Skip to content

Instantly share code, notes, and snippets.

@sedaghatfar
Created December 28, 2017 01:37
Show Gist options
  • Save sedaghatfar/690e4d999c1aa496b086cf01ff5fea03 to your computer and use it in GitHub Desktop.
Save sedaghatfar/690e4d999c1aa496b086cf01ff5fea03 to your computer and use it in GitHub Desktop.
eng_eth.py
import os
import tempfile
import time
import numpy as np
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
from catalyst.exchange.exchange_utils import get_exchange_symbols
# 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'
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 Neo in Ether.
context.market = symbol('eng_eth')
context.base_price = None
context.current_day = None
context.RSI_OVERSOLD = 30
context.RSI_OVERBOUGHT = 80
context.CANDLE_SIZE = '5T'
context.start_time = time.time()
# context.set_commission(maker=0.1, taker=0.2)
context.set_slippage(spread=0.0001)
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.market 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.market,
fields='close',
bar_count=50,
frequency=context.CANDLE_SIZE
)
# 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.market, 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(
volume=current['volume'],
price=price,
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
# TODO: retest with open orders
# 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.market)
if len(orders) > 0:
log.info('exiting because orders are open: {}'.format(orders))
return
# Exit if we cannot trade
if not data.can_trade(context.market):
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.market].amount
if rsi[-1] <= context.RSI_OVERSOLD and pos_amount == 0:
log.info(
'{}: buying - price: {}, rsi: {}'.format(
data.current_dt, price, rsi[-1]
)
)
# Set a style for limit orders,
limit_price = price * 1.005
order_target_percent(
context.market, 1, limit_price=limit_price
)
context.traded_today = True
elif rsi[-1] >= context.RSI_OVERBOUGHT and pos_amount > 0:
log.info(
'{}: selling - price: {}, rsi: {}'.format(
data.current_dt, price, rsi[-1]
)
)
limit_price = price * 0.995
order_target_percent(
context.market, 0, limit_price=limit_price
)
context.traded_today = True
def analyze(context=None, perf=None):
end = time.time()
log.info('elapsed time: {}'.format(end - context.start_time))
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\nValue\n({})'.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}\n({base})'.format(
asset=context.market.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.floor('1 min'), 'price'],
marker='^',
s=100,
c='green',
label=''
)
ax2.scatter(
sell_df.index.to_pydatetime(),
perf.loc[sell_df.index.floor('1 min'), '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\n({})'.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\nChange')
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')
if not transaction_df.empty:
ax6.scatter(
buy_df.index.to_pydatetime(),
perf.loc[buy_df.index.floor('1 min'), 'rsi'],
marker='^',
s=100,
c='green',
label=''
)
ax6.scatter(
sell_df.index.to_pydatetime(),
perf.loc[sell_df.index.floor('1 min'), 'rsi'],
marker='v',
s=100,
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.
plt.gcf().set_size_inches(18, 8)
plt.show()
pass
if __name__ == '__main__':
# The execution mode: backtest or live
MODE = 'backtest'
if MODE == 'backtest':
folder = os.path.join(
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
run_algorithm(
capital_base=0.1,
data_frequency='minute',
initialize=initialize,
handle_data=handle_data,
analyze=analyze,
exchange_name='bittrex',
algo_namespace=NAMESPACE,
base_currency='eth',
start=pd.to_datetime('2017-11-30', utc=True),
end=pd.to_datetime('2017-12-24', utc=True),
output=out
)
log.info('saved perf stats: {}'.format(out))
elif MODE == 'live':
run_algorithm(
capital_base=0.05,
initialize=initialize,
handle_data=handle_data,
analyze=analyze,
exchange_name='bittrex',
live=True,
algo_namespace=NAMESPACE,
base_currency='eth',
live_graph=False,
simulate_orders=True,
stats_output=None
)
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment