def macdTRIX(self, df):
df = df.dropna()
#1. Calculate ATR for potential trade
atr = atrcalc.ATRcalc(df)
##b. MACD
macdInput = df.head(34)
macdInput = macdInput.iloc[::-1]
MACDclose = macdInput['close'].values
macd, macdsignal, macdhist = MACDFIX(MACDclose, signalperiod=9)
# print("MACD\n", macd[-1])
# print("Signal\n", macdsignal[-1])
##c. DelayedMACD
delayedmacdInput = df.iloc[1:].head(34)
delayedmacdInput = delayedmacdInput.iloc[::-1]
delayedMACDclose = delayedmacdInput['close'].values
delayedmacd, delayedmacdsignal, delayedmacdhist = MACDFIX(
delayedMACDclose, signalperiod=9)
trixInput = df.head(60)
trixInput = df.iloc[::-1]
trixOutput = TRIX(trixInput['close'].values, timeperiod=20)
# print("EMA\n", ema[-1])
##d. current price action
priceaction = df.head(1)
pricehigh = priceaction['high'].values[0]
pricelow = priceaction['low'].values[0]
priceclose = priceaction['close'].values[0]
# print("pricehigh\n", pricehigh)
# print("pricelow\n", pricelow)
if delayedmacd[-1] < delayedmacdsignal[-1] and macd[-1] > macdsignal[
-1] and trixOutput[-1] < 0:
position = 1
elif delayedmacd[-1] > delayedmacdsignal[-1] and macd[-1] < macdsignal[
-1] and trixOutput[-1] > 0:
position = -1
else:
position = 0
amount = 50
confidence = 0
if position == 1:
stoploss = priceclose - 1.05 * atr
takeprofit = priceclose + 1.45 * atr
# amount = priceclose / (priceclose - stoploss)
if (priceclose - stoploss) * amount < 0.1:
position = 0
amount = 0
stoploss = 0
takeprofit = 0
else:
pattern, patterntype = check(df.head(20))
if position * pattern > 0:
confidence = abs(pattern)
elif position * pattern < 0:
confidence = abs(1 / pattern)
else:
confidence = 1
elif position == -1:
stoploss = priceclose + 1.05 * atr
takeprofit = priceclose - 1.45 * atr
# amount = priceclose / (stoploss - priceclose)
if (stoploss - priceclose) * amount < 0.1:
position = 0
amount = 0
stoploss = 0
takeprofit = 0
else:
pattern, patterntype = check(df.head(20))
if position * pattern > 0:
confidence = abs(pattern)
elif position * pattern < 0:
confidence = abs(1 / pattern)
else:
confidence = 1
else:
stoploss = 0
takeprofit = 0
amount = 0
##For test
# position = 1
# ##FOR TEST
# position = 1
return [position, amount, priceclose, stoploss, takeprofit, confidence]
def bbands200(self, df):
df = df.dropna()
#1. Calculate ATR for potential trade
atr = atrcalc.ATRcalc(df)
pattern, patterntype = check(df.head(20))
##b. get BBands
bband = df.head(21)
bband = bband.iloc[1:]
bband = bband.iloc[::-1]
bbandInput = bband['close'].values
upperband, middleband, lowerband = BBANDS(bbandInput,
timeperiod=20,
nbdevup=2,
nbdevdn=2,
matype=0)
##c. 200EMA
emaInput = df.head(200)
emaInput = emaInput.iloc[::-1]
EMAclose = emaInput['close'].values
ema = EMA(EMAclose, timeperiod=200)
##d. current price action
priceaction = df.head(1)
pricehigh = priceaction['high'].values[0]
pricelow = priceaction['low'].values[0]
priceclose = priceaction['close'].values[0]
if pricehigh > upperband[-1]: breakBand = -1
elif pricelow < lowerband[-1]: breakBand = 1
else: breakBand = 0
if pricelow > ema[-1]: marketEMA = 1
elif pricehigh < ema[-1]: marketEMA = -1
else: marketEMA = 0
##OUTPUT
if marketEMA == 1 and pattern > 0 and patterntype == -1 and breakBand == 1:
position = 1
elif marketEMA == -1 and pattern < 0 and patterntype == -1 and breakBand == -1:
position = -1
else:
position = 0
amount = 50
confidence = 0
if position == 1:
stoploss = priceclose - (1.05 * atr)
takeprofit = priceclose + (1.45 * atr)
# amount = priceclose / (priceclose - stoploss)
if (stoploss - priceclose) * amount < 0.1:
position = 0
amount = 0
stoploss = 0
takeprofit = 0
else:
if position * pattern > 0:
confidence = abs(pattern)
elif position * pattern < 0:
confidence = abs(1 / pattern)
else:
confidence = 1
elif position == -1:
stoploss = priceclose + (1.05 * atr)
takeprofit = priceclose - (1.45 * atr)
# amount = priceclose / (stoploss - priceclose)
if (stoploss - priceclose) * amount < 0.1:
position = 0
amount = 0
stoploss = 0
takeprofit = 0
else:
if position * pattern > 0:
confidence = abs(pattern)
elif position * pattern < 0:
confidence = abs(1 / pattern)
else:
confidence = 1
else:
stoploss = 0
takeprofit = 0
amount = 0
# ##FOR TEST
# position = 1
return [position, amount, priceclose, stoploss, takeprofit, confidence]
def trix200(self, df):
df = df.dropna()
#1. Calculate ATR for potential trade
atr = atrcalc.ATRcalc(df)
trixInput = df.head(60)
trixInput = trixInput.iloc[::-1]
trixOutput = TRIX(trixInput['close'].values, timeperiod=14)
##c. 200EMA
emaInput = df.head(200)
emaInput = emaInput.iloc[::-1]
EMAclose = emaInput['close'].values
ema = EMA(EMAclose, timeperiod=200)
# print("EMA\n", ema[-1])
##d. current price action
priceaction = df.head(1)
pricehigh = priceaction['high'].values[0]
pricelow = priceaction['low'].values[0]
priceclose = priceaction['close'].values[0]
# print("pricehigh\n", pricehigh)
# print("pricelow\n", pricelow)
if trixOutput[-2] < 0 and trixOutput[-1] > 0:
crossover = 1
elif trixOutput[-2] > 0 and trixOutput[-1] < 0:
crossover = -1
else:
crossover = 0
if pricelow > ema[-1]: marketEMA = 1
elif pricehigh < ema[-1]: marketEMA = -1
else: marketEMA = 0
##OUTPUT
if marketEMA == 1 and crossover > 0: position = 1
elif marketEMA == -1 and crossover < 0: position = -1
else: position = 0
amount = 50
confidence = 0
if position == 1:
stoploss = priceclose - 1.05 * atr
takeprofit = priceclose + 1.45 * atr
# amount = priceclose / (priceclose - stoploss)
if (priceclose - stoploss) * amount < 0.1:
position = 0
amount = 0
stoploss = 0
takeprofit = 0
else:
pattern, patterntype = check(df.head(20))
if position * pattern > 0:
confidence = abs(pattern)
elif position * pattern < 0:
confidence = abs(1 / pattern)
else:
confidence = 1
elif position == -1:
stoploss = priceclose + 1.05 * atr
takeprofit = priceclose - 1.45 * atr
# amount = priceclose / (stoploss - priceclose)
if (stoploss - priceclose) * amount < 0.1:
position = 0
amount = 0
stoploss = 0
takeprofit = 0
else:
pattern, patterntype = check(df.head(20))
if position * pattern > 0:
confidence = abs(pattern)
elif position * pattern < 0:
confidence = abs(1 / pattern)
else:
confidence = 1
else:
stoploss = 0
takeprofit = 0
amount = 0
# ##FOR TEST
# position = 1
return [position, amount, priceclose, stoploss, takeprofit, confidence]
def macd200(self, df):
#1. Calculate ATR for potential trade
atr = atrcalc.ATRcalc(df)
#####PLACEHOLDER
# df = pd.read_csv('./database/AAPL.csv')
#####END_PLACEHOLDER
df = df.dropna()
###1. Getting Parameters
##b. MACD
macdInput = df.head(34)
macdInput = macdInput.iloc[::-1]
MACDclose = macdInput['close'].values
macd, macdsignal, macdhist = MACDFIX(MACDclose, signalperiod=9)
# print("MACD\n", macd[-1])
# print("Signal\n", macdsignal[-1])
##c. DelayedMACD
delayedmacdInput = df.iloc[1:].head(34)
delayedmacdInput = delayedmacdInput.iloc[::-1]
delayedMACDclose = delayedmacdInput['close'].values
delayedmacd, delayedmacdsignal, delayedmacdhist = MACDFIX(
delayedMACDclose, signalperiod=9)
##c. 200EMA
emaInput = df.head(200)
emaInput = emaInput.iloc[::-1]
EMAclose = emaInput['close'].values
ema = EMA(EMAclose, timeperiod=200)
# print("EMA\n", ema[-1])
##d. current price action
priceaction = df.head(1)
pricehigh = priceaction['high'].values[0]
pricelow = priceaction['low'].values[0]
priceclose = priceaction['close'].values[0]
# print("pricehigh\n", pricehigh)
# print("pricelow\n", pricelow)
###2. Analysing using the data provided
##macd-signal crossover type
## -1 means negative crossover
## 1 means positive crossover
## 0 means both
if delayedmacd[-1] < delayedmacdsignal[-1] and macd[-1] > macdsignal[
-1]:
crossover = 1
elif delayedmacd[-1] > delayedmacdsignal[-1] and macd[-1] < macdsignal[
-1]:
crossover = -1
else:
crossover = 0
##market-ema type
## 1 means low > 200EMA
## -1 means high < 200EMA
## 0 means otherwise
if pricelow > ema[-1]: marketEMA = 1
elif pricehigh < ema[-1]: marketEMA = -1
else: marketEMA = 0
##OUTPUT
if marketEMA == 1 and crossover > 0 and macd[-1] < 0: position = 1
elif marketEMA == -1 and crossover < 0 and macd[-1] > 0: position = -1
else: position = 0
amount = 50
confidence = 0
if position == 1:
stoploss = priceclose - 1.05 * atr
takeprofit = priceclose + 1.45 * atr
# amount = priceclose / (priceclose - stoploss)
if (priceclose - stoploss) * amount < 0.1:
position = 0
amount = 0
stoploss = 0
takeprofit = 0
else:
pattern, patterntype = check(df.head(20))
if position * pattern > 0:
confidence = abs(pattern)
elif position * pattern < 0:
confidence = abs(1 / pattern)
else:
confidence = 1
elif position == -1:
stoploss = priceclose + 1.05 * atr
takeprofit = priceclose - 1.45 * atr
# amount = priceclose / (stoploss - priceclose)
if (stoploss - priceclose) * amount < 0.1:
position = 0
amount = 0
stoploss = 0
takeprofit = 0
else:
pattern, patterntype = check(df.head(20))
if position * pattern > 0:
confidence = abs(pattern)
elif position * pattern < 0:
confidence = abs(1 / pattern)
else:
confidence = 1
else:
stoploss = 0
takeprofit = 0
amount = 0
##For test
# position = 1
# ##FOR TEST
# position = 1
return [position, amount, priceclose, stoploss, takeprofit, confidence]
def ichimoku200(self, df):
## Step 1:
#####PLACEHOLDER
# df = pd.read_csv('./database/TSLA/temp2.csv')
#####END_PLACEHOLDER
df = df.dropna()
###1. Getting Parameters
##a. Senkou Span B Ahead
Currentfifty_two = df.head(52)
Currentfifty_two_high = Currentfifty_two['high'].max()
Currentfifty_two_low = Currentfifty_two['low'].min()
AheadSenkouB = (Currentfifty_two_high + Currentfifty_two_low) / 2
# print("SenkouBAhead\n", AheadSenkouB)
##b. Current Kijun-Sen
Currenttwenty_six = Currentfifty_two.head(26)
Currenttwenty_six_high = Currenttwenty_six['high'].max()
Currenttwenty_six_low = Currenttwenty_six['low'].min()
CurrentKijun = (Currenttwenty_six_high + Currenttwenty_six_low) / 2
# print("Kijun-Sen\n" , CurrentKijun)
##c. Current Tenkan-Sen
Currentnine = Currenttwenty_six.head(9)
Currentnine_high = Currentnine['high'].max()
Currentnine_low = Currentnine['low'].min()
CurrentTenkan = (Currentnine_high + Currentnine_low) / 2
# print("Tenkan-Sen\n", CurrentTenkan)
##d. Senkou Span A Ahead
AheadSenkouA = (CurrentKijun + CurrentTenkan) / 2
# print("SenkouAAhead\n", AheadSenkouA)
##e. Senkou Span B Current
Pastfifty_two = df.iloc[26:].head(52)
Pastfifty_two_high = Pastfifty_two['high'].max()
Pastfifty_two_low = Pastfifty_two['low'].min()
CurrentSenkouB = (Pastfifty_two_high + Pastfifty_two_low) / 2
# print("SenkouBCurrent\n", CurrentSenkouB)
##f. Past Kijun-Sen
Pasttwenty_six = Pastfifty_two.head(26)
Pasttwenty_six_high = Pasttwenty_six['high'].max()
Pasttwenty_six_low = Pasttwenty_six['low'].min()
PastKijun = (Pasttwenty_six_low + Pasttwenty_six_high) / 2
# print("PastKijun-Sen\n",PastKijun)
##g. Past Tenkan-Sen
Pastnine = Pasttwenty_six.head(9)
Pastnine_high = Pastnine['high'].max()
Pastnine_low = Pastnine['low'].min()
PastTenkan = (Pastnine_high + Pastnine_low) / 2
# print("PastTenkan-Sen\n", PastTenkan)
##h. Senkou Span A Current
CurrentSenkouA = (PastKijun + PastTenkan) / 2
# print("SenkouACurrent\n", CurrentSenkouA)
##i. Senkou Span B Past
PastPastfifty_two = df.iloc[52:].head(52)
PastPastfifty_two_high = PastPastfifty_two['high'].max()
PastPastfifty_two_low = PastPastfifty_two['low'].min()
PastSenkouB = (PastPastfifty_two_high + PastPastfifty_two_low) / 2
##j. Past Past Kijun - Sen
PastPasttwenty_six = PastPastfifty_two.head(26)
PastPasttwenty_six_high = PastPasttwenty_six['high'].max()
PastPasttwenty_six_low = PastPasttwenty_six['low'].min()
PastPastKijun = (PastPasttwenty_six_low + PastPasttwenty_six_high) / 2
##k. Past Past Tenkan-Sen
PastPastnine = PastPasttwenty_six.head(9)
PastPastnine_high = PastPastnine['high'].max()
PastPastnine_low = PastPastnine['low'].min()
PastPastTenkan = (PastPastnine_high + PastPastnine_low) / 2
##l. Senkou Span A Past
PastSenkouA = (PastPastKijun + PastPastTenkan) / 2
##m. 200EMA
emaInput = df.head(200)
emaInput = emaInput.iloc[::-1]
EMAclose = emaInput['close'].values
ema = EMA(EMAclose, timeperiod=200)
# print("EMA\n", ema[-1])
##n. current price action
priceaction = df.head(1)
pricehigh = priceaction['high'].values[0]
pricelow = priceaction['low'].values[0]
priceclose = priceaction['close'].values[0]
### IMPT CHIKOU SPAN = PRICE CLOSE
# print("pricehigh\n", pricehigh)
# print("pricelow\n", pricelow)
### 2. Analysing using Data Provided
##tenkan-kijun crossover type
## -1 means negative crossover
## 1 means positive crossover
## 0 means both
delayOnePeriod = df.iloc[1:]
##b. Current Kijun-Sen
Delaytwenty_six = delayOnePeriod.head(26)
Delaytwenty_six_high = Delaytwenty_six['high'].max()
Delaytwenty_six_low = Delaytwenty_six['low'].min()
DelayKijun = (Delaytwenty_six_high + Delaytwenty_six_low) / 2
# print("Delay Kijun-Sen\n" , DelayKijun)
##c. Current Tenkan-Sen
Delaynine = Delaytwenty_six.head(9)
Delaynine_high = Delaynine['high'].max()
Delaynine_low = Delaynine['low'].min()
DelayTenkan = (Delaynine_high + Delaynine_low) / 2
# print("Tenkan-Sen\n", DelayTenkan)
if DelayTenkan <= DelayKijun and CurrentTenkan >= CurrentKijun and pricelow > CurrentTenkan:
crossover = 1
elif DelayTenkan >= DelayKijun and CurrentTenkan <= CurrentKijun and pricehigh < CurrentTenkan:
crossover = -1
else:
crossover = 0
##ahead cloud colour
## -1 means red cloud
## 1 means green cloud
## 0 means both
if AheadSenkouA > AheadSenkouB: AheadCloud = 1
elif AheadSenkouA < AheadSenkouB: AheadCloud = -1
else: AheadCloud = 0
##market-ema type
## 1 means low > 200EMA
## -1 means high < 200EMA
## 0 means otherwise
if pricelow > ema[-1]: marketEMA = 1
elif pricehigh < ema[-1]: marketEMA = -1
else: marketEMA = 0
##market-cloud type
## 1 means close is above current cloud, and close (lagging span) above the past cloud too
## -1 means close is below current cloud, and close (lagging span) below the past cloud too
## 0 means otherwise (absolutely no trading)
if pricelow > max(CurrentSenkouA, CurrentSenkouB) and pricelow > max(
PastSenkouB, PastSenkouA):
marketCloud = 1
elif pricehigh < min(CurrentSenkouB,
CurrentSenkouA) and pricehigh < min(
PastSenkouA, PastSenkouB):
marketCloud = -1
else:
marketCloud = 0
if marketCloud == 1 and marketEMA > 0 and AheadCloud >= 0 and crossover > 0:
position = 1 ##long
elif marketCloud == -1 and marketEMA < 0 and AheadCloud <= 0 and crossover < 0:
position = -1 ##short
else:
position = 0 ## no position
amount = 50
confidence = 0
if position == 1:
closeKijunDistance = priceclose - CurrentKijun
adjustedDistance = 1.05 * closeKijunDistance
stoploss = priceclose - adjustedDistance
# amount = priceclose / (priceclose-stoploss)
takeprofit = priceclose + 1.45 * (priceclose - stoploss)
if (takeprofit - priceclose) * amount < 0.1:
amount = 0
position = 0
stoploss = 0
takeprofit = 0
else:
pattern, patterntype = check(df.head(20))
if position * pattern > 0:
confidence = abs(pattern)
elif position * pattern < 0:
confidence = abs(1 / pattern)
else:
confidence = 1
elif position == -1:
closeKijunDistance = CurrentKijun - priceclose
adjustedDistance = 1.05 * closeKijunDistance
stoploss = priceclose + adjustedDistance
# amount = priceclose / (stoploss - priceclose)
takeprofit = priceclose - 1.45 * (stoploss - priceclose)
if (priceclose - takeprofit) * amount < 0.1:
amount = 0
position = 0
stoploss = 0
takeprofit = 0
else:
pattern, patterntype = check(df.head(20))
if position * pattern > 0:
confidence = abs(pattern)
elif position * pattern < 0:
confidence = abs(1 / pattern)
else:
confidence = 1
else:
amount = 0
stoploss = 0
takeprofit = 0
# ##FOR TEST
# position = 1
return [position, amount, priceclose, stoploss, takeprofit, confidence]
def SMA200(self, df):
df = df.dropna()
###1. Getting Parameters
##a. current price action
priceaction = df.head(1)
pricehigh = priceaction['high'].values[0]
pricelow = priceaction['low'].values[0]
priceclose = priceaction['close'].values[0]
##b. previous price action
prevaction = df.iloc[1:].head(1)
prevhigh = prevaction['high'].values[0]
prevlow = prevaction['low'].values[0]
prevclose = prevaction['close'].values[0]
##d. current SMAs
smaCurrentInput = df.head(20)
sma20Current = SMA(smaCurrentInput['close'].values, timeperiod=20)
smaCurrentInput = smaCurrentInput.head(10)
sma10Current = SMA(smaCurrentInput['close'].values, timeperiod=10)
##e. previous SMAs
smaPreviousInput = df.iloc[1:].head(20)
sma20Previous = SMA(smaPreviousInput['close'].values, timeperiod=20)
smaPreviousInput = smaPreviousInput.head(10)
sma10Previous = SMA(smaPreviousInput['close'].values, timeperiod=10)
##f. 200EMA
emaInput = df.head(200)
emaInput = emaInput.iloc[::-1]
EMAclose = emaInput['close'].values
ema = EMA(EMAclose, timeperiod=200)
##trade conditions
if sma10Previous[-1] < sma20Previous[-1] and sma10Current[
-1] > sma20Current[-1]:
crossover = 1
elif sma10Previous[-1] > sma20Previous[-1] and sma10Current[
-1] < sma20Current[-1]:
crossover = -1
else:
crossover = 0
## 200EMA filtering false signal
if pricelow > ema[-1]: marketEMA = 1
elif pricehigh < ema[-1]: marketEMA = -1
else: marketEMA = 0
if crossover == 1 and marketEMA == 1:
position = 1
elif crossover == -1 and marketEMA == -1:
position = -1
else:
position = 0
atr = atrcalc.ATRcalc(df)
amount = 50
confidence = 0
if position == 1:
stoploss = priceclose - 1.05 * atr
takeprofit = priceclose + 1.45 * atr
# amount = priceclose / (priceclose - stoploss)
if (priceclose - stoploss) * amount < 0.1:
position = 0
amount = 0
stoploss = 0
takeprofit = 0
else:
pattern, patterntype = check(df.head(20))
if position * pattern > 0:
confidence = abs(pattern)
elif position * pattern < 0:
confidence = abs(1 / pattern)
else:
confidence = 1
elif position == -1:
stoploss = priceclose + 1.05 * atr
takeprofit = priceclose - 1.45 * atr
# amount = priceclose / (stoploss - priceclose)
if (stoploss - priceclose) * amount < 0.1:
position = 0
amount = 0
stoploss = 0
takeprofit = 0
else:
pattern, patterntype = check(df.head(20))
if position * pattern > 0:
confidence = abs(pattern)
elif position * pattern < 0:
confidence = abs(1 / pattern)
else:
confidence = 1
else:
stoploss = 0
takeprofit = 0
amount = 0
# ##FOR TEST
# position = 1
return [position, amount, priceclose, stoploss, takeprofit, confidence]
def parabolic200(self, df):
df = df.dropna()
###1. Getting Parameters
##a. current price action
priceaction = df.head(1)
pricehigh = priceaction['high'].values[0]
pricelow = priceaction['low'].values[0]
priceclose = priceaction['close'].values[0]
##b. SAR current
sarCurrentInput = df.head(2)
sarCurrentInput = sarCurrentInput.iloc[::-1]
sarCurrentInputHigh = sarCurrentInput['high'].values
sarCurrentInputLow = sarCurrentInput['low'].values
sarCurrent = SAR(sarCurrentInputHigh,
sarCurrentInputLow,
acceleration=0,
maximum=0)
##c. previous price action
prevaction = df.iloc[1:].head(1)
prevhigh = prevaction['high'].values[0]
prevlow = prevaction['low'].values[0]
prevclose = prevaction['close'].values[0]
##d. previous SAR
sarPreviousInput = df.iloc[1:].head(2)
sarPreviousInput = sarPreviousInput.iloc[::-1]
sarPreviousInputHigh = sarPreviousInput['high'].values
sarPreviousInputLow = sarPreviousInput['low'].values
sarPrevious = SAR(sarPreviousInputHigh,
sarPreviousInputLow,
acceleration=0,
maximum=0)
##b. 200EMA
emaInput = df.head(200)
emaInput = emaInput.iloc[::-1]
EMAclose = emaInput['close'].values
ema = EMA(EMAclose, timeperiod=200)
## SAR reversal
## 1 if from -ve become +ve
## -1 if from +ve become -ve
## 0 otherwise
if prevclose < sarPrevious[-1] and priceclose > sarCurrent[-1]:
change = 1
elif prevclose > sarPrevious[-1] and priceclose < sarCurrent[-1]:
change = -1
else:
change = 0
## 200EMA filtering false signal
if pricelow > ema[-1]: marketEMA = 1
elif pricehigh < ema[-1]: marketEMA = -1
else: marketEMA = 0
##OUTPUT
if marketEMA == 1 and change == 1: position = 1
elif marketEMA == -1 and change == -1: position = -1
else: position = 0
amount = 50
confidence = 0
if position == 1:
stoploss = sarCurrent[-1]
takeprofit = priceclose + 1.45 * (priceclose - sarCurrent[-1])
# amount = priceclose / (priceclose - stoploss)
if (priceclose - stoploss) * amount < 0.1:
position = 0
amount = 0
stoploss = 0
takeprofit = 0
else:
pattern, patterntype = check(df.head(20))
if position * pattern > 0:
confidence = abs(pattern)
elif position * pattern < 0:
confidence = abs(1 / pattern)
else:
confidence = 1
elif position == -1:
stoploss = sarCurrent[-1]
takeprofit = priceclose - 1.45 * (sarCurrent[-1] - priceclose)
# amount = priceclose / (stoploss - priceclose)
if (stoploss - priceclose) * amount < 0.1:
position = 0
amount = 0
stoploss = 0
takeprofit = 0
else:
pattern, patterntype = check(df.head(20))
if position * pattern > 0:
confidence = abs(pattern)
elif position * pattern < 0:
confidence = abs(1 / pattern)
else:
confidence = 1
else:
stoploss = 0
takeprofit = 0
amount = 0
##For test
# position = 1
# ##FOR TEST
# position = 1
return [position, amount, priceclose, stoploss, takeprofit, confidence]