def handle_data(context, data):
trailing_window = data.history(context.asset, 'price', 40, '1d')
if trailing_window.isnull().values.any():
return
short_ema = data.history(context.asset,
'price',
bar_count=20,
frequency="1d").mean()
long_ema = data.history(context.asset,
'price',
bar_count=40,
frequency="1d").mean()
# short_ema = EMA(trailing_window.values, timeperiod=20)
# long_ema = EMA(trailing_window.values, timeperiod=40)
buy = False
sell = False
if (short_ema[-1] > long_ema[-1]) and not context.invested:
order(context.asset, 100)
context.invested = True
buy = True
elif (short_ema[-1] < long_ema[-1]) and context.invested:
order(context.asset, -100)
context.invested = False
sell = True
record(AAPL=data.current(context.asset, "price"),
short_ema=short_ema[-1],
long_ema=long_ema[-1],
buy=buy,
sell=sell)
def rebalance(context, data):
log.info("rebalance) " + str(normalize_date(get_datetime())))
# Pipeline data will be a dataframe with boolean columns named 'longs' and
# 'shorts'.
pipeline_data = context.pipeline_data.dropna()
log.info(pipeline_data.head())
all_assets = pipeline_data.index
longs = all_assets[pipeline_data.longs]
shorts = all_assets[pipeline_data.shorts]
record(universe_size=len(all_assets))
# Build a 2x-leveraged, equal-weight, long-short portfolio.
one_third = 1.0 / 3.0
for asset in longs:
order_target_percent(asset, one_third)
for asset in shorts:
order_target_percent(asset, -one_third)
# Remove any assets that should no longer be in our portfolio.
portfolio_assets = longs | shorts
positions = context.portfolio.positions
for asset in viewkeys(positions) - set(portfolio_assets):
# This will fail if the asset was removed from our portfolio because it
# was delisted.
if data.can_trade(asset):
order_target_percent(asset, 0)
def rebalance(context, data):
history = data.history(assets=context.asset,
fields=['close'],
bar_count=context.tema_window * 4,
frequency='1d')
date = history.index.values[-1]
close = history['close'].values
tema = ta.TEMA(close, timeperiod=context.tema_window)
current_price = data[context.asset].price
record(price=current_price)
buy_signal_triggered = False
sell_signal_triggered = False
if current_price > tema[-1]:
buy_signal_triggered = True
elif current_price < tema[-1]:
sell_signal_triggered = True
current_position = context.portfolio.positions[context.asset].amount
if buy_signal_triggered and current_position == 0:
print(str(date) + '==>Buy')
order_target_percent(context.asset, 1.0)
elif sell_signal_triggered and current_position > 0:
print(str(date) + '==>Sell')
order_target_percent(context.asset, 0.0)
else:
print("No trading")
def rebalance(context, data):
history = data.history(assets=context.asset,
fields=['open', 'high', 'low', 'close'],
bar_count=context.cci_window + 1,
frequency='1d')
date = history.index.values[-1]
high = history['high'].values
low = history['low'].values
close = history['close'].values
# 计算CCI指标
cci = ta.CCI(high, low, close, timeperiod=context.cci_window)
current_price = data[context.asset].price
record(price=current_price)
buy_signal_triggered = False
sell_signal_triggered = False
if cci[-1] > -100 >= cci[-2]:
buy_signal_triggered = True
elif cci[-1] < 100 <= cci[-2]:
sell_signal_triggered = True
current_position = context.portfolio.positions[context.asset].amount
if buy_signal_triggered and current_position == 0:
print(str(date) + '==>Buy')
order_target_percent(context.asset, 0.5)
elif sell_signal_triggered and current_position > 0:
print(str(date) + '==>Sell')
order_target_percent(context.asset, 0.0)
else:
print("No trading")
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 etfDataLog(self, etf_prices: np.array, etf_weights: np.array):
"""Function to log ETF data, specifically ETF prices and corresponding
portfolio weights.
Arguments:
etf_prices {np.array} -- ETF prices.
etf_weights {np.array} -- Portfolio ETF weights.
"""
# Building log object
log_dict = dict()
# Building labels for dictionary
etf_price_labels = [
'_'.join(['etf', i])
for i in config.sector_universe.getSectorLabels()
]
port_weights_label = [
'_'.join(['etf_weight', i])
for i in config.sector_universe.getSectorLabels()
]
# Adding to dictionary
log_dict.update(zip(etf_price_labels, etf_prices))
log_dict.update(zip(port_weights_label, etf_weights))
# Adding to zipline record
record(**log_dict)
def handle_data(context, data):
short_ema = context.short_ema_trans.handle_data(data)
long_ema = context.long_ema_trans.handle_data(data)
if short_ema is None or long_ema is None:
return
buy = False
sell = False
if (short_ema > long_ema).all() and not context.invested:
order(context.asset, 100)
context.invested = True
buy = True
elif (short_ema < long_ema).all() and context.invested:
order(context.asset, -100)
context.invested = False
sell = True
record(
AAPL=data[context.asset].price,
short_ema=short_ema[context.asset],
long_ema=long_ema[context.asset],
buy=buy,
sell=sell,
)
def handle_data(context, data):
# 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('600000')].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)
order(symbol('600000'), 10)
record(Close=data['Close'])
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 daily_check(context, data):
c = context
#global c.BULL, c.COUNT, OUT_DAY
vola = data.history(c.MKT, 'price', c.VOLA + 1,
'1d').pct_change().std() * np.sqrt(252)
WAIT_DAYS = int(vola * c.RET_INITIAL)
RET = int((1.0 - vola) * c.RET_INITIAL)
P = data.history([c.A, c.B, c.C, c.D], 'price', RET + 2,
'1d').iloc[:-1].dropna()
ratio_ab = (P[c.A].iloc[-1] / P[c.A].iloc[0]) / (P[c.B].iloc[-1] /
P[c.B].iloc[0])
ratio_cd = (P[c.C].iloc[-1] / P[c.C].iloc[0]) / (P[c.D].iloc[-1] /
P[c.D].iloc[0])
exit = ratio_ab < c.LB and ratio_cd < c.LB
if exit:
c.BULL = 0
c.OUT_DAY = c.COUNT
elif (c.COUNT >= c.OUT_DAY + WAIT_DAYS):
c.BULL = 1
c.COUNT += 1
wt_stk = c.LEV if c.BULL else 0
wt_bnd = 0 if c.BULL else c.LEV
for sec in c.STOCKS:
c.wt[sec] = wt_stk / len(c.STOCKS)
for sec in c.BONDS:
c.wt[sec] = wt_bnd / len(c.BONDS)
for sec, weight in c.wt.items():
order_target_percent(sec, weight)
record(wt_bnd=wt_bnd, wt_stk=wt_stk)
def rebalance(context, data):
history = data.history(assets=context.asset,
fields=['close'],
bar_count=context.cmo_window + 1,
frequency='1d')
date = history.index.values[-1]
close = history['close'].values
# 计算CMO指标
cmo = ta.CMO(close, timeperiod=context.cmo_window)
print(cmo[-1])
current_price = data[context.asset].price
record(price=current_price)
buy_signal_triggered = False
sell_signal_triggered = False
if cmo[-1] < context.over_sell:
buy_signal_triggered = True
elif cmo[-1] > context.over_buy:
sell_signal_triggered = True
current_position = context.portfolio.positions[context.asset].amount
if buy_signal_triggered and current_position == 0:
print(str(date) + '==>Buy')
order_target_percent(context.asset, 1.0)
elif sell_signal_triggered and current_position > 0:
print(str(date) + '==>Sell')
order_target_percent(context.asset, 0.0)
else:
print("No trading")
def record_vars(context, data):
"""
Plot variables at the end of each day.
"""
record(leverage=context.account.leverage,
longs=context.longs,
shorts=context.shorts)
def rebalance(context, data):
history = data.history(context.asset, ['close'], 40, '1d')
close = history['close'].values
date = history.index.values[-1]
current_position = context.portfolio.positions[context.asset].amount
print("当前持仓==>%d" % current_position)
price = data[context.asset].price
record(price=price)
# 计算指标
sma_data = talib.SMA(close)[-1]
wma_data = talib.WMA(close)[-1]
mom_data = talib.MOM(close)[-1]
# 添加今日的特征
features = []
x = []
features.append(sma_data)
features.append(wma_data)
features.append(mom_data)
x.append(features)
flag = context.svm_module.predict(x) # 预测的涨跌结果
if bool(flag) and current_position == 0:
order_target_percent(context.asset, 0.5)
print(str(date) + "==>买入信号")
elif bool(flag) is False and current_position > 0:
order_target_percent(context.asset, 0.0)
print(str(date) + "==>卖出信号")
else:
print(str(date) + "==>无交易信号")
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):
# 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):
#print('handle data')
prices = data.history(context.asset, 'price', argcount,
'1d')
normalizedPrices = (np.divide(prices, np.mean(prices)))
scaledPrice = np.divide(
(normalizedPrices - np.min(normalizedPrices)),
(np.max(normalizedPrices) - np.min(normalizedPrices)))
dp = tradingRule(
*scaledPrice
) #[0],scaledPrice[1],scaledPrice[2],scaledPrice[3],scaledPrice[4],scaledPrice[5])
# print(dp)
# dpList.append(dp)
if dp < 0:
desiredPosition = max(-1, dp)
else:
desiredPosition = min(1, dp)
# if desired position varies from previous desired position by more than 10%, order to target percentage
currentPosition = np.divide(
(context.portfolio.positions[context.asset].amount) *
(context.portfolio.positions[context.asset].cost_basis
), context.portfolio.portfolio_value)
if np.abs(desiredPosition - currentPosition) > 0.1:
order_target_percent(context.asset, desiredPosition)
context.tradeCount += 1
context.dayCount += 1
record(Asset=data.current(context.asset, 'price'))
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 my_func(context, data):
# Order 100 shares of AAPL.
# order_target(context.asset, 1000)
close = data.current(context.asset, "close")
uplimit = data.current(context.asset, "up_limit") / 1000
# print(context.asset, "价格限制", 'close', close, 'uplimit', uplimit)
if (close >= uplimit):
print(context.asset, "价格限制", 'close', close, 'uplimit', uplimit)
else:
order(context.asset, 1000)
# Retrieve all open orders.
open_orders = get_open_orders()
# If there are any open orders.
if open_orders:
# open_orders is a dictionary keyed by sid, with values that are lists of orders. Iterate over the dictionary
for security, orders in open_orders.items():
# Iterate over the orders and log the total open amount
# for each order.
for oo in orders:
message = 'Open order for {amount} shares in {stock}'
message = message.format(amount=oo.amount, stock=security)
log.info(message)
record(AAPL=data.current(context.asset, 'price'))
def handle_data(context, data):
#assert context.portfolio.cash > 0.0, "ERROR: negative context.portfolio.cash"
#assert len(context.raw_data) == context.training_data_length; "ERROR: "
# data stored as (open, high, low, close, volume, price)
feed_data = ([
data[context.security].open,
data[context.security].high,
data[context.security].low,
data[context.security].close
#data[context.security].volume,
#data[context.security].close,
])
#keep track of history.
context.raw_data.pop(0)
context.raw_data.append(feed_data)
context.normalized_data = Manager.preprocessData(context.raw_data)[:-2]
prediction = context.strategy.predict(context.normalized_data)[-1]
print "Value: $%.2f Cash: $%.2f Predict: %.5f" % (context.portfolio.portfolio_value, context.portfolio.cash, prediction[0])
# Do nothing if there are open orders:
if has_orders(context, data):
print('has open orders - doing nothing!')
# Put entire position in
elif prediction > 0.5:
order_target_percent(context.security, .95)
# Take entire position out
else:
order_target_percent(context.security, 0)
#order_target_percent(context.security, -.99)
record(BENCH=data[context.security].price)
record(SPY=data[context.benchmark].price)
def rebalance(context, data):
month = context.get_datetime().month
if month in [2, 5, 8, 11] and context.reb_flag:
# 原始持仓权重
weight_old = get_weight(context)
# 目标持仓权重
print context.rank_score
h = np.nanpercentile(context.rank_score,
100 - context.percent,
interpolation='higher')
fund_pool = context.rank_score[context.rank_score >= h]
# 过滤年轻基金
longevit = 252 * 1
fund_data = data.history(fund_pool.index, 'close', longevit, '1d')
selected = fund_pool[fund_data.dropna(how='any', axis=0).columns]
print selected
weight_new = pd.Series(0.98 / len(selected), index=selected.index)
# 仓位变动百分比计算
change = {}
for stock in weight_new.keys():
if stock not in weight_old.keys():
change[stock] = weight_new[stock]
else:
change[stock] = weight_new[stock] - weight_old[stock]
# 下订单
for stock in sorted(change, key=change.get):
order_percent(stock, change[stock])
# 残余头寸清仓处理
for stock in weight_old.keys():
if stock not in weight_new.keys():
order_target_percent(stock, 0)
record(weights=weight_new)
print '调仓了：'
print weight_new
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(self, context, data):
if not context.has_ordered:
for stock in context.stocks:
order(symbol(stock), 100)
context.has_ordered = True
record(AAPL=data.current(context.asset, 'price'))
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_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_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 rebalance(context, data):
# Pipeline data will be a dataframe with boolean columns named 'longs' and
# 'shorts'.
pipeline_data = context.pipeline_data
all_assets = pipeline_data.index
longs = all_assets[pipeline_data.longs]
shorts = all_assets[pipeline_data.shorts]
record(universe_size=len(all_assets))
# Build a 2x-leveraged, equal-weight, long-short portfolio.
one_third = 1.0 / 3.0
for asset in longs:
order_target_percent(asset, one_third)
for asset in shorts:
order_target_percent(asset, -one_third)
# Remove any assets that should no longer be in our portfolio.
portfolio_assets = longs | shorts
positions = context.portfolio.positions
for asset in viewkeys(positions) - set(portfolio_assets):
# This will fail if the asset was removed from our portfolio because it
# was delisted.
if data.can_trade(asset):
order_target_percent(asset, 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 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, 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):
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 200 days to get full windows
context.i += 1
if context.i < 200:
return
# Compute averages
short_mavg = data.history(context.asset, 'price',
bar_count=50, frequency="1d").mean()
long_mavg = data.history(context.asset, 'price',
bar_count=200, frequency="1d").mean()
# Trading logic
open_orders = get_open_orders()
if context.asset not in open_orders:
if short_mavg > long_mavg:
# order_target orders as many shares as needed to
# achieve the desired number of shares.
order_target_percent(context.asset, 1.0)
elif short_mavg < long_mavg:
order_target_percent(context.asset, 0.0)
# 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):
# 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 rebalanceLog(self, old_weights: np.array, new_weights: np.array,
new_prices: np.array):
"""Function to log portfolio data during a rebalancing process. Records
dollar value turnover (i.e. the trades) to rebalance
the portfolio, and total rebalancing turnover for the portfolio.
Arguments:
old_weights {np.array} -- Old ETF weights (last rebalance).
new_weights {np.array} -- New ETF weights (current rebalance).
new_prices {np.array} -- New ETF prices (current rebalance).
"""
# Computing absolute weights delta
abs_weights_delta = np.abs(new_weights - old_weights)
# Computing portfolio turnover (dollar value) for each of the ETFs,
# using current asset prices
port_rebal_turnover = np.multiply(abs_weights_delta, new_prices)
# Computing total turnover
port_rebal_total_turnover = np.sum(port_rebal_turnover)
# Building log object
log_dict = dict()
# Adding to dictionary
log_dict.update(
zip(self.port_rebal_turnover_labels, port_rebal_turnover))
# Total turnover
log_dict[self.total_port_rebal_turnover_label[0]] = \
port_rebal_total_turnover
# Adding to zipline record
record(**log_dict)
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 stop_loss(context, stock_to_sell, data):
# 止损： 将 stock 仓位降低为0，并调入货币基金
# replace_stock： 调入的货币基金， 目前使用OF110006
# stock: 将要进行止损的基金
# flag: 判断 replace_stock 是否存在于原有仓位中
# weight 原有仓位权重
# stop_weight： 止损仓位，等于原始仓位重stock 的权重
flag = False #初始值为货币基金不在持仓当中
replace_stock = "110006.OF"
weight = get_weight(context)
stop_weight = weight[stock_to_sell]
stocks = get_weight(context).keys()
for stock in stocks:
if stock.symbol == replace_stock:
flag = True
# 记录仓位及价格变动
weight = weight.drop(stock_to_sell)
if flag:
weight[symbol(
replace_stock)] = stop_weight + weight[symbol(replace_stock)]
else:
weight[symbol(replace_stock)] = stop_weight
weight = weight / np.sum(weight)
# 更新 调入货币基金 的成本价格
context.init_value[symbol(replace_stock)] = data[symbol(
replace_stock)]["price"]
record(weights=weight)
record(rebalance_reason=STOPLOSS)
# 止损+调入货币基金下单
order_target_percent(stock_to_sell, 0)
order_percent(symbol(replace_stock), stop_weight)
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 rebalance(context, data):
history = data.history(assets=context.asset,
fields=['close'],
bar_count=context.bar_window,
frequency='1d')
date = history.index.values[-1]
close = history['close'].values
current_position = context.portfolio.positions[context.asset].amount
current_price = data[context.asset].price
record(price=current_price)
print("持仓数==>%d" % current_position)
short_value = ta.SMA(close, timeperiod=5)
long_value = ta.SMA(close, timeperiod=15)
buy_signal_triggered = False
sell_signal_triggered = False
if short_value[-1] >= long_value[-1] and short_value[-2] < long_value[-2]:
buy_signal_triggered = True
elif short_value[-1] <= long_value[-1] and short_value[-2] > long_value[-2]:
sell_signal_triggered = True
if buy_signal_triggered and current_position == 0:
print(str(date) + '==>买入信号')
order_target_percent(context.asset, 1.0)
elif sell_signal_triggered and current_position > 0:
print(str(date) + '==>卖出信号')
order_target_percent(context.asset, 0.0)
else:
print(str(date) + '==>无交易信号')
def rebalance(context, data):
print(data.current_dt)
# Pipeline data will be a dataframe with boolean columns named 'longs' and
# 'shorts'.
pipeline_data = context.pipeline_data
print(pipeline_data.head())
all_assets = pipeline_data.index
longs = all_assets[pipeline_data.longs]
shorts = all_assets[pipeline_data.shorts]
record(universe_size=len(all_assets))
# Build a 2x-leveraged, equal-weight, long-short portfolio.
for asset in longs:
order_target_percent(asset, ONE_THIRD)
for asset in shorts:
order_target_percent(asset, -ONE_THIRD)
# Remove any assets that should no longer be in our portfolio.
portfolio_assets = longs | shorts
positions = context.portfolio.positions
for asset in viewkeys(positions) - set(portfolio_assets):
# This will fail if the asset was removed from our portfolio because it
# was delisted.
if data.can_trade(asset):
order_target_percent(asset, 0)
def handle_data(context, data):
context.i += 1
avg_days = 20
if context.i < avg_days:
record(data=data, context=context)
return
short_mavg = data.history(context.asset,
'price',
bar_count=avg_days,
frequency="1d")
Mean = short_mavg.mean()
Std = short_mavg.std()
bollinger_high = Mean + Std * 2
bollinger_low = Mean - Std * 2
curr_price = data.history(context.asset,
'price',
bar_count=1,
frequency="1d").mean()
open_orders = get_open_orders()
if context.asset not in open_orders:
if curr_price > bollinger_high:
order(context.asset, -1) #-1 indicates selling 1 stock
elif curr_price < bollinger_low:
order(context.asset, 1)
record(price=data.current(symbol(stock), 'price'),
short_mavg=short_mavg,
data=data,
context=context)
def handle_data(context, data):
context.i += 1
stock_name = context.panel.axes[0][0]
if context.i == 60:
order(symbol(stock_name), 10)
if context.i == 150:
order(symbol(stock_name), -10)
record(Prices=data[symbol(stock_name)].price)
def handle_data(context, data):
print "================================="
print "New iteration"
print data
order(symbol('AAPL'), 10)
record(AAPL=data[symbol('AAPL')].price)
def handle_data(context, data):
if context.prev_day_close_price:
percent_change = (data[0]["close"] - context.prev_day_close_price) * 100 / context.prev_day_close_price
open_close_percent_change = (data[0]["open"] - context.prev_day_close_price) * 100 / context.prev_day_close_price
if percent_change < 0:
context.pattern.append(-1)
else:
context.pattern.append(1)
pattern_length = len(context.buy_pattern_to_match)
if context.pattern[-pattern_length:] == context.buy_pattern_to_match:
order(context.symbol, 10, limit_price = data[0]["open"])
if context.take_profit_target and (data[0]["open"] + context.take_profit_target) <= data[0]["high"]:
target_price = data[0]["open"] + context.take_profit_target
order(context.symbol, -10, limit_price = target_price)
pnl_cents = target_price - data[0]["open"]
context.cents = context.cents + pnl_cents
print "{0}, {1}, pnl: {2}, accum.pnl: {3}".format(data[0]["dt"], "BUY", pnl_cents, context.cents)
else:
order(context.symbol, -10, limit_price = data[0]["close"])
pnl_cents = data[0]["close"] - data[0]["open"]
context.cents = context.cents + pnl_cents
print "{0}, {1}, pnl: {2}, accum.pnl: {3}".format(data[0]["dt"], "BUY", pnl_cents, context.cents)
pattern_length = len(context.sell_pattern_to_match)
if context.pattern[-pattern_length:] == context.sell_pattern_to_match:
order(context.symbol, -10, limit_price = data[0]["open"])
if context.take_profit_target and (data[0]["open"] - context.take_profit_target) >= data[0]["low"]:
target_price = data[0]["open"] - context.take_profit_target
order(context.symbol, 10, limit_price = target_price)
pnl_cents = data[0]["open"] - target_price
context.cents = context.cents + pnl_cents
print "{0}, {1}, pnl: {2}, accum.pnl: {3}".format(data[0]["dt"], "SELL", pnl_cents, context.cents)
else:
order(context.symbol, 10, limit_price = data[0]["close"])
pnl_cents = data[0]["open"] - data[0]["close"]
context.cents = context.cents + pnl_cents
print "{0}, {1}, pnl: {2}, accum.pnl: {3}".format(data[0]["dt"], "SELL", pnl_cents, context.cents)
#pattern_length = len(context.follow_uptrend_pattern_to_match)
#if context.pattern[-pattern_length:] == context.follow_uptrend_pattern_to_match:
#
# order(context.symbol, 10, limit_price = data[0]["open"])
# order(context.symbol, -10, limit_price = data[0]["close"])
#
# context.cents = context.cents + (data[0]["close"] - data[0]["open"])
# print "{0}, {1}, pnl: {2}, accum.pnl: {3}".format(data[0]["dt"], "FLW UPTREND BUY", data[0]["close"] - data[0]["open"], context.cents)
context.prev_day_close_price = data[0]["close"]
record(SMBL = data[0]["close"])
def handle_data_api(context, data):
if context.incr == 0:
assert 0 not in context.portfolio.positions
else:
assert context.portfolio.positions[0]["amount"] == context.incr, "Orders not filled immediately."
assert context.portfolio.positions[0]["last_sale_price"] == data[0].price, "Orders not filled at current price."
context.incr += 1
order(0, 1)
record(incr=context.incr)
def handle_data_api(context, data):
if context.incr == 0:
assert 0 not in context.portfolio.positions
else:
assert context.portfolio.positions[0].amount == context.incr, "Orders not filled immediately."
assert context.portfolio.positions[0].last_sale_price == data.current(
sid(0), "price"
), "Orders not filled at current price."
context.incr += 1
order(sid(0), 1)
record(incr=context.incr)
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_data(context, data):
context.panel # Here we have access to training data also.
# Make solution using the result of learning:
if not int(data[symbol('AAPL')].price) % context.result:
order(symbol('AAPL'), 10)
# Record some values for analysis in 'analyze()'.
sids = context.panel.axes[0].values
prices = [data[symbol(sid)].price for sid in sids]
record(Prices=prices)
record(Prediction=3 * data[symbol('AAPL')].price - 2.2 * context.previous)
# Record current price to use it in future.
context.previous = data[symbol('AAPL')].price
def handle_data(context, data):
#On-Line Moving Average Reversal (OLMAR)
context.days += 1
if context.days < context.window_length:
return
if context.init:
rebalance_portfolio(context, data, context.b_t)
context.init=False
return
m = context.m #num assets
x_tilde = np.zeros(m) #relative mean deviation
b = np.zeros(m) #weights
#compute moving average price for each asset
mavgs = history(context.window_length, '1d', 'price').mean()
#mavgs = data.history(context.sids, 'price', context.window_length, '1d').mean()
for i, stock in enumerate(context.stocks):
price = data[stock]['price']
x_tilde[i] = mavgs[i] / price
x_bar = x_tilde.mean()
market_rel_dev = x_tilde - x_bar #relative deviation
exp_return = np.dot(context.b_t, x_tilde)
weight = context.eps - exp_return
variability = (np.linalg.norm(market_rel_dev))**2
if variability == 0.0:
step_size = 0
else:
step_size = np.max((0, weight/variability))
b = context.b_t + step_size * market_rel_dev
b_norm = simplex_projection(b)
rebalance_portfolio(context, data, b_norm)
context.b_t = b_norm
#save values for plotting
record(AAPL = data[symbol('AAPL')].price,
MSFT = data[symbol('MSFT')].price,
step_size = step_size,
variability = variability
)
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 deallocate_(context):
# order_target_percent(symbol(REFERENCE_GROUP_SYMBOL['Cash']), 1.0)
for symbol_ in context.active_orders:
order_target_percent(symbol(symbol_), 0.0)
# order_target(symbol(symbol_), 0.0)
context.active_orders = []
if context.verbose:
print "Record Variables for Performance Analysis."
out_dict = {}
for symbol_ in context.symbols:
out_dict[symbol_] = 0.0
if context.verbose:
print "Recording weight %s for symbol %s." %(0.0, symbol_)
record(allocation = out_dict)
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_api(context, data):
if context.incr == 0:
assert 0 not in context.portfolio.positions
else:
assert (
context.portfolio.positions[0].amount ==
context.incr
), "Orders not filled immediately."
assert (
context.portfolio.positions[0].last_sale_date ==
context.get_datetime()
), "Orders not filled at current datetime."
context.incr += 1
order(sid(0), 1)
record(incr=context.incr)
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 handle_data(context, data):
#print "Cash: $" + str(context.portfolio.cash), "Data: ", str(len(context.training_data))
#assert context.portfolio.cash > 0.0, "ERROR: negative context.portfolio.cash"
assert len(context.training_data) == context.training_data_length; "ERROR: "
context.security = symbol(BACKTEST_STOCK)
# data stored as (open, high, low, close, volume, price)
if IS_NORMALIZE:
feed_data = ([
data[context.security].open - data[context.security].open,
data[context.security].high - data[context.security].open,
data[context.security].low - data[context.security].open,
data[context.security].close - data[context.security].open
#data[context.security].volume,
#data[context.security].close,
])
else:
feed_data = ([
data[context.security].open,
data[context.security].high,
data[context.security].low,
data[context.security].close
#data[context.security].volume,
#data[context.security].close,
])
#keep track of history.
context.training_data.pop(0)
context.training_data.append(feed_data)
context.normalized_data = Manager.normalize(context.training_data) # will have to redo every time step
#print len(context.training_data), len(context.normalized_data), len(context.normalized_data[0])
prediction = context.strategy.predict(context.training_data)[-1]
print "Value: $%.2f Cash: $%.2f Predict: %.5f" % (context.portfolio.portfolio_value, context.portfolio.cash, prediction[0])
# Do nothing if there are open orders:
if has_orders(context, data):
print('has open orders - doing nothing!')
# Put entire position in
elif prediction > 0.5:
order_target_percent(context.security, .98)
# Take entire position out
else:
order_target_percent(context.security, 0)
#order_target_percent(context.security, -.99)
record(BENCH=data[context.security].price)
record(SPY=data[context.benchmark].price)
def handle_data(context, data):
context.day_count += 1
if context.day_count < 100:
return
prices = history(950, '1d', 'price').dropna()
security_index = 0;
daily_returns = np.zeros((len(context.stocks), 950))
for security in context.stocks:
if data.has_key(security):
for day in range(0, 99):
day_of = prices[security][day]
day_before = prices[security][day - 1]
daily_returns[security_index][day] = (day_of - day_before) / day_before
security_index = security_index + 1
covars = cov(daily_returns)
covars = covars * 250
###########################################################
returns = prices.pct_change().dropna()
bnds = ((0,1),(0,1),(0,1),(0,1),(0,1),(0,1),(0,1),(0,1),(0,1),(0,1))
cons = ({'type': 'eq', 'fun': lambda x: np.sum(x)-1.0})
res = scipy.optimize.minimize(compute_var,
context.x0,
cov(daily_returns)*255,
method='SLSQP',
constraints=cons,
bounds=bnds)
allocation = res.x
allocation[allocation < 0] = 0 # jamais de vente, que des achats
denom = np.sum(allocation)
if denom != 0:
allocation = allocation/denom
context.x0 = allocation
record(stocks=np.sum(allocation[0:-1]))
record(bonds=allocation[-1])
for i, stock in enumerate(context.stocks):
order_target_percent(stock, allocation[i])
def handle_data(context, data):
# Record and plot the leverage of our portfolio over time.
record(leverage = context.account.leverage)
context.base_counter += 1
context.base_prices = add_prices(context, data)
# 1. Add prices and determine the weight of stationary portfolio on X freq
if context.freq_counter <= context.lookback:
if context.base_counter % context.freq == 0:
print 'Pre_trade freq_counter: %d' %context.freq_counter
context.freq_counter += 1
pct_allocation = pre_trade(context, data)
if pct_allocation:
context.pct_allocation = pct_allocation
return
else:
return
# 2. Trade using the weights trained earlier
if (context.freq_counter >= context.lookback
and context.freq_counter < context.lookback + context.trade_bars):
if context.base_counter % context.freq == 0:
print 'Go2_trade freq_counter: %d' %context.freq_counter
context.freq_counter += 1
pct_allocation = context.pct_allocation
else:
return
else:
context.freq_counter = context.lookback
context.base_counter = 0
return
# Last check before placing order
if pct_allocation is None:
return
# Place orders.
print '-'*10, 'Trade'
for stock, j in zip(data, range(0,len(data))):
price = data[stock].price
record(stock, price)
print stock
log.info(pct_allocation[j])
order_target_percent(stock, pct_allocation[j])
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):
#print context.portfolio.cash
context.day += 1
i = context.day
s = context.maShort[i]
l = context.maLong[i]
pres = s
prel = l
if i!=0:
pres = context.maShort[i-1]
prel = context.maLong[i-1]
if i>=longWin-1:
if s>l and pres<=prel and not context.invested:
order_percent(symbol(code), 1.0)
context.invested = True
elif s<l and context.invested:
order_percent(symbol(code), -1.0)
context.invested = False
record(maShort=s, maLong=l)
pass
def handle_data(context, data):
context.recent_prices.append(data[context.security].price) # Update the recent prices
if len(context.recent_prices) == context.window_length+2: # If there's enough recent price data
# Make a list of 1's and 0's, 1 when the price increased from the prior bar
changes = np.diff(context.recent_prices) > 0
context.X.append(changes[:-1]) # Add independent variables, the prior changes
context.Y.append(changes[-1]) # Add dependent variable, the final change
if len(context.Y) >= 100: # There needs to be enough data points to make a good model
context.classifier.fit(context.X, context.Y) # Generate the model
context.prediction = context.classifier.predict(changes[1:]) # Predict
# If prediction = 1, buy all shares affordable, if 0 sell all shares
order_target_percent(context.security, context.prediction)
record(prediction=int(context.prediction))
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)