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 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 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 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 getEMA(volume, mov_date):
real = EMA(volume, mov_date)
return real
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]
def process(self):
logging.info('process')
# Open positions
response = API.position.list_open(ACCOUNT_ID)
if response.status != 200:
print(response.body)
positions = set([p.instrument for p in response.body['positions']])
# Pending orders
response = API.order.list_pending(ACCOUNT_ID)
if response.status != 200:
print(response.body)
orders = [o for o in filter(
lambda o: o.type in ['STOP', 'LIMIT'], response.body['orders'])]
for o in orders:
positions.add(o.instrument)
# Prices
response = API.pricing.get(ACCOUNT_ID, instruments=','.join(SYMBOLS))
prices = response.body['prices']
# Order
for i, instrument in enumerate(self.instruments):
symbol = instrument.symbol
if symbol in positions:
continue
data = instrument.data
t = data['time'].values[-1]
close = data['close'].values
ema6 = EMA(close, timeperiod=6)
ema18 = EMA(close, timeperiod=18)
units = SIZE
buy = False
ask = prices[i].asks[0].price
bid = prices[i].bids[0].price
price = ask
logging.info('symbol=%s, time=%s, ask=%.5f, bid=%.5f', symbol, t, ask, bid)
if cross_up(ema6, ema18):
logging.info('buy %s', symbol)
buy = True
elif cross_down(ema6, ema18):
logging.info('sell %s', symbol)
price = bid
units = -units
else:
continue
response = API.order.stop(
ACCOUNT_ID,
instrument=symbol,
units=units,
price=instrument.price_value(
price + instrument.pips_value(DELTA, buy)),
takeProfitOnFill=transaction.TakeProfitDetails(
price=instrument.price_value(price + instrument.pips_value(PROFIT, buy))),
stopLossOnFill=transaction.StopLossDetails(
price=instrument.price_value(price - instrument.pips_value(LOSS, buy))),
timeInForce='GTD',
gtdTime=str(time.time() + 60)
)
if response.status != 201:
print(response.body)
pattern = -1
else:
pattern = 0
##b. get BBands
upperband, middleband, lowerband = BBANDS(ad,
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
def process_col(data, col="", *argv):
params = '_'.join(str(x) for x in argv)
if(col+"_"+params in data.columns):
return
if(col=="zero"):
data['zero'] = np.full(len(data), 0)
if(col=="atr_risk"):
from talib import ATR
data["atr_risk_"+params]= ATR(data['high'].values, data['low'].values, data['close'].values ,timeperiod=argv[0])
if(col=="macd"):
from talib import MACD
data['macd_'+params], data['macd_signal_'+params], data['macd_hist_'+params] = MACD(data['close'], fastperiod=argv[0], slowperiod=argv[1], signalperiod=argv[2])
if(col=="rsi"):
from talib import RSI
data['rsi_'+params] = RSI(data['close'].values, timeperiod=argv[0])
if(col=="adx"):
from talib import ADX
data['adx_'+params] = ADX(data['high'].values, data['low'].values, data['close'].values, timeperiod=argv[0])
if(col=="kijunsen"):
data['kijunsen_'+params] = KIJUNSEN(data['high'],data['low'], timeperiod=argv[0])
if(col=="ema"):
from talib import EMA
data['ema_'+params] = EMA(data[argv[0]], timeperiod=argv[1])
if(col=="sma"):
from talib import SMA
data['sma_'+params] = SMA(data[argv[0]], timeperiod=argv[1])
if(col=="hma"):
data['hma_'+params] = HMA(data[argv[0]], timeperiod=argv[1])
if(col=="linearreg"):
from talib import LINEARREG_ANGLE
data['linearreg_'+params] = LINEARREG_ANGLE(data[argv[0]], timeperiod=argv[1])
if(col=="linearreg"):
from talib import LINEARREG_ANGLE
data['linearreg_'+params] = LINEARREG_ANGLE(data[argv[0]], timeperiod=argv[1])
if(col=="atr_stoploss"):
from talib import LINEARREG_ANGLE
data['atr_stoploss_'+params] = ATR_STOPLOSS(close = data.close.values,
high = data.high.values,
low = data.low.values,
times=argv[0], stop_early_times=argv[1], early_stop_profit=argv[2], period=argv[3], repaint=True)[0]
if(col=="atr"):
from talib import ATR
data['atr_'+params] = ATR(data['high'].values, data['low'].values, data['close'].values ,timeperiod=argv[0])
if(col=="ssl"):
data["ssl_up_"+params],data["ssl_down_"+params]= SSL(data['high'].values, data['low'].values, data['close'].values ,timeperiod=argv[0])
if(col=="ha"):
data["ha_open"],data["ha_high"],data["ha_low"],data["ha_close"] = HEIKIN_ASHI(data['open'].values, data['high'].values, data['low'].values, data['close'].values)
if(col=="rvi"):
data["rvi_"+params],data["rvi_signal_"+params]= RVI(data['high'].values, data['low'].values, data['close'].values, data['open'].values,timeperiod=argv[0])
if(col=="waddah"):
data["waddah_bull_"+params],data["waddah_bear_"+params],data["waddah_explo_"+params],data["waddah_dead_"+params]= WADDAH_ATTAR_EXPLOSION(data['close'].values,data['high'].values,data['low'].values, sensitive = argv[0] , fast_period= argv[1], slow_period = argv[2], channel_period = argv[3], channel_mult = argv[4], dead_zone= argv[5])
if(col=="ash"):
data["ASH_bull_"+params],data["ASH_bear_"+params]= ASH(data['close'].values, timerperiod=argv[0], smooth =argv[1])
##Overlap Studies
brsr6CSV = pd.read_csv("data/brsr6_ponto.csv")
abertura = brsr6CSV['ABERTURA'].values
fechamento = brsr6CSV['FECHAMENTO'].values
abertura = brsr6CSV['ABERTURA'].values
minimo = brsr6CSV['MINIMO'].values
maximo = brsr6CSV['MAXIMO'].values
variacao = brsr6CSV['VARIACAO'].values
volume = brsr6CSV['VOLUME'].values
upBR, midBR, lowBR = BBANDS(fechamento,
timeperiod=20,
nbdevup=2,
nbdevdn=2,
matype=0)
ema = EMA(fechamento,
timeperiod=30) #curto prazo 21. Medio prazo 24. Longo prazo 89
wma = WMA(fechamento, timeperiod=30)
# Momentum Indicators
macd, macdsignal, macdhist = MACD(fechamento,
fastperiod=12,
slowperiod=26,
signalperiod=9)
# print('-----------------------------------MACD-----------------------------------')
# print(macd[34:37])
# print(macdsignal[34:37])
# print(macdhist[34:37])
#
rsi = RSI(fechamento, timeperiod=14)
# print('-----------------------------------RSI-----------------------------------')
# print(rsi[34:37])
def handler(event, context):
print(sys.version)
# def main():
# print(sys.version)
# stock = input('Stock to plot: ')
outputDf = pd.DataFrame()
# symbolList = ['^NSEI','^NSEBANK']
symbolList = [
'3MINDIA.NS', 'ACC.NS', 'AIAENG.NS', 'APLAPOLLO.NS', 'AUBANK.NS',
'AARTIIND.NS', 'AAVAS.NS', 'ABBOTINDIA.NS', 'ADANIGAS.NS',
'ADANIGREEN.NS', 'ADANIPORTS.NS', 'ADANIPOWER.NS', 'ADANITRANS.NS',
'ABCAPITAL.NS', 'ABFRL.NS', 'ADVENZYMES.NS', 'AEGISCHEM.NS',
'AJANTPHARM.NS', 'AKZOINDIA.NS', 'APLLTD.NS', 'ALKEM.NS',
'ALLCARGO.NS', 'AMARAJABAT.NS', 'AMBER.NS', 'AMBUJACEM.NS',
'APOLLOHOSP.NS', 'APOLLOTYRE.NS', 'ARVINDFASN.NS', 'ASAHIINDIA.NS',
'ASHOKLEY.NS', 'ASHOKA.NS', 'ASIANPAINT.NS', 'ASTERDM.NS',
'ASTRAZEN.NS', 'ASTRAL.NS', 'ATUL.NS', 'AUROPHARMA.NS',
'AVANTIFEED.NS', 'DMART.NS', 'AXISBANK.NS', 'BASF.NS', 'BEML.NS',
'BSE.NS', 'BAJAJ-AUTO.NS', 'BAJAJCON.NS', 'BAJAJELEC.NS',
'BAJFINANCE.NS', 'BAJAJFINSV.NS', 'BAJAJHLDNG.NS', 'BALKRISIND.NS',
'BALMLAWRIE.NS', 'BALRAMCHIN.NS', 'BANDHANBNK.NS', 'BANKBARODA.NS',
'BANKINDIA.NS', 'MAHABANK.NS', 'BATAINDIA.NS', 'BAYERCROP.NS',
'BERGEPAINT.NS', 'BDL.NS', 'BEL.NS', 'BHARATFORG.NS', 'BHEL.NS',
'BPCL.NS', 'BHARTIARTL.NS', 'INFRATEL.NS', 'BIOCON.NS',
'BIRLACORPN.NS', 'BSOFT.NS', 'BLISSGVS.NS', 'BLUEDART.NS',
'BLUESTARCO.NS', 'BBTC.NS', 'BOMDYEING.NS', 'BOSCHLTD.NS',
'BRIGADE.NS', 'BRITANNIA.NS', 'CARERATING.NS', 'CCL.NS', 'CESC.NS',
'CRISIL.NS', 'CADILAHC.NS', 'CANFINHOME.NS', 'CANBK.NS',
'CAPLIPOINT.NS', 'CGCL.NS', 'CARBORUNIV.NS', 'CASTROLIND.NS',
'CEATLTD.NS', 'CENTRALBK.NS', 'CDSL.NS', 'CENTURYPLY.NS', 'CERA.NS',
'CHALET.NS', 'CHAMBLFERT.NS', 'CHENNPETRO.NS', 'CHOLAHLDNG.NS',
'CHOLAFIN.NS', 'CIPLA.NS', 'CUB.NS', 'COALINDIA.NS', 'COCHINSHIP.NS',
'COLPAL.NS', 'CONCOR.NS', 'COROMANDEL.NS', 'CREDITACC.NS',
'CROMPTON.NS', 'CUMMINSIND.NS', 'CYIENT.NS', 'DBCORP.NS', 'DCBBANK.NS',
'DCMSHRIRAM.NS', 'DLF.NS', 'DABUR.NS', 'DALBHARAT.NS', 'DEEPAKNTR.NS',
'DELTACORP.NS', 'DHFL.NS', 'DBL.NS', 'DISHTV.NS', 'DCAL.NS',
'DIVISLAB.NS', 'DIXON.NS', 'LALPATHLAB.NS', 'DRREDDY.NS',
'EIDPARRY.NS', 'EIHOTEL.NS', 'EDELWEISS.NS', 'EICHERMOT.NS',
'ELGIEQUIP.NS', 'EMAMILTD.NS', 'ENDURANCE.NS', 'ENGINERSIN.NS',
'EQUITAS.NS', 'ERIS.NS', 'ESCORTS.NS', 'ESSELPACK.NS', 'EXIDEIND.NS',
'FDC.NS', 'FEDERALBNK.NS', 'FMGOETZE.NS', 'FINEORG.NS', 'FINCABLES.NS',
'FINPIPE.NS', 'FSL.NS', 'FORTIS.NS', 'FCONSUMER.NS', 'FLFL.NS',
'FRETAIL.NS', 'GAIL.NS', 'GEPIL.NS', 'GET&D.NS', 'GHCL.NS',
'GMRINFRA.NS', 'GALAXYSURF.NS', 'GARFIBRES.NS', 'GAYAPROJ.NS',
'GICRE.NS', 'GILLETTE.NS', 'GLAXO.NS', 'GLENMARK.NS', 'GODFRYPHLP.NS',
'GODREJAGRO.NS', 'GODREJCP.NS', 'GODREJIND.NS', 'GODREJPROP.NS',
'GRANULES.NS', 'GRAPHITE.NS', 'GRASIM.NS', 'GESHIP.NS',
'GREAVESCOT.NS', 'GRINDWELL.NS', 'GUJALKALI.NS', 'GUJGASLTD.NS',
'GMDCLTD.NS', 'GNFC.NS', 'GPPL.NS', 'GSFC.NS', 'GSPL.NS',
'GULFOILLUB.NS', 'HEG.NS', 'HCLTECH.NS', 'HDFCAMC.NS', 'HDFCBANK.NS',
'HDFCLIFE.NS', 'HFCL.NS', 'HATSUN.NS', 'HAVELLS.NS', 'HEIDELBERG.NS',
'HERITGFOOD.NS', 'HEROMOTOCO.NS', 'HEXAWARE.NS', 'HSCL.NS',
'HIMATSEIDE.NS', 'HINDALCO.NS', 'HAL.NS', 'HINDCOPPER.NS',
'HINDPETRO.NS', 'HINDUNILVR.NS', 'HINDZINC.NS', 'HONAUT.NS',
'HUDCO.NS', 'HDFC.NS', 'ICICIBANK.NS', 'ICICIGI.NS', 'ICICIPRULI.NS',
'ISEC.NS', 'ICRA.NS', 'IDBI.NS', 'IDFCFIRSTB.NS', 'IDFC.NS',
'IFBIND.NS', 'IFCI.NS', 'IIFL.NS', 'IRB.NS', 'IRCON.NS', 'ITC.NS',
'ITDCEM.NS', 'ITI.NS', 'INDIACEM.NS', 'ITDC.NS', 'IBULHSGFIN.NS',
'IBULISL.NS', 'IBREALEST.NS', 'IBVENTURES.NS', 'INDIANB.NS', 'IEX.NS',
'INDHOTEL.NS', 'IOC.NS', 'IOB.NS', 'INDOSTAR.NS', 'IGL.NS',
'INDUSINDBK.NS', 'INFIBEAM.NS', 'NAUKRI.NS', 'INFY.NS',
'INOXLEISUR.NS', 'INTELLECT.NS', 'INDIGO.NS', 'IPCALAB.NS',
'JBCHEPHARM.NS', 'JKCEMENT.NS', 'JKLAKSHMI.NS', 'JKPAPER.NS',
'JKTYRE.NS', 'JMFINANCIL.NS', 'JSWENERGY.NS', 'JSWSTEEL.NS',
'JAGRAN.NS', 'JAICORPLTD.NS', 'JISLJALEQS.NS', 'J&KBANK.NS',
'JAMNAAUTO.NS', 'JINDALSAW.NS', 'JSLHISAR.NS', 'JSL.NS',
'JINDALSTEL.NS', 'JCHAC.NS', 'JUBLFOOD.NS', 'JUBILANT.NS',
'JUSTDIAL.NS', 'JYOTHYLAB.NS', 'KPRMILL.NS', 'KEI.NS', 'KNRCON.NS',
'KPITTECH.NS', 'KRBL.NS', 'KAJARIACER.NS', 'KALPATPOWR.NS',
'KANSAINER.NS', 'KTKBANK.NS', 'KARURVYSYA.NS', 'KSCL.NS', 'KEC.NS',
'KIRLOSENG.NS', 'KOLTEPATIL.NS', 'KOTAKBANK.NS', 'L&TFH.NS', 'LTTS.NS',
'LICHSGFIN.NS', 'LAXMIMACH.NS', 'LAKSHVILAS.NS', 'LTI.NS', 'LT.NS',
'LAURUSLABS.NS', 'LEMONTREE.NS', 'LINDEINDIA.NS', 'LUPIN.NS',
'LUXIND.NS', 'MASFIN.NS', 'MMTC.NS', 'MOIL.NS', 'MRF.NS', 'MAGMA.NS',
'MGL.NS', 'MAHSCOOTER.NS', 'MAHSEAMLES.NS', 'M&MFIN.NS', 'M&M.NS',
'MAHINDCIE.NS', 'MHRIL.NS', 'MAHLOG.NS', 'MANAPPURAM.NS', 'MRPL.NS',
'MARICO.NS', 'MARUTI.NS', 'MFSL.NS', 'METROPOLIS.NS', 'MINDTREE.NS',
'MINDACORP.NS', 'MINDAIND.NS', 'MIDHANI.NS', 'MOTHERSUMI.NS',
'MOTILALOFS.NS', 'MPHASIS.NS', 'MCX.NS', 'MUTHOOTFIN.NS',
'NATCOPHARM.NS', 'NBCC.NS', 'NCC.NS', 'NESCO.NS', 'NHPC.NS',
'NIITTECH.NS', 'NLCINDIA.NS', 'NMDC.NS', 'NTPC.NS', 'NH.NS',
'NATIONALUM.NS', 'NFL.NS', 'NBVENTURES.NS', 'NAVINFLUOR.NS',
'NESTLEIND.NS', 'NETWORK18.NS', 'NILKAMAL.NS', 'NAM-INDIA.NS',
'OBEROIRLTY.NS', 'ONGC.NS', 'OIL.NS', 'OMAXE.NS', 'OFSS.NS',
'ORIENTCEM.NS', 'ORIENTELEC.NS', 'ORIENTREF.NS', 'PCJEWELLER.NS',
'PIIND.NS', 'PNBHOUSING.NS', 'PNCINFRA.NS', 'PTC.NS', 'PVR.NS',
'PAGEIND.NS', 'PARAGMILK.NS', 'PERSISTENT.NS', 'PETRONET.NS',
'PFIZER.NS', 'PHILIPCARB.NS', 'PHOENIXLTD.NS', 'PIDILITIND.NS',
'PEL.NS', 'POLYCAB.NS', 'PFC.NS', 'POWERGRID.NS', 'PRAJIND.NS',
'PRESTIGE.NS', 'PRSMJOHNSN.NS', 'PGHL.NS', 'PGHH.NS', 'PNB.NS',
'QUESS.NS', 'RBLBANK.NS', 'RECLTD.NS', 'RITES.NS', 'RADICO.NS',
'RVNL.NS', 'RAIN.NS', 'RAJESHEXPO.NS', 'RALLIS.NS', 'RCF.NS',
'RATNAMANI.NS', 'RAYMOND.NS', 'REDINGTON.NS', 'RELAXO.NS',
'RELCAPITAL.NS', 'RELIANCE.NS', 'RELINFRA.NS', 'RPOWER.NS',
'REPCOHOME.NS', 'RESPONIND.NS', 'SHK.NS', 'SBILIFE.NS', 'SJVN.NS',
'SKFINDIA.NS', 'SRF.NS', 'SADBHAV.NS', 'SANOFI.NS', 'SCHAEFFLER.NS',
'SIS.NS', 'SFL.NS', 'SHILPAMED.NS', 'SHOPERSTOP.NS', 'SHREECEM.NS',
'RENUKA.NS', 'SHRIRAMCIT.NS', 'SRTRANSFIN.NS', 'SIEMENS.NS',
'SOBHA.NS', 'SOLARINDS.NS', 'SONATSOFTW.NS', 'SOUTHBANK.NS',
'STARCEMENT.NS', 'SBIN.NS', 'SAIL.NS', 'STRTECH.NS', 'STAR.NS',
'SUDARSCHEM.NS', 'SPARC.NS', 'SUNPHARMA.NS', 'SUNTV.NS',
'SUNCLAYLTD.NS', 'SUNDARMFIN.NS', 'SUNDRMFAST.NS', 'SUNTECK.NS',
'SUPRAJIT.NS', 'SUPREMEIND.NS', 'SUZLON.NS', 'SWANENERGY.NS',
'SYMPHONY.NS', 'SYNGENE.NS', 'TCIEXP.NS', 'TCNSBRANDS.NS',
'TTKPRESTIG.NS', 'TVTODAY.NS', 'TV18BRDCST.NS', 'TVSMOTOR.NS',
'TAKE.NS', 'TASTYBITE.NS', 'TCS.NS', 'TATAELXSI.NS', 'TATAINVEST.NS',
'TATAMTRDVR.NS', 'TATAMOTORS.NS', 'TATAPOWER.NS', 'TATASTLBSL.NS',
'TATASTEEL.NS', 'TEAMLEASE.NS', 'TECHM.NS', 'TECHNOE.NS', 'NIACL.NS',
'RAMCOCEM.NS', 'THERMAX.NS', 'THYROCARE.NS', 'TIMETECHNO.NS',
'TIMKEN.NS', 'TITAN.NS', 'TORNTPHARM.NS', 'TORNTPOWER.NS', 'TRENT.NS',
'TRIDENT.NS', 'TRITURBINE.NS', 'TIINDIA.NS', 'UCOBANK.NS', 'UFLEX.NS',
'UPL.NS', 'UJJIVAN.NS', 'ULTRACEMCO.NS', 'UNIONBANK.NS', 'UBL.NS',
'MCDOWELL-N.NS', 'VGUARD.NS', 'VMART.NS', 'VIPIND.NS', 'VRLLOG.NS',
'VSTIND.NS', 'WABAG.NS', 'VAIBHAVGBL.NS', 'VAKRANGEE.NS', 'VTL.NS',
'VARROC.NS', 'VBL.NS', 'VEDL.NS', 'VENKEYS.NS', 'VINATIORGA.NS',
'IDEA.NS', 'VOLTAS.NS', 'WABCOINDIA.NS', 'WELCORP.NS', 'WELSPUNIND.NS',
'WHIRLPOOL.NS', 'WIPRO.NS', 'WOCKPHARMA.NS', 'YESBANK.NS', 'ZEEL.NS',
'ZENSARTECH.NS', 'ZYDUSWELL.NS', 'ECLERX.NS'
]
for symbol in symbolList:
print(symbol)
period = '2y'
pd.options.display.max_rows = 2000
# period = '6mo'
# fetching 2 year 1h data as that is the limiit for 1 hr
content = fetch_data(symbol, period, '1d')
content = content.tz_localize(tz='Etc/UTC')
content = content.tz_convert(tz='Asia/Kolkata')
content.reset_index(inplace=True)
content.rename(columns={'index': 'startTime'}, inplace=True)
# hma = myHMACalc(content, 21)
# content['21HMA'] = hma.values
# ohlc = content["close"].resample("1h").ohlc()
# print("Dummy")
content['Symbol'] = symbol
close = content['Close'].values
# up, mid, low = BBANDS(close, timeperiod=20, nbdevup=2, nbdevdn=2, matype=0)
for hmaperiod in [5, 8, 15, 21, 50, 100, 200]:
hma = myHMACalc(close, hmaperiod)
hma = np.round(hma, 2)
se = pd.Series(hma)
content["{0}HMA".format(hmaperiod)] = se.values
for emaperiod in [5, 8, 15, 22, 50, 100, 200]:
ema = EMA(close, timeperiod=emaperiod)
ema = np.round(ema, 2)
se = pd.Series(ema)
content["{0}EMA".format(emaperiod)] = se.values
for smaperiod in [5, 8, 15, 22, 50, 100, 200]:
sma = SMA(close, timeperiod=smaperiod)
sma = np.round(sma, 2)
se = pd.Series(sma)
content["{0}SMA".format(smaperiod)] = se.values
# populating a column if it is index or stock
if symbol in ['^NSEI', '^NSEBANK']:
content['Type'] = "Index"
else:
content['Type'] = "Stock"
# GEt just the last 2 lines
content = content.tail(2)
# The last 2 lines is the data we operate on
# Create multiple lists based on the criteria, like close to 200, crossed etc
# then consolidate each to its own output
# then alert mail or SNS or what not
# Based on the columnval create a new column and populate all B
# cloumnval-ARB - meaning above or below the columnval with the diff percent
# however dilemma is if diffpercent non 0 then it can neither be above or below
# for now default 0 and can address later
columnval = "200SMA"
diffPercent = 0.0
ARBcolumnval = 'ARB-{}'.format(columnval)
content[ARBcolumnval] = 'B'
# If condition is above set A for the rows
content[ARBcolumnval][
content[content['Close'] >= (1.0 + (0.01 * diffPercent)) *
content[columnval]].index] = 'A'
# if content[columnval] >= (1.0 + (0.01 * diffPercent)) * content['Close']:
# content['{}-ARB'.format(columnval)] = 'A'
# else:
# content['{}-ARB'.format(columnval)] = 'B'
# Below block gets prev N rows val of a column and create a column
# It will create column for the chose columnval and also the close
nrowsback = 1
columnvalback = ARBcolumnval
# Create empty columns equal to n previos rows
for itervar in range(1, nrowsback + 1, 1):
content["{0}prev{1}".format(columnvalback, itervar)] = ''
# Populating the above created columns
for indexval, row in content.iterrows():
for itervar in range(1, nrowsback + 1, 1):
content["{0}prev{1}".format(
columnvalback, itervar)] = content.tail(itervar + 1).head(
1)[columnvalback].values[0]
# tail the last line and append to the outputDf
# Then from that filter 4 lists as per criteria
# essential idea is one row with all data for all stocks and then filer and mail
# That is the model for additional criteria
content = content.tail(1)
# Iterating through the list of index/stock and then appending the processed data to outputDf
outputDf = outputDf.append(content, ignore_index=True)
india_tz = tz.gettz('Asia/Kolkata')
dtString = datetime.datetime.now(tz=india_tz).strftime("%d-%m-%Y")
# print(outputDf)
tmpPath = '/tmp/daily_MA_HMA_{}.csv'.format(dtString)
uploadBucket = 'dart-analytics-dnd'
outputDf.to_csv(tmpPath)
s3_client = boto3.client('s3')
s3_client.upload_file(tmpPath, uploadBucket,
'daily/daily_MA_HMA_{}.csv'.format(dtString))
above2002percentNoCross = outputDf[
(outputDf['Close'] >= outputDf['200SMA'])
& (outputDf['Close'] <= 1.02 * outputDf['200SMA']) &
(outputDf['ARB-200SMA'] == outputDf['ARB-200SMAprev1'])]
below2002percentNoCross = outputDf[
(outputDf['Close'] < outputDf['200SMA'])
& (outputDf['Close'] >= 0.98 * outputDf['200SMA']) &
(outputDf['ARB-200SMA'] == outputDf['ARB-200SMAprev1'])]
above2002percentCross = outputDf[
(outputDf['Close'] >= outputDf['200SMA'])
& (outputDf['Close'] <= 1.02 * outputDf['200SMA']) &
(outputDf['ARB-200SMA'] != outputDf['ARB-200SMAprev1'])]
below2002percentCross = outputDf[
(outputDf['Close'] < outputDf['200SMA'])
& (outputDf['Close'] >= 0.98 * outputDf['200SMA']) &
(outputDf['ARB-200SMA'] != outputDf['ARB-200SMAprev1'])]
# print(above2002percentNoCross['Symbol'].tolist())
# print("\n".join(above2002percentNoCross['Symbol'].tolist()[0:]))
numberofStocksAbove200SMA = len(
outputDf[(outputDf['Close'] >= outputDf['200SMA'])].index)
numberofStocksBelow200SMA = len(
outputDf[(outputDf['Close'] < outputDf['200SMA'])].index)
print(numberofStocksAbove200SMA)
print(numberofStocksBelow200SMA)
SUBJECT = "Daily stock tracker"
# The email body for recipients with non-HTML email clients.
BODY_TEXT = "Number of stocks above 200 SMA"
BODY_TEXT = BODY_TEXT + "\n\n" + str(numberofStocksAbove200SMA)
BODY_TEXT = BODY_TEXT + "\n\n" + "Number of stocks below 200 SMA"
BODY_TEXT = BODY_TEXT + "\n\n" + str(numberofStocksBelow200SMA)
BODY_TEXT = BODY_TEXT + "\n\n" + "List of stocks aobve 200 SMA - within 2 percent"
BODY_TEXT = BODY_TEXT + "\n\n" + "\n".join(
above2002percentNoCross['Symbol'].tolist()[0:])
BODY_TEXT = BODY_TEXT + "\n\n" + "List of stocks below 200 SMA - within 2 percent"
BODY_TEXT = BODY_TEXT + "\n\n" + "\n".join(
below2002percentNoCross['Symbol'].tolist()[0:])
BODY_TEXT = BODY_TEXT + "\n\n" + "List of stocks aobve 200 SMA and corssed 200 SMA"
BODY_TEXT = BODY_TEXT + "\n\n" + "\n".join(
above2002percentCross['Symbol'].tolist()[0:])
BODY_TEXT = BODY_TEXT + "\n\n" + "List of stocks below 200 SMA and crossed 200 SMA"
BODY_TEXT = BODY_TEXT + "\n\n" + "\n".join(
below2002percentCross['Symbol'].tolist()[0:])
# print(BODY_TEXT)
sendAWSMailAttachment('*****@*****.**', SUBJECT, BODY_TEXT,
tmpPath)
# sendAWSMail(SUBJECT, BODY_TEXT)
# above2002percentNoCross['Symbol'].series.to_csv('/home/ec2-user/environment/dart-stock-scan-lambda/above2002percentNoCross')
# below2002percentNoCross['Symbol'].series.to_csv('/home/ec2-user/environment/dart-stock-scan-lambda/below2002percentNoCross')
# above2002percentCross['Symbol'].series.to_csv('/home/ec2-user/environment/dart-stock-scan-lambda/above2002percentCross')
# below2002percentCross['Symbol'].series.to_csv('/home/ec2-user/environment/dart-stock-scan-lambda/below2002percentCross')
# print(above2002percent)
# print(below2002percent)
data = {
'output': 'Hello World 11',
'timestamp': datetime.datetime.utcnow().isoformat()
}
return {
'statusCode': 200,
'body': json.dumps(data),
'headers': {
'Content-Type': 'application/json'
}
}
print("Hello")
def priceTechnicalIndicator(value, tot_lags, prefix):
lags = np.array(tot_lags)
lags = lags[lags >= 2]
data_result = pd.DataFrame([])
# MA series
for lag in lags:
# SMA
sma = pd.Series(SMA(value, timeperiod=lag), name='%sMA_%dD' % (prefix, lag))
data_result = pd.concat([data_result, sma], axis=1)
# SMA derivatives
tmp = ma_derivative_indicator(value, sma, 'MA', lag, prefix)
data_result = data_result.join(tmp)
# EMA
ema = pd.Series(EMA(value, lag), name='%sEMA_%dD' % (prefix, lag))
data_result = data_result.join(ema)
# EMA derivatives
tmp = ma_derivative_indicator(value, ema, 'EMA', lag, prefix)
data_result = data_result.join(tmp)
# WMA
wma = pd.Series(WMA(value, lag), name='%sWMA_%dD' % (prefix, lag))
data_result = data_result.join(wma)
# WMA derivatives
tmp = ma_derivative_indicator(value, wma, 'WMA', lag, prefix)
data_result = data_result.join(tmp)
# change percent
lags = tot_lags
for lag in lags:
tmp = pd.Series(value.diff(lag) / value.shift(lag), name='%sRET_%dD' % (prefix, lag))
data_result = data_result.join(tmp)
# volatility
lags = np.array(tot_lags)
lags = lags[lags >= 5]
for lag in lags:
tmp = pd.Series(value.rolling(window=lag).std(), name='%sSTD_%dD' % (prefix, lag))
data_result = data_result.join(tmp)
# technical indicators
lags = [7, 14, 21, 28] # ****** 待修改，技术指标的参数只用最常用的一套
for lag in lags:
# bollinger brands
tmp_upper, tmp_middle, tmp_lower = BBANDS(value, lag, 2, 2)
tmp_upper.name = '%sBBANDS_UPPER_%dD' % (prefix, lag)
tmp_lower.name = '%sBBANDS_LOWER_%dD' % (prefix, lag)
data_result = data_result.join(tmp_upper)
data_result = data_result.join(tmp_lower)
# MACD
tmp, tmp_signal, tmp_hist = MACD(value, 12, 26, lag)
tmp.name = '%sMACD_DIF_12_26D' % prefix # macd 对应 macd_dif 线
tmp_signal.name = '%sMACD_DEA_12_26_%dD' % (prefix, lag) # macd_signal 对应 macd_dea 线
tmp_hist = 2 * tmp_hist
tmp_hist.name = '%sMACD_12_26_%dD' % (prefix, lag) # 2 * macd_hist 对应 macd 线
if tmp.name not in data_result.columns: # macd_dif is the same for all lags
data_result = data_result.join(tmp)
data_result = data_result.join(tmp_signal)
data_result = data_result.join(tmp_hist)
return data_result
# Do MinMax normalization
maxValue = train_df.to_numpy().max()
minValue = train_df.to_numpy().min()
diff = maxValue - minValue
train = train_df.transform(lambda x: (x - minValue) / diff)
# test = test_df.transform(lambda x: (x - minValue) / diff)
# Use technical analysis to expand the data
train["upperband"], train["middleband"], train["lowerband"] = BBANDS(
train.close.to_numpy())
train["sar"] = SAR(train.high.to_numpy(), train.low.to_numpy())
train["rsi"] = RSI(train.close.to_numpy(), timeperiod=5)
train["slowk"], train["slowd"] = STOCH(train.high.to_numpy(),
train.low.to_numpy(),
train.close.to_numpy())
train["ema"] = EMA(train.close.to_numpy(), timeperiod=5)
train["willr"] = WILLR(train.high.to_numpy(),
train.low.to_numpy(),
train.close.to_numpy(),
timeperiod=5)
train_data = train.dropna()
train_data = train_data.reset_index(drop=True)
"""
test["upperband"], test["middleband"], test["lowerband"] = BBANDS(test.close.to_numpy())
test["sar"] = SAR(test.high.to_numpy(), test.low.to_numpy())
test["rsi"] = RSI(test.close.to_numpy(), timeperiod=5)
test["slowk"], test["slowd"] = STOCH(test.high.to_numpy(), test.low.to_numpy(), test.close.to_numpy())
"""
# 2->bullish, 0->bearish, 1->do nothing
y = list()
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 calculate_signals(self):
for symbol in self.symbol_list:
print('Searching for {1} signals ...'.format(symbol))
last_price = self.data_handler.current_price(symbol)[0]
h1_highs = self.data_handler.get_latest_bars_values(
symbol, 'high', timeframe='H1', N=self.bars_window)
h1_lows = self.data_handler.get_latest_bars_values(
symbol, 'low', timeframe='H1', N=self.bars_window)
h1_closes = self.data_handler.get_latest_bars_values(
symbol, 'close', timeframe='H1', N=self.bars_window)
h1_hlc3 = (h1_highs + h1_lows + h1_closes) / 3
h1_ema_50 = EMA(h1_hlc3, timeperiod=50)
h1_ema_100 = EMA(h1_hlc3, timeperiod=100)
h1_ema_200 = EMA(h1_hlc3, timeperiod=200)
m15_highs = self.data_handler.get_latest_bars_values(
symbol, 'high', timeframe='m15', N=self.bars_window)
m15_lows = self.data_handler.get_latest_bars_values(
symbol, 'low', timeframe='m15', N=self.bars_window)
m15_closes = self.data_handler.get_latest_bars_values(
symbol, 'close', timeframe='m15', N=self.bars_window)
m15_hlc3 = (m15_highs + m15_lows + m15_closes) / 3
m15_ema_50 = EMA(m15_hlc3, timeperiod=50)
m15_ema_100 = EMA(m15_hlc3, timeperiod=100)
m15_ema_200 = EMA(m15_hlc3, timeperiod=200)
m15_price_bb_up, m15_price_bb_mid, m15_price_bb_low = BBANDS(
m15_hlc3, timeperiod=20, nbdevup=2, nbdevdn=2)
m15_price_bb_low_dist = (m15_price_bb_mid + m15_price_bb_low) / 2
m15_price_bb_up_dist = (m15_price_bb_up + m15_price_bb_mid) / 2
m15_rsi = RSI(m15_closes, timeperiod=14)
m15_rsi_bb_up, m15_rsi_bb_mid, m15_rsi_bb_low = BBANDS(
m15_rsi, timeperiod=20, nbdevup=2, nbdevdn=2)
m15_stochrsi = STOCHRSI(pd.Series(m15_closes), timeperiod=14)
m15_atr = ATR(m15_highs, m15_lows, m15_closes, timeperiod=14)
datetime = self.data_handler.get_latest_bar_datetime(symbol)[0]
direction = None
status = None
position = 'Not yet'
print(datetime)
if last_price > h1_ema_50[-1] and last_price > h1_ema_100[
-1] and last_price > h1_ema_200[-1]:
print('Uprend detected')
if last_price > m15_ema_50[-1] and last_price > m15_ema_100[
-1] and last_price > m15_ema_200[-1]:
print('Uprend confirmed')
direction = 'LONG'
if m15_rsi[-1] < m15_rsi_bb_low[-1]:
print('Reversal Detected')
status = 'Reversal detected'
if last_price < m15_price_bb_low_dist[-1]:
print('Reversal confirmed')
status = 'Reversal confirmed'
if m15_stochrsi[-1] < 25:
print('Go LONG')
position = 'Go ahead'
elif last_price < h1_ema_50[-1] and last_price < h1_ema_100[
-1] and last_price < h1_ema_200[-1]:
print('Downrend detected')
if last_price < m15_ema_50[-1] and last_price < m15_ema_100[
-1] and last_price < m15_ema_200[-1]:
print('Downrend confirmed')
direction = 'SHORT'
if m15_rsi[-1] > m15_rsi_bb_up[-1]:
print('Reversal Detected')
status = 'Reversal detected'
if last_price > m15_price_bb_up_dist[-1]:
print('Reversal confirmed')
status = 'Reversal confirmed'
if m15_stochrsi[-1] > 75:
print('Go SHORT')
position = 'Go ahead'
else:
pass
print(symbol, direction, status, position, datetime)
if status is not None:
self.telegram.send_text({
'symbol':
symbol,
'direction':
direction,
'status':
status,
'position':
position,
'atr':
np.around(m15_atr[-1], decimals=5),
'datetime':
datetime
})
def calcEMA(self, dates, volume, mov_date):
real = EMA(volume, mov_date)
EMA_, isExist = self.createDateFrame(dates, real, ['timestamp', 'EMA'])
return real, EMA_, isExist
def fetch(self,
symbol: str,
interval: Interval = Interval.DAY,
window: Window = Window.YEAR,
indicators: Indicators = [],
verbose=False) -> pd.DataFrame:
"""
Fetch symbol stock OHLCV from Yahoo Finance API
Args:
symbol (str): Symbol to fetch
interval (Interval, optional): Interval (hour, day, week, ...) of data. Defaults to Interval.DAY.
window (Window, optional): Length (day, week, month, year) of interval. Defaults to Window.YEAR.
indicators (Indicators, optional): Array of indicators to include in the result. Defaults to empty array.
Returns:
pd.DataFrame: OHLCV pandas DataFrame with interval on window length and indicators if specified
"""
try:
if verbose:
print(
f"Fetching OHLCV {symbol} stock data on {interval.name} interval and {window.name} window"
)
# Generic url to fetch
url = f"https://query1.finance.yahoo.com/v8/finance/chart/{symbol}?region=FR&lang=fr-FR&includePrePost=false&interval={interval.value}&range={window.value}&corsDomain=fr.finance.yahoo.com&.tsrc=finance"
req = requests.get(url)
# Testing request status code
if req.status_code == 200:
# Extracting data as json
data = req.json()
# Creating new DataFrame
df = pd.DataFrame()
# Extract date from object
if interval in [
Interval.MINUTE, Interval.TWO_MINUTE,
Interval.FIVE_MINUTE, Interval.FIFTEEN_MINUTE,
Interval.THIRTY_MINUTE, Interval.HOUR
]:
dateFromUnix = [
datetime.utcfromtimestamp(dt).strftime(
"%Y-%m-%d %H:%M:%S")
for dt in data["chart"]["result"][0]["timestamp"]
]
else:
dateFromUnix = [
datetime.utcfromtimestamp(dt).strftime("%Y-%m-%d")
for dt in data["chart"]["result"][0]["timestamp"]
]
# Date & OHLCV to DataFrame
df["date"] = pd.to_datetime(dateFromUnix)
df["open"] = data["chart"]["result"][0]["indicators"]["quote"][
0]["open"]
df["high"] = data["chart"]["result"][0]["indicators"]["quote"][
0]["high"]
df["low"] = data["chart"]["result"][0]["indicators"]["quote"][
0]["low"]
df["close"] = data["chart"]["result"][0]["indicators"][
"quote"][0]["close"]
df["volume"] = data["chart"]["result"][0]["indicators"][
"quote"][0]["volume"]
# Drop NaN on close col
df.dropna(subset=["close"], inplace=True)
# Divide volume column by a 1 000
df["volume"] = df["volume"].div(1000)
# Set date column as index
df.set_index("date", inplace=True)
for indicator in indicators:
# ADX
if indicator == Indicators.ADX:
df[indicator.value] = ADX(df["high"], df["low"],
df["close"])
# BBANDS
elif indicator == Indicators.BBANDS:
df[Indicators.BBANDS.value[0]], df[
Indicators.BBANDS.value[1]], df[
Indicators.BBANDS.value[2]] = BBANDS(
df["close"])
df["p_band"] = 100 - \
(df["l_band"] / df["u_band"] * 100)
df["p_band_ma_5"] = MA(df["p_band"], timeperiod=5)
# EMA
elif indicator == Indicators.EMA:
for ema in Indicators.EMA.value:
df[ema] = EMA(df["close"],
timeperiod=int(ema.split("_")[1]))
# MA
elif indicator == Indicators.MA:
for ma in Indicators.MA.value:
df[ma] = MA(df["close"],
timeperiod=int(ma.split("_")[1]))
# OBV
elif indicator == Indicators.OBV:
df[indicator.value] = OBV(df["close"], df["volume"])
df[indicator.value] = df[indicator.value].div(1000)
# PSAR
elif indicator == Indicators.PSAR:
df[indicator.value] = SAR(df["high"], df["low"])
# PERCENT CHANGE
elif indicator == Indicators.P_CHANGE:
for p_change in Indicators.P_CHANGE.value:
df[p_change] = df["close"].pct_change(
int(p_change.split(" ")[1])) * 100
# RSI
elif indicator == Indicators.RSI:
df[indicator.value] = RSI(df["close"])
return df.round(decimals=2)
except Exception as e:
print(e)