def before_trading_start(context, data):
log.info("RUNNING before_trading_start")
# Prevent running more than once a day:
# https://docs.alpaca.markets/platform-migration/zipline-to-pylivetrader/#deal-with-restart
today = get_datetime().floor('1D')
last_date = getattr(context, 'last_date', None)
if today == last_date:
log.info(
"Skipping before_trading_start because it's already ran today")
return
context.output = pipeline_output('my_pipeline')
context.stocks_worst = context.output[
context.output['stocks_worst']].index.tolist()
context.MyCandidate = cycle(context.stocks_worst)
# Update ages
for stock in context.portfolio.positions:
if stock in context.age:
context.age[stock] += 1
else:
log.info("Could not find %s in context.age" % stock.symbol)
context.age[stock] = 1
# Remove stale ages
context.age = {
stock: age
for stock, age in context.age.items()
if stock not in context.portfolio.positions
}
# Track the last run
context.last_date = today
def before_trading_start(context, data):
# over simplistic tracking of position age
if not hasattr(context, 'age') or not context.age:
context.age = {}
today = get_datetime().floor('1D')
last_date = getattr(context, 'last_date', None)
if today != last_date:
# Gets our pipeline output every day.
context.output = pipeline_output('my_pipeline')
context.stocks_worst = context.output[
context.output['stocks_worst']].index.tolist()
context.stocks_worst_weight = my_compute_weights(context)
context.MyCandidate = cycle(context.stocks_worst)
context.LowestPrice = context.MyLeastPrice # reset beginning of day
print(len(context.portfolio.positions))
for stock in context.portfolio.positions:
CurrPrice = float(data.current([stock], 'price'))
if CurrPrice < context.LowestPrice:
context.LowestPrice = CurrPrice
if stock in context.age:
context.age[stock] += 1
else:
context.age[stock] = 1
for stock in context.age:
if stock not in context.portfolio.positions:
context.age[stock] = 0
message = 'stock.symbol: {symbol} : age: {age}'
log.info(
message.format(symbol=stock.symbol, age=context.age[stock]))
context.last_date = today
def handle_data(context, data):
t = get_datetime()
print('current time', t)
assets = symbols('AAPL', 'GOOG', 'MSFT', 'AMZN')
safe = symbol('TLT')
assets_hist = data.history(assets, 'price', 5, '1m')
print(assets_hist)
'''
def handle_data(context, data):
today = get_datetime().floor('1D')
last_date = getattr(context, 'last_ran_buy', None)
if today != last_date:
my_rebalance(context, data)
context.last_ran_buy = today
else:
for stock, position in context.portfolio.positions.items():
if get_open_orders(stock):
continue
current_price = data.current(stock, 'price')
cost_basis = position.cost_basis
stop_loss = -0.05
current_loss = (current_price - cost_basis) / cost_basis
if current_loss < stop_loss:
log.info('selling early %s (%.2f)' % (stock.symbol, current_loss))
order_target_percent(stock, 0)
def before_trading_start(context, data):
log.info("RUNNING before_trading_start")
# Prevent running more than once a day:
# https://docs.alpaca.markets/platform-migration/zipline-to-pylivetrader/#deal-with-restart
today = get_datetime().floor('1D')
last_date = getattr(context, 'last_date', None)
if today == last_date:
log.info("Skipping before_trading_start because it's already ran today")
return
pipe_results = pipeline_output('my_pipeline')
log.info(pipe_results)
context.longs = []
for sec in pipe_results[pipe_results['longs']].index.tolist():
context.longs.append(sec)
context.shorts = []
for sec in pipe_results[pipe_results['shorts']].index.tolist():
context.shorts.append(sec)
# Track the last run
context.last_date = today
def clear(context, data):
log.info(get_datetime())
log.info("clearing")
for s in context.currently_holding:
order_target_percent(s, 0)
context.currently_holding = set()
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