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()
log.info("buying position cheaper than cost basis {} < {}".format(
context.price, context.cost_basis))
order(
asset=context.asset,
amount=context.buy_increment,
limit_price=context.price * (1 + context.SLIPPAGE_ALLOWED),
)
@strat.sell_order
def sell(context):
profit = (context.price * context.position.amount) - (
context.cost_basis * context.position.amount)
log.info("closing position, taking profit: {}".format(profit))
order_target_percent(asset=context.asset,
target=0,
limit_price=context.price *
(1 - context.SLIPPAGE_ALLOWED))
@strat.analyze()
def analyze(context, results, pos):
ending_cash = results.cash[-1]
log.info("Ending cash: ${}".format(ending_cash))
log.info("Completed for {} trading periods".format(context.i))
if __name__ == "__main__":
log.info("Strategy Schema:\n{}".format(strat.serialize()))
strat.run()