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BLD: adjusted the example algorithms

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This commit is contained in:
Frederic Fortier
2017-12-12 15:13:57 -05:00
parent ddf0c480a0
commit d41d9095a1
6 changed files with 53 additions and 263 deletions
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catalyst/examples/buy_low_sell_high_live.py
+1 -1
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@@ -152,7 +152,7 @@ if __name__ == '__main__':
initialize=initialize,
handle_data=handle_data,
analyze=analyze,
exchange_name='bittrex',
exchange_name='binance',
live=True,
algo_namespace=algo_namespace,
base_currency='btc',
catalyst/examples/dual_vwap.py
-190
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@@ -1,190 +0,0 @@
#!/usr/bin/env python
#
# Copyright 2017 Enigma MPC, Inc.
# Copyright 2014 Quantopian, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from catalyst.api import (
order_target_percent,
record,
symbol,
get_open_orders,
set_max_leverage,
schedule_function,
date_rules,
attach_pipeline,
pipeline_output,
)
from catalyst.pipeline import Pipeline
from catalyst.pipeline.data import CryptoPricing
from catalyst.pipeline.factors.crypto import VWAP
def initialize(context):
context.ASSET_NAME = 'USDT_BTC'
context.TARGET_INVESTMENT_RATIO = 0.8
context.SHORT_WINDOW = 30
context.LONG_WINDOW = 100
# For all trading pairs in the poloniex bundle, the default denomination
# currently supported by Catalyst is 1/1000th of a full coin. Use this
# constant to scale the price of up to that of a full coin if desired.
context.TICK_SIZE = 1000.0
context.i = 0
context.asset = symbol(context.ASSET_NAME)
set_max_leverage(1.0)
attach_pipeline(make_pipeline(context), 'vwap_pipeline')
schedule_function(
rebalance,
time_rules=date_rules.every_minute(),
)
def before_trading_start(context, data):
context.pipeline_data = pipeline_output('vwap_pipeline')
def make_pipeline(context):
return Pipeline(
columns={
'price': CryptoPricing.open.latest,
'volume': CryptoPricing.volume.latest,
'short_mavg': VWAP(window_length=context.SHORT_WINDOW),
'long_mavg': VWAP(window_length=context.LONG_WINDOW),
}
)
def rebalance(context, data):
context.i += 1
# skip first LONG_WINDOW bars to fill windows
if context.i < context.LONG_WINDOW:
return
# get pipeline data for asset of interest
pipeline_data = context.pipeline_data
pipeline_data = pipeline_data[pipeline_data.index == context.asset].iloc[0]
# retrieve long and short moving averages from pipeline
short_mavg = pipeline_data.short_mavg
long_mavg = pipeline_data.long_mavg
price = pipeline_data.price
volume = pipeline_data.volume
# check that order has not already been placed
open_orders = get_open_orders()
if context.asset not in open_orders:
# check that the asset of interest can currently be traded
if data.can_trade(context.asset):
# adjust portfolio based on comparison of long and short vwap
if short_mavg > long_mavg:
order_target_percent(
context.asset,
context.TARGET_INVESTMENT_RATIO,
)
elif short_mavg < long_mavg:
order_target_percent(
context.asset,
0.0,
)
record(
price=price,
cash=context.portfolio.cash,
leverage=context.account.leverage,
short_mavg=short_mavg,
long_mavg=long_mavg,
volume=volume,
)
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)')
ax2 = plt.subplot(612, sharex=ax1)
ax2.set_ylabel('{asset} (USD)'.format(asset=context.ASSET_NAME))
(context.TICK_SIZE*results[['price',
'short_mavg',
'long_mavg']]).plot(ax=ax2)
trans = results.ix[[t != [] for t in results.transactions]]
buys = trans.ix[
[t[0]['amount'] > 0 for t in trans.transactions]
]
sells = trans.ix[
[t[0]['amount'] < 0 for t in trans.transactions]
]
ax2.plot(
buys.index,
context.TICK_SIZE * results.price[buys.index],
'^',
markersize=10,
color='g',
)
ax2.plot(
sells.index,
context.TICK_SIZE * results.price[sells.index],
'v',
markersize=10,
color='r',
)
ax3 = plt.subplot(613, sharex=ax1)
results[['leverage', 'alpha', 'beta']].plot(ax=ax3)
ax3.set_ylabel('Leverage (USD)')
ax4 = plt.subplot(614, sharex=ax1)
results[['cash']].plot(ax=ax4)
ax4.set_ylabel('Cash (USD)')
results[[
'treasury',
'algorithm',
'benchmark',
]] = results[[
'treasury_period_return',
'algorithm_period_return',
'benchmark_period_return',
]]
ax5 = plt.subplot(615, sharex=ax1)
results[[
'treasury',
'algorithm',
'benchmark',
]].plot(ax=ax5)
ax5.set_ylabel('Percent Change')
ax6 = plt.subplot(616, sharex=ax1)
results[['volume']].plot(ax=ax6)
ax6.set_ylabel('Volume (mBTC/day)')
plt.legend(loc=3)
# Show the plot.
plt.gcf().set_size_inches(18, 8)
plt.show()
catalyst/examples/mean_reversion_simple.py
+1 -1
View File
@@ -245,7 +245,7 @@ def analyze(context=None, perf=None):
if __name__ == '__main__':
# The execution mode: backtest or live
MODE = 'live'
MODE = 'backtest'
if MODE == 'backtest':
folder = os.path.join(
catalyst/examples/portfolio_optimization.py
+22 -21
View File
@@ -43,14 +43,14 @@ def handle_data(context, data):
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')
bar_count=n + 1, frequency='1d')
pr = np.asmatrix(prices)
t_prices = prices.iloc[1:n+1]
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)
r = np.asmatrix(t_val/tminus_val-1)
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
m = np.asmatrix(np.mean(r, axis=0))
@@ -59,20 +59,20 @@ def handle_data(context, data):
# Compute excess returns matrix (xr)
xr = r - m
# Matrix algebra to get variance-covariance matrix
cov_m = np.dot(np.transpose(xr), xr)/n
cov_m = np.dot(np.transpose(xr), xr) / n
# Compute asset correlation matrix (informative only)
corr_m = cov_m/np.dot(np.transpose(stds), stds)
corr_m = cov_m / np.dot(np.transpose(stds), stds)
# Define portfolio optimization parameters
n_portfolios = 50000
results_array = np.zeros((3+context.nassets, n_portfolios))
results_array = np.zeros((3 + context.nassets, n_portfolios))
for p in xrange(n_portfolios):
weights = np.random.random(context.nassets)
weights /= np.sum(weights)
w = np.asmatrix(weights)
p_r = np.sum(np.dot(w, np.transpose(m)))*365
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)
np.transpose(w))) * np.sqrt(365)
# store results in results array
results_array[0, p] = p_r
@@ -82,13 +82,13 @@ def handle_data(context, data):
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]
results_array[3 + i, p] = weights[i]
i += 1
# convert results array to Pandas DataFrame
results_frame = pd.DataFrame(np.transpose(results_array),
columns=['r', 'stdev', 'sharpe']
+ context.assets)
+ 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
@@ -129,20 +129,21 @@ def handle_data(context, data):
def analyze(context=None, results=None):
# Form DataFrame with selected data
data = results[['pr', 'r', 'm', 'stds', 'max_sharpe_port', 'corr_m',
'portfolio_value']]
'portfolio_value']]
# 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.
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,
handle_data=handle_data,
analyze=analyze,
start=start,
end=end,
exchange_name='poloniex',
capital_base=100000, )
if __name__ == '__main__':
# 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,
handle_data=handle_data,
analyze=analyze,
start=start,
end=end,
exchange_name='poloniex',
capital_base=100000, )
catalyst/examples/rsi_profit_target.py
+16 -26
View File
@@ -114,7 +114,7 @@ def _handle_data_rsi_only(context, data):
prices = data.history(
context.asset,
fields='price',
bar_count=17,
bar_count=20,
frequency='30T'
)
except Exception as e:
@@ -156,7 +156,7 @@ def handle_data(context, data):
dt = data.current_dt
if context.last_bar is None or (
context.last_bar + timedelta(minutes=15)) <= dt:
context.last_bar + timedelta(minutes=15)) <= dt:
context.last_bar = dt
else:
return
@@ -249,27 +249,17 @@ def analyze(context=None, results=None):
pass
# run_algorithm(
# initialize=initialize,
# handle_data=handle_data,
# analyze=analyze,
# exchange_name='bittrex',
# live=True,
# algo_namespace=algo_namespace,
# base_currency='btc',
# live_graph=False
# )
# Backtest
run_algorithm(
capital_base=0.5,
data_frequency='minute',
initialize=initialize,
handle_data=handle_data,
analyze=analyze,
exchange_name='poloniex',
algo_namespace=algo_namespace,
base_currency='btc',
start=pd.to_datetime('2017-9-1', utc=True),
end=pd.to_datetime('2017-10-1', utc=True),
)
if __name__ == '__main__':
# Backtest
run_algorithm(
capital_base=0.5,
data_frequency='minute',
initialize=initialize,
handle_data=handle_data,
analyze=analyze,
exchange_name='poloniex',
algo_namespace=algo_namespace,
base_currency='btc',
start=pd.to_datetime('2017-9-1', utc=True),
end=pd.to_datetime('2017-10-1', utc=True),
)
catalyst/examples/simple_loop.py
+13 -24
View File
@@ -110,27 +110,16 @@ def analyze(context, perf):
pass
# run_algorithm(
# capital_base=250,
# start=pd.to_datetime('2017-11-9 0:00', utc=True),
# end=pd.to_datetime('2017-11-10 23:59', utc=True),
# data_frequency='minute',
# initialize=initialize,
# handle_data=handle_data,
# analyze=analyze,
# exchange_name='bitfinex',
# algo_namespace='simple_loop',
# base_currency='usd'
# )
run_algorithm(
capital_base=1,
initialize=initialize,
handle_data=handle_data,
analyze=None,
exchange_name='poloniex',
live=True,
algo_namespace='simple_loop',
base_currency='eth',
live_graph=False,
simulate_orders=True
)
if __name__ == '__main__':
run_algorithm(
capital_base=1,
initialize=initialize,
handle_data=handle_data,
analyze=None,
exchange_name='poloniex',
live=True,
algo_namespace='simple_loop',
base_currency='eth',
live_graph=False,
simulate_orders=True
)