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DOC: mean_reversion_simple.py minor edits, and added to doc website

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Victor Grau Serrat
2017-11-20 09:12:43 -07:00
parent 698b19c8fa
commit 9cfd50dc4f
2 changed files with 280 additions and 9 deletions
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catalyst/examples/mean_reversion_simple.py
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@@ -4,22 +4,21 @@
import pandas as pd
import talib
# To run an algorithm in Catalyst, you need two functions: initialize and
# handle_data.
from logbook import Logger
from catalyst import run_algorithm
from catalyst.api import symbol, record, order_target_percent, \
get_open_orders
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.exchange.stats_utils import extract_transactions
NAMESPACE = 'mean_reversion_simple'
log = Logger(NAMESPACE)
# 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
@@ -216,7 +215,7 @@ def analyze(context=None, perf=None):
if __name__ == '__main__':
# The execution mode: backtest or live
MODE = 'live'
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
docs/source/example-algos.rst
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@@ -6,8 +6,10 @@ This section documents a small number of example algorithms to complement the
beginner tutorial, and show how other trading algorithms can be implemented
using Catalyst:
Buy and Hodl
~~~~~~~~~~~~
.. _buy_and_hodl:
Buy and Hodl Algorithm
~~~~~~~~~~~~~~~~~~~~~~
source: `examples/buy_and_hodl.py <https://github.com/enigmampc/catalyst/blob/master/catalyst/examples/buy_and_hodl.py>`_
@@ -170,3 +172,273 @@ one day prior to the current date.
plt.gcf().set_size_inches(18, 8)
plt.show()
.. _mean_reversion:
Mean Reversion Algorithm
~~~~~~~~~~~~~~~~~~~~~~~~
source: `examples/mean_reversion_simple.py <https://github.com/enigmampc/catalyst/blob/master/catalyst/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
that we need, with minute resolution:
.. code-block:: bash
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:
.. code-block:: bash
python mean_reversion_simple.py
.. code-block:: python
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.
NAMESPACE = 'mean_reversion_simple'
log = Logger(NAMESPACE)
# 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
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.
# 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_usd 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,
fields='close',
bar_count=50,
frequency='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
def analyze(context=None, perf=None):
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))
# 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
))
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=''
)
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))
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')
ax6 = plt.subplot(615, sharex=ax1)
perf.loc[:, 'rsi'].plot(ax=ax6, label='RSI')
ax6.axhline(70, color='darkgoldenrod')
ax6.axhline(30, color='darkgoldenrod')
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)
# 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':
# 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
)