+
-
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
-
+
+
Development Guidelines
@@ -309,30 +174,30 @@
All contributions, bug reports, bug fixes, documentation improvements, enhancements and ideas are welcome. We track issues on GitHub and also have a discord group where you can ask questions.
Creating a Development Environment
-
First, you’ll need to clone Catalyst by running:
-
$ git clone git@github.com:enigmampc/catalyst.git
+First, you’ll need to clone Catalyst by running:
+Then check out to a new branch where you can make your changes:
-$ git checkout -b some-short-descriptive-name
+If you don’t already have them, you’ll need some C library dependencies. You can follow the install guide to get the appropriate dependencies.
+If you don’t already have them, you’ll need some C library dependencies. You can follow the install guide to get the appropriate dependencies.
The following section assumes you already have virtualenvwrapper and pip installed on your system. Suggested installation of Python library dependencies used for development:
-$ mkvirtualenv catalyst
-$ ./etc/ordered_pip.sh ./etc/requirements.txt
-$ pip install -r ./etc/requirements_dev.txt
-$ pip install -r ./etc/requirements_blaze.txt
+Finally, you can build the C extensions by running:
-$ python setup.py build_ext --inplace
+
Development with Docker
-
If you want to work with zipline using a Docker container, you’ll need to
+
If you want to work with zipline using a Docker container, you’ll need to
build the Dockerfile Dockerfile-dev Dockerfile Dockerfile-dev
@@ -344,22 +209,22 @@ build the
Dockerfile
Contributing to the Docs
-
If you’d like to contribute to the documentation on enigmampc.github.io, you can navigate to docs/source/ reStructuredText file is a separate section there. To add a section, create a new file called some-descriptive-name.rst some-descriptive-name index.rst
+
If you’d like to contribute to the documentation on enigmampc.github.io, you can navigate to docs/source/ reStructuredText file is a separate section there. To add a section, create a new file called some-descriptive-name.rst some-descriptive-name index.rst
We use Sphinx to generate documentation for Catalyst, which you will need to install by running:
-
$ pip install -r ./etc/requirements_docs.txt
+To build and view the docs locally, run:
-# assuming you're in the Catalyst root directory
-$ cd docs
-$ make html
-$ { BROWSER} build/html/index.html
+# assuming you're in the Catalyst root directory
+$ cd docs
+$ make html
+$ { BROWSER} build/html/index.html
There is a documented issue
with sphinx docutils
-Exception occurred:
+If you get this error, you need to downgrade your version of docutils
-
$ pip install docutils == 0.12
+docutils == 0 .12
Commit messages
Standard prefixes to start a commit message:
-
BLD: change related to building Catalyst
+Some commit style guidelines:
Commit lines should be no longer than 72 characters . The first line of the commit should include one of the above prefixes. There should be an empty line between the commit subject and the body of the commit. In general, the message should be in the imperative tense. Best practice is to include not only what the change is, but why the change was made.
Example:
-MAINT: Remove unused calculations of max_leverage, et al.
+
+
+
+
@@ -454,7 +324,8 @@ the algorithm has little cash on hand.
VERSION:'0.4',
COLLAPSE_INDEX:false,
FILE_SUFFIX:'.html',
- HAS_SOURCE: true
+ HAS_SOURCE: true,
+ SOURCELINK_SUFFIX: '.txt'
};
diff --git a/example-algos.html b/example-algos.html
index 40f0dc77..a3bf5def 100644
--- a/example-algos.html
+++ b/example-algos.html
@@ -13,6 +13,8 @@
+
+
@@ -30,6 +32,9 @@
+
+
@@ -41,21 +46,28 @@
+
-
-
+
-
-
- Catalyst
+
+ Catalyst
+
-
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
-
+
+
@@ -314,29 +180,29 @@ using Catalyst.
Overview
-Buy BTC Simple Buy BTC Simple initialize() handle_data() beginner tutorial beginner tutorial Buy and Hodl buy and hold that
+Buy and Hodl buy and hold that
makes one single buy at the very beginning. Introduces the notions of
cash orders order_target_value analyze() matplotlib Dual Moving Average Crossover Dual Moving Average Crossover beginner tutorial to introduce the
data.history() matplotlib analyze() Mean Reversion Algorithm Mean Reversion Algorithm two-part video tutorial to show how
to get started in backtesting and live trading with Catalyst.Simple Universe Simple Universe Portfolio Optimization Portfolio Optimization
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
@@ -369,11 +235,11 @@ writting the following article:
from catalyst.api import order , record , symbol
def initialize ( context ):
- context . asset = symbol ( 'btc_usd' )
+ context . asset = symbol ( 'btc_usd' )
def handle_data ( context , data ):
order ( context . asset , 1 )
- record ( btc = data . current ( context . asset , 'price' ))
+ record ( btc = data . current ( context . asset , 'price' ))
This simple algorithm does not produce any output nor displays any chart.
@@ -382,40 +248,40 @@ writting the following article:
Buy and Hodl Algorithm
Source code: examples/buy_and_hodl.py
First ingest the historical pricing data needed to run this algorithm:
-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 bitfinex -c btc -o bah.pickle
+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
plot the resulting performance using the matplotlib library. You can choose any
date interval with the --start --end
-#!/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.
+#!/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
@@ -425,7 +291,7 @@ one day prior to the current date.
def initialize ( context ):
- context . ASSET_NAME = 'btc_usd'
+ context . ASSET_NAME = 'btc_usd'
context . TARGET_HODL_RATIO = 0.8
context . RESERVE_RATIO = 1.0 - context . TARGET_HODL_RATIO
@@ -442,23 +308,23 @@ one day prior to the current date.
target_hodl_value = context . TARGET_HODL_RATIO * starting_cash
reserve_value = context . RESERVE_RATIO * starting_cash
- # Cancel any outstanding orders
+ # 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
+ # 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
+ # 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
+ print ( 'buying' )
+ # Place order to make position in asset equal to target_hodl_value
order_target_value (
context . asset ,
target_hodl_value ,
@@ -467,7 +333,7 @@ one day prior to the current date.
record (
price = price ,
- volume = data . current ( context . asset , 'volume' ),
+ volume = data . current ( context . asset , 'volume' ),
cash = cash ,
starting_cash = context . portfolio . starting_cash ,
leverage = context . account . leverage ,
@@ -476,77 +342,77 @@ one day prior to the current date.
def analyze ( context = None , results = None ):
- # Plot the portfolio and asset data.
+ # Plot the portfolio and asset data.
ax1 = plt . subplot ( 611 )
- results [[ 'portfolio_value' ]] . plot ( ax = ax1 )
- ax1 . set_ylabel ( 'Portfolio Value (USD)' )
+ 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 . 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 ]
+ [ t [ 0 ][ 'amount' ] > 0 for t in trans . transactions ]
]
ax2 . scatter (
buys . index . to_pydatetime (),
results . price [ buys . index ],
- marker = '^' ,
+ marker = '^' ,
s = 100 ,
- c = 'g' ,
- label = ''
+ c = 'g' ,
+ label = ''
)
ax3 = plt . subplot ( 613 , sharex = ax1 )
- results [[ 'leverage' , 'alpha' , 'beta' ]] . plot ( ax = ax3 )
- ax3 . set_ylabel ( 'Leverage ' )
+ 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 [[ 'starting_cash' , 'cash' ]] . plot ( ax = ax4 )
+ ax4 . set_ylabel ( 'Cash (USD)' )
results [[
- 'treasury' ,
- 'algorithm' ,
- 'benchmark' ,
+ 'treasury' ,
+ 'algorithm' ,
+ 'benchmark' ,
]] = results [[
- 'treasury_period_return' ,
- 'algorithm_period_return' ,
- 'benchmark_period_return' ,
+ 'treasury_period_return' ,
+ 'algorithm_period_return' ,
+ 'benchmark_period_return' ,
]]
ax5 = plt . subplot ( 615 , sharex = ax1 )
results [[
- 'treasury' ,
- 'algorithm' ,
- 'benchmark' ,
+ 'treasury' ,
+ 'algorithm' ,
+ 'benchmark' ,
]] . plot ( ax = ax5 )
- ax5 . set_ylabel ( 'Percent Change' )
+ ax5 . set_ylabel ( 'Percent Change' )
ax6 = plt . subplot ( 616 , sharex = ax1 )
- results [[ 'volume' ]] . plot ( ax = ax6 )
- ax6 . set_ylabel ( 'Volume (mCoins/5min)' )
+ results [[ 'volume' ]] . plot ( ax = ax6 )
+ ax6 . set_ylabel ( 'Volume (mCoins/5min)' )
plt . legend ( loc = 3 )
- # Show the plot.
+ # Show the plot.
plt . gcf () . set_size_inches ( 18 , 8 )
plt . show ()
-if __name__ == '__main__' :
+if __name__ == '__main__' :
run_algorithm (
capital_base = 10000 ,
- data_frequency = 'daily' ,
+ 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 ),
+ 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 ),
)
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 numpy as np
import pandas as pd
from logbook import Logger
import matplotlib.pyplot as plt
@@ -567,90 +433,90 @@ one day prior to the current date.
get_open_orders )
from catalyst.exchange.stats_utils import extract_transactions
-NAMESPACE = 'dual_moving_average'
+NAMESPACE = 'dual_moving_average'
log = Logger ( NAMESPACE )
def initialize ( context ):
context . i = 0
- context . asset = symbol ( 'ltc_usd' )
+ context . asset = symbol ( 'ltc_usd' )
context . base_price = None
def handle_data ( context , data ):
- # define the windows for the moving averages
+ # 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
+ # 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 ()
+ # 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' )
+ # 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 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
+ # 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.
+ # 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
+ # 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
+ # We check what's our position on our portfolio and trade accordingly
pos_amount = context . portfolio . positions [ context . asset ] . amount
- # Trading logic
+ # Trading logic
if short_mavg > long_mavg and pos_amount == 0 :
- # we buy 100% of our portfolio for this asset
+ # 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
+ # 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
+ # 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
+ # First chart: Plot portfolio value using base_currency
ax1 = plt . subplot ( 411 )
- perf . loc [:, [ 'portfolio_value' ]] . plot ( ax = ax1 )
+ perf . loc [:, [ 'portfolio_value' ]] . plot ( ax = ax1 )
ax1 . legend_ . remove ()
- ax1 . set_ylabel ( 'Portfolio Value \n ({})' . format ( base_currency ))
+ 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
+ # 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' )
+ perf . loc [:, [ 'price' , 'short_mavg' , 'long_mavg' ]] . plot ( ax = ax2 , label = 'Price' )
ax2 . legend_ . remove ()
- ax2 . set_ylabel ( '{asset} \n ({base})' . format (
+ ax2 . set_ylabel ( '{asset} \n ({base})' . format (
asset = context . asset . symbol ,
base = base_currency
))
@@ -659,56 +525,56 @@ one day prior to the current date.
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 ]
+ 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 = '^' ,
+ perf . loc [ buy_df . index , 'price' ],
+ marker = '^' ,
s = 100 ,
- c = 'green' ,
- label = ''
+ c = 'green' ,
+ label = ''
)
ax2 . scatter (
sell_df . index . to_pydatetime (),
- perf . loc [ sell_df . index , 'price' ],
- marker = 'v' ,
+ perf . loc [ sell_df . index , 'price' ],
+ marker = 'v' ,
s = 100 ,
- c = 'red' ,
- label = ''
+ c = 'red' ,
+ label = ''
)
- # Third chart: Compare percentage change between our portfolio
- # and the price of the asset
+ # 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 )
+ perf . loc [:, [ 'algorithm_period_return' , 'price_change' ]] . plot ( ax = ax3 )
ax3 . legend_ . remove ()
- ax3 . set_ylabel ( 'Percent Change' )
+ 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
+ # Fourth chart: Plot our cash
ax4 = plt . subplot ( 414 , sharex = ax1 )
perf . cash . plot ( ax = ax4 )
- ax4 . set_ylabel ( 'Cash \n ({})' . format ( base_currency ))
+ 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__' :
+if __name__ == '__main__' :
run_algorithm (
capital_base = 1000 ,
- data_frequency = 'minute' ,
+ data_frequency = 'minute' ,
initialize = initialize ,
handle_data = handle_data ,
analyze = analyze ,
- exchange_name = 'bitfinex' ,
+ 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 ),
+ base_currency = 'usd' ,
+ start = pd . to_datetime ( '2017-9-22' , utc = True ),
+ end = pd . to_datetime ( '2017-9-23' , utc = True ),
)
Mean Reversion Algorithm
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.
+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 backtest this trading algorithm with the neo_usd Bitfinex
-catalyst ingest-exchange -x bitfinex -f minute -i neo_usd
+To run this algorithm, we are opting for the Python interpreter, instead of the
command line (CLI). All of the parameters for the simulation are specified in
lines 218-245, so in order to run the algorithm we just type:
-python mean_reversion_simple.py
+import os
+import os
import tempfile
import time
@@ -744,124 +610,124 @@ lines 218-245, so in order to run the algorithm we just type:
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.
+# 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'
+NAMESPACE = 'mean_reversion_simple'
log = Logger ( NAMESPACE )
-# To run an algorithm in Catalyst, you need two functions: initialize and
-# handle_data.
+# 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.
+ # 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 Neo in USD.
- context . neo_eth = 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 . 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
- # minute-level tick data, and each minute is called a frame. This function
- # runs on each frame of the 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.
- # 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' )
+ # 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
- # 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.
+ # 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.
- # 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
+ # 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' ,
+ fields = 'close' ,
bar_count = 50 ,
frequency = context . CANDLE_SIZE
)
- # Ta-lib calculates various technical indicator based on price and
- # volume arrays.
+ # Ta-lib calculates various technical indicator based on price and
+ # volume arrays.
- # In this example, we are comp
+ # 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_eth , fields = [ 'close' , 'volume' ])
- price = current [ 'close' ]
+ # 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' ]
- # 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 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.
+ # 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' ],
+ 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.
+ # 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.
+ # 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
+ # 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.
+ # 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 (
+ '{}: buying - price: {}, rsi: {}' . format (
data . current_dt , price , rsi [ - 1 ]
)
)
- # Set a style for limit orders,
+ # Set a style for limit orders,
limit_price = price * 1.005
order_target_percent (
context . neo_eth , 1 , limit_price = limit_price
@@ -870,7 +736,7 @@ lines 218-245, so in order to run the algorithm we just type:
elif rsi [ - 1 ] >= context . RSI_OVERBOUGHT and pos_amount > 0 :
log . info (
- '{}: selling - price: {}, rsi: {}' . format (
+ '{}: selling - price: {}, rsi: {}' . format (
data . current_dt , price , rsi [ - 1 ]
)
)
@@ -883,129 +749,129 @@ lines 218-245, so in order to run the algorithm we just type:
def analyze ( context = None , perf = None ):
end = time . time ()
- log . info ( 'elapsed time: {}' . format ( end - context . start_time ))
+ log . info ( 'elapsed time: {}' . format ( end - context . start_time ))
import matplotlib.pyplot as plt
- # The base currency of the algo exchange
+ # The base currency of the algo exchange
base_currency = context . exchanges . values ()[ 0 ] . base_currency . upper ()
- # Plot the portfolio value over time.
+ # Plot the portfolio value over time.
ax1 = plt . subplot ( 611 )
- perf . loc [:, 'portfolio_value' ] . plot ( ax = ax1 )
- ax1 . set_ylabel ( 'Portfolio \n Value \n ({})' . format ( base_currency ))
+ perf . loc [:, 'portfolio_value' ] . plot ( ax = ax1 )
+ ax1 . set_ylabel ( 'Portfolio \n Value \n ({})' . format ( base_currency ))
- # Plot the price increase or decrease over time.
+ # Plot the price increase or decrease over time.
ax2 = plt . subplot ( 612 , sharex = ax1 )
- perf . loc [:, 'price' ] . plot ( ax = ax2 , label = 'Price' )
+ perf . loc [:, 'price' ] . plot ( ax = ax2 , label = 'Price' )
- ax2 . set_ylabel ( '{asset} \n ({base})' . format (
+ 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 ]
+ 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 = '^' ,
+ perf . loc [ buy_df . index . floor ( '1 min' ), 'price' ],
+ marker = '^' ,
s = 100 ,
- c = 'green' ,
- label = ''
+ c = 'green' ,
+ label = ''
)
ax2 . scatter (
sell_df . index . to_pydatetime (),
- perf . loc [ sell_df . index . floor ( '1 min' ), 'price' ],
- marker = 'v' ,
+ perf . loc [ sell_df . index . floor ( '1 min' ), 'price' ],
+ marker = 'v' ,
s = 100 ,
- c = 'red' ,
- label = ''
+ c = 'red' ,
+ label = ''
)
ax4 = plt . subplot ( 613 , sharex = ax1 )
- perf . loc [:, 'cash' ] . plot (
- ax = ax4 , label = 'Base Currency ({})' . format ( base_currency )
+ perf . loc [:, 'cash' ] . plot (
+ ax = ax4 , label = 'Base Currency ({})' . format ( base_currency )
)
- ax4 . set_ylabel ( 'Cash \n ({})' . format ( base_currency ))
+ ax4 . set_ylabel ( 'Cash \n ({})' . format ( base_currency ))
- perf [ 'algorithm' ] = perf . loc [:, 'algorithm_period_return' ]
+ 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 \n Change' )
+ perf . loc [:, [ 'algorithm' , 'price_change' ]] . plot ( ax = ax5 )
+ ax5 . set_ylabel ( 'Percent \n Change' )
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' )
+ 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 = '^' ,
+ perf . loc [ buy_df . index . floor ( '1 min' ), 'rsi' ],
+ marker = '^' ,
s = 100 ,
- c = 'green' ,
- label = ''
+ c = 'green' ,
+ label = ''
)
ax6 . scatter (
sell_df . index . to_pydatetime (),
- perf . loc [ sell_df . index . floor ( '1 min' ), 'rsi' ],
- marker = 'v' ,
+ perf . loc [ sell_df . index . floor ( '1 min' ), 'rsi' ],
+ marker = 'v' ,
s = 100 ,
- c = 'red' ,
- label = ''
+ 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.
+ # 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 __name__ == '__main__' :
+ # The execution mode: backtest or live
+ MODE = 'backtest'
- if MODE == 'backtest' :
+ if MODE == 'backtest' :
folder = os . path . join (
- tempfile . gettempdir (), 'catalyst' , NAMESPACE
+ 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
+ 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' ,
+ data_frequency = 'minute' ,
initialize = initialize ,
handle_data = handle_data ,
analyze = analyze ,
- exchange_name = 'bitfinex' ,
+ 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 ),
+ 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 ))
+ log . info ( 'saved perf stats: {}' . format ( out ))
- elif MODE == 'live' :
+ elif MODE == 'live' :
run_algorithm (
capital_base = 0.5 ,
initialize = initialize ,
handle_data = handle_data ,
analyze = analyze ,
- exchange_name = 'bittrex' ,
+ exchange_name = 'bittrex' ,
live = True ,
algo_namespace = NAMESPACE ,
- base_currency = 'usd' ,
+ base_currency = 'usd' ,
live_graph = False
)
@@ -1040,14 +906,14 @@ environment). The first one retrieves all the pricing data needed for this
script to run (only needs to be run once), and the second one executes this
script with the parameters specified in the run_algorithm() call at the end
of the file:
-
catalyst ingest-exchange -x bitfinex -f minute
+python simple_universe.py
+Credits: This code was originally submitted by Abner Ayala-Acevedo . Thank you!
-from datetime import timedelta
+from datetime import timedelta
import numpy as np
import pandas as pd
@@ -1058,55 +924,55 @@ of the file:
def initialize ( context ):
- context . i = - 1 # minute counter
+ context . i = - 1 # minute counter
context . exchange = context . exchanges . values ()[ 0 ] . name . lower ()
context . base_currency = context . exchanges . values ()[ 0 ] . base_currency . lower ()
def handle_data ( context , data ):
context . i += 1
- lookback_days = 7 # 7 days
+ lookback_days = 7 # 7 days
- # current date & time in each iteration formatted into a string
+ # current date & time in each iteration formatted into a string
now = data . current_dt
- date , time = now . strftime ( '%Y-%m- %d %H:%M:%S' ) . split ( ' ' )
+ date , time = now . strftime ( '%Y-%m- %d %H:%M:%S' ) . split ( ' ' )
lookback_date = now - timedelta ( days = lookback_days )
- # keep only the date as a string, discard the time
- lookback_date = lookback_date . strftime ( '%Y-%m- %d %H:%M:%S' ) . split ( ' ' )[ 0 ]
+ # keep only the date as a string, discard the time
+ lookback_date = lookback_date . strftime ( '%Y-%m- %d %H:%M:%S' ) . split ( ' ' )[ 0 ]
- one_day_in_minutes = 1440 # 60 * 24 assumes data_frequency='minute'
- # update universe everyday at midnight
+ one_day_in_minutes = 1440 # 60 * 24 assumes data_frequency='minute'
+ # update universe everyday at midnight
if not context . i % one_day_in_minutes :
context . universe = universe ( context , lookback_date , date )
- # get data every 30 minutes
+ # get data every 30 minutes
minutes = 30
- # get lookback_days of history data: that is 'lookback' number of bins
+ # get lookback_days of history data: that is 'lookback' number of bins
lookback = one_day_in_minutes / minutes * lookback_days
if not context . i % minutes and context . universe :
- # we iterate for every pair in the current universe
+ # we iterate for every pair in the current universe
for coin in context . coins :
pair = str ( coin . symbol )
- # Get 30 minute interval OHLCV data. This is the standard data
- # required for candlestick or indicators/signals. Return Pandas
- # DataFrames. 30T means 30-minute re-sampling of one minute data.
- # Adjust it to your desired time interval as needed.
- opened = fill ( data . history ( coin , 'open' ,
- bar_count = lookback , frequency = '30T' )) . values
- high = fill ( data . history ( coin , 'high' ,
- bar_count = lookback , frequency = '30T' )) . values
- low = fill ( data . history ( coin , 'low' ,
- bar_count = lookback , frequency = '30T' )) . values
- close = fill ( data . history ( coin , 'price' ,
- bar_count = lookback , frequency = '30T' )) . values
- volume = fill ( data . history ( coin , 'volume' ,
- bar_count = lookback , frequency = '30T' )) . values
+ # Get 30 minute interval OHLCV data. This is the standard data
+ # required for candlestick or indicators/signals. Return Pandas
+ # DataFrames. 30T means 30-minute re-sampling of one minute data.
+ # Adjust it to your desired time interval as needed.
+ opened = fill ( data . history ( coin , 'open' ,
+ bar_count = lookback , frequency = '30T' )) . values
+ high = fill ( data . history ( coin , 'high' ,
+ bar_count = lookback , frequency = '30T' )) . values
+ low = fill ( data . history ( coin , 'low' ,
+ bar_count = lookback , frequency = '30T' )) . values
+ close = fill ( data . history ( coin , 'price' ,
+ bar_count = lookback , frequency = '30T' )) . values
+ volume = fill ( data . history ( coin , 'volume' ,
+ bar_count = lookback , frequency = '30T' )) . values
- # close[-1] is the last value in the set, which is the equivalent
- # to current price (as in the most recent value)
- # displays the minute price for each pair every 30 minutes
- print ( '{now}: {pair} - \t O:{o}, \t H:{h}, \t L:{c}, \t C{c}, \t V:{v}' . format (
+ # close[-1] is the last value in the set, which is the equivalent
+ # to current price (as in the most recent value)
+ # displays the minute price for each pair every 30 minutes
+ print ( '{now}: {pair} - \t O:{o}, \t H:{h}, \t L:{c}, \t C{c}, \t V:{v}' . format (
now = now ,
pair = pair ,
o = opened [ - 1 ],
@@ -1116,37 +982,37 @@ of the file:
v = volume [ - 1 ],
))
- # -------------------------------------------------------------
- # --------------- Insert Your Strategy Here -------------------
- # -------------------------------------------------------------
+ # -------------------------------------------------------------
+ # --------------- Insert Your Strategy Here -------------------
+ # -------------------------------------------------------------
def analyze ( context = None , results = None ):
pass
-# Get the universe for a given exchange and a given base_currency market
-# Example: Poloniex BTC Market
+# Get the universe for a given exchange and a given base_currency market
+# Example: Poloniex BTC Market
def universe ( context , lookback_date , current_date ):
- # get all the pairs for the given exchange
+ # get all the pairs for the given exchange
json_symbols = get_exchange_symbols ( context . exchange )
- # convert into a DataFrame for easier processing
+ # convert into a DataFrame for easier processing
df = pd . DataFrame . from_dict ( json_symbols ) . transpose () . astype ( str )
- df [ 'base_currency' ] = df . apply ( lambda row : row . symbol . split ( '_' )[ 1 ], axis = 1 )
- df [ 'market_currency' ] = df . apply ( lambda row : row . symbol . split ( '_' )[ 0 ], axis = 1 )
+ df [ 'base_currency' ] = df . apply ( lambda row : row . symbol . split ( '_' )[ 1 ], axis = 1 )
+ df [ 'market_currency' ] = df . apply ( lambda row : row . symbol . split ( '_' )[ 0 ], axis = 1 )
- # Filter all the pairs to get only the ones for a given base_currency
- df = df [ df [ 'base_currency' ] == context . base_currency ]
+ # Filter all the pairs to get only the ones for a given base_currency
+ df = df [ df [ 'base_currency' ] == context . base_currency ]
- # Filter all the pairs to ensure that pair existed in the current date range
+ # Filter all the pairs to ensure that pair existed in the current date range
df = df [ df . start_date < lookback_date ]
df = df [ df . end_daily >= current_date ]
- context . coins = symbols ( * df . symbol ) # convert all the pairs to symbols
+ context . coins = symbols ( * df . symbol ) # convert all the pairs to symbols
return df . symbol . tolist ()
-# Replace all NA, NAN or infinite values with its nearest value
+# Replace all NA, NAN or infinite values with its nearest value
def fill ( series ):
if isinstance ( series , pd . Series ):
return series . replace ([ np . inf , - np . inf ], np . nan ) . ffill () . bfill ()
@@ -1158,21 +1024,21 @@ of the file:
return series
-if __name__ == '__main__' :
- start_date = pd . to_datetime ( '2017-11-10' , utc = True )
- end_date = pd . to_datetime ( '2017-11-13' , utc = True )
+if __name__ == '__main__' :
+ start_date = pd . to_datetime ( '2017-11-10' , utc = True )
+ end_date = pd . to_datetime ( '2017-11-13' , utc = True )
performance = run_algorithm ( start = start_date , end = end_date ,
- capital_base = 100.0 , # amount of base_currency
+ capital_base = 100.0 , # amount of base_currency
initialize = initialize ,
handle_data = handle_data ,
analyze = analyze ,
- exchange_name = 'bitfinex' ,
- data_frequency = 'minute' ,
- base_currency = 'btc' ,
+ exchange_name = 'bitfinex' ,
+ data_frequency = 'minute' ,
+ base_currency = 'btc' ,
live = False ,
live_graph = False ,
- algo_namespace = 'simple_universe' )
+ algo_namespace = 'simple_universe' )
@@ -1183,7 +1049,7 @@ select the portfolio with the maximum Sharpe Ratio. The parameters are set to
use 180 days of historical data and rebalance every 30 days. This code was used
in writting the following article:
Markowitz Portfolio Optimization for Cryptocurrencies .
-
'''
+'''
You can run this code using the Python interpreter:
$ python portfolio_optimization.py
@@ -1201,49 +1067,49 @@ in writting the following article:
from catalyst.api import record , symbol , symbols , order_target_percent
from catalyst.utils.run_algo import run_algorithm
-np . set_printoptions ( threshold = 'nan' , suppress = True )
+np . set_printoptions ( threshold = 'nan' , suppress = True )
def initialize ( context ):
- # Portfolio assets list
- context . assets = symbols ( 'btc_usdt' , 'eth_usdt' , 'ltc_usdt' , 'dash_usdt' ,
- 'xmr_usdt' )
+ # Portfolio assets list
+ context . assets = symbols ( 'btc_usdt' , 'eth_usdt' , 'ltc_usdt' , 'dash_usdt' ,
+ 'xmr_usdt' )
context . nassets = len ( context . assets )
- # Set the time window that will be used to compute expected return
- # and asset correlations
+ # Set the time window that will be used to compute expected return
+ # and asset correlations
context . window = 180
- # Set the number of days between each portfolio rebalancing
+ # Set the number of days between each portfolio rebalancing
context . rebalance_period = 30
context . i = 0
def handle_data ( context , data ):
- # Only rebalance at the beggining of the algorithm execution and
- # every multiple of the rebalance period
+ # Only rebalance at the beggining of the algorithm execution and
+ # every multiple of the rebalance period
if context . i == 0 or context . i % context . rebalance_period == 0 :
n = context . window
- prices = data . history ( context . assets , fields = 'price' ,
- bar_count = n + 1 , frequency = '1d' )
+ prices = data . history ( context . assets , fields = 'price' ,
+ bar_count = n + 1 , frequency = '1d' )
pr = np . asmatrix ( prices )
t_prices = prices . iloc [ 1 : n + 1 ]
t_val = t_prices . values
tminus_prices = prices . iloc [ 0 : n ]
tminus_val = tminus_prices . values
- # Compute daily returns (r)
+ # Compute daily returns (r)
r = np . asmatrix ( t_val / tminus_val - 1 )
- # Compute the expected returns of each asset with the average
- # daily return for the selected time window
+ # Compute the expected returns of each asset with the average
+ # daily return for the selected time window
m = np . asmatrix ( np . mean ( r , axis = 0 ))
- # ###
+ # ###
stds = np . std ( r , axis = 0 )
- # Compute excess returns matrix (xr)
+ # Compute excess returns matrix (xr)
xr = r - m
- # Matrix algebra to get variance-covariance matrix
+ # Matrix algebra to get variance-covariance matrix
cov_m = np . dot ( np . transpose ( xr ), xr ) / n
- # Compute asset correlation matrix (informative only)
+ # Compute asset correlation matrix (informative only)
corr_m = cov_m / np . dot ( np . transpose ( stds ), stds )
- # Define portfolio optimization parameters
+ # Define portfolio optimization parameters
n_portfolios = 50000
results_array = np . zeros (( 3 + context . nassets , n_portfolios ))
for p in xrange ( n_portfolios ):
@@ -1253,38 +1119,38 @@ in writting the following article:
p_r = np . sum ( np . dot ( w , np . transpose ( m ))) * 365
p_std = np . sqrt ( np . dot ( np . dot ( w , cov_m ), np . transpose ( w ))) * np . sqrt ( 365 )
- #store results in results array
+ #store results in results array
results_array [ 0 , p ] = p_r
results_array [ 1 , p ] = p_std
- #store Sharpe Ratio (return / volatility) - risk free rate element
- #excluded for simplicity
+ #store Sharpe Ratio (return / volatility) - risk free rate element
+ #excluded for simplicity
results_array [ 2 , p ] = results_array [ 0 , p ] / results_array [ 1 , p ]
i = 0
for iw in weights :
results_array [ 3 + i , p ] = weights [ i ]
i += 1
- #convert results array to Pandas DataFrame
+ #convert results array to Pandas DataFrame
results_frame = pd . DataFrame ( np . transpose ( results_array ),
- columns = [ 'r' , 'stdev' , 'sharpe' ] + context . assets )
- #locate position of portfolio with highest Sharpe Ratio
- max_sharpe_port = results_frame . iloc [ results_frame [ 'sharpe' ] . idxmax ()]
- #locate positon of portfolio with minimum standard deviation
- min_vol_port = results_frame . iloc [ results_frame [ 'stdev' ] . idxmin ()]
+ columns = [ 'r' , 'stdev' , 'sharpe' ] + context . assets )
+ #locate position of portfolio with highest Sharpe Ratio
+ max_sharpe_port = results_frame . iloc [ results_frame [ 'sharpe' ] . idxmax ()]
+ #locate positon of portfolio with minimum standard deviation
+ min_vol_port = results_frame . iloc [ results_frame [ 'stdev' ] . idxmin ()]
- #order optimal weights for each asset
+ #order optimal weights for each asset
for asset in context . assets :
if data . can_trade ( asset ):
order_target_percent ( asset , max_sharpe_port [ asset ])
- #create scatter plot coloured by Sharpe Ratio
- plt . scatter ( results_frame . stdev , results_frame . r , c = results_frame . sharpe , cmap = 'RdYlGn' )
- plt . xlabel ( 'Volatility' )
- plt . ylabel ( 'Returns' )
+ #create scatter plot coloured by Sharpe Ratio
+ plt . scatter ( results_frame . stdev , results_frame . r , c = results_frame . sharpe , cmap = 'RdYlGn' )
+ plt . xlabel ( 'Volatility' )
+ plt . ylabel ( 'Returns' )
plt . colorbar ()
- #plot red star to highlight position of portfolio with highest Sharpe Ratio
- plt . scatter ( max_sharpe_port [ 1 ], max_sharpe_port [ 0 ], marker = 'o' , color = 'b' , s = 200 )
- #plot green star to highlight position of minimum variance portfolio
+ #plot red star to highlight position of portfolio with highest Sharpe Ratio
+ plt . scatter ( max_sharpe_port [ 1 ], max_sharpe_port [ 0 ], marker = 'o' , color = 'b' , s = 200 )
+ #plot green star to highlight position of minimum variance portfolio
plt . show ()
print ( max_sharpe_port )
record ( pr = pr , r = r , m = m , stds = stds , max_sharpe_port = max_sharpe_port , corr_m = corr_m )
@@ -1292,15 +1158,15 @@ in writting the following article:
def analyze ( context = None , results = None ):
- # Form DataFrame with selected data
- data = results [[ 'pr' , 'r' , 'm' , 'stds' , 'max_sharpe_port' , 'corr_m' , 'portfolio_value' ]]
+ # Form DataFrame with selected data
+ data = results [[ 'pr' , 'r' , 'm' , 'stds' , 'max_sharpe_port' , 'corr_m' , 'portfolio_value' ]]
- # Save results in CSV file
- filename = os . path . splitext ( os . path . basename ( __file__ ))[ 0 ]
- data . to_csv ( filename + '.csv' )
+ # Save results in CSV file
+ filename = os . path . splitext ( os . path . basename ( __file__ ))[ 0 ]
+ data . to_csv ( filename + '.csv' )
-# Bitcoin data is available from 2015-3-2. Dates vary for other tokens.
+# Bitcoin data is available from 2015-3-2. Dates vary for other tokens.
start = datetime ( 2017 , 1 , 1 , 0 , 0 , 0 , 0 , pytz . utc )
end = datetime ( 2017 , 8 , 16 , 0 , 0 , 0 , 0 , pytz . utc )
results = run_algorithm ( initialize = initialize ,
@@ -1308,7 +1174,7 @@ in writting the following article:
analyze = analyze ,
start = start ,
end = end ,
- exchange_name = 'poloniex' ,
+ exchange_name = 'poloniex' ,
capital_base = 100000 , )
+
+
+
+
-
-
+
-
-
- Catalyst
+
+ Catalyst
+
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+
Features
@@ -325,7 +187,7 @@ details.
structure in backtesting and paper trading.
Standardized naming convention for all asset pairs trading on any exchange in
the form {market_currency}_{base_currency} Naming Convention
+
Naming Convention .
Output of performance statistics based on Pandas DataFrames to integrate
nicely into the existing PyData ecosystem.
Support for accessing multiple exchanges per algorithm, which opens the door
@@ -342,7 +204,7 @@ can be specified through set_be
Support for Python2 and Python3.
For additional details on the functionality added on recent releases, see the
-Release Notes
+Release Notes
Upcoming features
@@ -374,29 +236,29 @@ exchange. Catalyst will always expect in all its functions that you will refer t
the asset pairs by using the Catalyst naming convention.
If at any point, you input the wrong name for an asset pair, you will get an error
of that pair not found in the given exchange, and a list of pairs available on that exchange:
-
$ catalyst ingest-exchange -x poloniex -i btc_usd
+Ingesting exchange bundle poloniex...
-Error traceback: /Volumes/Data/Users/victoris/Desktop/Enigma/user-install/catalyst-dev/catalyst/exchange/exchange.py (line 175)
-SymbolNotFoundOnExchange: Symbol btc_usd not found on exchange Poloniex.
-Choose from: ['rep_usdt', 'gno_btc', 'xvc_btc', 'pink_btc', 'sys_btc',
-'emc2_btc', 'rads_btc', 'note_btc', 'maid_btc', 'bch_btc', 'gnt_btc',
-'bcn_btc', 'rep_btc', 'bcy_btc', 'cvc_btc', 'nxt_xmr', 'zec_usdt',
-'fct_btc', 'gas_btc', 'pot_btc', 'eth_usdt', 'btc_usdt', 'lbc_btc',
-'dcr_btc', 'etc_usdt', 'omg_eth', 'amp_btc', 'xpm_btc', 'nxt_btc',
-'vtc_btc', 'steem_eth', 'blk_xmr', 'pasc_btc', 'zec_xmr', 'grc_btc',
-'nxc_btc', 'btcd_btc', 'ltc_btc', 'dash_btc', 'naut_btc', 'zec_eth',
-'zec_btc', 'burst_btc', 'zrx_eth', 'bela_btc', 'steem_btc', 'etc_btc',
-'eth_btc', 'huc_btc', 'strat_btc', 'lsk_btc', 'exp_btc', 'clam_btc',
-'rep_eth', 'dash_xmr', 'cvc_eth', 'bch_usdt', 'zrx_btc', 'dash_usdt',
-'blk_btc', 'xrp_btc', 'nxt_usdt', 'neos_btc', 'omg_btc', 'bts_btc',
-'doge_btc', 'gnt_eth', 'sbd_btc', 'gno_eth', 'xcp_btc', 'ltc_usdt',
-'btm_btc', 'xmr_usdt', 'lsk_eth', 'omni_btc', 'nav_btc', 'fldc_btc',
-'ppc_btc', 'xbc_btc', 'dgb_btc', 'sc_btc', 'btcd_xmr', 'vrc_btc',
-'ric_btc', 'str_btc', 'maid_xmr', 'xmr_btc', 'sjcx_btc', 'via_btc',
-'xem_btc', 'nmc_btc', 'etc_eth', 'ltc_xmr', 'ardr_btc', 'gas_eth',
-'flo_btc', 'xrp_usdt', 'game_btc', 'bch_eth', 'bcn_xmr', 'str_usdt']
+Ingesting exchange bundle poloniex ...
+Error traceback : / Volumes / Data / Users / victoris / Desktop / Enigma / user - install / catalyst - dev / catalyst / exchange / exchange . py ( line 175 )
+SymbolNotFoundOnExchange : Symbol btc_usd not found on exchange Poloniex .
+Choose from : [ 'rep_usdt' , 'gno_btc' , 'xvc_btc' , 'pink_btc' , 'sys_btc' ,
+'emc2_btc' , 'rads_btc' , 'note_btc' , 'maid_btc' , 'bch_btc' , 'gnt_btc' ,
+'bcn_btc' , 'rep_btc' , 'bcy_btc' , 'cvc_btc' , 'nxt_xmr' , 'zec_usdt' ,
+'fct_btc' , 'gas_btc' , 'pot_btc' , 'eth_usdt' , 'btc_usdt' , 'lbc_btc' ,
+'dcr_btc' , 'etc_usdt' , 'omg_eth' , 'amp_btc' , 'xpm_btc' , 'nxt_btc' ,
+'vtc_btc' , 'steem_eth' , 'blk_xmr' , 'pasc_btc' , 'zec_xmr' , 'grc_btc' ,
+'nxc_btc' , 'btcd_btc' , 'ltc_btc' , 'dash_btc' , 'naut_btc' , 'zec_eth' ,
+'zec_btc' , 'burst_btc' , 'zrx_eth' , 'bela_btc' , 'steem_btc' , 'etc_btc' ,
+'eth_btc' , 'huc_btc' , 'strat_btc' , 'lsk_btc' , 'exp_btc' , 'clam_btc' ,
+'rep_eth' , 'dash_xmr' , 'cvc_eth' , 'bch_usdt' , 'zrx_btc' , 'dash_usdt' ,
+'blk_btc' , 'xrp_btc' , 'nxt_usdt' , 'neos_btc' , 'omg_btc' , 'bts_btc' ,
+'doge_btc' , 'gnt_eth' , 'sbd_btc' , 'gno_eth' , 'xcp_btc' , 'ltc_usdt' ,
+'btm_btc' , 'xmr_usdt' , 'lsk_eth' , 'omni_btc' , 'nav_btc' , 'fldc_btc' ,
+'ppc_btc' , 'xbc_btc' , 'dgb_btc' , 'sc_btc' , 'btcd_xmr' , 'vrc_btc' ,
+'ric_btc' , 'str_btc' , 'maid_xmr' , 'xmr_btc' , 'sjcx_btc' , 'via_btc' ,
+'xem_btc' , 'nmc_btc' , 'etc_eth' , 'ltc_xmr' , 'ardr_btc' , 'gas_eth' ,
+'flo_btc' , 'xrp_usdt' , 'game_btc' , 'bch_eth' , 'bcn_xmr' , 'str_usdt' ]
In the example above, exchange Poloniex does not use USD, but uses instead the
@@ -404,26 +266,30 @@ USDT cryptocurrency asset that is issued on the Bitcoin blockchain via the Omni
Layer Protocol. Each USDT unit is backed by a U.S Dollar held in the reserves of
Tether Limited. USDT can be transferred, stored, and spent, just like bitcoins
or any other cryptocurrency. Given its 1:1 mapping to the USD, is a viable alternative.
-$ catalyst ingest-exchange -x poloniex -i btc_usdt
+Ingesting exchange bundle poloniex...
- [====================================] Fetching poloniex daily candles: : 100%
+Ingesting exchange bundle poloniex ...
+ [ ==================================== ] Fetching poloniex daily candles : : 100 %
+
+
+
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+
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- Catalyst
+
+ Catalyst
+
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+
- Docs »Docs »Index
+
+
-
+
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+
+
-
+
+
Index
@@ -327,582 +184,370 @@
A
B
C
D
E
F
G
H
I
L
M
N
O
P
R
S
T
V
W
+
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+
+
@@ -935,7 +581,8 @@
VERSION:'0.4',
COLLAPSE_INDEX:false,
FILE_SUFFIX:'.html',
- HAS_SOURCE: true
+ HAS_SOURCE: true,
+ SOURCELINK_SUFFIX: '.txt'
};
diff --git a/index.html b/index.html
index dae5ea4b..14cf06af 100644
--- a/index.html
+++ b/index.html
@@ -13,6 +13,8 @@
+
+
@@ -30,6 +32,9 @@
+
+
@@ -40,21 +45,28 @@
+
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+ Catalyst
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Catalyst is an algorithmic trading library for crypto-assets written in Python.
It allows trading strategies to be easily expressed and backtested against
historical data (with daily and minute resolution), providing analytics and
-insights regarding a particular strategy’s performance. Catalyst also supports
+insights regarding a particular strategy’s performance. Catalyst also supports
live-trading of crypto-assets starting with three exchanges (Bitfinex, Bittrex,
and Poloniex) with more being added over time. Catalyst empowers users to share
and curate data and build profitable, data-driven investment strategies. Please
@@ -373,12 +230,16 @@ performance across trading algorithms.
+
+
+
+
@@ -412,7 +274,8 @@ performance across trading algorithms.
VERSION:'0.4',
COLLAPSE_INDEX:false,
FILE_SUFFIX:'.html',
- HAS_SOURCE: true
+ HAS_SOURCE: true,
+ SOURCELINK_SUFFIX: '.txt'
};
diff --git a/install.html b/install.html
index 3887a974..1ba9708e 100644
--- a/install.html
+++ b/install.html
@@ -13,6 +13,8 @@
+
+
@@ -30,6 +32,9 @@
+
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@@ -41,21 +46,28 @@
+
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Install
@@ -311,29 +190,29 @@ Like any other piece of software, Catalyst has a number of dependencies
well. We recommend using a software named
Conda that will manage all
these dependencies for you, and set up the environment needed to get you up
and running as easily as possible. This is the recommended installation method
-for Windows, MacOS and Linux. See
Installing with Conda .
+for Windows, MacOS and Linux. See
Installing with Conda .
What conda does is create a pre-configured environment, and inside that
-environment install Catalyst using pip pip pip Installing with pip
+environemnt). See
Installing with pip .
Alternatively you can install Catalyst using pipenv Installing with pipenv
+and virtualenv. See
Installing with pipenv .
Regardless of the method, each operating system (OS), has its own
prerequisites, make sure to review the corresponding sections for your system:
-Linux MacOS Windows
+
Linux ,
MacOS and
Windows .
Installing with conda
The preferred method to install Catalyst is via the conda Anaconda distribution.
+which comes as part of Continuum Analytics’
Anaconda distribution.
The primary advantage of using Conda over pip numpy scipy conda
For Windows, you will first need to install the
Microsoft Visual C++
-Compiler for Python 2.7 . Follow the instructions on the
Windows section and come back here.
+Compiler for Python 2.7. Follow the instructions on the
Windows section and come back here.
For instructions on how to install conda Conda Installation
Documentation . Alternatively,
you can install MiniConda, which is a smaller footprint (fewer packages and
@@ -347,18 +226,18 @@ for your Operating System.
window, which should print the list of packages installed with Conda.
-For Windows, if you accepted the default installation options, you didn’t
+
For Windows, if you accepted the default installation options, you didn’t
check an option to add Conda to the PATH, so trying to run conda Command Prompt 'conda'
is no recognized as an internal or external command, operatble program or
-batch file Anaconda
+batch file Anaconda
Prompt
Once either Conda or MiniConda has been set up you can install Catalyst:
Download the file python2.7-environment.yml .
-To download, simply click on the ‘Raw’ button and save the file locally
+
To download, simply click on the ‘Raw’ button and save the file locally
to a folder you can remember. Make sure that the file gets saved with the
.yml .txt
@@ -367,23 +246,23 @@ to a folder you can remember. Make sure that the file gets saved with the
saved the above
python2.7-environment.yml file.
Install using this file. This step can take about 5-10 minutes to install.
-conda env create -f python2.7-environment.yml
+ Activate the environment (which you need to do every time you start a new
session to run Catalyst):
Linux or MacOS:
-source activate catalyst
+
Windows:
-activate catalyst
+
Verify that Catalyst is install correctly:
-catalyst --version
+
which should display the current version.
@@ -399,32 +278,32 @@ with the following steps:
If the above installation failed, and you have a partially set up catalyst
environment, remove it first. If you are starting from scratch, proceed to
step #2:
-conda env remove --name catalyst
+ Create the environment:
-conda create --name catalyst python = 2.7 scipy zlib
+python = 2 .7 scipy zlib
Activate the environment:
Linux or MacOS:
-source activate catalyst
+
Windows:
-activate catalyst
+
Install the Catalyst inside the environment:
-pip install enigma-catalyst matplotlib
+ Verify that Catalyst is installed correctly:
-catalyst --version
+
which should display the current version.
@@ -446,26 +325,26 @@ CPython header files for your Python installation.
numerical array computing in Python. Numpy depends on having the LAPACK linear algebra routines available.
Because LAPACK and the CPython headers are non-Python dependencies, the
-correctway to install them varies from platform to platform. If you’d rather
-use a single tool to install Python and non-Python dependencies, or if you’re
+correctway to install them varies from platform to platform. If you’d rather
+use a single tool to install Python and non-Python dependencies, or if you’re
already using Anaconda as your Python
-distribution, refer to the Installing with Conda
+distribution, refer to the Installing with Conda If you use Python for anything other than Catalyst, we strongly recommend
-that you install in a virtualenv . The Hitchhiker’s Guide to
-Python provides an excellent tutorial on virtualenv . Here’s a
+that you install in a virtualenv . The Hitchhiker’s Guide to
+Python provides an excellent tutorial on virtualenv . Here’s a
summarized version:
-$ pip install virtualenv
-$ virtualenv catalyst-venv
-$ source ./catalyst-venv/bin/activate
+source ./catalyst-venv/bin/activate
Once you’ve installed the necessary additional dependencies for your system
-(GNU/Linux Requirements MacOS Requirements Windows Requirements and have activated your virtualenv , you should be able to simply run
-$ pip install enigma-catalyst matplotlib
+Once you’ve installed the necessary additional dependencies for your system
+(GNU/Linux Requirements MacOS Requirements Windows Requirements and have activated your virtualenv , you should be able to simply run
+Note that in the command above we install two different packages. The second
-one, matplotlib matplotlib
@@ -476,11 +355,11 @@ recommend you install it, as well.
Package enigma-catalyst cannot be found
Solution :Make sure you have the most up-to-date version of pip installed, by running:
-$ pip install --upgrade pip
+On Windows, the recommended command is:
-$ python -m pip install --upgrade pip
+Issue :Package enigma-catalyst cannot still be found, even after upgrading pip
(see above), with an error similar to:
-Downloading/unpacking enigma-catalyst
+( from versions: 0.1.dev9, 0.2.dev2, 0.1.dev4, 0.1.dev5, 0.1.dev3,
-0.2.dev1, 0.1.dev8, 0.1.dev6)
+( from versions: 0 .1.dev9, 0 .2.dev2, 0 .1.dev4, 0 .1.dev5, 0 .1.dev3,
+0 .2.dev1, 0 .1.dev8, 0 .1.dev6)
Cleaning up...
No distributions matching the version for enigma-catalyst
@@ -503,9 +382,9 @@ No distributions matching the version
for enigma-catalyst
In some systems (this error has been reported in Ubuntu), pip is configured
to only find stable versions by default. Since Catalyst is in alpha
version, pip cannot find a matching version that satisfies the installation
-requirements. The solution is to include the –pre flag to include
+requirements. The solution is to include the –pre flag to include
pre-release and development versions:
-$ pip install --pre enigma-catalyst
+Package enigma-catalyst fails to install because of outdated setuptools
Solution :Upgrade to the most up-to-date setuptools package by running:
-$ pip install --upgrade pip setuptools
+Missing required packages
Solution :Download requirements.txt
-(click on the Raw button and Right click -> Save As...) and use it to
+(click on the Raw button and Right click -> Save As…) and use it to
install all the required dependencies by running:
-$ pip install -r requirements.txt
+$ sudo apt-get install python-dev
+Installing with pipenv
Installing Catalyst via pipenv pip pipenv pip
-$ pip install pipenv
+
Once pipenv
-$ mkdir project
-$ cd project
-$ pipenv --two
-$ pipenv install enigma-catalyst matplotlib
+cd project
+$ pipenv --two
+$ pipenv install enigma-catalyst matplotlib
Until now the workflow compared to pip pipenv run prefix to run the catalyst command as follows:
-$ pipenv run catalyst --version
+