def macd(coin_name, amount):
model = SeqRegressor(coin=coin_name.lower(), model=MODEL)
model.to(DEVICE)
dataloaderX = cryptoData(coin_name.lower(), DEVICE=DEVICE)
DAYS = len(dataloaderX)
model.eval(dataloaderX[33][0].unsqueeze(1))
macd_line = []
signal_line = []
x = []
start_amount = amount
no_of_coins = 0
print("\n", coin_name.upper(), ":")
for i, (x_input, target) in enumerate(
dataloaderX
): # TODO: Discuss standard time slot across all algorithms
if i < 34: # let dataloader catchup with macd range
continue
if i == DAYS:
break
predictedPrice = model(x_input.unsqueeze(1)) * dataloaderX.pmax
predictedPrice = predictedPrice.squeeze()
(macd, macd_9, ema_12, ema_26, price) = dataloaderX.getMacDData(i)
ema_12 = ema_12 + (predictedPrice - ema_12) * dataloaderX.multiplier_12
ema_26 = ema_26 + (predictedPrice - ema_26) * dataloaderX.multiplier_26
macd = ema_12 - ema_26
macd_9 = macd_9 + (macd - macd_9) * dataloaderX.multiplier_9
x.append(i)
macd_line.append(macd)
signal_line.append(macd_9)
# If MACD crosses over Signal Line then bullish market
if macd - macd_9 > 0 and no_of_coins == 0:
no_of_coins = amount / price
amount = 0
# If MACD crosses below Signal Line then bearish market
if macd - macd_9 <= 0 and no_of_coins != 0:
amount = no_of_coins * price
no_of_coins = 0
if amount == 0:
amount = no_of_coins * price
print("\nStart: ", start_amount, " End: ", amount)
macd_line = np.asarray(macd_line)
signal_line = np.asarray(signal_line)
x = np.asarray(x)
def boll_band(coin, amount):
model = SeqRegressor(coin=coin, model=MODEL)
model.to(DEVICE)
print("\n", coin.upper(), ":")
dataloader = cryptoData(coin, DEVICE=DEVICE)
model.eval(dataloader[28][0].unsqueeze(1))
start = amount
cash = amount
no_of_coins = 0
mean = []
ub = []
lb = []
x = []
for i, (x_input, target) in enumerate(
dataloader
): # TODO: Discuss standard time slot across all algorithms
# Wait for the Moving Average
if (i < 34):
continue
(sma_20, sma_30, sma_5, ubb, lbb,
price) = dataloader.getBollBandData(i)
trend = sma_5 - sma_30
x.append(i)
ub.append(ubb)
lb.append(lbb)
mean.append(sma_20)
predictedPrice = model(x_input.unsqueeze(1)) * dataloader.pmax
predictedPrice = predictedPrice.squeeze()
if trend > 0:
# If Upward Trend and price touches Upper Bollinger Band then Buy
if predictedPrice - ubb >= 0 and no_of_coins == 0:
# print("Buy\n")
no_of_coins = cash / price
cash = 0
else:
# If Downward Trend and price touches Lower Bollinger Band then Sell
if predictedPrice - lbb <= 0 and no_of_coins != 0:
# print("Sell\n")
cash = no_of_coins * price
no_of_coins = 0
if no_of_coins != 0:
cash = no_of_coins * price
no_of_coins = 0
print("\nStart: ", start, " End: ", cash)
def awesomeLSTM(coin_name, amount):
model = SeqRegressor(coin=coin_name.lower(),model= MODEL)
model.to(DEVICE)
dataloaderX = cryptoData(coin_name.lower(),DEVICE=DEVICE)
DAYS = len(dataloaderX)
model.eval(dataloaderX[33][0].unsqueeze(1))
sma5_line = []
sma34_line = []
ao_line = []
start_amount = amount
no_of_coins = 0
print("\n",coin_name.upper(),":")
for i,(x_input,target) in enumerate(dataloaderX): # TODO: Discuss standard time slot across all algorithms
if i < 34: # let dataloader catchup with macd range
continue
if i == DAYS:
break
predictedPrice = model(x_input.unsqueeze(1)) * dataloaderX.pmax
sma_5,sma_34,AO,price,(new_sma5_lower,new_sma34_lower) = dataloaderX.getAOData(i)
sma5_line.append(sma_5)
sma34_line.append(sma_34)
ao_line.append(AO)
predicted_sma5 = (new_sma5_lower + predictedPrice)/2
predicted_sma34 = (new_sma34_lower + predictedPrice)/2
# Averaging out AO and predicted AO
AO = (predicted_sma5 -predicted_sma34 + AO)/2
if AO > 0 and no_of_coins == 0:
no_of_coins = amount/price
amount = 0
if AO <= 0 and no_of_coins != 0:
amount = no_of_coins*price
no_of_coins = 0
if amount == 0:
amount = no_of_coins*price
print("\nStart: ",start_amount," End: ",amount)
sma5_line = np.asarray(sma5_line)
sma34_line = np.asarray(sma34_line)
ao_line = np.asarray(ao_line)
import torch
from torch import nn
from torch.optim import Adam
from torch.nn.init import xavier_normal as xavier
import matplotlib.pyplot as plt
from data.unorm_loader import cryptoData
from models.model import SeqRegressor
DEVICE = torch.device("cpu")
MODE = "train"
if __name__ == "__main__":
model = SeqRegressor()
optimizer = Adam(model.parameters(), lr=0.00001, weight_decay=0.000001)
lossfn = nn.MSELoss(reduction='mean')
if MODE == "train":
test = False
else:
test = True
dataloader = cryptoData("btc", test=test, DEVICE=DEVICE, window=7)
model.to(DEVICE)
breaker = len(dataloader)
for j in range(700):
tots = 0
from torch.optim import Adam
from torch.nn.init import xavier_normal as xavier
import matplotlib.pyplot as plt
from data.loader import cryptoData
from models.model import SeqRegressor
DEVICE = torch.device("cpu")
MODE = "test"
COIN = "eth"
MODEL = "unorm"
STOCHASTIC = True
if __name__ == "__main__":
model = SeqRegressor(stochastic=STOCHASTIC, model=MODEL, coin=COIN)
if MODE == "train":
test = False
else:
test = True
dataloader = cryptoData(currency=COIN,
test=test,
DEVICE=DEVICE,
model=MODEL)
model.to(DEVICE)
breaker = len(dataloader)
for j in range(100):