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Removed use of global variables from example strategies

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This commit is contained in:
Conner Fromknecht
2017-07-06 15:11:05 -07:00
parent c4bbbb4732
commit c4e53a7ceb
2 changed files with 57 additions and 67 deletions
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catalyst/examples/buy_and_hodl.py
+17 -27
View File
@@ -23,24 +23,24 @@ from catalyst.api import (
get_open_orders,
)
ASSET = 'USDT_BTC'
TARGET_HODL_RATIO = 0.8
RESERVE_RATIO = 1.0 - TARGET_HODL_RATIO
# For all trading pairs in the poloniex bundle, the default denomination
# currently supported by Catalyst is 1/10th of a full coin. Use this constant
# to scale the price of up to that of a full coin if desired.
UNITS_PER_COIN = 10.0
def initialize(context):
context.ASSET_NAME = 'USDT_ETH'
context.TARGET_HODL_RATIO = 0.8
context.RESERVE_RATIO = 1.0 - context.TARGET_HODL_RATIO
# 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.is_buying = True
context.asset = symbol(ASSET)
context.asset = symbol(context.ASSET_NAME)
def handle_data(context, data):
cash = context.portfolio.cash
target_hodl_value = TARGET_HODL_RATIO * context.portfolio.starting_cash
reserve_value = RESERVE_RATIO * context.portfolio.starting_cash
starting_cash = context.portfolio.starting_cash
target_hodl_value = context.TARGET_HODL_RATIO * starting_cash
reserve_value = context.RESERVE_RATIO * starting_cash
# Cancel any outstanding orders
orders = get_open_orders(context.asset) or []
@@ -48,6 +48,7 @@ def handle_data(context, data):
cancel_order(order)
# Stop buying after passing the reserve threshold
cash = context.portfolio.cash
if cash <= reserve_value:
context.is_buying = False
@@ -79,8 +80,8 @@ def analyze(context=None, results=None):
ax1.set_ylabel('Portfolio Value (USD)')
ax2 = plt.subplot(512, sharex=ax1)
ax2.set_ylabel('{asset} (USD)'.format(asset=ASSET))
(UNITS_PER_COIN * results[['price']]).plot(ax=ax2)
ax2.set_ylabel('{asset} (USD)'.format(asset=context.ASSET_NAME))
(context.TICK_SIZE * results[['price']]).plot(ax=ax2)
trans = results.ix[[t != [] for t in results.transactions]]
buys = trans.ix[
@@ -88,7 +89,7 @@ def analyze(context=None, results=None):
]
ax2.plot(
buys.index,
UNITS_PER_COIN * results.price[buys.index],
context.TICK_SIZE * results.price[buys.index],
'^',
markersize=10,
color='g',
@@ -125,14 +126,3 @@ def analyze(context=None, results=None):
# Show the plot.
plt.gcf().set_size_inches(18, 8)
plt.show()
def _test_args():
"""Extra arguments to use when catalyst's automated tests run this example.
"""
import pandas as pd
return {
'start': pd.Timestamp('2008', tz='utc'),
'end': pd.Timestamp('2013', tz='utc'),
}
catalyst/examples/dual_vwap.py
+40 -40
View File
@@ -31,24 +31,24 @@ from catalyst.pipeline import Pipeline
from catalyst.pipeline.data import CryptoPricing
from catalyst.pipeline.factors.crypto import VWAP
ASSET = 'USDT_BTC'
TARGET_INVESTMENT_RATIO = 0.8
SHORT_WINDOW = 30
LONG_WINDOW = 100
# For all trading pairs in the poloniex bundle, the default denomination
# currently supported by Catalyst is 1/10th of a full coin. Use this constant
# to scale the price of up to that of a full coin if desired.
UNITS_PER_COIN = 10.0
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(ASSET)
context.asset = symbol(context.ASSET_NAME)
set_max_leverage(1.0)
attach_pipeline(make_pipeline(), 'vwap_pipeline')
attach_pipeline(make_pipeline(context), 'vwap_pipeline')
schedule_function(
rebalance,
@@ -59,12 +59,13 @@ def initialize(context):
def before_trading_start(context, data):
context.pipeline_data = pipeline_output('vwap_pipeline')
def make_pipeline():
def make_pipeline(context):
return Pipeline(
columns={
'price': CryptoPricing.open.latest,
'short_mavg': VWAP(window_length=SHORT_WINDOW),
'long_mavg': VWAP(window_length=LONG_WINDOW),
'volume': CryptoPricing.volume.latest,
'short_mavg': VWAP(window_length=context.SHORT_WINDOW),
'long_mavg': VWAP(window_length=context.LONG_WINDOW),
}
)
@@ -72,7 +73,7 @@ def rebalance(context, data):
context.i += 1
# skip first LONG_WINDOW bars to fill windows
if context.i < LONG_WINDOW:
if context.i < context.LONG_WINDOW:
return
# get pipeline data for asset of interest
@@ -83,6 +84,7 @@ def rebalance(context, data):
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()
@@ -91,9 +93,15 @@ def rebalance(context, data):
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, TARGET_INVESTMENT_RATIO)
order_target_percent(
context.asset,
context.TARGET_INVESTMENT_RATIO,
)
elif short_mavg < long_mavg:
order_target_percent(context.asset, 0.0)
order_target_percent(
context.asset,
0.0,
)
record(
price=price,
@@ -101,23 +109,22 @@ def rebalance(context, data):
leverage=context.account.leverage,
short_mavg=short_mavg,
long_mavg=long_mavg,
volume=volume,
)
# Note: this function can be removed if running
# this algorithm on quantopian.com
def analyze(context=None, results=None):
import matplotlib.pyplot as plt
# Plot the portfolio and asset data.
ax1 = plt.subplot(511)
ax1 = plt.subplot(611)
results[['portfolio_value']].plot(ax=ax1)
ax1.set_ylabel('Portfolio value (USD)')
ax2 = plt.subplot(512, sharex=ax1)
ax2.set_ylabel('{asset} (USD)'.format(asset=ASSET))
(UNITS_PER_COIN*results[['price', 'short_mavg', 'long_mavg']]).plot(ax=ax2)
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]]
amounts = [t[0]['amount'] for t in trans.transactions]
@@ -131,24 +138,24 @@ def analyze(context=None, results=None):
ax2.plot(
buys.index,
UNITS_PER_COIN * results.price[buys.index],
context.TICK_SIZE * results.price[buys.index],
'^',
markersize=10,
color='g',
)
ax2.plot(
sells.index,
UNITS_PER_COIN * results.price[sells.index],
context.TICK_SIZE * results.price[sells.index],
'v',
markersize=10,
color='r',
)
ax3 = plt.subplot(513, sharex=ax1)
ax3 = plt.subplot(613, sharex=ax1)
results[['leverage', 'alpha', 'beta']].plot(ax=ax3)
ax3.set_ylabel('Leverage (USD)')
ax4 = plt.subplot(514, sharex=ax1)
ax4 = plt.subplot(614, sharex=ax1)
results[['cash']].plot(ax=ax4)
ax4.set_ylabel('Cash (USD)')
@@ -162,7 +169,7 @@ def analyze(context=None, results=None):
'benchmark_period_return',
]]
ax5 = plt.subplot(515, sharex=ax1)
ax5 = plt.subplot(615, sharex=ax1)
results[[
'treasury',
'algorithm',
@@ -170,19 +177,12 @@ def analyze(context=None, results=None):
]].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()
def _test_args():
"""Extra arguments to use when catalyst's automated tests run this example.
"""
import pandas as pd
return {
'start': pd.Timestamp('2014-01-01', tz='utc'),
'end': pd.Timestamp('2014-11-01', tz='utc'),
}