def run_strat(
strat_json,
strat_id,
user_id=None,
telegram_id=None,
live=False,
simulate_orders=True,
):
log.info(f"Worker received job for strat {strat_id}")
strat_dict = json.loads(strat_json)
strat = Strategy.from_dict(strat_dict)
strat.id = strat_id
strat.telegram_id = telegram_id
strat.run(
viz=False,
live=live,
simulate_orders=simulate_orders,
user_id=user_id,
as_job=True,
)
result_df = strat.quant_results
if result_df is None:
log.warning("No results from strategy")
return
return result_df.to_json()
def run_indicator(indicator_name):
strat = Strategy(indicator_name)
strat.add_market_indicator(indicator_name)
click.secho('Running {}'.format(indicator_name), fg='cyan')
strat.run(viz=False)
click.secho('{}: {}'.format(indicator_name,
strat.quant_results['net_profit_pct']),
fg='cyan')
# import pdb; pdb.set_trace()
return strat
def run(datasets):
"""Runs s strategy based on provided Blockchain dataset codes
\b
Example:
bchain -s NTRAN -s CPTRA
\b
Available Dataset Codes:
- TOTBC - Total Bitcoins
- MKTCP - Bitcoin Market Capitalization
- TRFEE - Bitcoin Total Transaction Fees
- TRFUS - Bitcoin Total Transaction Fees USD
- NETDF - Bitcoin Network Deficit
- NTRAN - Bitcoin Number of Transactions
- NTRAT - Bitcoin Total Number of Transactions
- NTREP - Bitcoin Number of Transactions Excluding Popular Addresses
- NADDU - Bitcoin Number of Unique Bitcoin Addresses Used
- NTRBL - Bitcoin Number of Transaction per Block
- TOUTV - Bitcoin Total Output Volume
- ETRAV - Bitcoin Estimated Transaction Volume
- ETRVU - Bitcoin Estimated Transaction Volume USD
- TRVOU - Bitcoin USD Exchange Trade Volume
- TVTVR - Bitcoin Trade Volume vs Transaction Volume Ratio
- MKPRU - Bitcoin Market Price USD
- CPTRV - Bitcoin Cost % of Transaction Volume
- CPTRA - Bitcoin Cost Per Transaction
- HRATE - Bitcoin Hash Rate
- MIREV - Bitcoin Miners Revenue
- ATRCT - Bitcoin Median Transaction Confirmation Time
- BCDDC - Bitcoin Days Destroyed Cumulative
- BCDDE - Bitcoin Days Destroyed
- BCDDW - Bitcoin Days Destroyed (Minimum Age 1 Week)
- BCDDM - Bitcoin Days Destroyed (Minimum Age 1 Month)
- BCDDY - Bitcoin Days Destroyed (Minimum Age 1 Year)
- BLCHS - Bitcoin api.blockchain Size
- AVBLS - Bitcoin Average Block Size
- MWTRV - Bitcoin My Wallet Transaction Volume
- MWNUS - Bitcoin My Wallet Number of Users
- MWNTD - Bitcoin My Wallet Number of Transaction Per Day
- MIOPM - Bitcoin Mining Operating Margin
- DIFF - Bitcoin Difficulty
"""
click.secho("Executing using datasets:\n{}".format(datasets), fg="white")
strat = Strategy()
strat.use_dataset("quandl", columns=list(datasets))
strat.run()
def run(keywords, asset):
"""Runs strategy using Google Search Trends
Example:
trends 'btc' 'btc usd' 'btc price'
"""
keywords = list(keywords)
if asset:
keywords.append(CONFIG["ASSET"].replace("_", " "))
strat = Strategy()
strat.use_dataset("google", columns=keywords)
click.secho("Analysis Google Trends:\n{}".format(keywords), fg="white")
strat.run()
from kryptos.strategy import Strategy
from kryptos.strategy.indicators import technical
from catalyst.api import order_target_percent, order, record
import logbook
log = logbook.Logger("BUY_LOW_SELL_HIGH")
log.level = logbook.INFO
strat = Strategy("BUY_LOW_SELL_HIGH", data_frequency="daily")
rsi = technical.get_indicator("RSI")
rsi.update_param("timeperiod", 14)
strat.add_market_indicator(rsi)
@strat.init
def init(context):
context.TARGET_POSITIONS = 30
context.PROFIT_TARGET = 0.1
context.SLIPPAGE_ALLOWED = 0.02
context.cost_basis = None
context.buy_increment = None
context.i = 0
@strat.handle_data
def handle_data(context, data):
context.i += 1
from kryptos.strategy import Strategy
from kryptos.strategy.indicators import technical
from catalyst.api import order_target_percent, order, record, get_open_orders
import logbook
log = logbook.Logger("DUAL_MOVING_AVG")
log.level = logbook.INFO
strat = Strategy("DUAL_MOVING_AVG", data_frequency="daily")
#
# short_avg = technical.get_indicator('SMA')
# long_avg = technical.get_indicator('SMA')
#
# strat.add_market_indicator(short_avg)
# strat.add_market_indicator(long_avg)
strat.load_json_file('dual_moving_average.json')
@strat.init
def init(context):
context.i = 0
context.base_price = None
@strat.handle_data
def trade_logic(context, data):
# define the windows for the moving averages
short_window = 50
# 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.
import time
import talib
from kryptos.strategy import Strategy
from kryptos.strategy.indicators import technical
from catalyst.api import order_target_percent, order, record, get_open_orders, symbol
import logbook
strat = Strategy("MEAN_REVERSION", data_frequency="daily")
strat.load_json_file('mean_reversion.json')
log = logbook.Logger(strat.name)
# To run an algorithm in Catalyst, you need two functions: initialize and
# handle_data.
@strat.initialize
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.
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from logbook import Logger
from catalyst import run_algorithm
from catalyst.api import (
record,
symbol,
order_target_percent,
)
from catalyst.exchange.utils.stats_utils import extract_transactions
from kryptos.strategy import Strategy
strat = Strategy('PAPER')
NAMESPACE = 'dual_moving_average'
log = Logger(NAMESPACE)
def initialize(context):
context.i = 0
context.asset = symbol('ltc_usd')
context.base_price = None
def handle_data(context, data):
# define the windows for the moving averages
short_window = 50
long_window = 200
from kryptos.strategy import Strategy
from kryptos.strategy.indicators import technical
from catalyst.api import order_target_percent, record
import logbook
log = logbook.Logger("BEAR_MARKET")
log.level = logbook.INFO
strat = Strategy("BEAR_MARKET", data_frequency="daily")
@strat.init
def init(context):
log.info("Algo is being initialzed, setting up context")
context.i = 0
context.IS_MARKET_BEAR = False
@strat.handle_data
def handle_data(context, data):
log.debug("Processing new trading step")
context.i += 1
# Get price history for the last two months. Find peak, bottom, and last
# prices for the period
price_history = data.history(context.asset,
fields="price",
bar_count=60,
frequency="1d")
context.peak = price_history.max()
from kryptos.strategy import Strategy
from kryptos.strategy.indicators import technical
import logbook
log = logbook.Logger('EXAMPLE')
log.level = logbook.INFO
strat = Strategy('Simple Stragey', data_frequency='daily')
bbands = technical.get_indicator('BBANDS')
bbands.update_param('matype', 'EMA')
stoch = technical.get_indicator('STOCH')
strat.add_market_indicator(bbands)
strat.add_market_indicator(stoch)
strat.use_dataset('quandl', columns=['MKTCP'])
strat.use_dataset('google', columns=['bitcoin futures'])
strat.add_data_indicator('google', 'relchange', col='bitcoin futures')
# trading config can be set via json or api
# Note that minute data is not supported for external datasets
# strat.trading_info['CAPITAL_BASE'] = 10000
# strat.trading_info['DATA_FREQ'] = 'minute'
# strat.trading_info['HISTORY_FREQ'] = '1m'
# strat.trading_info['START'] = '2017-12-10'
# strat.trading_info['END'] = '2017-12-11'
import talib as ta
from kryptos.strategy import Strategy
from kryptos.strategy.indicators import technical
from catalyst.api import order_target_percent, order, record, get_open_orders
import logbook
strat = Strategy("BBANDS_PSAR", data_frequency="daily")
strat.load_json_file('bbands_psar.json')
log = logbook.Logger(strat.name)
@strat.init
def initialize(context):
context.swallow_errors = True
context.errors = []
# Bars to look at per iteration should be bigger than SMA_SLOW
# context.BARS = 365
context.ORDER_SIZE = 0.5
# context.SLIPPAGE_ALLOWED =
@strat.handle_data
def trade_logic(context, data):
log.info("handling bar {}".format(data.current_dt))
from kryptos.strategy import Strategy
from kryptos.strategy.signals import utils
from kryptos.utils import viz
import matplotlib.pyplot as plt
config = './sma_crossover.json'
strat = Strategy()
strat.load_json_file(config)
sma_fast = strat.indicator('SMA_FAST')
sma_slow = strat.indicator('SMA_SLOW')
@strat.signal_sell(override=True)
def signal_sell(context, data):
return utils.cross_below(sma_fast.outputs.SMA_FAST,
sma_slow.outputs.SMA_SLOW)
@strat.signal_buy(override=True)
def signal_buy(context, data):
return utils.cross_above(sma_fast.outputs.SMA_FAST,
sma_slow.outputs.SMA_SLOW)
@strat.analyze(num_plots=1)
def extra_plot(context, results, pos):
viz.plot_column(results,
'SMA_FAST',
def load_from_cli(
market_indicators,
machine_learning_models,
dataset,
columns,
data_indicators,
json_file,
python_script,
):
strat = Strategy()
if python_script is not None:
strat = get_strat(python_script)
columns = list(columns)
for i in market_indicators:
click.echo(f"Attaching indicator: {i.upper()}")
strat.add_market_indicator(i.upper())
for i in machine_learning_models:
click.echo(f"Attaching ml model: {i.upper()}")
strat.add_ml_models(i.upper())
# currently assigns -i indicator to the column provided at the same index
if dataset is not None:
click.echo(f"Using dataset: {dataset}")
strat.use_dataset(dataset, columns)
for i, ind in enumerate(data_indicators):
strat.add_data_indicator(dataset, ind.upper(), col=columns[i])
if json_file is not None:
click.echo(f"Loading from json file {json_file}")
strat.load_json_file(json_file)
click.secho(strat.serialize(), fg="white")
return strat
from kryptos.strategy import Strategy
from kryptos.strategy.indicators import technical
from catalyst.api import order_target_percent, order, record, get_open_orders
import logbook
log = logbook.Logger("DYNAMIC_REBALANCE")
log.level = logbook.INFO
strat = Strategy("DYNAMIC_REBALANCE", data_frequency="daily")
@strat.init
def initialize(context):
context.i = 0
context.base_price = None
@strat.handle_data
def trade_logic(context, data):
# Cancel any outstanding orders
orders = get_open_orders(context.asset) or []
for order in orders:
cancel_order(order)
# Define base price and make initial trades to achieve target investment ratio of 0.5
order_target_percent(context.asset, 0.5)
# Retrieve current asset price from pricing data
price = data.current(context.asset, "price")
from kryptos.strategy import Strategy
from kryptos.strategy.indicators import technical
from catalyst.api import order_target_value, record
import logbook
log = logbook.Logger("BUY_AND_HODL")
log.level = logbook.INFO
strat = Strategy("BUY_AND_HODL", data_frequency="daily")
@strat.init
def init(context):
log.info("Algo is being initialzed, setting up context")
context.TARGET_HODL_RATIO = 0.8
context.RESERVE_RATIO = 1.0 - context.TARGET_HODL_RATIO
context.starting_cash = context.portfolio.starting_cash
context.target_hodl_value = context.TARGET_HODL_RATIO * context.starting_cash
context.reserve_value = context.RESERVE_RATIO * context.starting_cash
context.is_buying = True
context.i = 0
@strat.handle_data
def handle_data(context, data):
context.i += 1
if context.i == 1:
from kryptos.strategy import Strategy
from kryptos.strategy.indicators import technical
# from kryptos.strategy.signals import utils
# from kryptos.utils import viz
# import matplotlib.pyplot as plt
import logbook
log = logbook.Logger('EXAMPLE')
log.level = logbook.INFO
strat = Strategy('MacdFix')
macdfix_9 = technical.get_indicator('MACDFIX', label='MACDFIX_9')
macdfix_18 = technical.get_indicator('MACDFIX', label='MACDFIX_18')
macdfix_18.update_param('signalperiod', 18)
strat.add_market_indicator(macdfix_9)
strat.add_market_indicator(macdfix_18)
@strat.init
def init(context):
log.info('Algo is being initialzed, setting up context')
context.i = 0
@strat.handle_data
def handle_data(context, data):
log.debug('Processing new trading step')
context.i += 1