def handle_data(context, data):
# Compute averages
# data.history() has to be called with the same params
# from above and returns a pandas dataframe.
short_mavg = data.history(context.asset,
'price',
bar_count=100,
frequency="1m").mean()
long_mavg = data.history(context.asset,
'price',
bar_count=300,
frequency="1m").mean()
# Trading logic
if short_mavg > long_mavg:
# order_target orders as many shares as needed to
# achieve the desired number of shares.
order_target(context.asset, 100)
elif short_mavg < long_mavg:
order_target(context.asset, 0)
# Save values for later inspection
record(AAPL=data.current(context.asset, 'price'),
short_mavg=short_mavg,
long_mavg=long_mavg)
def handle_data(context, data):
# The handle_data method is called by pylivetrader every minute when
# new data is received. This is where we'll execute our trading logic. For
# an explanation of pylivetrader function scheduling, please see here:
# https://github.com/alpacahq/pylivetrader#run.
# Compute averages
# data.history() will return a pandas dataframe with price information.
# pandas' EWM method will give us our exponential moving averages.
# Calculate short-term EMA (using data from the past 12 minutes.)
short_periods = 12
short_data = data.history(context.asset,
'price',
bar_count=short_periods,
frequency="1m")
short_ema = pd.Series.ewm(short_data, span=short_periods).mean().iloc[-1]
# Calculate long-term EMA (using data from the past 26 minutes.)
long_periods = 26
long_data = data.history(context.asset,
'price',
bar_count=long_periods,
frequency="1m")
long_ema = pd.Series.ewm(long_data, span=long_periods).mean().iloc[-1]
macd = short_ema - long_ema
# Trading logic
if macd > 0:
# order_target_percent allocates a specified percentage of your
# portfolio to a long position in a given asset. (A value of 1
# means that 100% of your portfolio will be allocated.)
order_id = order_target(context.asset, BUY_AMOUNT)
if order_id:
log.info("Bought {} shares of {}".format(BUY_AMOUNT,
context.asset.symbol))
elif macd < 0:
# You can supply a negative value to short an asset instead.
order_id = order_target(context.asset, 0)
if order_id:
log.info("Closed position for {}".format(context.asset.symbol))
# Save values for later inspection
record(AA=data.current(context.asset, 'price'),
short_mavg=short_ema,
long_mavg=long_ema)
def handle_data(context, data):
# Morning Margin Check (UTC timezone) - LONG ONLY
if get_datetime().hour == 14 and get_datetime().minute == 35:
context.requiredMargin = marginRequirements(context.portfolio) * 3.0
if context.portfolio.cash < 0.:
context.usedMargin = abs(context.portfolio.cash)
else:
context.usedMargin = 0.
if context.requiredMargin < context.usedMargin:
log.warn('MARGIN REQUIREMENTS EXCEEDED. ' +\
'Used Margin = ' + str(context.usedMargin) +\
' Allowable Margin = ' + str(context.requiredMargin))
# Liquidate if total value falls 10% or more (disable margin use after 5% loss)
if 0.9 * (context.portfolio.positions_value +
context.portfolio.cash) > context.trailingStopPortfolioValue:
context.trailingStopPortfolioValue = 0.90 * (
context.portfolio.positions_value + context.portfolio.cash)
context.disableMarginPortfolioValue = 0.95 * (
context.portfolio.positions_value + context.portfolio.cash)
if (context.portfolio.positions_value +
context.portfolio.cash) < context.trailingStopPortfolioValue:
log.warn('*** L I Q U I D A T E ***')
liquidate(context.portfolio)
context.trailingStopPortfolioValue = 0.90 * (
context.portfolio.positions_value + context.portfolio.cash)
if (context.portfolio.positions_value +
context.portfolio.cash) < context.disableMarginPortfolioValue:
log.info('*** MARGIN USE DISABLED ***')
context.allowableMargin = 1.
context.enableMarginPortfolioValue = 1.10 * (
context.portfolio.positions_value + context.portfolio.cash)
elif (context.portfolio.positions_value +
context.portfolio.cash) > context.enableMarginPortfolioValue:
log.info('*** MARGIN USE ENABLED ***')
context.allowableMargin = 2.
# End of Day
if get_datetime().hour == 20 and get_datetime().minute == 55:
for stock in list(data.keys()):
closeAnyOpenOrders(stock)
#if loc_dt.month != context.previous_month:
### if (get_datetime() - context.lastPortfolioUpdate) >= timedelta(weeks=context.rebalanceFrequency):
### context.lastPortfolioUpdate = get_datetime()
### log.debug('Number of secruities to be considered: ' + str(len(data.keys())))
if get_datetime().hour == 14 and get_datetime().minute == 35:
all_prices = history(250, '1d', 'price')
daily_returns = all_prices.pct_change().dropna()
dr = np.array(daily_returns)
(rr, cc) = dr.shape
expreturns, covars = assets_meanvar(dr, list(data.keys()))
R = expreturns
C = covars
rf = 0.015
expreturns = np.array(expreturns)
frontier_mean, frontier_var, frontier_weights = solve_frontier(
R, C, rf, context)
f_w = array(frontier_weights)
(row_1, col_1) = f_w.shape
# Choose an allocation along the efficient frontier
wts = frontier_weights[context.risk_tolerance]
new_weights = wts
# Set leverage to 1
leverage = sum(abs(new_weights))
portfolio_value = (context.portfolio.positions_value +
context.portfolio.cash) / leverage
record(PV=portfolio_value)
record(Cash=context.portfolio.cash)
# Reweight portfolio
i = 0
for sec in list(data.keys()):
if wts[i] < 0.01:
wts[i] = 0.0
if wts[i] < 0.01 and context.portfolio.positions[sec].amount == 0:
i = i + 1
continue
order_target_percent(sec, wts[i], None, None)
log.info('Adjusting ' + str(sec) + ' to ' + str(wts[i] * 100.0) +
'%')
i = i + 1
def rebalance(context, data):
order(symbol('AAPL'), 2)
record(AAPL=data.current(symbol('AAPL'), 'price'))