df_init = df_init.iloc[::-1]
pred_day = '-'.join(df_init.loc[df_init.index[-1], 'Date'].split('.'))
# PREPROCESSING
df_init['Date'] = list(map(h.cast_date, df_init['Date']))
df_init.set_index("Date", inplace=True)
emas = []
for ema in config['emas']:
emas.append(df_init.loc[:, ['Close']].ewm(span=ema, adjust=False).mean().rename(columns={'Close':f'EMA {ema}'}))
df_init = pd.concat([df_init, *emas], axis=1)
pocids = []
for pocid in config['pocids']:
pocids.append(pd.DataFrame(to_categorical(h.pocid_series(df_init['Close'].shift(pocid), df_init['Close'])), columns=[f'Down {pocid}',f'Up {pocid}']).set_index(df_init.index))
#df_init[f'POCID {pocid}'] = h.pocid_series(df_init['Close'].shift(pocid), df_init['Close'])
df_init = pd.concat([df_init, *pocids], axis=1)
for macd in macds_config:
df_init[f'MACD {macd[0]}-{macd[1]}'] = h.macd_series(df_init['Close'], macd[0], macd[1])
for rsi in config['rsis']:
df_init[f'RSI {rsi}'] = h.rsi_series(df_init['Close'], rsi)
df_init.insert(0, f'Close {config["pred_offset"]}', df_init['Close'].shift(-config['pred_offset']))
pred_data = {
'close': float(df_init.loc[df_init.index[-1], 'Close']) if test else float('nan')
}
df_init['Date'] = list(map(h.cast_date, df_init['Date']))
df_init.set_index("Date", inplace=True)
emas = []
for ema in config['emas']:
emas.append(df_init.loc[:, ['Close']].ewm(
span=ema, adjust=False).mean().rename(
columns={'Close': f'EMA {ema}'}))
df_init = pd.concat([df_init, *emas], axis=1)
pocids = []
for pocid in config['pocids']:
pocids.append(
pd.DataFrame(to_categorical(
h.pocid_series(df_init['Close'].shift(pocid),
df_init['Close'])),
columns=[
f'Down {pocid}', f'Up {pocid}'
]).set_index(df_init.index))
#df_init[f'POCID {pocid}'] = h.pocid_series(df_init['Close'].shift(pocid), df_init['Close'])
df_init = pd.concat([df_init, *pocids], axis=1)
for macd in macds_config:
df_init[f'MACD {macd[0]}-{macd[1]}'] = h.macd_series(
df_init['Close'], macd[0], macd[1])
for rsi in config['rsis']:
df_init[f'RSI {rsi}'] = h.rsi_series(
df_init['Close'], rsi)
df_pocid_future = pd.DataFrame(
df_init = df_init.head(2000)
df_init = df_init.iloc[::-1]
# PREPROCESSING
df_init['Date'] = list(map(h.cast_date, df_init['Date']))
df_init.set_index("Date", inplace=True)
# Close
#df_init.insert(0, f'Close {config["pred_offset"]}', df_init['Close'].shift(-offset))
# Min Max
#df_init.insert(0, f'Max {config["pred_offset"]}', df_init['Max'].shift(-offset))
#df_init.insert(0, f'Min {config["pred_offset"]}', df_init['Min'].shift(-offset))
# Pocid
df_pocid_future = pd.DataFrame(to_categorical(
h.pocid_series(df_init['Close'], df_init['Close'].shift(-5))),
columns=[f'Down Future {5}', f'Up Future {5}'])
df_init = pd.concat([df_pocid_future.set_index(df_init.index), df_init],
axis=1)
cfg_outputs = 2
cfg_range = 75
cfg_nodes = 100
cfg_batch = 50
cfg_epoch = 75
accs = []
for i in range(10):
accs.append([])
df_init['Date'] = list(map(h.cast_date, df_init['Date']))
df_init.set_index("Date", inplace=True)
emas = []
for ema in config['emas']:
emas.append(df_init.loc[:, ['Close']].ewm(
span=ema, adjust=False).mean().rename(
columns={'Close': f'EMA {ema}'}))
df_init = pd.concat([df_init, *emas], axis=1)
pocids = []
for pocid in config['pocids']:
pocids.append(
pd.DataFrame(to_categorical(
h.pocid_series(df_init['Close'].shift(pocid),
df_init['Close'])),
columns=[f'Down {pocid}', f'Up {pocid}'
]).set_index(df_init.index))
#df_init[f'POCID {pocid}'] = h.pocid_series(df_init['Close'].shift(pocid), df_init['Close'])
df_init = pd.concat([df_init, *pocids], axis=1)
for macd in macds_config:
df_init[f'MACD {macd[0]}-{macd[1]}'] = h.macd_series(
df_init['Close'], macd[0], macd[1])
for rsi in config['rsis']:
df_init[f'RSI {rsi}'] = h.rsi_series(df_init['Close'], rsi)
df_pocid_future = pd.DataFrame(
to_categorical(
h.pocid_series(