'''
#---------GLOBAL SETTINGS-------------------
path = 'D:\\BITBUCKET_PROJECTS\\Forecasting 1.0\\'
STRATEGY = '3'
DEVIATION = MULTIPLIER = 2
PERIOD = 20
DATA_LIMIT = 400
#--------RSI_SETTINGS------------------------
LOWER_BOUND = 30
UPPER_BOUND = 70
#--------MACD SETTINGS-----------------------
FAST = 12
SLOW = 26
SIGNAL = 9
loc.set_path(path + 'DATASET')
#-------get the data we need------------------
STOK_list = ls_STOK()
Signal_Gen = {}
for ii in range(DATA_LIMIT):
print('{}'.format(STOK_list[ii]))
data = loc.read_csv('{}'.format(STOK_list[ii]) + str('.csv'))
data.index = pd.to_datetime(data.index)
#-----convert to the stock class--------------
stock_data = stock(data)
Fibo_SUP_RES_ = stock_data.fibonacci_pivot_point()
df_RSI = RSI_signal(data,
PERIOD,
lw_bound=LOWER_BOUND,
up_bound=UPPER_BOUND)
'days', #trading days
'months', #months
'DayOfTheWeek', #days of week
'time_epoch', #time epoch
'wday_sin', #sine of trading day
'wday_cos', #cosine of trading day
'mday_sin', #sine of days of the month
'mday_cos', #cosine of days of the month
'yday_sin', #sine of day of year
'yday_cos', #cosine of day of year
'month_sin', #sine of month
'month_cos'
] #cosine of month
#set working directory
loc.set_path('D:\\BITBUCKET_PROJECTS\\Forecasting 1.0\\DATASET')
#stock list
STOCK_list_ = ls_STOK()
#window
forecast = {}
#//Extract Forecast window
for pr in price:
forecast_window, trad_days, dt_range = window(MIN_LAG, MAX_LAG, STEP,
STOCK_name, pr, next_day)
#train test
X_train, X_test, Y_train, Y_test = Scale_train_test(
forecast_window, trad_days)
#yhat for all models
Avg_price = Modeller(X_train, X_test, Y_train, Y_test, dt_range,
params, EPOCHS)
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import make_pipeline
from sklearn.decomposition import PCA
from sklearn.naive_bayes import GaussianNB
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestRegressor
'''
Create date holder
start date: indicates the last date in the series
End date: indicates the numbers of days we want to project into
'''
#set directory to fetch data
loc.set_path('D:\\GIT PROJECT\\ERIC_PROJECT101\\FREELANCE_KENNETH\\DATASET')
def predict_OHLC(NXT_DAY):
'''
:Arguments:
STOCKLIST: List of downloaded stock in the dataset folder
NXTDAY: nextday to predict
stock_data: stock class
:Return:
Next day Open, High, Low, Close for all stock
'''
#get ojects in the dataset folder and