def watch(context, data): # On schedule, process any sids in watch_list
prices = data.history(context.watch_list, 'close', 2,
'1d') # Daily, couple days
for s in context.watch_list:
if s not in context.portfolio.positions: # If sold elsewhere, drop it from watch
context.watch_list.remove(s)
continue
# Slope of prices, minute
slp = slope(
data.history(s, 'close', 60,
'1m').dropna()) # Minutes, note dropna(), important
if slp < 0: # Close if downward
log.info('sell {} at {} prv {} {}%'.format(
s.symbol, data.current(s, 'price'), '%.2f' % prices[s][0],
'%.0f' % (100 * data.current(s, 'price') / prices[s][0])))
order_target(s, 0) # Close/sell
context.watch_list.remove(s)
# Any new for price jump watch_list
prices = data.history(context.portfolio.positions.keys(), 'close', 2,
'1d') # Couple days
for s in context.portfolio.positions: # Add to watch_list with price jump
if not data.can_trade(s): continue
if data.current(
s,
'price') > 1.10 * prices[s][0]: # If price has jumped upward
if s in context.watch_list: continue
log.info('{} to watch_list at {} prv {} {}%'.format(
s.symbol, data.current(s, 'price'), '%.2f' % prices[s][0],
'%.0f' % (100 * data.current(s, 'price') / prices[s][0])))
context.watch_list.append(s)
def my_rebalance(context, data):
GDXJ_prices = data.history(context.GDXJ, "price", 10000,
"1m").resample('30T',
closed='right',
label='right').last().dropna()
#GDXJ_prices = data.history(context.GDXJ, "price", 100, "1d")
ema12 = talib.EMA(GDXJ_prices, 12)
ema26 = talib.EMA(GDXJ_prices, 26)
macd = ema12 - ema26
signal = talib.EMA(macd, 9)
record(SIG=macd[-1] - signal[-1])
record(MACD=macd[-1])
if macd[-2] < signal[-2] and macd[-1] >= signal[-1] and not context.bought:
set_fixed_stop_long(context, data)
order_target_percent(context.GDXJ, context.allocation)
context.bought = True
context.sold = False
if macd[-1] < signal[-1] and not context.sold:
set_fixed_stop_short(context, data)
order_target_percent(context.GDXJ, 0)
context.bought = False
context.sold = True
def my_rebalance_close(context, data):
#If we have a position check sell conditions
DBC_prices = data.history(context.DBC, "price", 100, "1d")
ema12 = talib.EMA(DBC_prices, 12)
ema26 = talib.EMA(DBC_prices, 26)
macd = ema12 - ema26
signal = talib.EMA(macd, 9)
if macd[-2] > signal[-2] and macd[-1] < signal[
-1] and context.portfolio.positions[
context.DBC].amount != 0 and context.bought:
order_target_percent(context.DBC, 0)
log.info(
"Closed Long Position; MACD_val: %.5f; PNL: %.5f; Market Value: %.5f"
% (macd[-1] - signal[-1], context.portfolio.pnl,
context.portfolio.portfolio_value))
# log.info(context.portfolio.positions)
context.bought = False
if macd[-2] < signal[-2] and macd[-1] > signal[
-1] and context.portfolio.positions[
context.DBC].amount != 0 and context.sold:
order_target_percent(context.DBC, 0)
log.info(
"Closed Short Position; MACD_val: %.5f; PNL: %.5f; Market Value: %.5f"
% (macd[-1] - signal[-1], context.portfolio.pnl,
context.portfolio.portfolio_value))
# log.info(context.portfolio.positions)
context.sold = False
def my_rebalance(context, data):
DBC_prices = data.history(context.DBC, "price", 100, "1d")
ema12 = talib.EMA(DBC_prices, 12)
ema26 = talib.EMA(DBC_prices, 26)
macd = ema12 - ema26
signal = talib.EMA(macd, 9)
record(MACD_val=macd[-1] - signal[-1])
record(macd=macd[-1])
record(exp3=signal[-1])
if macd[-2] < signal[-2] and macd[-1] > signal[-1] and not context.bought:
order(context.DBC, 40)
log.info("------------------------------------")
log.info(
"Opened Long Position; MACD_val: %.3f; PNL: %.3f; Market Value: %.5f"
% (macd[-1] - signal[-1], context.portfolio.pnl,
context.portfolio.portfolio_value))
# log.info(context.portfolio.positions)
context.boughtPrice = data.current(context.DBC, 'price')
context.bought = True
context.sold = False
if macd[-2] > signal[-2] and macd[-1] < signal[-1] and not context.sold:
order(context.DBC, -40)
log.info("------------------------------------")
log.info(
"Opened Short Position; MACD_val: %.5f; PNL: %.5f; Market Value: %.5f"
% (macd[-1] - signal[-1], context.portfolio.pnl,
context.portfolio.portfolio_value))
# log.info(context.portfolio.positions)
context.boughtPrice = data.current(context.DBC, 'price')
context.bought = False
context.sold = True
def _get_macd(data, ticker):
prices = data.history(ticker, "price", 100, "1d")
prices.dropna(inplace=True)
ema12 = talib.EMA(prices, 12)
ema26 = talib.EMA(prices, 26)
macd = ema12 - ema26
signal = talib.EMA(macd, 9)
return macd, signal
def my_record_vars(context, data):
DBC_prices = data.history(context.DBC, "price", 100, "1d")
ema12 = talib.EMA(DBC_prices, 12)
ema26 = talib.EMA(DBC_prices, 26)
macd = ema12 - ema26
signal = talib.EMA(macd, 9)
if context.bought or context.sold:
log.info(
"Holding position; MACD_val: %.5f; PNL: %.5f; Market Value: %.5f" %
(macd[-1] - signal[-1], context.portfolio.pnl,
context.portfolio.portfolio_value))
def check_rsi(context,data):
vxx_highs = data.history(context.VXX, "high", context.wvf_length*2, "1d")
vxx_prices = data.history(context.VXX, "price", context.wvf_length*2, "1d")
vxx_lows = data.history(context.VXX, "low", context.wvf_length*2, "1d")
vxx_highest = vxx_highs.rolling(window = context.wvf_length, center=False).max()
WVF = ((vxx_highest - vxx_lows)/(vxx_highest)) * 100
context.SmoothedWVF1 = talib.EMA(WVF, timeperiod=context.ema1)
context.SmoothedWVF2 = talib.EMA(WVF, timeperiod=context.ema2)
SMA30VXX = talib.SMA(vxx_prices, 30)
SMA5VXX = talib.SMA(vxx_prices, 5)
record(WVF1=WVF[-1])
#record(SMA30=SMA30VXX[-1]) USELESS WITHOUT VXX PRICE!
record(EMA5=context.SmoothedWVF1[-1])
record(EMA20=context.SmoothedWVF2[-1])
print("VXX[-2]: {} VXX[-1]: {} SMA5: {} SMA30[-2]: {} SMA30[-1]: {} WVF[-2]: {} WVF[-1]: {} SmoothedWVF1[-2]: {} SmoothedWVF1[-1]: {} SmoothedWVF2[-2]: {} SmoothedWVF2[-1]: {}".format(vxx_prices[-2], vxx_prices[-1], SMA5VXX[-1], SMA30VXX[-2], SMA30VXX[-1], WVF[-2], WVF[-1], context.SmoothedWVF1[-2], context.SmoothedWVF1[-1], context.SmoothedWVF2[-2], context.SmoothedWVF2[-1]))
## BUY RULES
if context.portfolio.positions[context.XIV].amount == 0:
if ((WVF[-1] > context.SmoothedWVF1[-1] and WVF[-2] < context.SmoothedWVF1[-2] and WVF[-1] < context.SmoothedWVF2[-1]) or (WVF[-1] > context.SmoothedWVF1[-1] and WVF[-2] < context.SmoothedWVF1[-2] and WVF[-1] > context.SmoothedWVF2[-1] and WVF[-2] < context.SmoothedWVF2[-2])) and vxx_prices[-1] < SMA5VXX[-1]:
set_fixed_stop(context, data)
order_target_percent(context.XIV, 1.0)
print("bought")
## SELL RULES
if context.portfolio.positions[context.XIV].amount > 0 and len(get_open_orders()) == 0:
#if vxx crosses above SMA30: sell
if vxx_prices[-2] < SMA30VXX[-2] and vxx_prices[-1] > SMA30VXX[-1]:
context.sell = True
print("vxx crosses above SMA30: sell")
#if wvf crosses under smoothwvf2: sell
if WVF[-2] > context.SmoothedWVF2[-2] and WVF[-1] < context.SmoothedWVF2[-1]:
context.sell = True
print("wvf crosses under smoothwvf2: sell")
def my_rebalance(context, data):
# my_assigned_weights(context, data)
n = 28
WFV_limit = 14.5
vxx_prices = data.history(context.VXX, "price", n + 2, "1d")[:-1]
vxx_lows = data.history(context.VXX, "low", n + 2, "1d")[:-1]
vxx_highest = vxx_prices.rolling(window=n, center=False).max()
WVF = ((vxx_highest - vxx_lows) / (vxx_highest)) * 100
record(WVF=WVF[-1])
xn = 28
xiv_prices = data.history(context.XIV, "price", xn + 2, "1d")[:-1]
xiv_lows = data.history(context.XIV, "low", xn + 2, "1d")[:-1]
xiv_highest = xiv_prices.rolling(window=xn, center=False).max()
XWVF = ((xiv_highest - xiv_lows) / (xiv_highest)) * 100
record(XWVF=XWVF[-1])
if (WVF[-1] >= WFV_limit and WVF[-2] < WFV_limit):
BuyStock = context.XIV
context.Safety_Weight = 0.0
if context.XIV not in context.portfolio.positions:
order_target_percent(BuyStock, context.maxBuy)
message = 'BUY: {stock}'
log.info(message.format(stock=BuyStock.symbol))
if (WVF[-1] < WFV_limit
and WVF[-2] >= WFV_limit) or (WVF[-1] < XWVF[-1]
and WVF[-2] >= XWVF[-2]):
if context.XIV in context.portfolio.positions:
SellStock = context.XIV
order_target_percent(SellStock, 0.00)
message = 'Sell: {stock}'
log.info(message.format(stock=SellStock.symbol))
context.Safety_Weight = 1.00
def rebalance(context, data):
# check if it is January
if get_datetime().month not in [1]:
return
# read and produce returns dataframe
context.returns_df = pd.DataFrame()
for ticker in context.tickers:
prices = data.history(ticker, "price", 252, "1d")
df = pd.DataFrame(data={ticker: prices})
context.returns_df = pd.concat([context.returns_df, df], axis=1)
context.returns_df.dropna(inplace=True)
pct = context.returns_df
#协方差矩阵
cov_mat = pct.cov()
# log.info(cov_mat)
if not isinstance(cov_mat, pd.DataFrame):
raise ValueError('cov_mat should be pandas DataFrame！')
omega = np.matrix(cov_mat.values) # 协方差矩阵
a, b = np.linalg.eig(np.array(cov_mat)) #a为特征值,b为特征向量
a = np.matrix(a)
b = np.matrix(b)
# 定义目标函数
def fun1(x):
tmp = (omega * np.matrix(x).T).A1
risk = x * tmp / np.sqrt(np.matrix(x) * omega * np.matrix(x).T).A1[0]
delta_risk = [sum((i - risk)**2) for i in risk]
return sum(delta_risk)
# 初始值 + 约束条件
x0 = np.ones(omega.shape[0]) / omega.shape[0]
bnds = tuple((0, None) for x in x0)
cons = ({'type': 'eq', 'fun': lambda x: sum(x) - 1})
options = {'disp': False, 'maxiter': 1000, 'ftol': 1e-20}
try:
res = minimize(fun1,
x0,
bounds=bnds,
constraints=cons,
method='SLSQP',
options=options)
except:
raise ValueError('method error！！！')
# 权重调整
if res['success'] == False:
# print res['message']
pass
wts = pd.Series(index=cov_mat.index, data=res['x'])
# weight adjust
wts = wts / wts.sum()
risk = pd.Series(wts * (omega * np.matrix(wts).T).A1 /
np.sqrt(np.matrix(wts) * omega * np.matrix(wts).T).A1[0],
index=cov_mat.index)
risk[risk < 0.0] = 0.0
context.weights = wts
def my_rebalance(context,data):
xiv_prices = data.history(context.XIV, "price", 1600, "1m").resample('30T', closed='right', label='right').last().dropna()
#convert to 2 hour:
xiv2hr = []
if '20:00:00+00:00' in str(xiv_prices.index[-1]) or '21:00:00+00:00' in str(xiv_prices.index[-1]):
xiv2hr.append([xiv_prices.index[-28], xiv_prices[-28]])
xiv2hr.append([xiv_prices.index[-27], xiv_prices[-27]])
xiv2hr.append([xiv_prices.index[-23], xiv_prices[-23]])
xiv2hr.append([xiv_prices.index[-19], xiv_prices[-19]])
xiv2hr.append([xiv_prices.index[-15], xiv_prices[-15]])
xiv2hr.append([xiv_prices.index[-14], xiv_prices[-14]])
xiv2hr.append([xiv_prices.index[-10], xiv_prices[-10]])
xiv2hr.append([xiv_prices.index[-6], xiv_prices[-6]])
xiv2hr.append([xiv_prices.index[-2], xiv_prices[-2]])
xiv2hr.append([xiv_prices.index[-1], xiv_prices[-1]])
context.last_bar = True
elif '19:30:00+00:00' in str(xiv_prices.index[-1]) or '20:30:00+00:00' in str(xiv_prices.index[-1]):
xiv2hr.append([xiv_prices.index[-31], xiv_prices[-31]])
xiv2hr.append([xiv_prices.index[-27], xiv_prices[-27]])
xiv2hr.append([xiv_prices.index[-26], xiv_prices[-26]])
xiv2hr.append([xiv_prices.index[-22], xiv_prices[-22]])
xiv2hr.append([xiv_prices.index[-18], xiv_prices[-18]])
xiv2hr.append([xiv_prices.index[-14], xiv_prices[-14]])
xiv2hr.append([xiv_prices.index[-13], xiv_prices[-13]])
xiv2hr.append([xiv_prices.index[-9], xiv_prices[-9]])
xiv2hr.append([xiv_prices.index[-5], xiv_prices[-5]])
xiv2hr.append([xiv_prices.index[-1], xiv_prices[-1]])
context.last_bar = False
elif '17:30:00+00:00' in str(xiv_prices.index[-1]) or '18:30:00+00:00' in str(xiv_prices.index[-1]):
xiv2hr.append([xiv_prices.index[-31], xiv_prices[-31]])
xiv2hr.append([xiv_prices.index[-27], xiv_prices[-27]])
xiv2hr.append([xiv_prices.index[-23], xiv_prices[-23]])
xiv2hr.append([xiv_prices.index[-22], xiv_prices[-22]])
xiv2hr.append([xiv_prices.index[-18], xiv_prices[-18]])
xiv2hr.append([xiv_prices.index[-14], xiv_prices[-14]])
xiv2hr.append([xiv_prices.index[-10], xiv_prices[-10]])
xiv2hr.append([xiv_prices.index[-9], xiv_prices[-9]])
xiv2hr.append([xiv_prices.index[-5], xiv_prices[-5]])
xiv2hr.append([xiv_prices.index[-1], xiv_prices[-1]])
context.last_bar = False
elif '15:30:00+00:00' in str(xiv_prices.index[-1]) or '16:30:00+00:00' in str(xiv_prices.index[-1]):
xiv2hr.append([xiv_prices.index[-31], xiv_prices[-31]])
xiv2hr.append([xiv_prices.index[-27], xiv_prices[-27]])
xiv2hr.append([xiv_prices.index[-23], xiv_prices[-23]])
xiv2hr.append([xiv_prices.index[-19], xiv_prices[-19]])
xiv2hr.append([xiv_prices.index[-18], xiv_prices[-18]])
xiv2hr.append([xiv_prices.index[-14], xiv_prices[-14]])
xiv2hr.append([xiv_prices.index[-10], xiv_prices[-10]])
xiv2hr.append([xiv_prices.index[-6], xiv_prices[-6]])
xiv2hr.append([xiv_prices.index[-5], xiv_prices[-5]])
xiv2hr.append([xiv_prices.index[-1], xiv_prices[-1]])
context.last_bar = False
else:
log.error("2 HOUR CONVERSION FAILURE")
return
dates, vals = zip(*xiv2hr)
s = pd.Series(vals, index=dates)
rsi = talib.RSI(s,2)
RSI5 = talib.RSI(s, 5)
# XIV
# BUY RULE
if context.xiv_buy is False and rsi[-2] < 70 and rsi[-1] >= 70 and context.portfolio.positions[context.XIV].amount == 0:
context.xiv_buy = True
# SELL RULE
if rsi[-2] > 85 and rsi[-1] <= 85 and context.portfolio.positions[context.XIV].amount > 0:
order_target_percent(context.XIV, 0)
context.xiv_buy = False
# VXX
if RSI5[-1] < 70:
if rsi[-2] > 85 and rsi[-1] <= 85:
if context.portfolio.positions[context.VXX].amount == 0:
#if len(get_open_orders()) == 0 and context.portfolio.positions[context.VXX].amount == 0:
context.vxx_buy = True
if context.portfolio.positions[context.VXX].amount > 0 and len(get_open_orders()) == 0:
if rsi[-2] < 70 and rsi[-1] >= 70:
order_target_percent(context.VXX, 0)
# panic button
high = (data.history(context.XIV, "high", 119, "1m")).max()
if context.last_bar:
high = (data.history(context.XIV, "high", 29, "1m")).max()
price = data.current(context.XIV, 'price')
if ((high/price) - 1) > .1 and len(get_open_orders()) == 0:
order_target_percent(context.XIV, 0)
context.xiv_sell = False
context.xiv_buy = False
def my_rebalance(context, data):
xiv_prices = data.history(context.XIV, "price", 1600,
"1m").resample('30T',
closed='right',
label='right').last().dropna()
#convert to 2 hour:
xiv2hr = []
if '20:00:00+00:00' in str(
xiv_prices.index[-1]) or '21:00:00+00:00' in str(
xiv_prices.index[-1]):
xiv2hr.append([xiv_prices.index[-28], xiv_prices[-28]])
xiv2hr.append([xiv_prices.index[-27], xiv_prices[-27]])
xiv2hr.append([xiv_prices.index[-23], xiv_prices[-23]])
xiv2hr.append([xiv_prices.index[-19], xiv_prices[-19]])
xiv2hr.append([xiv_prices.index[-15], xiv_prices[-15]])
xiv2hr.append([xiv_prices.index[-14], xiv_prices[-14]])
xiv2hr.append([xiv_prices.index[-10], xiv_prices[-10]])
xiv2hr.append([xiv_prices.index[-6], xiv_prices[-6]])
xiv2hr.append([xiv_prices.index[-2], xiv_prices[-2]])
xiv2hr.append([xiv_prices.index[-1], xiv_prices[-1]])
context.last_bar = True
elif '19:30:00+00:00' in str(
xiv_prices.index[-1]) or '20:30:00+00:00' in str(
xiv_prices.index[-1]):
xiv2hr.append([xiv_prices.index[-31], xiv_prices[-31]])
xiv2hr.append([xiv_prices.index[-27], xiv_prices[-27]])
xiv2hr.append([xiv_prices.index[-26], xiv_prices[-26]])
xiv2hr.append([xiv_prices.index[-22], xiv_prices[-22]])
xiv2hr.append([xiv_prices.index[-18], xiv_prices[-18]])
xiv2hr.append([xiv_prices.index[-14], xiv_prices[-14]])
xiv2hr.append([xiv_prices.index[-13], xiv_prices[-13]])
xiv2hr.append([xiv_prices.index[-9], xiv_prices[-9]])
xiv2hr.append([xiv_prices.index[-5], xiv_prices[-5]])
xiv2hr.append([xiv_prices.index[-1], xiv_prices[-1]])
context.last_bar = False
elif '17:30:00+00:00' in str(
xiv_prices.index[-1]) or '18:30:00+00:00' in str(
xiv_prices.index[-1]):
xiv2hr.append([xiv_prices.index[-31], xiv_prices[-31]])
xiv2hr.append([xiv_prices.index[-27], xiv_prices[-27]])
xiv2hr.append([xiv_prices.index[-23], xiv_prices[-23]])
xiv2hr.append([xiv_prices.index[-22], xiv_prices[-22]])
xiv2hr.append([xiv_prices.index[-18], xiv_prices[-18]])
xiv2hr.append([xiv_prices.index[-14], xiv_prices[-14]])
xiv2hr.append([xiv_prices.index[-10], xiv_prices[-10]])
xiv2hr.append([xiv_prices.index[-9], xiv_prices[-9]])
xiv2hr.append([xiv_prices.index[-5], xiv_prices[-5]])
xiv2hr.append([xiv_prices.index[-1], xiv_prices[-1]])
context.last_bar = False
elif '15:30:00+00:00' in str(
xiv_prices.index[-1]) or '16:30:00+00:00' in str(
xiv_prices.index[-1]):
xiv2hr.append([xiv_prices.index[-31], xiv_prices[-31]])
xiv2hr.append([xiv_prices.index[-27], xiv_prices[-27]])
xiv2hr.append([xiv_prices.index[-23], xiv_prices[-23]])
xiv2hr.append([xiv_prices.index[-19], xiv_prices[-19]])
xiv2hr.append([xiv_prices.index[-18], xiv_prices[-18]])
xiv2hr.append([xiv_prices.index[-14], xiv_prices[-14]])
xiv2hr.append([xiv_prices.index[-10], xiv_prices[-10]])
xiv2hr.append([xiv_prices.index[-6], xiv_prices[-6]])
xiv2hr.append([xiv_prices.index[-5], xiv_prices[-5]])
xiv2hr.append([xiv_prices.index[-1], xiv_prices[-1]])
context.last_bar = False
else:
log.error("2 HOUR CONVERSION FAILURE")
return
dates, vals = zip(*xiv2hr)
s = pd.Series(vals, index=dates)
rsi = talib.RSI(s, 2)
#rsi7 = talib.RSI(s,7)
record(RSI_2=rsi[-1])
#FR#4: Improve buy/sell condition
if context.rsi_last == -1:
context.rsi_last = rsi[-2]
#Using Agent conditions
if context.buy is False and context.rsi_last < TraderAgent_RSIBASED_RGQF.RsiUpperLimit and rsi[
-1] >= TraderAgent_RSIBASED_RGQF.RsiUpperLimit and context.portfolio.positions[
context.XIV].amount == 0:
context.buy = True
if context.rsi_last > TraderAgent_RSIBASED_RGQF.RsiUpperLimitTrend and rsi[
-1] <= TraderAgent_RSIBASED_RGQF.RsiUpperLimitTrend and context.portfolio.positions[
context.XIV].amount > 0 and len(get_open_orders()) == 0:
order_target_percent(context.XIV, 0)
context.buy = False
if context.buyVXX is False and rsi[
-1] <= TraderAgent_RSIBASED_RGQF.RsiLowerLimit and context.portfolio.positions[
context.VXX].amount == 0:
context.buyVXX = True
# Buy rule
if context.buyVXX is False and context.rsi_last > TraderAgent_RSIBASED_RGQF.RsiLowerLimit and rsi[
-1] <= TraderAgent_RSIBASED_RGQF.RsiLowerLimit and context.portfolio.positions[
context.XIV].amount == 0 and context.portfolio.positions[
context.VXX].amount == 0:
context.buyVXX = True
# Sell rule
if context.rsi_last < TraderAgent_RSIBASED_RGQF.RsiLowerLimitTrend and rsi[
-1] >= TraderAgent_RSIBASED_RGQF.RsiLowerLimitTrend and context.portfolio.positions[
context.VXX].amount > 0:
order_target_percent(context.VXX, 0)
context.buyVXX = False
#End of agent usage
# panic button hard for safety:
high = (data.history(context.XIV, "high", 119, "1m")).max()
if context.last_bar:
high = (data.history(context.XIV, "high", 29, "1m")).max()
price = data.current(context.XIV, 'price')
if ((high / price) - 1) > .1:
order_target_percent(context.XIV, 0)
context.sell = False
context.buy = False
#Update for next cicle
context.rsi_last = rsi[-1]