def __init__(self, df, params):
self.condList = (params["condList"] if "condList" in params.keys() else
("maBelow", "isWhiteBlack"))
self.numPeriods = params["numPeriods"] if "numPeriods" in params.keys(
) else 2
self.maLong = params["maLong"] if "maLong" in params.keys() else 50
self.maShort = params["maShort"] if "maShort" in params.keys() else 10
self.df = Attr(
Attr(
Attr(
Attr(df).add("IsWhiteBlack",
params={
"whiteList": [0, 1],
"blackList": [2]
})).add("Hammer", params={"delay": 1})).add(
"MA",
params={
"numPeriods": self.maLong,
"attrName": "maLong"
})).add("MA",
params={
"numPeriods": self.maShort,
"attrName": "maShort"
})
self.df["maBelow"] = self.df["maLong"] > self.df["maShort"]
def __init__(self, df, params):
self.numPeriods = params["numPeriods"] if "numPeriods" in params.keys() else 100
self.maxPeriod = params["maxPeriod"] if "maxPeriod" in params.keys() else 32
isHalves = params["isHalves"] if "isHalves" in params.keys() else True
self.df = Attr(
Attr(df).add(
"MA", params={"col": "volume", "numPeriods": 100, "attrName": "volMA"}
)
).add("PivotLines", params={"numPeriods": self.numPeriods})
self.pivots = (
[
"s3",
"s2.5",
"s2",
"s1.5",
"s1",
"s0.5",
"p",
"r0.5",
"r1",
"r1.5",
"r2",
"r2.5",
"r3",
]
if isHalves
else ["s3", "s2", "s1", "p", "r1", "r2", "r3"]
)
def __init__(self, df, params):
self.condList = (
params["condList"] if "condList" in params.keys() else ("maBelow", "conf")
)
self.numPeriods = params["numPeriods"] if "numPeriods" in params.keys() else 2
self.maLong = params["maLong"] if "maLong" in params.keys() else 50
self.maShort = params["maShort"] if "maShort" in params.keys() else 10
self.df = Attr(
Attr(
Attr(df).add("BullSqueeze", params={"numPeriods": self.numPeriods})
).add("MA", params={"numPeriods": self.maLong, "attrName": "maLong"})
).add("MA", params={"numPeriods": self.maShort, "attrName": "maShort"})
self.df["maBelow"] = self.df["maLong"] > self.df["maShort"]
self.df["conf"] = self.df["close"] > self.df["bullConf"]
def __init__(self, df, params):
self.delay = params["delay"] if "delay" in params.keys() else 0
self.volMALongPeriods = (params["numPeriodsVolLong"] if
"numPeriodsVolLong" in params.keys() else 100)
self.volMAShortPeriods = (params["numPeriodsVolShort"] if
"numPeriodsVolShort" in params.keys() else 5)
self.volCoef = params["volCoef"] if "volCoef" in params.keys() else 1
self.volDir = params["volDir"] if "volDir" in params.keys() else "high"
self.maPeriods = (params["numPeriodsMA"]
if "numPeriodsMA" in params.keys() else 30)
self.numStd = params["numStd"] if "numStd" in params.keys() else 2
self.bolCoef = params["bolCoef"] if "bolCoef" in params.keys() else 1
self.volCol = (params["volCol"]
if "volCol" in params.keys() else "takerBaseAssetVol")
if len(df) > self.volMALongPeriods:
self.df = (Attr(
Attr(
Attr(
Attr(df).add(
"MA",
params={
"numPeriods": self.maPeriods,
"attrName": "ma"
},
)).add(
"Bollinger",
params={
"numPeriods": self.maPeriods,
"numStd": self.numStd,
"maField": "ma",
"attrName": "bollinger",
},
)).add(
"MA",
params={
"col": self.volCol,
"numPeriods": self.volMALongPeriods,
"attrName": "volMALong",
},
)).add(
"MA",
params={
"col": self.volCol,
"numPeriods": self.volMAShortPeriods,
"attrName": "volMAShort",
},
).iloc[max(self.volMALongPeriods, self.maPeriods):])
else:
self.df = df
def __init__(self, df, params, enterList, isDebug=False):
self.numMAPeriods = (
params["numMAPeriods"] if "numMAPeriods" in params.keys() else 5
)
self.numRPeriods = (
params["numRPeriods"] if "numRPeriods" in params.keys() else 10
)
self.coef = params["coef"] if "coef" in params.keys() else 0.99
self.df = Attr(
Attr(df).add(
"MA", params={"attrName": "exitMA", "numPeriods": self.numMAPeriods}
)
).add("Support", params={"numPeriods": self.numRPeriods})
self.enterList = enterList
self.isDebug = isDebug
class InsideUp:
"""
Pairs a Bull Squeeze candlestick formation with a confirming white session
numPeriods > 2 then -> squeeze
12h/1d effective
"""
def __init__(self, df, params):
self.condList = (
params["condList"] if "condList" in params.keys() else ("maBelow", "conf")
)
self.numPeriods = params["numPeriods"] if "numPeriods" in params.keys() else 2
self.maLong = params["maLong"] if "maLong" in params.keys() else 50
self.maShort = params["maShort"] if "maShort" in params.keys() else 10
self.df = Attr(
Attr(
Attr(df).add("BullSqueeze", params={"numPeriods": self.numPeriods})
).add("MA", params={"numPeriods": self.maLong, "attrName": "maLong"})
).add("MA", params={"numPeriods": self.maShort, "attrName": "maShort"})
self.df["maBelow"] = self.df["maLong"] > self.df["maShort"]
self.df["conf"] = self.df["close"] > self.df["bullConf"]
def run(self):
self.df["bullSqueezeConf"] = self.df.apply(self.conditions, axis=1)
return list(self.df[self.df["bullSqueezeConf"]]["TS"].values)
def conditions(self, row):
return False not in [row[val] for val in self.condList] + [row["bullSqueeze"]]
class LadderBottom:
"""
Three consecutive down periods followed by a gap white
** Not promising
"""
def __init__(self, df, params):
self.gapCoef = params["gapCoef"] if "gapCoef" in params.keys(
) else 1.005
self.gapPeriods = params["gapPeriods"] if "gapPeriods" in params.keys(
) else 2
self.numBSPeriods = (params["numBSPeriods"]
if "numBSPeriods" in params.keys() else 3)
self.df = Attr(
Attr(df).add("UpGap",
params={
"numPeriods": self.gapPeriods,
"coef": self.gapCoef
})).add("BlackSoldiers",
params={
"numPeriods": self.numBSPeriods,
"delay": 1
})
def run(self):
self.df["ladderBottom"] = self.df.apply(self.conditions, axis=1)
return list(self.df[self.df["ladderBottom"]]["TS"].values)
def conditions(self, row):
return False not in [row["blackSoldiers"], row["upGap"]]
def __init__(self, df, params):
self.gapCoef = params["gapCoef"] if "gapCoef" in params.keys(
) else 1.005
self.gapPeriods = params["gapPeriods"] if "gapPeriods" in params.keys(
) else 2
self.numBSPeriods = (params["numBSPeriods"]
if "numBSPeriods" in params.keys() else 3)
self.df = Attr(
Attr(df).add("UpGap",
params={
"numPeriods": self.gapPeriods,
"coef": self.gapCoef
})).add("BlackSoldiers",
params={
"numPeriods": self.numBSPeriods,
"delay": 1
})
def test_Bollinger():
numPeriods = 5
df = Attr(testingLoad()).add("MA", params={"numPeriods": numPeriods})
maCol = df["ma%s" % numPeriods]
bol = Bollinger(df=df, params={
"numPeriods": numPeriods,
"numStd": 10
}).run()
p = 8
assert bol[0][1][p] == round(
float(df["ma%s" % numPeriods].iloc[p] +
10 * np.std(df["close"].iloc[p - numPeriods:p])),
6,
)
p = 5
assert bol[1][1][p] == round(
float(df["ma%s" % numPeriods].iloc[p] -
10 * np.std(df["close"].iloc[p - numPeriods:p])),
6,
)
class BullHammer:
"""
Could make cleaner
* Not promising
"""
def __init__(self, df, params):
self.condList = (params["condList"] if "condList" in params.keys() else
("maBelow", "isWhiteBlack"))
self.numPeriods = params["numPeriods"] if "numPeriods" in params.keys(
) else 2
self.maLong = params["maLong"] if "maLong" in params.keys() else 50
self.maShort = params["maShort"] if "maShort" in params.keys() else 10
self.df = Attr(
Attr(
Attr(
Attr(df).add("IsWhiteBlack",
params={
"whiteList": [0, 1],
"blackList": [2]
})).add("Hammer", params={"delay": 1})).add(
"MA",
params={
"numPeriods": self.maLong,
"attrName": "maLong"
})).add("MA",
params={
"numPeriods": self.maShort,
"attrName": "maShort"
})
self.df["maBelow"] = self.df["maLong"] > self.df["maShort"]
def run(self):
self.df["hammerConf"] = self.df.apply(self.conditions, axis=1)
return list(self.df[self.df["hammerConf"]]["TS"].values)
def conditions(self, row):
return False not in [row[val]
for val in self.condList] + [row["hammer"]]
def __init__(self, df, params):
self.numBSPeriods = (params["numBSPeriods"]
if "numBSPeriods" in params.keys() else 3)
self.maLong = params["maLong"] if "maLong" in params.keys() else 50
self.maShort = params["maShort"] if "maShort" in params.keys() else 10
self.volPeriods = params["volPeriods"] if "volPeriods" in params.keys(
) else 40
self.condList = (params["condList"] if "condList" in params.keys() else
("isWhite", "maBelow", "volCond"))
self.df = Attr(
Attr(
Attr(
Attr(
Attr(df).add(
"MA",
params={
"numPeriods": self.volPeriods,
"col": "takerBaseAssetVol",
"attrName": "meanVol",
},
)).add(
"Bollinger",
params={
"maField": "meanVol",
"numStd": 0.5,
"attrName": "bolVol",
},
)).add("MA",
params={
"numPeriods": self.maLong,
"attrName": "maLong"
})).add("MA",
params={
"numPeriods": self.maShort,
"attrName": "maShort"
})).add("BullSqueeze",
params={
"numPeriods":
self.numBSPeriods,
"delay": 1
})
self.df["isWhite"] = self.df["close"] > self.df["open"]
self.df["maBelow"] = self.df["maLong"] > self.df["maShort"]
self.runStats = {}
class BullSqueezeVol:
"""
MA50 > MA10
Bull squeeze pattern
Vol for init > Mean(Vol_20) + std
Vol for last squeeze period + 1 < Mean(Vol_20)
Squeeze +1 white
"""
def __init__(self, df, params):
self.numBSPeriods = (params["numBSPeriods"]
if "numBSPeriods" in params.keys() else 3)
self.maLong = params["maLong"] if "maLong" in params.keys() else 50
self.maShort = params["maShort"] if "maShort" in params.keys() else 10
self.volPeriods = params["volPeriods"] if "volPeriods" in params.keys(
) else 40
self.condList = (params["condList"] if "condList" in params.keys() else
("isWhite", "maBelow", "volCond"))
self.df = Attr(
Attr(
Attr(
Attr(
Attr(df).add(
"MA",
params={
"numPeriods": self.volPeriods,
"col": "takerBaseAssetVol",
"attrName": "meanVol",
},
)).add(
"Bollinger",
params={
"maField": "meanVol",
"numStd": 0.5,
"attrName": "bolVol",
},
)).add("MA",
params={
"numPeriods": self.maLong,
"attrName": "maLong"
})).add("MA",
params={
"numPeriods": self.maShort,
"attrName": "maShort"
})).add("BullSqueeze",
params={
"numPeriods":
self.numBSPeriods,
"delay": 1
})
self.df["isWhite"] = self.df["close"] > self.df["open"]
self.df["maBelow"] = self.df["maLong"] > self.df["maShort"]
self.runStats = {}
def run(self):
self.df["volCond"] = self.volCond()
self.df["bullSqueezeVol"] = self.df.apply(self.conditions, axis=1)
for cond in ["bullSqueeze", "isWhite", "maBelow", "volCond"]:
self.runStats[cond] = round(sum(self.df[cond]) / len(self.df), 4)
print(self.runStats)
return list(self.df[self.df["bullSqueezeVol"]]["TS"].values)
def conditions(self, row):
return False not in [row[val]
for val in self.condList] + [row["bullSqueeze"]]
def volCond(self):
volList = [False for _ in range(len(self.df))]
for i in range(self.numBSPeriods, len(self.df)):
row = self.df.iloc[i]
if (self.df.iloc[i - self.numBSPeriods - 1]["takerBaseAssetVol"] >
row["bolVolUp"] and row["takerBaseAssetVol"] <
(row["meanVol"] + row["bolVolUp"]) / 2):
volList[i] = True
return volList
def test_add(loc=None):
df = testingLoad(loc=loc)
A = Attr(df)
df_ = A.add("TestAttr", params={"attrName": "attrName"})
assert df.iloc[-1]["open"] + df.iloc[-1]["close"] == df_.iloc[-1]["attrName1"]
assert df.iloc[0]["high"] + df.iloc[0]["low"] == df_.iloc[0]["attrName2"]