wassname/catalyst
1
0
Fork 0
mirror of https://github.com/wassname/catalyst.git synced 2026-08-14 12:10:24 +08:00
Code Issues Packages Projects Releases Wiki Activity

BUG: resolve issue #61 by adjusting our perf conventions to match zipline exactly.

Browse Source
This commit is contained in:
fredfortier
2017-11-10 17:39:36 -05:00
parent 631cbcd352
commit b64e5929b4
2 changed files with 59 additions and 63 deletions
Download Patch File Download Diff File Expand all files Collapse all files
catalyst/examples/momemtum.py
+35 -32
View File
@@ -1,18 +1,17 @@
# For this example, we're going to write a simple momentum script. When the
# stock goes up quickly, we're going to buy; when it goes down quickly, we're
# going to sell. Hopefully we'll ride the waves.
from collections import OrderedDict
import pandas as pd
import talib
# To run an algorithm in Catalyst, you need two functions: initialize and
# 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.exchange import stats_utils
from catalyst.finance.execution import LimitOrder
# 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`
@@ -37,27 +36,30 @@ def initialize(context):
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.
today = data.current_dt.floor('1D')
if today != context.current_day:
context.traded_today = False
context.current_day = today
# 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're computing the volume-weighted-average-price of the security
# We're computing the volume-weighted-average-price of the security
# defined above, in the context.eth_btc variable. For this example, we're
# using three bars on the daily chart.
bars = data.history(
prices = data.history(
context.eth_btc,
fields=['close', 'volume'],
fields='close',
bar_count=100,
frequency='30T'
)
# Use TA-Lib to calculate MACD data using calibrated settings
macd_raw, signal, macd_hist = talib.MACD(
bars['close'].values, fastperiod=30, slowperiod=40, signalperiod=45
prices.values, fastperiod=30, slowperiod=40, signalperiod=45
)
# We need a variable for the current price of the security to compare to
@@ -99,7 +101,7 @@ def handle_data(context, data):
context.traded_today = True
elif macd_hist[-1] < 0 and data.can_trade(context.eth_btc) \
and pos_amount > 0 and context.traded_today:
and pos_amount > 0 and not context.traded_today:
order_target_percent(context.eth_btc, 0)
context.traded_today = True
@@ -129,8 +131,8 @@ def analyze(context=None, results=None):
# Transaction have an exact timestamp while stVats are daily.
# We adjust the time to the end of each period to place them on the graph.
for t in all_trans:
t['dt'] = t['dt'].replace(hour=23, minute=59)
# for t in all_trans:
# t['dt'] = t['dt'].replace(hour=23, minute=59)
buys = results.loc[[t['dt'] for t in all_trans if t['amount'] > 0], :]
sells = results.loc[[t['dt'] for t in all_trans if t['amount'] < 0], :]
@@ -161,23 +163,24 @@ def analyze(context=None, results=None):
ax5.set_ylabel('Percent Change')
ax6 = plt.subplot(615, sharex=ax1)
results.loc[:, 'macd'].plot(ax=ax6)
ax6.set_ylabel('MACD')
results.loc[:, 'macd'].plot(ax=ax6, label='macd')
ax6.plot(
buys.index,
results.loc[buys.index, 'macd'],
'^',
markersize=10,
color='g',
)
ax6.plot(
sells.index,
results.loc[sells.index, 'macd'],
'v',
markersize=10,
color='r',
)
# ax6.plot(
# buys.index,
# results.loc[buys.index, 'macd'],
# '^',
# markersize=10,
# color='g',
# label='buys'
# )
# ax6.plot(
# sells.index,
# results.loc[sells.index, 'macd'],
# 'v',
# markersize=10,
# color='r',
# label='sells'
# )
plt.legend(loc=3)
@@ -197,6 +200,6 @@ run_algorithm(
exchange_name='poloniex',
algo_namespace=algo_namespace,
base_currency='usdt',
start=pd.to_datetime('2016-10-1', utc=True),
end=pd.to_datetime('2017-10-31', utc=True),
start=pd.to_datetime('2017-6-1', utc=True),
end=pd.to_datetime('2017-8-1', utc=True),
)
catalyst/exchange/exchange_algorithm.py
+24 -31
View File
@@ -16,6 +16,7 @@ import signal
import sys
from collections import deque
from datetime import timedelta
from itertools import chain
from os import listdir
from os.path import isfile, join
from time import sleep
@@ -23,12 +24,11 @@ from time import sleep
import logbook
import pandas as pd
from catalyst.assets._assets import TradingPair
from six import itervalues
import catalyst.protocol as zp
from catalyst.algorithm import TradingAlgorithm
from catalyst.constants import LOG_LEVEL
from catalyst.data.minute_bars import BcolzMinuteBarWriter, \
BcolzMinuteBarReader
from catalyst.errors import OrderInBeforeTradingStart
from catalyst.exchange.exchange_blotter import ExchangeBlotter
from catalyst.exchange.exchange_errors import (
@@ -182,17 +182,19 @@ class ExchangeTradingAlgorithmBase(TradingAlgorithm):
# we want the key to be absent, not just empty
# Only include transactions for given dt
stats['transactions'] = dict()
stats['transactions'] = []
for date in period.processed_transactions:
if start_dt <= date < end_dt:
stats['transactions'][date] = \
period.processed_transactions[date]
transactions = period.processed_transactions[date]
for t in transactions:
stats['transactions'].append(t.to_dict())
stats['orders'] = dict()
stats['orders'] = []
for date in period.orders_by_modified:
if start_dt <= date < end_dt:
stats['orders'][date] = \
period.orders_by_modified[date]
orders = period.orders_by_modified[date]
for order in orders:
stats['orders'].append(orders[order].to_dict())
return stats
@@ -201,6 +203,7 @@ class ExchangeTradingAlgorithmBacktest(ExchangeTradingAlgorithmBase):
def __init__(self, *args, **kwargs):
super(ExchangeTradingAlgorithmBacktest, self).__init__(*args, **kwargs)
self.frame_stats = list()
self.blotter = ExchangeBlotter(
data_frequency=self.data_frequency,
# Default to NeverCancel in catalyst
@@ -245,6 +248,19 @@ class ExchangeTradingAlgorithmBacktest(ExchangeTradingAlgorithmBase):
else:
return MarketOrder()
def handle_data(self, data):
super(ExchangeTradingAlgorithmBacktest, self).handle_data(data)
minute_stats = self.prepare_period_stats(
data.current_dt, data.current_dt + timedelta(minutes=1))
self.frame_stats.append(minute_stats)
def analyze(self, perf):
stats = pd.DataFrame(self.frame_stats)
stats.set_index('period_close', inplace=True, drop=False)
super(ExchangeTradingAlgorithmBacktest, self).analyze(perf)
class ExchangeTradingAlgorithmLive(ExchangeTradingAlgorithmBase):
def __init__(self, *args, **kwargs):
@@ -273,34 +289,11 @@ class ExchangeTradingAlgorithmLive(ExchangeTradingAlgorithmBase):
self.stats_minutes = 5
super(ExchangeTradingAlgorithmLive, self).__init__(*args, **kwargs)
# TODO: fix precision before re-enabling
# self._create_minute_writer()
signal.signal(signal.SIGINT, self.signal_handler)
log.info('initialized trading algorithm in live mode')
def _create_minute_writer(self):
root = get_exchange_minute_writer_root(self.exchange.name)
filename = os.path.join(root, 'metadata.json')
if os.path.isfile(filename):
writer = BcolzMinuteBarWriter.open(
root, self.sim_params.end_session)
else:
# TODO: need to be able to write more precise numbers
writer = BcolzMinuteBarWriter(
rootdir=root,
calendar=self.trading_calendar,
minutes_per_day=1440,
start_session=self.sim_params.start_session,
end_session=self.sim_params.end_session,
write_metadata=True
)
self.exchange.minute_writer = writer
self.exchange.minute_reader = BcolzMinuteBarReader(root)
def signal_handler(self, signal, frame):
"""
Handles the keyboard interruption signal.