def handle_data(context, data):
# Request history for the stock
equities_hist = data.history(context.stock, "close",
context.index_average_window, "1d")
# Compute averages
short_mavg = data.history(context.stock,
'price',
bar_count=20,
frequency="1d").mean()
long_mavg = data.history(context.stock,
'price',
bar_count=50,
frequency="1d").mean()
# Trading logic
if short_mavg > long_mavg:
order_target(context.stock, 100)
elif short_mavg < long_mavg:
order_target(context.stock, 0)
# Save values for later inspection
record(AAPL=data.current(context.stock, 'price'),
short_mavg=short_mavg,
long_mavg=long_mavg)
def handle_data(context, data):
#order((symbol('AAPL'), 10))
#record(AAPL=data.current(symbol('AAPL'), 'price'))
MA1 = data[context.security].mavg(50)
MA2 = data[context.security].mavg(100)
date = str(data[context.security].datetime)[:10]
current_price = data[context.security].price
current_positions = context.portfolio.positions[symbol('AAPL')].amount
cash = context.portfolio.cash
value = context.portfolio.portfolio_value
current_pnl = context.portfolio.pnl
#code (this will come under handle_data function only)
if (MA1 > MA2) and current_positions == 0:
number_of_shares = int(cash/current_price)
order(context.security, number_of_shares)
record(date=date,MA1 = MA1, MA2 = MA2, Price=
current_price,status="buy",shares=number_of_shares,PnL=current_pnl,cash=cash,value=value)
elif (MA1 < MA2) and current_positions != 0:
order_target(context.security, 0)
record(date=date,MA1 = MA1, MA2 = MA2, Price= current_price,status="sell",shares="--",PnL=current_pnl,cash=cash,value=value)
else:
record(date=date,MA1 = MA1, MA2 = MA2, Price= current_price,status="--",shares="--",PnL=current_pnl,cash=cash,value=value)
def handle_data(context, data):
context.i += 1
if context.i < 20:
return
buy = False
sell = False
# 5일 이동평균선과 20일 이동평균선 구함
ma5 = data.history(context.sym, 'price', 5, '1d').mean()
ma20 = data.history(context.sym, 'price', 20, '1d').mean()
# 주식을 보유하지 않는 상태에서 골든크로스가 발생하면 100주 매수
if ma5 > ma20 and context.hold == False:
order_target(context.sym, 1)
context.hold = True
buy = True
# 주식을 보유한 상태에서 데드크로스가 발생하면 100주를 매도
elif ma5 < ma20 and context.hold == True:
order_target(context.sym, -1)
context.hold = False
sell = True
record(AAPL=data.current(context.sym, "price"),
ma5=ma5,
ma20=ma20,
buy=buy,
sell=sell)
def handle_data(context, data):
context.i += 1
if (context.i < 20):
return
buy = False
sell = False
ma5 = data.history(context.sym, 'price', 5, '1d').mean()
ma20 = data.history(context.sym, 'price', 20, '1d').mean()
if (ma5 > ma20 and context.hold == False):
# 100주 매수
order_target(context.sym, 100)
context.hold = True
elif (ma5 < ma20 and context.hold == True):
# 100주 매도
order_target(context.sym, -100)
context.hold = False
sell = True
record(AAPL=data.current(context.sym, 'price'),
ma5=ma5,
ma20=ma20,
buy=buy,
sell=sell)
def handle_data(context, data):
context.i += 1
if context.i < 20:
return
ma5 = history(5, '1d', 'price').mean()
ma20 = history(20, '1d', 'price').mean()
buy = False
sell = False
sym = symbol('HMD')
if ma5[sym] > ma20[sym] and context.investment == False:
order_target(sym, 1)
context.investment = True
buy = True
elif ma5[sym] < ma20[sym] and context.investment == True:
order_target(sym, -1)
context.investment = False
sell = True
record(HMD=data[sym].price,
ma5=ma5[sym],
ma20=ma20[sym],
buy=buy,
sell=sell)
def handle_data(context, data):
# Load historical pricing data for the stocks, using daily frequncy and a rolling 20 days
prices = data.history(context.stocks, 'price', bar_count=20, frequency="1d")
rsis = {}
# Loop through our list of stocks
for stock in context.stocks:
# Get the rsi of this stock.
rsi = talib.RSI(prices[stock], timeperiod=14)[-1]
rsis[stock] = rsi
current_position = context.portfolio.positions[stock].amount
# RSI is above 70 and we own shares, time to sell
if rsi > context.HIGH_RSI and current_position > 0 and data.can_trade(stock):
order_target(stock, 0)
# RSI is below 30 and we don't have any shares, time to buy
elif rsi < context.LOW_RSI and current_position == 0 and data.can_trade(stock):
order_target_percent(stock, context.target_pct_per_stock)
# record the current RSI values of each stock for later ispection
record(fb_rsi=rsis[symbol('FB')],
amzn_rsi=rsis[symbol('AMZN')],
aapl_rsi=rsis[symbol('AAPL')],
nflx_rsi=rsis[symbol('NFLX')],
googl_rsi=rsis[symbol('GOOGL')])
def handle_data(context, data):
context.i += 1
if context.i < 20:
return
buy = False
sell = False
ma5 = data.history(context.sym, 'price', 5, '1d').mean()
ma20 = data.history(context.sym, 'price', 20, '1d').mean()
if ma5 > ma20 and context.hold == False:
order_target(context.sym, 100)
context.hold = True
buy = True
elif ma5 < ma20 and context.hold == True:
order_target(context.sym, -100)
context.hold = False
sell = True
record(LKTB=data.current(context.sym, "price"),
ma5=ma5,
ma20=ma20,
buy=buy,
sell=sell)
def handle_data(context, data):
context.i += 1
if context.i < 20:
return
buy = False
sell = False
ma5 = data.history(context.asset, 'close', 5, '1d').mean()
ma20 = data.history(context.asset, 'close', 20, '1d').mean()
'''
수정 이전의 이동평균선 전략
if ma5 > ma20:
order_target(context.asset, 1)
else:
order_target(context.asset, -1)
'''
#수정 이후의 이동평균선 전략
# 구현한 전략과의 차이는 매수/매도가 일정 "구간" 동안 이뤄지는 것이
# 아니라 골든크로스/데드크로스 시점에서만 발생한다는 것입니다.
if ma5 > ma20 and context.hold == False:
order_target(context.asset, 10)
context.hold = True
elif ma5 < ma20 and context.hold == True:
order_target(context.asset, -10)
context.hold = False
record(BTC=data.current(context.asset, "close"), ma5=ma5, ma20=ma20)
def handle_data(self, data):
from zipline.api import (
order_percent,
order_target,
order_target_percent,
order_target_value,
order_value,
)
for style in [
MarketOrder(),
LimitOrder(10),
StopOrder(10),
StopLimitOrder(10, 10)
]:
with assert_raises(UnsupportedOrderParameters):
order(self.asset, 10, limit_price=10, style=style)
with assert_raises(UnsupportedOrderParameters):
order(self.asset, 10, stop_price=10, style=style)
with assert_raises(UnsupportedOrderParameters):
order_value(self.asset, 300, limit_price=10, style=style)
with assert_raises(UnsupportedOrderParameters):
order_value(self.asset, 300, stop_price=10, style=style)
with assert_raises(UnsupportedOrderParameters):
order_percent(self.asset, .1, limit_price=10, style=style)
with assert_raises(UnsupportedOrderParameters):
order_percent(self.asset, .1, stop_price=10, style=style)
with assert_raises(UnsupportedOrderParameters):
order_target(self.asset, 100, limit_price=10, style=style)
with assert_raises(UnsupportedOrderParameters):
order_target(self.asset, 100, stop_price=10, style=style)
with assert_raises(UnsupportedOrderParameters):
order_target_value(self.asset,
100,
limit_price=10,
style=style)
with assert_raises(UnsupportedOrderParameters):
order_target_value(self.asset, 100, stop_price=10, style=style)
with assert_raises(UnsupportedOrderParameters):
order_target_percent(self.asset,
.2,
limit_price=10,
style=style)
with assert_raises(UnsupportedOrderParameters):
order_target_percent(self.asset,
.2,
stop_price=10,
style=style)
def handle_data(context, data):
# Skip first 300 days to get full windows
context.i += 1
if context.i < 300:
return
# Compute averages
# data.history() has to be called with the same params
# from above and returns a pandas dataframe.
ma100 = data.history(context.asset, 'price', bar_count=100,
frequency="1d").mean()
ma300 = data.history(context.asset, 'price', bar_count=300,
frequency="1d").mean()
# Trading logic
if ma100 > ma300:
# order_target orders as many shares as needed to
# achieve the desired number of shares.
order_target(context.asset, 100)
elif ma100 < ma300:
order_target(context.asset, 0)
# Save values for later inspection
record(mydata=data.current(context.asset, 'price'),
ma100=ma100,
ma300=ma300)
def handle_data(context, data):
# Skip first 300 days to get full windows
context.i += 1
if context.i < 300:
return
# Compute averages
# history() has to be called with the same params
# from above and returns a pandas dataframe.
short_mavg = data.history(context.sym, "price", 100, "1d").mean()
long_mavg = data.history(context.sym, "price", 300, "1d").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.sym, 100)
elif short_mavg < long_mavg:
order_target(context.sym, 0)
# Save values for later inspection
record(
AAPL=data.current(context.sym, "price"),
short_mavg=short_mavg,
long_mavg=long_mavg,
)
def handle_data(context, data):
logging.debug('enter handle_data')
context.i += 1
if context.i < context.rsi_window:
return
# get the last RSI value
prices = history(context.rsi_window, '1d', 'price')
sec_rsi = talib.RSI(prices[context.security].values,
timeperiod=context.rsi_window - 1)
# buy and sell flags
buy = False
sell = False
if sec_rsi[-1] < context.LOW_RSI and not context.invested:
# RSI under 30 indicates oversold, time to buy
order_target(context.security, 1000)
logging.debug('Buying {}'.format(context.security))
context.invested = True
buy = True
elif sec_rsi[-1] > context.HIGH_RSI and context.invested:
# RSI over 70 indicates overbought, sell everything
order_target(context.security, 0)
logging.debug('Selling {}'.format(context.security))
context.invested = False
sell = True
# record data for each time increment
record(secRSI=sec_rsi[-1],
price=data[context.security].price,
buy=buy,
sell=sell)
logging.info(context.portfolio.cash)
def handle_data_bband(context, data):
context.i += 1
if context.i < 20:
return
buy = False
sell = False
sym = symbol(code)
count = int(100000 / data[sym].price)
prices = history(20, '1d', 'price')
upper, middle, lower = ta.BBANDS(
prices[sym].values,
timeperiod=20,
nbdevup=2,
nbdevdn=2,
matype=0)
if context.investment == False:
if lower[-1] > data[sym].price:
order_target(sym, count)
context.investment = True
context.buy_price = data[sym].price
buy = True
context.position = 1
else:
if (data[sym].price > context.buy_price + (context.buy_price * sell_point)):
order_target(sym, -count)
context.investment = False
sell = True
record(code=data[sym].price, upper=upper[-1], lower=lower[-1], makeBacktestingDataFrame=middle[-1], buy=buy, sell=sell)
def handle_data(context, data):
# Skip first 300 days to get full windows
date = get_datetime()
context.i += 1
if context.i < 10:
return
prices = history(25, '1d', 'price')
for sym in data:
upper, middle, lower = talib.BBANDS(
np.array(prices[sym]),
timeperiod=20,
nbdevup=2,
nbdevdn=2,
matype=0
)
potential_buy = []
buy = False
sell = False
if data[sym].price > upper[-1] and context.portfolio.positions[sym].amount == 0:
# log.info('buy')
# log.info(get_datetime())
# log.info(data[sym].price)
# log.info(upper[-1])
order_target_percent(sym, 1.0, limit_price=data[sym].price)
elif data[sym].price < middle[-1] and context.portfolio.positions[sym].amount > 0:
# log.info('sell')
# log.info(get_datetime())
# log.info(data[sym].price)
# log.info(middle[-1])
order_target(sym, 0, limit_price=data[sym].price)
def handle_data(context, data):
#trading algorithm (executed on every event)
#skip first 300 days to get full windows
context.i += 1
if context.i < 300:
return
#compute short and long moving averages:
short_mavg = history(100, '1d', 'price').mean()
long_mavg = history(300, '1d', 'price').mean()
buy = False
sell = False
#trading logic
if (short_mavg[0] > long_mavg[0]) and not context.invested:
buy = True
context.invested = True
order_target(symbol('AAPL'), 100)
elif (short_mavg[0] < long_mavg[0]) and context.invested:
sell = True
context.invested = False
order_target(symbol('AAPL'), -100)
#save values for plotting
record(AAPL = data[symbol('AAPL')].price,
short_mavg = short_mavg[0],
long_mavg = long_mavg[0],
buy=buy,
sell=sell)
def handle_data(context, data):
print context.portfolio.portfolio_value
# Skip first 300 days to get full windows
context.i += 1
if context.i < 300:
return
# Compute averages
# history() has to be called with the same params
# from above and returns a pandas dataframe.
short_mavg = history(100, '1d', 'price').mean()
long_mavg = history(300, '1d', 'price').mean()
sym = symbol('AAPL')
# Trading logic
if short_mavg[sym] > long_mavg[sym]:
# order_target orders as many shares as needed to
# achieve the desired number of shares.
order_target(sym, 100)
elif short_mavg[sym] < long_mavg[sym]:
order_target(sym, 0)
# Save values for later inspection
record(AAPL=data[sym].price,
short_mavg=short_mavg[sym],
long_mavg=long_mavg[sym])
def handle_data(context, data):
# Skip first 60 mins to get full windows
context.i += 1
if context.i < 60:
return
# 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=30, frequency="1m").mean()
long_mavg = data.history(context.asset, 'price',
bar_count=60, 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(MDJT=data.current(context.asset, 'price'),
short_mavg=short_mavg,
long_mavg=long_mavg)
def handle_data(context, panel):
#calculating moving average
MA1 = panel[context.security].mavg(50)
MA2 = panel[context.security].mavg(100)
date = str(panel[context.security].datetime)[:10]
#calculating price, pnl, portfolio value
current_price = panel[context.security].price
current_positions = context.portfolio.positions[symbol('SPY')].amount
cash = context.portfolio.cash
value = context.portfolio.portfolio_value
current_pnl = context.portfolio.pnl
#to buy stock
if (MA1 > MA2) and current_positions == 0:
number_of_shares = int(cash / current_price)
order(context.security, number_of_shares)
#recording the data
record(date=date,MA1 = MA1, MA2 = MA2, Price= current_price,status="buy",shares=number_of_shares,\
PnL=current_pnl,cash=cash,value=value)
csv_data.append([date,format(MA1, '.2f'),format(MA2, '.2f'),format(current_price, '.2f'),\
"buy",number_of_shares,format(current_pnl, '.2f'),format(cash, '.2f'),format(value, '.2f')])
#to sell stocks
elif (MA1 < MA2) and current_positions != 0:
order_target(context.security, 0)
record(date=date,MA1 = MA1, MA2 = MA2, Price= current_price,status="sell",shares="--",\
PnL=current_pnl,cash=cash,value=value)
csv_data.append([date,format(MA1, '.2f'),format(MA2, '.2f'),format(current_price, '.2f'),\
"sell","--",format(current_pnl, '.2f'),format(cash, '.2f'),format(value, '.2f')])
#do nothing just record the data
else:
record(date=date,MA1 = MA1, MA2 = MA2, Price= current_price,status="--",shares="--",\
PnL=current_pnl,cash=cash,value=value)
csv_data.append([date,format(MA1, '.2f'),format(MA2, '.2f'),format(current_price, '.2f'),\
"--","--",format(current_pnl, '.2f'),format(cash, '.2f'),format(value, '.2f')])
def rebalance(context, data):
"""
Execute orders according to our schedule_function() timing.
"""
predictions = context.predicted_returns
# Drop stocks that can not be traded
predictions = predictions.loc[data.can_trade(predictions.index)]
longs = (predictions[predictions > 0]
.sort_values(ascending=False)[:N_LONGS]
.index
.tolist())
shorts = (predictions[predictions < 0]
.sort_values()[:N_SHORTS]
.index
.tolist())
targets = set(longs + shorts)
for position in context.portfolio.positions:
if position not in targets:
order_target(position, 0)
n_longs, n_shorts = len(longs), len(shorts)
if n_longs > MIN_POSITIONS and n_shorts > MIN_POSITIONS:
for stock in longs:
order_target_percent(stock, target=1/n_longs)
for stock in shorts:
order_target_percent(stock, target=-1/n_shorts)
else:
for stock in targets:
if stock in context.portfolio.positions:
order_target(stock, 0)
def handle_data_macd(context, data):
context.i += 1
if context.i < 60:
return
buy = False
sell = False
sym = symbol(code)
count = int(100000 / data[sym].price)
prices = history(40, '1d', 'price')
macd = prices.apply(MACD, fastperiod=12, slowperiod=26, signalperiod=9)
if context.investment == False:
if macd[sym] > 0 and context.position == -1:
order_target(sym, count)
context.investment = True
context.buy_price = data[sym].price
buy = True
context.position = 1
else:
if (data[sym].price > context.buy_price + (context.buy_price * sell_point)):
order_target(sym, -count)
context.investment = False
sell = True
if macd[sym] < 0 :
context.position = -1
if macd[sym] > 0 :
context.position = 1
record(code=data[sym].price, macd=macd[sym], buy=buy, sell=sell)
def handle_data(context, data):
# Skip first 300 days to get full windows
context.i += 1
if context.i < 10:
return
# Compute averages
# history() has to be called with the same params
# from above and returns a pandas dataframe.
short_mavg = history(5, '1d', 'price').mean()
long_mavg = history(10, '1d', 'price').mean()
for sym in data:
# sym = data.keys()[0]
# sym = data.keys()[0]
# Trading logic
if short_mavg[sym] > long_mavg[sym] and not context.invested:
# order_target orders as many shares as needed to
# achieve the desired number of shares.
order_target(sym, 5000)
context.invested = True
elif short_mavg[sym] < long_mavg[sym] and context.invested:
order_target(sym, 0)
context.invested = False
def portfolio_logic(context):
longs_to_remove = []
for asset in context.longs_portfolio: # search portfolio for positions to close out
if asset not in context.longs.index:
longs_to_remove.append(asset)
order_target(asset, 0)
for asset in context.longs.index: # search context.longs for stocks to add to portfolio
order_target_percent(asset, .00025)
if asset not in context.longs_portfolio:
context.longs_portfolio[asset] = True
for key in longs_to_remove:
context.longs_portfolio.pop(key)
shorts_to_remove = []
for asset in context.shorts_portfolio: # search portfolio for positions to close out
if asset not in context.shorts.index:
shorts_to_remove.append(asset)
order_target(asset, 0)
for asset in context.shorts.index: # search context.shorts for stocks to add to portfolio
if asset not in context.shorts_portfolio:
context.shorts_portfolio[asset] = True
order_target_percent(asset, -0.00025)
for key in shorts_to_remove:
context.shorts_portfolio.pop(key)
def handle_data_magc(context, data):
context.i += 1
if context.i < 60:
return
ma20 = history(20, '1d', 'price').mean()
ma60 = history(60, '1d', 'price').mean()
buy = False
sell = False
sym = symbol(code)
count = int(100000 / data[sym].price)
if context.investment == False:
if ma20[sym] > ma60[sym] :
order_target(sym, count)
context.investment = True
context.buy_price = data[sym].price
buy = True
else:
if (data[sym].price > context.buy_price + (context.buy_price * sell_point)):
order_target(sym, -count)
context.investment = False
sell = True
record(code=data[sym].price, ma20=ma20[sym], ma60=ma60[sym], buy=buy, sell=sell)
def handle_data(context, data):
context.i += 1
if context.i < 20:
return
buy = False # 해당 거래일 매수 여부
sell = False # 해당 거래일 매도 여부
ma5 = data.history(context.sym, 'price', 5, '1d').mean()
ma20 = data.history(context.sym, 'price', 20, '1d').mean()
# 골든크로스 발생 시 100주 매수
if ma5 > ma20 and context.hold == False:
order_target(context.sym, 100)
context.hold = True
buy = True
# 데드크로스 발생 시 100주 매도
elif ma5 < ma20 and context.hold == True:
order_target(context.sym, -100)
context.hold = False
sell = True
record(AAPL=data.current(context.sym, "price"),
ma5=ma5,
ma20=ma20,
buy=buy,
sell=sell)
def handle_data(context, data):
# Skip first 300 days to get full windows
context.i += 1
dp = context.history_container.digest_panels
for k in dp.keys():
df = dp[k].buffer['price']
a = df.dropna()
print('No.',context.i,':Len.',len(a))
print('Contents:')
print(a)
print(context.history_container.buffer_panel.buffer['price'])
if context.i < 40:
return
# Compute averages
# history() has to be called with the same params
# from above and returns a pandas dataframe.
short_mavg = history(20, '1d', 'price').mean()
long_mavg = history(40, '1d', 'price').mean()
# Trading logic
if short_mavg[context.sym] > long_mavg[context.sym]:
# order_target orders as many shares as needed to
# achieve the desired number of shares.
order_target(context.sym, 100)
elif short_mavg[context.sym] < long_mavg[context.sym]:
order_target(context.sym, 0)
# Save values for later inspection
record(AAPL=data[context.sym].price,
short_mavg=short_mavg[context.sym],
long_mavg=long_mavg[context.sym])
def handle_data(context, data):
context.i += 1
if context.i < 20:
return
buy = False
sell = False
ma5 = data.history(context.sym, 'price', 5,
'1d').mean() #5-day simple moving average
ma20 = data.history(context.sym, 'price', 20,
'1d').mean() #20-day simple moving average
if ma5 > ma20 and context.hold == False: #Golden-cross
order_target(context.sym, 100)
context.hold = True
buy = True
elif ma5 < ma20 and context.hold == True: #Dead-cross
order_target(context.sym, -100)
context.hold = False
sell = True
record(AAPL=data.current(context.sym, "price"),
ma5=ma5,
ma20=ma20,
buy=buy,
sell=sell)
def handle_data(context, data):
# Определить окна для скользящих средних
short_window = 40
long_window = 100
# Пропустить первые 100 элементов, чтобы получить полное окно
context.i += 1
if context.i < long_window:
return
# Вычислим средние скользящие
short_mavg = data.history(context.asset, 'close', short_window,
'1m').mean()
long_mavg = data.history(context.asset, 'close', long_window, '1m').mean()
#Мы проверяем, какова наша позиция в нашем портфеле и соответственно
portfolio = context.portfolio
# Логика стратегии
if short_mavg > long_mavg:
# Покупаеи акцивы в размере 10
order_target(context.asset, 10)
elif short_mavg < long_mavg:
order_target(context.asset, 0)
# Сохранить значения для последующей визуализации
record(XBTUSD=data.current(context.asset, 'close'),
cash=context.portfolio.cash,
portfolio_value=context.portfolio.portfolio_value,
positions=context.portfolio.positions,
short_mavg=short_mavg,
long_mavg=long_mavg)
def handle_data(context, data):
#skip first 300 days to get full windos
context.i += 1
if context.i < 300:
return
# compute averages
# data.history() has to be called with the same params
# from above and returns a pandas data frame
short_mavg = data.history(context.asset,
'price',
bar_count=100,
frequency='1d').mean()
long_mavg = data.history(context.asset,
'price',
bar_count=300,
frequency='1d').mean()
#trading logic
if short_mavg > long_mavg:
#order_targer 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):
# Historical data
prices = data.history(context.stocks, 'price', bar_count=20, frequency='1d')
rsis = {}
# Loop thru the stocks and determine when to buy and sell them
for stock in context.stocks:
rsi = ta.RSI(prices[stock], timeperiod=14)[-1]
rsis[stock] = rsi
current_position = context.portfolio.positions[stock].amount
if rsi > context.high_rsi and current_position > 0 and data.can_trade(stock):
order_target(stock, 0)
elif rsi < context.low_rsi and current_position == 0 and data.can_trade(stock):
order_target_percent(stock, context.target_pct_per_stock)
record(fb_rsi=rsis[symbol('FB')],
amzn_rsi=rsis[symbol('AMZN')],
aapl_rsi=rsis[symbol('AAPL')],
nflx_rsi=rsis[symbol('NFLX')],
googl_rsi=rsis[symbol('GOOGL')])
def handle_data(context, data):
# Skip first 300 days to get full windows
context.i += 1
dp = context.history_container.digest_panels
for k in dp.keys():
df = dp[k].buffer['price']
a = df.dropna()
print('No.', context.i, ':Len.', len(a))
print('Contents:')
print(a)
print(context.history_container.buffer_panel.buffer['price'])
if context.i < 40:
return
# Compute averages
# history() has to be called with the same params
# from above and returns a pandas dataframe.
short_mavg = history(20, '1d', 'price').mean()
long_mavg = history(40, '1d', 'price').mean()
# Trading logic
if short_mavg[context.sym] > long_mavg[context.sym]:
# order_target orders as many shares as needed to
# achieve the desired number of shares.
order_target(context.sym, 100)
elif short_mavg[context.sym] < long_mavg[context.sym]:
order_target(context.sym, 0)
# Save values for later inspection
record(AAPL=data[context.sym].price,
short_mavg=short_mavg[context.sym],
long_mavg=long_mavg[context.sym])
def handle_data(context, data):
# Skip first 300 days to get full windows
context.i += 1
if context.i < 300:
return
# Compute averages
# history() has to be called with the same params
# from above and returns a pandas dataframe.
short_mavg = history(100, '1d', 'price').mean()
long_mavg = history(300, '1d', 'price').mean()
# price_history = data.history(assets=symbol('TEST'), fields="price", bar_count=5, frequency="1d")
# Trading logic
if short_mavg[0] > long_mavg[0]:
# order_target orders as many shares as needed to
# achieve the desired number of shares.
order_target(symbol('AAPL'), 100)
elif short_mavg[0] < long_mavg[0]:
order_target(symbol('AAPL'), 0)
# Save values for later inspection
record(AAPL=data[symbol('AAPL')].price,
short_mavg=short_mavg[0],
long_mavg=long_mavg[0])
def handle_data(context, data):
# Skip first 300 days to get full windows
context.i += 1
if context.i < 300:
return
# Compute averages
# history() has to be called with the same params
# from above and returns a pandas dataframe.
short_mavg = history(100, '1d', 'price').mean()
long_mavg = history(300, '1d', 'price').mean()
sym = symbol('AAPL')
# Trading logic
if short_mavg[sym] > long_mavg[sym]:
# order_target orders as many shares as needed to
# achieve the desired number of shares.
order_target(sym, 100)
elif short_mavg[sym] < long_mavg[sym]:
order_target(sym, 0)
# Save values for later inspection
record(AAPL=data[sym].price,
short_mavg=short_mavg[sym],
long_mavg=long_mavg[sym])
def close_positions(context, data):
""" This function is called before market close everyday and closes all open positions. """
for position in context.portfolio.positions.itervalues():
log.debug('Closing position for ' + str(position.sid) + ', amount: ' + str(position.amount) + ', cost: ' + str(position.cost_basis))
order_target(position.sid, 0)
context.my_record_vars(context, data)
def handle_data(context, data):
if context.live_trading is True:
for symbol, position in context.portfolio.positions.items():
data.current(symbol, 'price')
time.sleep(60)
positions = list(context.portfolio.positions.values())
stop_list = context.stop_loss_list
# update stop loss list
for i1, s1 in stop_list.items():
stop_list = stop_list.drop(index=[i1])
s1 -= 1
if s1 > 0:
stop_list = stop_list.append(pd.Series([s1], index=[i1]))
benchmark_dma = get_dma_returns(context, 70, data.current_dt)
if benchmark_dma < 0:
sell_all(positions, context)
return
# Sell logic
position_list = []
for position in positions:
position_list.append(position.asset)
if not position.amount > 0:
continue
if position.last_sale_price == 0:
last_price = data.history(position.asset, 'close', 1, '1d')[0]
else:
last_price = position.last_sale_price
# sell at stop loss
net_gain_loss = float("{0:.2f}".format(
(last_price - position.cost_basis) * 100 / position.cost_basis))
if net_gain_loss < -3:
order_target(position.asset, 0)
strategy.SendMessage(
'Sell Order',
'Buy all shares of {}'.format(str(position.asset.symbol)))
context.turnover_count += 1
try:
context.sector_stocks[context.pipeline_data.loc[
position.asset].sector].remove(position.asset)
print("Stop loss triggered for: " + position.asset.symbol)
# add to stop loss list to prevent re-buy
stop_loss = pd.Series([stop_loss_prevention_days],
index=[position.asset])
stop_list = stop_list.append(stop_loss)
except Exception as e:
print(e)
context.stop_loss_list = stop_list
print("Daily handle data processed for {}".format(
data.current_dt.strftime('%d/%m/%Y')))
def core_logic(context, data):
if context.live_trading is True:
for symbol, position in context.portfolio.positions.items():
data.current(symbol, 'price')
time.sleep(60)
stop_list = context.stop_loss_list
# update stop loss list
for i1, s1 in stop_list.items():
stop_list = stop_list.drop(labels=[i1])
s1 -= 1
if s1 > 0:
stop_list = stop_list.append(pd.Series([s1], index=[i1]))
positions = list(context.portfolio.positions.values())
benchmark_dma = get_dma_returns(context, 200, data.current_dt)
if benchmark_dma < 0:
sell_all(positions, context)
return
elif context.shorting_on and benchmark_dma > 1:
sell_all_etfs(positions, context)
rebalance(context, data)
# The day dma turns positive and etfs are sold, there aren't any stocks left in portfolio, hence return
return
position_list = []
# Sell logic
for position in positions:
position_list.append(position.asset)
if not position.amount > 0:
continue
if position.last_sale_price == 0:
last_price = data.history(position.asset, 'close', 1, '1d')[0]
else:
last_price = position.last_sale_price
if last_price == 0:
raise ValueError("Prices not available")
net_gain_loss = float("{0:.2f}".format((last_price - position.cost_basis) * 100 / position.cost_basis))
if net_gain_loss < -3:
order_target(position.asset, 0)
strategy.SendMessage('Stop loss Sell Order', 'Sell all shares of {}'.format(str(position.asset.symbol)))
context.turnover_count += 1
try:
context.sector_stocks[context.pipeline_data.loc[position.asset].sector].remove(position.asset)
print("Stop loss triggered for: {} on {}".format(position.asset.symbol,
data.current_dt.strftime('%d/%m/%Y')))
stop_loss = pd.Series([stop_loss_prevention_days], index=[position.asset])
stop_list = stop_list.append(stop_loss)
except Exception as e:
print(e)
context.stop_loss_list = stop_list
print("Daily handle data processed for {}".format(data.current_dt.strftime('%d/%m/%Y')))
def handle_data(self, data):
from zipline.api import (
order_percent,
order_target,
order_target_percent,
order_target_value,
order_value,
)
for style in [MarketOrder(), LimitOrder(10),
StopOrder(10), StopLimitOrder(10, 10)]:
with assert_raises(UnsupportedOrderParameters):
order(self.asset, 10, limit_price=10, style=style)
with assert_raises(UnsupportedOrderParameters):
order(self.asset, 10, stop_price=10, style=style)
with assert_raises(UnsupportedOrderParameters):
order_value(self.asset, 300, limit_price=10, style=style)
with assert_raises(UnsupportedOrderParameters):
order_value(self.asset, 300, stop_price=10, style=style)
with assert_raises(UnsupportedOrderParameters):
order_percent(self.asset, .1, limit_price=10, style=style)
with assert_raises(UnsupportedOrderParameters):
order_percent(self.asset, .1, stop_price=10, style=style)
with assert_raises(UnsupportedOrderParameters):
order_target(self.asset, 100, limit_price=10, style=style)
with assert_raises(UnsupportedOrderParameters):
order_target(self.asset, 100, stop_price=10, style=style)
with assert_raises(UnsupportedOrderParameters):
order_target_value(self.asset, 100,
limit_price=10,
style=style)
with assert_raises(UnsupportedOrderParameters):
order_target_value(self.asset, 100,
stop_price=10,
style=style)
with assert_raises(UnsupportedOrderParameters):
order_target_percent(self.asset, .2,
limit_price=10,
style=style)
with assert_raises(UnsupportedOrderParameters):
order_target_percent(self.asset, .2,
stop_price=10,
style=style)
def handle_data(context, data):
lookback = 200
percent_baseline = 0.5 # RSI retracement amount
low = 30 # RSI low value
RSI_time_period = 3
divergence_strength = 0
context.days_traded += 1
prices_close = data.history(assets=context.stock,
bar_count=lookback,
frequency='1d',
fields='price')
prices = data.history(assets=context.stock,
bar_count=lookback,
frequency='1d',
fields='price')
rsi = RSI(prices, RSI_time_period)
rsi_prices = create_rsi_price_array(rsi, prices_close)
global num_shares
try:
num_shares = math.floor(context.max_notional /
data[context.stock].close_price)
except ValueError:
num_shares = 0
if num_shares > 0:
set_trailing_stop(context, data)
if data[context.stock].price < context.stop_price:
order_target(context.stock, 0) # Sell all the stock
ws.send(msg_placeholder %
("Sold because Price: %s is less than Stop Price: %s" %
(data[context.stock].price, context.stop_price)))
context.stop_price = 0
current_shares = context.portfolio.positions[context.stock].amount
divergence = bullish_divergence(rsi_prices, percent_baseline, low)
if divergence is not None and divergence[1] > 1 and current_shares == 0:
troughs = divergence[0]
trough_diff = troughs[1] - troughs[0] # strength of divergence
if trough_diff > divergence_strength:
order_target_percent(context.stock, 1.0)
ws.send(msg_placeholder % (
"Bought because Divergence Strength: %s is greater than %s"
% (trough_diff, divergence_strength)))
def close_all(context, data, exclude=[], log=None):
"""
Close all positions, except those in the exclude list
"""
if log:
print_portfolio(log, context)
for stock in context.portfolio.positions:
if stock in exclude:
continue
if data.can_trade(stock):
order_target(stock, 0)
def handle_data(context, data):
context.cur_time += 1
month = get_datetime().date().month
is_january = (month == 1)
new_prices = np.array([data[symbol(symbol_name)].price for symbol_name in context.symbols], dtype='float32')
record(Prices=new_prices)
new_prices = new_prices.reshape((context.N_STOCKS, 1))
# print context.returns_history.shape
# print new_prices.shape
# print context.previous_prices.shape
context.returns_history = np.concatenate([context.returns_history, new_prices / context.previous_prices], axis=1)
context.previous_prices = new_prices
if context.month != month:
# Trading in the beginning of month
context.month_sizes.append(context.day_of_month)
context.day_of_month = 1
context.count_month += 1
context.month_sizes.append(context.day_of_month)
context.day_of_month = 1
if context.count_month > N_MONTHS:
# Deleting too old returns
if context.count_month > N_MONTHS + 1:
context.returns_history = np.delete(context.returns_history, range(context.month_sizes[-14]), axis=1)
model_input = preprocess_data_for_model(context.returns_history, context.month_sizes[-13:], context.scaler)
is_january_column = np.array([is_january] * context.N_STOCKS).reshape((context.N_STOCKS, 1))
model_input = np.concatenate([is_january_column, model_input], axis=1)
# print 'Input shape', model_input.shape
predicted_proba = context.model.predict_proba(model_input)
# print predicted_proba
'''
half_trade = len(context.symbols) * 1 / 10
args_sorted = np.argsort(predicted_proba[:, 0])
buy_args = args_sorted[:half_trade]
sell_args = args_sorted[-half_trade:]
for arg in buy_args:
order_target(symbol(context.symbols[arg]), 1)
for arg in sell_args:
order_target(symbol(context.symbols[arg]), -1)
'''
for i in range(context.N_STOCKS):
if predicted_proba[i, 0] > 0.5:
order_target(symbol(context.symbols[i]), 1)
else:
order_target(symbol(context.symbols[i]), -1)
else:
context.day_of_month += 1
context.month = month
def handle_terminations(context):
idx_to_remove = []
for idx, position in enumerate(context.to_terminate):
ticker = position[0]
countdown = position[1] - 1
if countdown == 0:
order_target(ticker, 0)
idx_to_remove.append(idx)
else:
context.to_terminate[idx] = (ticker, countdown)
context.to_terminate = [
data for idx, data in enumerate(context.to_terminate) if idx not in idx_to_remove
]
def handle_data(context, data):
context.i += 1
if context.i < 20:
return
ma5 = data.history(context.sym, 'price', 5, '1d').mean()
ma20 = data.history(context.sym, 'price', 20, '1d').mean()
if ma5 > ma20:
order_target(context.sym, 1)
else:
order_target(context.sym, -1)
record(AAPL=data.current(context.sym, "price"), ma5=ma5, ma20=ma20)
def handle_data(context, data):
average_price = data[context.security].mavg(5)
current_price = data[context.security].price
cash = context.portfolio.cash
if current_price > 1.01*average_price and cash > current_price:
number_of_shares = int(cash/current_price)
order(context.security, +number_of_shares)
log.info("Buying %s" % (context.security.symbol))
elif current_price < average_price:
order_target(context.security, 0)
log.info("Selling %s" % (context.security.symbol))
record(stock_price=data[context.security].price)
def handle_data(context, data):
# order(symbol('035720'), 1)
context.i += 1
if context.i < 20:
return
ma5 = history(5, '1d', 'price').mean()
ma20 = history(20, '1d', 'price').mean()
sym = symbol(context.stockCd)
record(kakao=data[sym].price, ma5=ma5[sym], ma20=ma20[sym])
if ma5[sym] > ma20[sym]:
order_target(sym, 1)
else:
order_target(sym, -1)
def fire_sale(context, data):
# Sell everything in the portfolio, at market price
show_spacer = False
for stock in data:
if context.portfolio.positions[stock].amount != 0:
order_target(stock, 0.0)
value_of_open_orders(context, data)
availibleCash = context.portfolio.cash-context.cashCommitedToBuy-context.cashCommitedToSell
print(" * Exit {0:,d} of {1:s} at ${2:,.2f} for ${3:,.2f} / ${4:,.2f} @ {5:s}"\
.format(int(context.portfolio.positions[stock].amount),
stock,
data[stock]['price'],
data[stock]['price']*context.portfolio.positions[stock].amount,
availibleCash,
context.exchange_time))
show_spacer = True
if show_spacer:
print('') #This just gives us a space to make reading the 'daily' log sections more easily
def handle_data(context, data):
# Save price to window
context.short_window.append(data[symbol('AAPL')].price)
context.long_window.append(data[symbol('AAPL')].price)
# Compute averages
short_mavg = np.mean(context.short_window)
long_mavg = np.mean(context.long_window)
# Trading logic
if short_mavg > long_mavg:
order_target(symbol('AAPL'), 100)
elif short_mavg < long_mavg:
order_target(symbol('AAPL'), 0)
# Save values for later inspection
record(AAPL=data[symbol('AAPL')].price,
short_mavg=short_mavg,
long_mavg=long_mavg)
def handle_data(context, data):
# 获取股票的收盘价
close_data = history(12,'1d','close')
# 取得过去五天的平均价格
ma5 = close_data[-6:-2].mean()
# 取得过去10天的平均价格
ma10 = close_data[-11:-2].mean()
# 取得当前的现金
print get_datetime(),ma5,ma10
cash = context.portfolio.cash
#print ma5[sid(symbol(context.security))],ma10[sid(stock)],cash,symbol(context.security)
#如果当前有余额，并且五日均线大于十日均线
if ma5[sid(symbol(context.security))] > ma10[sid(symbol(context.security))]:
order_value(symbol(context.security), cash)
# 如果五日均线小于十日均线，并且目前有头寸
elif ma5[sid(symbol(context.security))] < ma10[sid(symbol(context.security))]:
# 全部卖出
order_target(symbol(context.security), 0)
def handle_data(context, data):
context.i += 1
if context.i < 20:
return
buy = False
sell = False
ma5 = data.history(context.sym, 'price', 5, '1d').mean()
ma20 = data.history(context.sym, 'price', 20, '1d').mean()
if ma5 > ma20 and context.hold == False:
order_target(context.sym, 100)
context.hold = True
buy = True
elif ma5 < ma20 and context.hold == True:
order_target(context.sym, -100)
context.hold = False
sell = True
record(AAPL=data.current(context.sym, "price"), ma5=ma5, ma20=ma20, buy=buy, sell=sell)
def handle_data(context, data):
# Skip first 300 days to get full windows
#print data['Close'].dt
#context.i += 1
#if context.i < 300:
# return
# Compute averages
# history() has to be called with the same params
# from above and returns a pandas dataframe.
sym = symbol('Close')
if data['short_mavg'].price > data['long_mavg'].price:
# order_target orders as many shares as needed to
# achieve the desired number of shares.
order_target(context.sym, 1000)
elif data['short_mavg'].price < data['long_mavg'].price:
order_target(context.sym, 0)
# Save values for later inspection
record(Close=data[context.sym].price,
short_mavg=data['short_mavg'].price,
long_mavg=data['long_mavg'].price)
def handle_data(context, data):
context.i += 1
if context.i < 20:
return
ma5 = history(5,'1d','price').mean()
ma20 = history(20, '1d', 'price').mean()
buy = False
sell = False
sym = symbol('HMD')
if ma5[sym] > ma20[sym] and context.investment == False:
order_target(sym, 1)
context.investment = True
buy = True
elif ma5[sym] < ma20[sym] and context.investment == True:
order_target(sym, -1)
context.investment = False
sell = True
record(HMD=data[sym].price, ma5=ma5[sym], ma20=ma20[sym], buy=buy, sell=sell)
def handle_data(context, data):
logging.debug('enter handle_data')
context.i += 1
if context.i < context.rsi_window:
return
# get the last RSI value
prices = history(context.rsi_window, '1d', 'price')
sec_rsi = talib.RSI(
prices[context.security].values,
timeperiod=context.rsi_window - 1)
# buy and sell flags
buy = False
sell = False
if sec_rsi[-1] < context.LOW_RSI and not context.invested:
# RSI under 30 indicates oversold, time to buy
order_target(context.security, 1000)
logging.debug('Buying {}'.format(context.security))
context.invested = True
buy = True
elif sec_rsi[-1] > context.HIGH_RSI and context.invested:
# RSI over 70 indicates overbought, sell everything
order_target(context.security, 0)
logging.debug('Selling {}'.format(context.security))
context.invested = False
sell = True
# record data for each time increment
record(secRSI=sec_rsi[-1],
price=data[context.security].price,
buy=buy,
sell=sell)
logging.info(context.portfolio.cash)
def handle_data(context, data):
# Skip first 300 days to get full windows
context.i += 1
if context.i < 300:
return
# Compute averages
# history() has to be called with the same params
# from above and returns a pandas dataframe.
short_mavg = data.history(context.sym, 'price', 100, '1d').mean()
long_mavg = data.history(context.sym, 'price', 300, '1d').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.sym, 100)
elif short_mavg < long_mavg:
order_target(context.sym, 0)
# Save values for later inspection
record(AAPL=data.current(context.sym, "price"),
short_mavg=short_mavg,
long_mavg=long_mavg)
def handle_data(context, data):
# Skip first 300 days to get full windows
context.n += 1
if context.n < 359:
return
context.day += 1
if context.day < 7:
return
# Compute averages
# history() has to be called with the same params
# from above and returns a pandas dataframe.
historical_data =history(365, '1d', 'price')
pastReturns = (historical_data - historical_data.shift(-1)) / historical_data.shift(-1)
short_mavg = history(42, '1d', 'price').mean()
long_mavg = history(84, '1d', 'price').mean()
diff = short_mavg / long_mavg - 1
diff = diff.dropna()
diff.sort()
buys = diff [diff > 0.03]
sells = diff[diff < -0.03]
buy_length = min(context.stocks_to_long, len(buys))
short_length = min(context.stocks_to_short, len(sells))
buy_weight = 1.0/buy_length if buy_length != 0 else 0
short_weight = -1.0/short_length if short_length != 0 else 0
buys.sort(ascending=False)
sells.sort()
buys = buys.iloc[:buy_length] if buy_weight != 0 else None
sells = sells.iloc[:short_length] if short_weight != 0 else None
stops = historical_data.iloc[-1] * 0.05
for i in range(len(context.syms)):
#: If the security exists in our sells.index then sell
if sells is not None and context.syms[i] in sells.index:
#print ('SHORT: %s'%context.syms[i])
order_target_percent(context.syms[i], short_weight)
#print 'sell'
#: If the security instead, exists in our buys index, buy
elif buys is not None and context.syms[i] in buys.index:
# print ('BUYS: %s'%context.syms[i])
order_target_percent(context.syms[i], buy_weight)
#print 'nothing'
#: If the security is in neither list, exit any positions we might have in that security
else:
order_target(context.syms[i], 0)
context.day = 0
# Keep track of the number of long and short positions in the portfolio
longs = shorts = 0
for position in context.portfolio.positions.itervalues():
if position.amount > 0:
longs += 1
if position.amount < 0:
shorts += 1
record(short_mavg=short_mavg[context.syms[1]], long_mavg=long_mavg[context.syms[1]], portfoliovalue = (context.portfolio.returns), long_count=longs, short_count=shorts)
def handle_data(context, data):
order_target(context.sym, 1)