def __init__(self):
# To control operation entries
self.orderid = None
# Indicators
poc = btind.SimpleMovingAverage(
self.data0.POC,
period=1,
plotname='POC',
subplot=False,
)
ppoc = btind.SimpleMovingAverage(
self.data0.PPOC,
period=1,
plotname='PPOC',
subplot=False,
)
self.atr = bt.talib.ATR(self.data0.high,
self.data0.low,
self.data0.close,
timeperiod=14)
#self.rsi = bt.talib.RSI(self.data0.close, timeperiod=14)
#self.macd = bt.talib.MACD(self.data0.close)
self.ematr = btind.ExponentialMovingAverage(self.atr, period=89)
self.signal1 = btind.CrossOver(self.data0.close, poc, plot=False)
self.signal2 = btind.CrossOver(self.data0.close, ppoc, plot=False)
self.flag = True
self.flag1 = False
def __init__(self):
"""Initialize the strategy"""
self.fastma = dict()
self.slowma = dict()
self.regime = dict()
if self.params.optim: # Use a tuple during optimization
self.params.fast, self.params.slow = self.params.optim_fs # fast and slow replaced by tuple's contents
if self.params.fast > self.params.slow:
raise ValueError(
"A SMAC strategy cannot have the fast moving average's window be " + \
"greater than the slow moving average window.")
for d in self.getdatanames():
# The moving averages
self.fastma[d] = btind.SimpleMovingAverage(
self.getdatabyname(d), # The symbol for the moving average
period=self.params.fast, # Fast moving average
plotname="FastMA: " + d)
self.slowma[d] = btind.SimpleMovingAverage(
self.getdatabyname(d), # The symbol for the moving average
period=self.params.slow, # Slow moving average
plotname="SlowMA: " + d)
# Get the regime
self.regime[d] = self.fastma[d] - self.slowma[
d] # Positive when bullish
def __init__(self):
sma = btind.SimpleMovingAverage(period=self.p.smaperiod)
sma1 = btind.SimpleMovingAverage(self.data, period=self.params.period1)
sma2 = btind.SimpleMovingAverage(period=self.params.period2)
myindicator = sma2 - sma1 + self.datas[0].close
self.sma = btind.SimpleMovingAverage(period=self.p.period)
# see the (delay) notation
self.cmpval = self.data.close(-1) > self.sma
datasum = btind.SumN(self.data, period=self.p.period1)
# using operators /
av = datasum / self.p.period1
self.line.sma = av
# cannot use "and" operators, backtrader provides several methods
close_over_sma = self.data.close > sma
self.sma_dist_to_high = self.data.high - sma
sma_dist_small = self.sma_dist_to_high < 3.5
sell_sig = bt.And(close_over_sma, sma_dist_small)
high_or_low = bt.If(sma1 > self.data.close, self.data.low,
self.data.high)
self.buysell = btind.CrossOver(self.data.close, sma, plot=True)
self.order = None
def __init__(self):
"""Initialize the strategy"""
self.fastma = dict()
self.slowma = dict()
self.regime = dict()
self._addobserver(
True, bt.observers.BuySell
) # CAUTION: Abuse of the method, I will change this in future code (see: https://community.backtrader.com/topic/473/plotting-just-the-account-s-value/4)
if self.params.optim: # Use a tuple during optimization
self.params.fast, self.params.slow = self.params.optim_fs # fast and slow replaced by tuple's contents
if self.params.fast > self.params.slow:
raise ValueError(
"A SMAC strategy cannot have the fast moving average's window be " + \
"greater than the slow moving average window.")
for d in self.getdatanames():
# The moving averages
self.fastma[d] = btind.SimpleMovingAverage(
self.getdatabyname(d), # The symbol for the moving average
period=self.params.fast, # Fast moving average
plotname="FastMA: " + d)
self.slowma[d] = btind.SimpleMovingAverage(
self.getdatabyname(d), # The symbol for the moving average
period=self.params.slow, # Slow moving average
plotname="SlowMA: " + d)
# Get the regime
self.regime[d] = self.fastma[d] - self.slowma[
d] # Positive when bullish
def set_datalines(self):
self.data.spread = btind.SimpleMovingAverage(self.datas[0] - self.datas[1], period=1)
self.data.spread.plotinfo.subplot = True
self.data.spread.plotinfo.plotabove = True
self.data.spread.plotinfo.plotname = list(self.p.asset_names)[0]
# Override stat line:
# self.stat_asset = btind.SimpleMovingAverage((self.datas[0] + self.datas[1]) / 2, period=1)
# self.stat_asset.plotinfo.plot = False
self.stat_asset = self.data.spread
self.data.std = btind.StdDev(self.data.spread, period=self.p.time_dim, safepow=True)
self.data.std.plotinfo.plot = False
self.data.features = [
# btind.SimpleMovingAverage(self.data.spread, period=period) for period in self.p.features_parameters
btind.EMA(self.data.spread, period=period) for period in self.p.features_parameters
]
initial_time_period = np.asarray(self.p.features_parameters).max() + self.p.time_dim
self.data.dim_sma = btind.SimpleMovingAverage(
self.datas[0],
period=initial_time_period
)
self.data.dim_sma.plotinfo.plot = False
def __init__(self):
self.sma5 = btind.SimpleMovingAverage(period=5) # 5日均线
self.sma10 = btind.SimpleMovingAverage(period=10) # 10日均线
# bt.And 中所有条件都满足时返回 1；有一个条件不满足就返回 0
self.And = bt.And(self.data > self.sma5, self.data > self.sma10,
self.sma5 > self.sma10)
# bt.Or 中有一个条件满足时就返回 1；所有条件都不满足时返回 0
self.Or = bt.Or(self.data > self.sma5, self.data > self.sma10,
self.sma5 > self.sma10)
# bt.If(a, b, c) 如果满足条件 a，就返回 b，否则返回 c
self.If = bt.If(self.data > self.sma5, 1000, 5000)
# bt.All,同 bt.And
self.All = bt.All(self.data > self.sma5, self.data > self.sma10,
self.sma5 > self.sma10)
# bt.Any，同 bt.Or
self.Any = bt.Any(self.data > self.sma5, self.data > self.sma10,
self.sma5 > self.sma10)
# bt.Max，返回同一时刻所有指标中的最大值
self.Max = bt.Max(self.data, self.sma10, self.sma5)
# bt.Min，返回同一时刻所有指标中的最小值
self.Min = bt.Min(self.data, self.sma10, self.sma5)
# bt.Sum，对同一时刻所有指标进行求和
self.Sum = bt.Sum(self.data, self.sma10, self.sma5)
# bt.Cmp(a,b), 如果 a>b ，返回 1；否则返回 -1
self.Cmp = bt.Cmp(self.data, self.sma5)
def __init__(self):
self.fastma = dict()
self.slowma = dict()
self.regime = dict()
#self._addobserver(True, bt.observers.BuySell)
# CAUTION: Abuse of the method, I will change this in future code (see: https://community.backtrader.com/topic/473/plotting-just-the-account-s-value/4)
#optimization
if self.params.optim:
self.params.fast, self.params.slow = self.params.optim_fs # fast and slow replaced by tuple's contents
for d in self.getdatanames():
# The moving averages
self.fastma[d] = btind.SimpleMovingAverage(
self.getdatabyname(d), # The symbol for the moving average
period=self.params.fast, # Fast moving average
plotname="FastMA: " + d)
self.slowma[d] = btind.SimpleMovingAverage(
self.getdatabyname(d), # The symbol for the moving average
period=self.params.slow, # Slow moving average
plotname="SlowMA: " + d)
# Get the regime
self.regime[d] = self.fastma[d] - self.slowma[
d] # Positive when bullish
def set_datalines(self):
self.data.features = [
btind.SimpleMovingAverage(self.datas[0], period=period) for period in self.features_parameters
]
self.data.dim_sma = btind.SimpleMovingAverage(
self.datas[0],
period=(np.asarray(self.features_parameters).max() + self.time_dim)
)
self.data.dim_sma.plotinfo.plot = False
def __init__(self):
# 12 hour sma
self.sma_12hr = btind.SimpleMovingAverage(period=self.p.pfast)
# 30 day sma
self.sma_30day = btind.SimpleMovingAverage(period=self.p.pslow)
self.gsma = btind.CrossOver(self.sma_12hr, self.sma_30day)
# used to determine if order has executed
self.order = None
# self.signal_add(bt.SIGNAL_LONG, bt.ind.CrossOver(sma_12hr, sma_30day))
self.dataclose = self.datas[0].close
def set_datalines(self):
self.data.sma_16 = btind.SimpleMovingAverage(self.datas[0], period=16)
self.data.sma_32 = btind.SimpleMovingAverage(self.datas[0], period=32)
self.data.sma_64 = btind.SimpleMovingAverage(self.datas[0], period=64)
self.data.sma_128 = btind.SimpleMovingAverage(self.datas[0],
period=128)
self.data.dim_sma = btind.SimpleMovingAverage(self.datas[0],
period=(128 +
self.time_dim))
self.data.dim_sma.plotinfo.plot = False
def __init__(self):
# To control operation entries
self.orderid = None
self.set_tradehistory(True)
# Indicators
poc = SimpleMovingAverage1(self.data0.POC,
period=1,
plotname='POC',
subplot=False)
ppoc = SimpleMovingAverage1(self.data0.PPOC,
period=1,
plotname='PPOC',
subplot=False)
poc3w = SimpleMovingAverage1(self.data0.POC3w,
period=1,
plotname='POC3w',
subplot=False)
pocw = SimpleMovingAverage1(self.data0.POCw,
period=1,
plotname='POCw',
subplot=False)
sma80 = btind.SimpleMovingAverage(self.data1.close,
period=80,
plotname='SMA80')
sma240 = btind.SimpleMovingAverage(self.data1.close,
period=240,
plotname='SMA240')
self.atr = bt.talib.ATR(self.data0.high,
self.data0.low,
self.data0.close,
timeperiod=14)
self.rsi = btind.RSI_EMA(self.data1.close, period=14)
self.macd = btind.MACDHistogram(self.data1.close)
#self.ematr = btind.ExponentialMovingAverage(self.atr, period=89)
self.signal1 = btind.CrossOver(self.data0.close, poc, plot=False)
self.signal2 = btind.CrossOver(self.data0.close, ppoc, plot=False)
self.sma80_slope = Slope(sma80, period=self.p.pslope, plot=False)
self.sma240_slope = Slope(sma240, period=self.p.pslope, plot=False)
self.macd_slope = Slope(self.macd, period=self.p.pslope, plot=False)
self.rsi_slope = Slope(self.rsi, period=self.p.pslope, plot=False)
self.diffPOC = (self.data0.POC - self.data0.PPOC)
self.diffPOC3w = (self.data0.POC - self.data0.POC3w)
self.diffPOCw = (self.data0.POC - self.data0.POCw)
self.diffClPOC = (self.data0.close - self.data0.POC)
#self.diffClPPOC = (self.data0.close - self.data0.PPOC)
self.diffClPOC3w = (self.data0.close - self.data0.POC3w)
self.diffClPPOC3w = (self.data0.close - self.data0.PPOC3w)
self.diffClPOCw = (self.data0.close - self.data0.POCw)
self.diffClPPOCw = (self.data0.close - self.data0.PPOCw)
self.diffSMA = (sma80 - sma240)
self.flag = True
self.counter = 0
self.nlost = 0
def __init__(self):
# Keep a reference to the "close" line in the data[0] dataseries
self.dataclose = self.datas[0].close
# To keep track of pending orders and buy price/commission
self.order = None
self.buyprice = None
self.buycomm = None
# Add a MovingAverageSimple indicator
self.sma10 = btind.SimpleMovingAverage(self.datas[0], period=self.p.pfast)
self.sma20 = btind.SimpleMovingAverage(self.datas[0], period=self.p.pslow)
self.crossover = btind.CrossOver(self.sma10, self.sma20) # crossover signal
def set_datalines(self):
self.data.spread = btind.SimpleMovingAverage(self.datas[0] -
self.datas[1],
period=1)
self.data.spread.plotinfo.subplot = True
self.data.spread.plotinfo.plotabove = True
self.data.spread.plotinfo.plotname = list(self.p.asset_names)[0]
initial_time_period = self.p.time_dim
self.data.dim_sma = btind.SimpleMovingAverage(
self.datas[0], period=initial_time_period)
self.data.dim_sma.plotinfo.plot = False
def __init__(self):
"""Initialize the strategy"""
self.fastma = dict()
self.slowma = dict()
self.regime = dict()
self.date_combos = [c for c in zip(self.p.start_dates, self.p.end_dates)]
# Error checking
if type(self.p.start_dates) is not list or type(self.p.end_dates) is not list or \
type(self.p.fast) is not list or type(self.p.slow) is not list:
raise ValueError("Must past lists filled with numbers to params start_dates, end_dates, fast, slow.")
elif len(self.p.start_dates) != len(self.p.end_dates) or \
len(self.p.fast) != len(self.p.start_dates) or len(self.p.slow) != len(self.p.start_dates):
raise ValueError("All lists passed to params must have same length.")
for d in self.getdatanames():
self.fastma[d] = dict()
self.slowma[d] = dict()
self.regime[d] = dict()
# Additional indexing, allowing for differing start/end dates
for sd, ed, f, s in zip(self.p.start_dates, self.p.end_dates, self.p.fast, self.p.slow):
# More error checking
if type(f) is not int or type(s) is not int:
raise ValueError("Must include only integers in fast, slow.")
elif f > s:
raise ValueError("Elements in fast cannot exceed elements in slow.")
elif f <= 0 or s <= 0:
raise ValueError("Moving average windows must be positive.")
if type(sd) is not dt.date or type(ed) is not dt.date:
raise ValueError("Only datetime dates allowed in start_dates, end_dates.")
elif ed - sd < dt.timedelta(0):
raise ValueError("Start dates must always be before end dates.")
# The moving averages
# Notice that different moving averages are obtained for different combinations of
# start/end dates
self.fastma[d][(sd, ed)] = btind.SimpleMovingAverage(self.getdatabyname(d),
period=f,
plot=False)
self.slowma[d][(sd, ed)] = btind.SimpleMovingAverage(self.getdatabyname(d),
period=s,
plot=False)
# Get the regime
self.regime[d][(sd, ed)] = self.fastma[d][(sd, ed)] - self.slowma[d][(sd, ed)]
def set_datalines(self):
self.data.high = self.data.low = self.data.close = self.data.open
self.data.std = btind.StdDev(self.data.open, period=self.p.time_dim, safepow=True)
self.data.std.plotinfo.plot = False
self.data.features = [
btind.SimpleMovingAverage(self.data.open, period=period) for period in self.p.features_parameters
]
initial_time_period = np.asarray(self.p.features_parameters).max() + self.p.time_dim
self.data.dim_sma = btind.SimpleMovingAverage(
self.datas[0],
period=initial_time_period
)
self.data.dim_sma.plotinfo.plot = False
def set_datalines(self):
# Override stat line:
self.stat_asset = self.data.spread = SpreadConstructor()
# Spy on reward behaviour:
self.reward_tracker = CumSumReward()
self.data.std1 = btind.StdDev(self.datas[0],
period=self.p.time_dim,
safepow=True)
self.data.std1.plotinfo.plot = False
self.data.std2 = btind.StdDev(self.datas[1],
period=self.p.time_dim,
safepow=True)
self.data.std2.plotinfo.plot = False
self.data.features1 = [
btind.EMA(self.datas[0], period=period)
for period in self.p.features_parameters
]
self.data.features2 = [
btind.EMA(self.datas[1], period=period)
for period in self.p.features_parameters
]
initial_time_period = np.asarray(
self.p.features_parameters).max() + self.p.time_dim
self.data.dim_sma = btind.SimpleMovingAverage(
self.datas[0], period=initial_time_period)
self.data.dim_sma.plotinfo.plot = False
def set_datalines(self):
initial_time_period = self.p.time_dim
self.data.dim_sma = btind.SimpleMovingAverage(
self.datas[0],
period=initial_time_period
)
self.data.dim_sma.plotinfo.plot = False
def set_datalines(self):
features_low = [
MinPool(self.data, period=period)
for period in self.features_parameters
]
features_high = [
MaxPool(self.data, period=period)
for period in self.features_parameters
]
# If `scale` was scalar - make it vector:
if len(np.asarray(self.p.state_ext_scale).shape) < 1:
self.p.state_ext_scale = np.repeat(
np.asarray(self.p.state_ext_scale), self.num_features)
# Sort features by `period` for .get_external_state() to estimate
# more or less sensible gradient; double-stretch scale vector accordingly:
# TODO: maybe 2 separate conv. encoders for hi/low?
self.data.features = []
for f1, f2 in zip(features_low, features_high):
self.data.features += [f1, f2]
self.p.state_ext_scale = np.repeat(self.p.state_ext_scale, 2)
# print('p.state_ext_scale: ', self.p.state_ext_scale, self.p.state_ext_scale.shape)
self.data.dim_sma = btind.SimpleMovingAverage(
self.datas[0],
period=(np.asarray(self.features_parameters).max() +
self.time_dim))
self.data.dim_sma.plotinfo.plot = False
def __init__(self):
self.rsi = btind.RSI(period=self.params.period)
# self.macd = btind.MACD()
# self.macd = btind.EMA(9) - btind.EMA(14)
# self.signal = btind.EMA(14)
self.sma = btind.SimpleMovingAverage(period=21)
self.ema = btind.ExponentialMovingAverage(period=14)
self.accDe = btind.AccDeOsc(period=14)
self.close_over_sma = self.data.close > self.sma
self.close_over_ema = self.data.close > self.ema
self.sma_ema_diff = self.sma - self.ema
self.buy_sig = bt.And(self.close_over_sma, self.close_over_ema,
self.sma_ema_diff > 0)
self.max_pos = 10
self.overSold = self.rsi < 50
self.overBought = self.rsi > 70
self.oscSig = self.accDe > 0
self.buy_price = []
self.sell_price = []
self.buy_price = []
self.sell_price = []
self.ave_buy = 0
self.ave_sell = 0
def __init__(self):
"""初始化一些参数
self.datas 用来存储feed进来的数据
SimpleMovingAverage 方法应该会生成一条叫做sma的lines
"""
# sma = btind.SimpleMovingAverage(self.data, period=self.params.period)
self.movav = btind.SimpleMovingAverage(self.data, period=self.p.period)
pass
def set_datalines(self):
# Here spread is for plotting and norm. tracking:
self.data.spread = btind.SimpleMovingAverage(self.datas[0] - self.datas[1], period=1)
self.data.spread.plotinfo.subplot = True
self.data.spread.plotinfo.plotabove = True
self.data.spread.plotinfo.plotname = list(self.p.asset_names)[0]
# Override stat line:
self.stat_asset = self.data.spread
initial_time_period = self.p.time_dim
self.data.dim_sma = btind.SimpleMovingAverage(
self.datas[0],
period=initial_time_period
)
self.data.dim_sma.plotinfo.plot = False
def __init__(self):
sma1 = btind.SimpleMovingAverage(self.data)
ema1 = btind.ExponentialMovingAverage()
close_over_sma = self.data.close > sma1
close_over_ema = self.data.close > ema1
sma_ema_diff = sma1 - ema1
buy_sig = bt.And(close_over_sma, close_over_ema, sma_ema_diff > 0)
def __init__(self):
self.sma1 = btind.SimpleMovingAverage(self.data)
self.ema1 = btind.ExponentialMovingAverage()
self.close_over_sma = self.data.close > self.sma1
self.close_over_ema = self.data.close > self.ema1
self.sma_ema_diff = self.sma1 - self.ema1
# 生成交易信号
self.buy_sig = bt.And(self.close_over_sma, self.close_over_ema,
self.sma_ema_diff > 0)
def set_datalines(self):
self.data_streams = {stream._name: stream for stream in self.datas}
# self.data = self.data_streams[self.p.base_dataline] # TODO: ??!!
self.data.dim_sma = btind.SimpleMovingAverage(
self.data,
period=(np.asarray(self.features_parameters).max() +
self.time_dim))
self.data.dim_sma.plotinfo.plot = False
def __init__(self):
self.order = None
self.previous_action: StateToRemember = None
# read from params
self.environment = self.params.environment
self.agent = self.environment.agent
self.sma = btind.SimpleMovingAverage(self.datas[0],
period=self.environment.window +
2) # meh
def __init__(self):
# 指标函数也支持简写 SimpleMovingAverage → SMA
# 最简方式：直接省略指向的数据集
self.sma1 = btind.SimpleMovingAverage(period=5)
# 只指定第一个数据表格
self.sma2 = btind.SMA(self.data, period=5)
# 指定第一个数据表格的 close 线
self.sma3 = btind.SMA(self.data.close, period=5)
# 完整写法
self.sma4 = btind.SMA(self.datas[0].lines[0], period=5)
def __init__(self):
sma1 = btind.SimpleMovingAverage(self.datas[0], period=self.p.period1)
# This 2nd Moving Average operates using sma1 as "data"
sma2 = btind.SimpleMovingAverage(sma1, period=self.p.period2)
# New data created via arithmetic operation
something = sma2 - sma1 + self.data.close
# This 3rd Moving Average operates using something as "data"
sma3 = btind.SimpleMovingAverage(something, period=self.p.period3)
# Comparison operators work too ...
greater = sma3 > sma1
# Pointless Moving Average of True/False values but valid
# This 4th Moving Average operates using greater as "data"
sma3 = btind.SimpleMovingAverage(greater, period=self.p.period4)
def __init__(self):
# To control operation entries
self.orderid = None
# Indicators
poc = btind.SimpleMovingAverage(self.data0.POC,
period=1,
plotname='POC')
ppoc = btind.SimpleMovingAverage(self.data0.PPOC,
period=1,
plotname='PPOC')
self.atr = bt.talib.ATR(self.data0.high,
self.data0.low,
self.data0.close,
period=14)
self.ematr = btind.ExponentialMovingAverage(self.atr, period=89)
self.dataclose = self.data0.close
self.anchor = 3.0
self.expand = 1.0
self.signal1 = btind.CrossOver(self.dataclose, poc, plotname='POC')
self.signal2 = btind.CrossOver(self.dataclose, ppoc, plotname='PPOC')
#self.trailing = True
self.flag = True
def __init__(self):
print('init strategy')
self.dataclose = dict()
self.sma_fast = dict()
self.sma_slow = dict()
self.longsignal = dict()
self.shortsignal = dict()
for symbol in self.getdatanames():
self.dataclose[symbol] = self.datas[0].close
self.sma_fast[symbol] = btind.SimpleMovingAverage(
self.getdatabyname(symbol),
period=self.p.fast,
plotname="FastMA: " + symbol)
self.sma_slow[symbol] = btind.SimpleMovingAverage(
self.getdatabyname(symbol),
period=self.p.slow,
plotname="SlowMA: " + symbol)
self.longsignal[symbol] = btind.CrossOver(self.sma_fast[symbol],
self.sma_slow[symbol])
self.shortsignal[symbol] = btind.CrossOver(self.sma_slow[symbol],
self.sma_fast[symbol])
def set_datalines(self):
# Discard superclass dataline, use SpreadConstructor instead:
self.data.spread = None
# Override stat line:
self.stat_asset = self.SpreadConstructor()
# Spy on reward behaviour:
self.reward_tracker = self.CumSumReward()
initial_time_period = self.p.time_dim
self.data.dim_sma = btind.SimpleMovingAverage(
self.datas[0], period=initial_time_period)
self.data.dim_sma.plotinfo.plot = False
