def __init__(self):
dfr['EMA_12'] = EMAIndicator(close=dfr['close'], n=9,
fillna=True).ema_indicator()
dfr['EMA_26'] = EMAIndicator(close=dfr['close'], n=26,
fillna=True).ema_indicator()
MACD = MACD(close=dfr['close'])
dfr['MACD'] = dfr['EMA_12'] - dfr['EMA_26']
dfr['MACD2'] = MACD.macd()
dfr['MACD_Signal'] = EMAIndicator(close=dfr['MACD'],
n=9).ema_indicator()
dfr['MACD_Signal2'] = MACD.macd_signal()
dfr['MACD_HIST'] = dfr['MACD'] - dfr['MACD_Signal']
dfr['MACD_HIST2'] = MACD.macd_diff()
new_col = 'MACD_HIST_TYPE'
new_col2 = 'MACD_HIST_TYPE2'
nr = dfr.shape[0]
dfr[new_col] = np.empty(nr)
for k in range(1, nr):
i1 = dfr.index.values[k - 1]
i = dfr.index.values[k]
if dfr.loc[i, 'MACD_HIST'] > 0 and dfr.loc[i1, 'MACD_HIST'] < 0:
dfr.loc[i, new_col] = 1 # Cross over
dfr.loc[i, new_col2] = 1 # Cross over
if i not in long_asset_list.keys():
tlist = []
tlist.append(table_name)
long_asset_list[i] = tlist
elif dfr.loc[i, 'MACD_HIST'] > 0 and dfr.loc[
i, 'MACD_HIST'] > dfr.loc[i1, 'MACD_HIST']:
dfr.loc[i, new_col] = 2 # Col grow above
dfr.loc[i, new_col2] = 2 # Cross over
elif dfr.loc[i, 'MACD_HIST'] > 0 and dfr.loc[
i, 'MACD_HIST'] < dfr.loc[i1, 'MACD_HIST']:
dfr.loc[i, new_col] = 3 # Col fall above
dfr.loc[i, new_col2] = 3 # Cross over
elif dfr.loc[i, 'MACD_HIST'] < 0 and dfr.loc[i1,
'MACD_HIST'] > 0:
a = (i, table_name)
dfr.loc[i, new_col] = -1 # Cross over
dfr.loc[i, new_col2] = -1 # Cross over
if i not in short_asset_list.keys():
tlist = []
tlist.append(table_name)
short_asset_list[i] = tlist
else:
short_asset_list[i].append(table_name)
elif dfr.loc[i, 'MACD_HIST'] < 0 and dfr.loc[
i, 'MACD_HIST'] < dfr.loc[i1, 'MACD_HIST']:
dfr.loc[i, new_col] = -2 # Col fall above
dfr.loc[i, new_col2] = -2 # Cross over
elif dfr.loc[i, 'MACD_HIST'] < 0 and dfr.loc[
i, 'MACD_HIST'] > dfr.loc[i1, 'MACD_HIST']:
dfr.loc[i, new_col] = -3 # Cross under
dfr.loc[i, new_col2] = -3 # Cross over
else:
dfr.loc[i, new_col] = 0
dfr.loc[i, new_col2] = 0 # Cross over
def get_MACD_Indicator(self, col='Close', fillna=False):
"""
get MACD Indicator
"""
stock_MACD = MACD(self.df[col], fillna=fillna)
self.df['MACD'] = stock_MACD.macd()
self.df['MACD_signal'] = stock_MACD.macd_signal()
self.df['MACD_diff'] = stock_MACD.macd_diff()
def analyze(self):
macd = MACD(self.df.close, n_fast=12, n_slow=26, n_sign=9)
self.df["macd"] = macd.macd()
self.df["signal"] = macd.macd_signal()
self.df["buy_signal_macd"] = (is_crossover(self.df['macd'],
self.df['signal']))
self.df["sell_signal_macd"] = (is_crossover(self.df['signal'],
self.df['macd']))
self.df["buy_signal"] = (self.df["buy_signal_macd"])
def ta_macd(df):
"""
Moving Average Convergence Divergence (MACD) calculation.
:param df: pandas dataframe
:return: pandas dataframe
"""
temp_df = df.copy()
temp = MACD(close=temp_df["Close"], fillna=False)
# temp = MACD(close=temp_df["Close"], window_slow=26, window_fast=12, window_sign=9, fillna=True)
temp_df["macd"] = temp.macd()
temp_df["macd_diff"] = temp.macd_diff()
temp_df["macd_signal"] = temp.macd_signal()
return temp_df
def action(self, indicator):
# Derive the action based on past data
# action: 1 means buy, -1 means sell, 0 means do nothing
close = indicator['c']
macd = MACD(close=close,
n_slow=self.parameters['ema26'],
n_fast=self.parameters['ema12'],
n_sign=self.parameters['ema9'])
indicator['trend_macd'] = macd.macd()
indicator['trend_macd_signal'] = macd.macd_signal()
indicator['trend_macd_diff'] = macd.macd_diff()
indicator['trend_macd_diff_prev'] = indicator['trend_macd_diff'].shift(1)
indicator['action'] = (np.sign(indicator['trend_macd_diff']) \
- np.sign(indicator['trend_macd_diff_prev'])) / 2
def get_macd(config, company):
close_prices = company.prices
dataframe = company.technical_indicators
window_slow = 26
signal = 9
window_fast = 12
macd = MACD(company.prices, window_slow, window_fast, signal)
dataframe['MACD'] = macd.macd()
dataframe['MACD_Histogram'] = macd.macd_diff()
dataframe['MACD_Signal'] = macd.macd_signal()
generate_buy_sell_signals(
lambda x, dataframe: dataframe['MACD'].values[x] < dataframe[
'MACD_Signal'].iloc[x], lambda x, dataframe: dataframe['MACD'].
values[x] > dataframe['MACD_Signal'].iloc[x], dataframe, 'MACD')
return dataframe
def action(self, indicator):
# Derive the action based on past data
# action: 1 means buy, -1 means sell, 0 means do nothing
close = indicator["c"]
macd = MACD(
close=close,
n_slow=self.parameters["ema26"],
n_fast=self.parameters["ema12"],
n_sign=self.parameters["ema9"],
)
indicator["trend_macd"] = macd.macd()
indicator["trend_macd_signal"] = macd.macd_signal()
indicator["trend_macd_diff"] = macd.macd_diff()
indicator["trend_macd_diff_prev"] = indicator["trend_macd_diff"].shift(
1)
indicator["action"] = (np.sign(indicator["trend_macd_diff"]) -
np.sign(indicator["trend_macd_diff_prev"])) / 2
def get_signal(self, input_df, ticker, run_id):
df = input_df.copy()
indicator_macd = MACD(
close=df["Close"],
window_slow=self.indicator["window_slow"],
window_fast=self.indicator["window_fast"],
window_sign=self.indicator["window_sign"],
fillna=self.indicator["fillna"],
)
# Add Bollinger Bands features
df["macd"] = indicator_macd.macd()
df["macd_signal"] = indicator_macd.macd_signal()
df["macd_diff"] = indicator_macd.macd_diff()
previous_row = df.iloc[-2]
row = df.iloc[-1]
if (row.macd_diff.item() < 0) and (previous_row.macd_diff.item() > 0):
sell_signal = {
"ticker": ticker,
"datetime": row.Date,
"indicator": self.name,
"param": self.param,
"reason": "MACD Downward Crossover",
"image": self.draw_image(df, ticker, run_id),
}
else:
sell_signal = None
if (previous_row.macd_diff.item() < 0) and (row.macd_diff.item() > 0):
buy_signal = {
"ticker": ticker,
"datetime": row.Date,
"indicator": self.name,
"param": self.param,
"reason": "MACD Upward Crossover",
"image": self.draw_image(df, ticker, run_id),
}
else:
buy_signal = None
return buy_signal, sell_signal
def add_trend_indicators(data: pd.DataFrame) -> pd.DataFrame:
"""Adds the trend indicators.
Parameters
----------
data : pd.DataFrame
A dataframe with daily stock values. Must include: open, high,
low, close and volume. It should also be sorted in a descending
manner.
Returns
-------
pd.DataFrame
The input dataframe with the indicators added.
"""
adx = ADXIndicator(data['high'], data['low'], data['close'])
ema = EMAIndicator(data['close'])
ema_200 = EMAIndicator(data['close'], n=200)
ichimoku = IchimokuIndicator(data['high'], data['low'])
macd = MACD(data['close'])
sma = SMAIndicator(data['close'], n=14)
sma_200 = SMAIndicator(data['close'], n=200)
data.loc[:, 'adx'] = adx.adx()
data.loc[:, 'adx_pos'] = adx.adx_pos()
data.loc[:, 'adx_neg'] = adx.adx_neg()
data.loc[:, 'ema'] = ema.ema_indicator()
data.loc[:, 'ema_200'] = ema_200.ema_indicator()
data.loc[:, 'ichimoku_a'] = ichimoku.ichimoku_a()
data.loc[:, 'ichimoku_b'] = ichimoku.ichimoku_b()
data.loc[:, 'ichimoku_base_line'] = ichimoku.ichimoku_base_line()
data.loc[:, 'ichimoku_conversion_line'] = (
ichimoku.ichimoku_conversion_line())
data.loc[:, 'macd'] = macd.macd()
data.loc[:, 'macd_diff'] = macd.macd_diff()
data.loc[:, 'macd_signal'] = macd.macd_signal()
data.loc[:, 'sma'] = sma.sma_indicator()
data.loc[:, 'sma_200'] = sma_200.sma_indicator()
return data
def _macd(self, df, close):
macd = MACD(close=close)
df['macd'] = macd.macd()
df['macd_signal'] = macd.macd_signal()
df['macd_diff'] = macd.macd_diff()
from ta.momentum import RSIIndicator
from keras.models import Sequential
from keras.layers import Conv1D, MaxPool1D, Bidirectional, LSTM, Dropout, TimeDistributed
from keras.layers import Dense, GlobalAveragePooling2D
from ta.trend import IchimokuIndicator
from sklearn.linear_model import LinearRegression
from ta import add_all_ta_features
from ta.utils import dropna
import matplotlib.pyplot as plt
filename = 'AAPL'
stock = pd.read_csv('Data/' + filename + '.csv')
indicator_bb = BollingerBands(close=stock["Close"], n=20, ndev=2)
macd = MACD(close=stock["Close"])
rsi = RSIIndicator(close=stock["Close"])
ichi = IchimokuIndicator(high=stock["High"], low=stock["Low"])
stock['macd'] = macd.macd()
stock['rsi'] = rsi.rsi()
stock['bb_bbm'] = indicator_bb.bollinger_mavg()
stock['bb_bbh'] = indicator_bb.bollinger_hband()
stock['bb_bbl'] = indicator_bb.bollinger_lband()
stock['ichi_a'] = ichi.ichimoku_a()
stock['ichi_b'] = ichi.ichimoku_b()
stock['ichi_base'] = ichi.ichimoku_base_line()
stock['ichi_conv'] = ichi.ichimoku_conversion_line()
stock = stock.fillna(0)
print(stock)
scaler = preprocessing.MinMaxScaler()
scaled_values = scaler.fit_transform(stock.iloc[:, 1:4])
stock.iloc[:, 1:4] = scaled_values
y_scaler = preprocessing.MinMaxScaler()
def add_trend_ta(
df: pd.DataFrame,
high: str,
low: str,
close: str,
fillna: bool = False,
colprefix: str = "",
vectorized: bool = False,
) -> pd.DataFrame:
"""Add trend technical analysis features to dataframe.
Args:
df (pandas.core.frame.DataFrame): Dataframe base.
high (str): Name of 'high' column.
low (str): Name of 'low' column.
close (str): Name of 'close' column.
fillna(bool): if True, fill nan values.
colprefix(str): Prefix column names inserted
vectorized(bool): if True, use only vectorized functions indicators
Returns:
pandas.core.frame.DataFrame: Dataframe with new features.
"""
# MACD
indicator_macd = MACD(close=df[close],
window_slow=26,
window_fast=12,
window_sign=9,
fillna=fillna)
df[f"{colprefix}trend_macd"] = indicator_macd.macd()
df[f"{colprefix}trend_macd_signal"] = indicator_macd.macd_signal()
df[f"{colprefix}trend_macd_diff"] = indicator_macd.macd_diff()
# SMAs
df[f"{colprefix}trend_sma_fast"] = SMAIndicator(
close=df[close], window=12, fillna=fillna).sma_indicator()
df[f"{colprefix}trend_sma_slow"] = SMAIndicator(
close=df[close], window=26, fillna=fillna).sma_indicator()
# EMAs
df[f"{colprefix}trend_ema_fast"] = EMAIndicator(
close=df[close], window=12, fillna=fillna).ema_indicator()
df[f"{colprefix}trend_ema_slow"] = EMAIndicator(
close=df[close], window=26, fillna=fillna).ema_indicator()
# Vortex Indicator
indicator_vortex = VortexIndicator(high=df[high],
low=df[low],
close=df[close],
window=14,
fillna=fillna)
df[f"{colprefix}trend_vortex_ind_pos"] = indicator_vortex.vortex_indicator_pos(
)
df[f"{colprefix}trend_vortex_ind_neg"] = indicator_vortex.vortex_indicator_neg(
)
df[f"{colprefix}trend_vortex_ind_diff"] = indicator_vortex.vortex_indicator_diff(
)
# TRIX Indicator
df[f"{colprefix}trend_trix"] = TRIXIndicator(close=df[close],
window=15,
fillna=fillna).trix()
# Mass Index
df[f"{colprefix}trend_mass_index"] = MassIndex(high=df[high],
low=df[low],
window_fast=9,
window_slow=25,
fillna=fillna).mass_index()
# DPO Indicator
df[f"{colprefix}trend_dpo"] = DPOIndicator(close=df[close],
window=20,
fillna=fillna).dpo()
# KST Indicator
indicator_kst = KSTIndicator(
close=df[close],
roc1=10,
roc2=15,
roc3=20,
roc4=30,
window1=10,
window2=10,
window3=10,
window4=15,
nsig=9,
fillna=fillna,
)
df[f"{colprefix}trend_kst"] = indicator_kst.kst()
df[f"{colprefix}trend_kst_sig"] = indicator_kst.kst_sig()
df[f"{colprefix}trend_kst_diff"] = indicator_kst.kst_diff()
# Ichimoku Indicator
indicator_ichi = IchimokuIndicator(
high=df[high],
low=df[low],
window1=9,
window2=26,
window3=52,
visual=False,
fillna=fillna,
)
df[f"{colprefix}trend_ichimoku_conv"] = indicator_ichi.ichimoku_conversion_line(
)
df[f"{colprefix}trend_ichimoku_base"] = indicator_ichi.ichimoku_base_line()
df[f"{colprefix}trend_ichimoku_a"] = indicator_ichi.ichimoku_a()
df[f"{colprefix}trend_ichimoku_b"] = indicator_ichi.ichimoku_b()
# Schaff Trend Cycle (STC)
df[f"{colprefix}trend_stc"] = STCIndicator(
close=df[close],
window_slow=50,
window_fast=23,
cycle=10,
smooth1=3,
smooth2=3,
fillna=fillna,
).stc()
if not vectorized:
# Average Directional Movement Index (ADX)
indicator_adx = ADXIndicator(high=df[high],
low=df[low],
close=df[close],
window=14,
fillna=fillna)
df[f"{colprefix}trend_adx"] = indicator_adx.adx()
df[f"{colprefix}trend_adx_pos"] = indicator_adx.adx_pos()
df[f"{colprefix}trend_adx_neg"] = indicator_adx.adx_neg()
# CCI Indicator
df[f"{colprefix}trend_cci"] = CCIIndicator(
high=df[high],
low=df[low],
close=df[close],
window=20,
constant=0.015,
fillna=fillna,
).cci()
# Ichimoku Visual Indicator
indicator_ichi_visual = IchimokuIndicator(
high=df[high],
low=df[low],
window1=9,
window2=26,
window3=52,
visual=True,
fillna=fillna,
)
df[f"{colprefix}trend_visual_ichimoku_a"] = indicator_ichi_visual.ichimoku_a(
)
df[f"{colprefix}trend_visual_ichimoku_b"] = indicator_ichi_visual.ichimoku_b(
)
# Aroon Indicator
indicator_aroon = AroonIndicator(close=df[close],
window=25,
fillna=fillna)
df[f"{colprefix}trend_aroon_up"] = indicator_aroon.aroon_up()
df[f"{colprefix}trend_aroon_down"] = indicator_aroon.aroon_down()
df[f"{colprefix}trend_aroon_ind"] = indicator_aroon.aroon_indicator()
# PSAR Indicator
indicator_psar = PSARIndicator(
high=df[high],
low=df[low],
close=df[close],
step=0.02,
max_step=0.20,
fillna=fillna,
)
# df[f'{colprefix}trend_psar'] = indicator.psar()
df[f"{colprefix}trend_psar_up"] = indicator_psar.psar_up()
df[f"{colprefix}trend_psar_down"] = indicator_psar.psar_down()
df[f"{colprefix}trend_psar_up_indicator"] = indicator_psar.psar_up_indicator(
)
df[f"{colprefix}trend_psar_down_indicator"] = indicator_psar.psar_down_indicator(
)
return df
indicator_sma_200 = SMAIndicator(close=df["adj_close"], window=200)
df['sma_200'] = indicator_sma_200.sma_indicator()
indicator_sma_100 = SMAIndicator(close=df["adj_close"], window=100)
df['sma_100'] = indicator_sma_100.sma_indicator()
indicator_sma_50 = SMAIndicator(close=df["adj_close"], window=50)
df['sma_50'] = indicator_sma_50.sma_indicator()
# RSI Indicator
indicator_rsi_6 = RSIIndicator(close=df["adj_close"], window=6)
df['rsi_6'] = indicator_rsi_6.rsi()
indicator_rsi_14 = RSIIndicator(close=df["adj_close"], window=14)
df['rsi_14'] = indicator_rsi_14.rsi()
# MACD Indicator
indicator_macd = MACD(close=df["adj_close"])
df['macd'] = indicator_macd.macd()
# df = df.dropna(how='all)
df.set_index('trading_date', inplace=True)
# Filter the latest ones
df = df[df.index > last_entry_dict[equity_id]]
for idx, row in df.iterrows():
trade_date = row['trading_date']
bb_bbh = row['bb_bbh']
bb_bbl = row['bb_bbl']
ema_200 = row['ema_200']
ema_100 = row['ema_100']
ema_50 = row['ema_50']
rsi_14 = row['rsi_14']
rsi_6 = row['rsi_6']
def applyIndicator(self, full_company_price):
self.data = full_company_price
high = self.data['high']
low = self.data['low']
close = self.data['close']
volume = self.data['volume']
EMA12 = EMAIndicator(close, 12, fillna=False)
EMA30 = EMAIndicator(close, 20, fillna=False)
EMA60 = EMAIndicator(close, 60, fillna=False)
MACD1226 = MACD(close, 26, 12, 9, fillna=False)
MACD2452 = MACD(close, 52, 24, 18, fillna=False)
ROC12 = ROCIndicator(close, 12, fillna=False)
ROC30 = ROCIndicator(close, 30, fillna=False)
ROC60 = ROCIndicator(close, 60, fillna=False)
RSI14 = RSIIndicator(close, 14, fillna=False)
RSI28 = RSIIndicator(close, 28, fillna=False)
RSI60 = RSIIndicator(close, 60, fillna=False)
AROON25 = AroonIndicator(close, 25, fillna=False)
AROON50 = AroonIndicator(close, 50, fillna=False)
AROON80 = AroonIndicator(close, 80, fillna=False)
MFI14 = MFIIndicator(high, low, close, volume, 14, fillna=False)
MFI28 = MFIIndicator(high, low, close, volume, 28, fillna=False)
MFI80 = MFIIndicator(high, low, close, volume, 80, fillna=False)
CCI20 = CCIIndicator(high, low, close, 20, 0.015, fillna=False)
CCI40 = CCIIndicator(high, low, close, 40, 0.015, fillna=False)
CCI100 = CCIIndicator(high, low, close, 100, 0.015, fillna=False)
WILLR14 = WilliamsRIndicator(high, low, close, 14, fillna=False)
WILLR28 = WilliamsRIndicator(high, low, close, 28, fillna=False)
WILLR60 = WilliamsRIndicator(high, low, close, 60, fillna=False)
BBANDS20 = BollingerBands(close, 20, 2, fillna=False)
KC20 = KeltnerChannel(high, low, close, 20, 10, fillna=False)
STOCH14 = StochasticOscillator(high, low, close, 14, 3, fillna=False)
STOCH28 = StochasticOscillator(high, low, close, 28, 6, fillna=False)
STOCH60 = StochasticOscillator(high, low, close, 60, 12, fillna=False)
CMI20 = ChaikinMoneyFlowIndicator(high,
low,
close,
volume,
20,
fillna=False)
CMI40 = ChaikinMoneyFlowIndicator(high,
low,
close,
volume,
40,
fillna=False)
CMI100 = ChaikinMoneyFlowIndicator(high,
low,
close,
volume,
100,
fillna=False)
self.data['ema12'] = (close - EMA12.ema_indicator()) / close
self.data['ema30'] = (close - EMA30.ema_indicator()) / close
self.data['ema60'] = (close - EMA60.ema_indicator()) / close
self.data['macd1226'] = MACD1226.macd() - MACD1226.macd_signal()
self.data['macd2452'] = MACD2452.macd() - MACD2452.macd_signal()
self.data['roc12'] = ROC12.roc()
self.data['roc30'] = ROC30.roc()
self.data['roc60'] = ROC60.roc()
self.data['rsi14'] = RSI14.rsi()
self.data['rsi28'] = RSI28.rsi()
self.data['rsi60'] = RSI60.rsi()
self.data['aroon25'] = AROON25.aroon_indicator()
self.data['aroon50'] = AROON50.aroon_indicator()
self.data['aroon80'] = AROON80.aroon_indicator()
self.data['mfi14'] = MFI14.money_flow_index()
self.data['mfi28'] = MFI28.money_flow_index()
self.data['mfi80'] = MFI80.money_flow_index()
self.data['cci20'] = CCI20.cci()
self.data['cci40'] = CCI40.cci()
self.data['cci100'] = CCI100.cci()
self.data['willr14'] = WILLR14.wr()
self.data['willr28'] = WILLR28.wr()
self.data['willr60'] = WILLR60.wr()
self.data['bband20up'] = (BBANDS20.bollinger_hband() - close) / close
self.data['bband20down'] = (close - BBANDS20.bollinger_lband()) / close
self.data['stoch14'] = STOCH14.stoch()
self.data['stoch28'] = STOCH28.stoch()
self.data['stoch60'] = STOCH60.stoch()
self.data['cmi20'] = CMI20.chaikin_money_flow()
self.data['cmi40'] = CMI40.chaikin_money_flow()
self.data['cmi100'] = CMI100.chaikin_money_flow()
self.data['kc20up'] = (KC20.keltner_channel_hband() - close) / close
self.data['kc20down'] = (close - KC20.keltner_channel_lband()) / close
return self.data
import pandas as pd
from ta.utils import dropna
import matplotlib.pyplot as plt
from ta.trend import MACD
import numpy as np
df = pd.read_csv('TSLA.csv', sep=',')
# clean NaN
df = dropna(df)
macd = MACD(close=df["Close"])
def normalize(col):
return (col - col.mean()) / col.std()
df["macd"] = normalize(macd.macd())
df["macd_signal"] = normalize(macd.macd_signal())
df["macd_diff"] = normalize(macd.macd_diff())
plt.plot(df.macd, label="MACD")
plt.plot(df.macd_signal, label="MACD signal")
plt.plot(df.macd_diff, label="MACD diff")
plt.title("TSLA MACD")
plt.show()
with open("TSLA_ta.csv", "w") as f:
f.write(df.to_csv())
df['Centerline Crossover'] = np.where(df['macd'] < 0, -1, df['Centerline Crossover'])
except TypeError as e:
df['Signal Line Crossover'] = 111
df['Signal Line Crossover'] = 111
df['Buy Sell signal line'] = 111
df['Centerline Crossover'] =111
df['Centerline Crossover'] =111
"""For MACD"""
indicator = MACD(close=df["close_price"], n_slow=26, n_fast=12, n_sign=9, fillna=False)
MACD_INDICATOR = "MACD_indicator"
# print(indicator)
df[MACD_INDICATOR] = indicator.macd()
df["MACD_SIGNAL_indicator"] = indicator.macd_signal()
df2 = df2.append(df)
df2.to_csv('MACD_price_buy_sell.csv', mode='w', header=False)
m=m+1
def pg_load_table(file_path, table_name1, dbname, host, port, user, pwd):
'''
This function upload csv to a target table
'''
try:
conn = psycopg2.connect(dbname=dbname, host=host, port=port,
user=user, password=pwd)
print("Connecting to Database")
cur = conn.cursor()
#pd.to_datetime(EURUSD_T_FX.index)
print(EURUSD_T_FX.head())
#input()
# Calculate the macd and macd_sig lines.
# Start an instance of a MACD object
EURUSD_T_FX_MACD = MACD(close = EURUSD_T_FX["Last"])
# Set up new columns in df to hold macd method outputs for macd line and the signal line
EURUSD_T_FX['MACD'] = EURUSD_T_FX_MACD.macd()
EURUSD_T_FX['MACD_sig'] = EURUSD_T_FX_MACD.macd_signal()
# Run the macd line and signal line though the cross-over trigger function
MACD_crossover_signal = MACD_crossover_trigger(EURUSD_T_FX.Last, EURUSD_T_FX['MACD'], EURUSD_T_FX['MACD_sig'])
MACD_buy_sig = MACD_crossover_signal[0]
MACD_sell_sig = MACD_crossover_signal[1]
length = len(MACD_buy_sig)
for i in range(0, length):
if str(MACD_buy_sig[i]) == 'nan':
MACD_buy_sig[i] = 0
def add_trend_ta(df: pd.DataFrame,
high: str,
low: str,
close: str,
fillna: bool = False,
colprefix: str = ""):
"""Add trend technical analysis features to dataframe.
Args:
df (pandas.core.frame.DataFrame): Dataframe base.
high (str): Name of 'high' column.
low (str): Name of 'low' column.
close (str): Name of 'close' column.
fillna(bool): if True, fill nan values.
colprefix(str): Prefix column names inserted
Returns:
pandas.core.frame.DataFrame: Dataframe with new features.
"""
# MACD
indicator_macd = MACD(close=df[close],
n_fast=12,
n_slow=26,
n_sign=9,
fillna=fillna)
df[f'{colprefix}trend_macd'] = indicator_macd.macd()
df[f'{colprefix}trend_macd_signal'] = indicator_macd.macd_signal()
df[f'{colprefix}trend_macd_diff'] = indicator_macd.macd_diff()
# EMAs
df[f'{colprefix}trend_ema_fast'] = EMAIndicator(
close=df[close], n=12, fillna=fillna).ema_indicator()
df[f'{colprefix}trend_ema_slow'] = EMAIndicator(
close=df[close], n=26, fillna=fillna).ema_indicator()
# Average Directional Movement Index (ADX)
indicator = ADXIndicator(high=df[high],
low=df[low],
close=df[close],
n=14,
fillna=fillna)
df[f'{colprefix}trend_adx'] = indicator.adx()
df[f'{colprefix}trend_adx_pos'] = indicator.adx_pos()
df[f'{colprefix}trend_adx_neg'] = indicator.adx_neg()
# Vortex Indicator
indicator = VortexIndicator(high=df[high],
low=df[low],
close=df[close],
n=14,
fillna=fillna)
df[f'{colprefix}trend_vortex_ind_pos'] = indicator.vortex_indicator_pos()
df[f'{colprefix}trend_vortex_ind_neg'] = indicator.vortex_indicator_neg()
df[f'{colprefix}trend_vortex_ind_diff'] = indicator.vortex_indicator_diff()
# TRIX Indicator
indicator = TRIXIndicator(close=df[close], n=15, fillna=fillna)
df[f'{colprefix}trend_trix'] = indicator.trix()
# Mass Index
indicator = MassIndex(high=df[high],
low=df[low],
n=9,
n2=25,
fillna=fillna)
df[f'{colprefix}trend_mass_index'] = indicator.mass_index()
# CCI Indicator
indicator = CCIIndicator(high=df[high],
low=df[low],
close=df[close],
n=20,
c=0.015,
fillna=fillna)
df[f'{colprefix}trend_cci'] = indicator.cci()
# DPO Indicator
indicator = DPOIndicator(close=df[close], n=20, fillna=fillna)
df[f'{colprefix}trend_dpo'] = indicator.dpo()
# KST Indicator
indicator = KSTIndicator(close=df[close],
r1=10,
r2=15,
r3=20,
r4=30,
n1=10,
n2=10,
n3=10,
n4=15,
nsig=9,
fillna=fillna)
df[f'{colprefix}trend_kst'] = indicator.kst()
df[f'{colprefix}trend_kst_sig'] = indicator.kst_sig()
df[f'{colprefix}trend_kst_diff'] = indicator.kst_diff()
# Ichimoku Indicator
indicator = IchimokuIndicator(high=df[high],
low=df[low],
n1=9,
n2=26,
n3=52,
visual=False,
fillna=fillna)
df[f'{colprefix}trend_ichimoku_a'] = indicator.ichimoku_a()
df[f'{colprefix}trend_ichimoku_b'] = indicator.ichimoku_b()
indicator = IchimokuIndicator(high=df[high],
low=df[low],
n1=9,
n2=26,
n3=52,
visual=True,
fillna=fillna)
df[f'{colprefix}trend_visual_ichimoku_a'] = indicator.ichimoku_a()
df[f'{colprefix}trend_visual_ichimoku_b'] = indicator.ichimoku_b()
# Aroon Indicator
indicator = AroonIndicator(close=df[close], n=25, fillna=fillna)
df[f'{colprefix}trend_aroon_up'] = indicator.aroon_up()
df[f'{colprefix}trend_aroon_down'] = indicator.aroon_down()
df[f'{colprefix}trend_aroon_ind'] = indicator.aroon_indicator()
# PSAR Indicator
indicator = PSARIndicator(high=df[high],
low=df[low],
close=df[close],
step=0.02,
max_step=0.20,
fillna=fillna)
df[f'{colprefix}trend_psar'] = indicator.psar()
df[f'{colprefix}trend_psar_up'] = indicator.psar_up()
df[f'{colprefix}trend_psar_down'] = indicator.psar_down()
df[f'{colprefix}trend_psar_up_indicator'] = indicator.psar_up_indicator()
df[f'{colprefix}trend_psar_down_indicator'] = indicator.psar_down_indicator(
)
return df
def macd(df):
indicator_macd = MACD(close=df["Close"])
df['macd'] = indicator_macd.macd()
df['macd_diff'] = indicator_macd.macd_diff()
df['macd_signal'] = indicator_macd.macd_signal()
FX_data['Last'] = Last
print(FX_data.head())
print("Data loaded")
print("###########")
# Calculate the macd and macd_sig lines.
# Start an instance of a MACD object
FX_data_MACD = MACD(close = FX_data["Last"])
# Set up new columns in df to hold macd method outputs for macd line and the signal line
FX_data['MACD'] = FX_data_MACD.macd()
FX_data['MACD_sig'] = FX_data_MACD.macd_signal()
# Run the macd line and signal line though the cross-over trigger function
MACD_crossover_signal = MACD_crossover_trigger(FX_data.Last, FX_data['MACD'], FX_data['MACD_sig'])
MACD_buy_sig = MACD_crossover_signal[0]
MACD_sell_sig = MACD_crossover_signal[1]
length = len(MACD_buy_sig)
for i in range(0, length):
if str(MACD_buy_sig[i]) == 'nan':
MACD_buy_sig[i] = 0
def AddFeatures(data, category):
"""Add feature to the data.
Args:
--
data : (DataFrame)
The DataFrame of the stocks.
category : (str)
Composed of 5 arguments.
"all" : Append all features.
"trend": Append all trend-related features.
"volatility": Append all volatile-related features.
"volume": Append all volume-related features.
"emaribbon": Append all ema + volume features.
"oscillators": Append all oscillators + volume features.
Returns:
pandas.core.frame.DataFrame: Dataframe with new features.
"""
# Store list of tradable stocks
if len(data["stock code"]) > 0:
data.drop("stock code", 1, inplace=True) # drop stock code
data = data.apply(pd.to_numeric)
if category == "all" or category == "momentum":
# Append all Momentum-related features
print(f'data ${data}')
data = ta.add_momentum_ta(data,
high="High",
low="Low",
close="Close",
volume="Volume")
elif category == "all" or category == "trend":
# Append all tren-related features
data = ta.add_trend_ta(data, high="High", low="Low", close="Close")
elif category == "all" or category == "volatility":
# Append all volatility-related features
data = ta.add_volatility_ta(data,
high="High",
low="Low",
close="Close")
elif category == "all" or category == "volume":
# Append all volume-related features
data = ta.add_volume_ta(data,
high="High",
low="Low",
close="Close",
volume="Volume")
elif category == "emaribbon":
# Append user-specified features
data = indicators.ema(data, period=9)
data = indicators.ema(data, period=20)
data = indicators.ema(data, period=25)
data = indicators.ema(data, period=30)
data = indicators.ema(data, period=35)
data = indicators.ema(data, period=40)
data = indicators.ema(data, period=45)
data = indicators.ema(data, period=50)
data = indicators.ema(data, period=55)
data = indicators.volume(data, period=20)
elif category == 'bottom_fish':
# BUY:
# CCI > -100
# DCHL/DCHLI: Candle must touch the the lower indicators0
data = ta.add_all_ta_features(data,
open='Open',
high='High',
low='Low',
close='Close',
volume='Volume')
bottom_fish_list = ["Open","High","Low","Close", "Volume", \
"volatility_dcl", "volatility_dch", "volatility_dcli", "volatility_dchi", "trend_cci"]
bottom_fish = data[bottom_fish_list]
bottom_fish['volatility_dcl'] = data['volatility_dcl']
bottom_fish['volatility_dch'] = data['volatility_dch']
bottom_fish['volatility_dcli'] = data['volatility_dcl']
bottom_fish['volatility_dchi'] = data['volatility_dch']
bottom_fish['trend_cci'] = data['trend_cci']
bottom_fish = indicators.volume(bottom_fish, period=20)
data = bottom_fish
elif category == 'macdo':
indicator_macd = MACD(close=data['Close'],
n_slow=20,
n_fast=5,
n_sign=5)
data['trend_macd'] = indicator_macd.macd()
data = indicators.ma(data, period=200)
data = indicators.ema(data, period=62)
data = indicators.ema(data, period=38)
data = indicators.volume(data, period=20)
elif category == 'almarsi':
alma_indicator = ALMAIndicator(close=data['Close'])
data['alma9'] = alma_indicator.alma()
# data['alma20'] = alma_indicator.alma_weights(window=20)
# data['alma50'] = alma_indicator.alma_weights(window=50)
data = indicators.rsi(data, period=9)
data = indicators.rsi(data, period=25)
data = indicators.rsi(data, period=12)
# print (data)
if category == "all":
data.dropna(inplace=True, axis=1)
else:
data.dropna(inplace=True)
# print (data)
# Scale the value
for col in data.columns[:]:
data[col] = preprocessing.scale(data[col].values)
# print (data)
return data
def create_env(config):
df = load_csv('btc_usdt_m5_history.csv')
from ta.trend import (
MACD,
ADXIndicator,
AroonIndicator,
CCIIndicator,
DPOIndicator,
EMAIndicator,
IchimokuIndicator,
KSTIndicator,
MassIndex,
PSARIndicator,
SMAIndicator,
STCIndicator,
TRIXIndicator,
VortexIndicator,
)
colprefix = ""
# MACD
indicator_macd = MACD(close=df['close'],
window_slow=26,
window_fast=12,
window_sign=9,
fillna=True)
df[f"{colprefix}trend_macd"] = indicator_macd.macd()
df[f"{colprefix}trend_macd_signal"] = indicator_macd.macd_signal()
df[f"{colprefix}trend_macd_diff"] = indicator_macd.macd_diff()
#df = ta.add_trend_ta(df,'high', 'low', 'close', fillna=True)
#df = ta.add_volume_ta(df,'high', 'low', 'close', 'volume', fillna=True)
#df = ta.add_volume_ta(df, 'high', 'low', 'close', 'volume', fillna=True)
#df = ta.add_volatility_ta(df, 'high', 'low', 'close', fillna=True)
price_history = df[['date', 'open', 'high', 'low', 'close',
'volume']] # chart data
df.drop(columns=['date', 'open', 'high', 'low', 'close', 'volume'],
inplace=True)
print(df.head(5))
with NameSpace("bitfinex"):
streams = [
Stream.source(df[c].tolist(), dtype="float").rename(c)
for c in df.columns
]
feed_ta_features = DataFeed(streams)
price_list = price_history['close'].tolist()
p = Stream.source(price_list, dtype="float").rename("USD-BTC")
bitfinex = Exchange("bitfinex", service=execute_order)(p)
cash = Wallet(bitfinex, 10000 * USD)
asset = Wallet(bitfinex, 0 * BTC)
portfolio = Portfolio(USD, [cash, asset])
reward_scheme = default.rewards.SimpleProfit(window_size=1)
#action_scheme = default.actions.BSHEX(
# cash=cash,
# asset=asset
#).attach(reward_scheme)
action_scheme = default.actions.SimpleOrders(trade_sizes=3)
renderer_feed_ptc = DataFeed([
Stream.source(list(price_history["date"])).rename("date"),
Stream.source(list(price_history["open"]),
dtype="float").rename("open"),
Stream.source(list(price_history["high"]),
dtype="float").rename("high"),
Stream.source(list(price_history["low"]), dtype="float").rename("low"),
Stream.source(list(price_history["close"]),
dtype="float").rename("close"),
Stream.source(list(price_history["volume"]),
dtype="float").rename("volume")
])
env = default.create(
feed=feed_ta_features,
portfolio=portfolio,
#renderer=PositionChangeChart(),
renderer=default.renderers.PlotlyTradingChart(),
action_scheme=action_scheme,
reward_scheme=reward_scheme,
renderer_feed=renderer_feed_ptc,
window_size=config["window_size"],
max_allowed_loss=0.1)
return env
def handle(self, *args, **options):
# import pdb
# pdb.set_trace()
if not options['update']:
NSETechnical.objects.all().delete()
symbols = Symbol.objects.all()
for symbol in symbols:
nse_history_data = NSEHistoricalData.objects.filter(
symbol__symbol_name=symbol).order_by('timestamp')
if not nse_history_data:
continue
nse_technical = pd.DataFrame(
list(
nse_history_data.values('timestamp', 'open', 'high', 'low',
'close', 'total_traded_quantity')))
'''
Moving average convergence divergence
'''
indicator_macd = MACD(close=nse_technical['close'],
window_slow=26,
window_fast=12,
window_sign=9,
fillna=False)
nse_technical["trend_macd"] = indicator_macd.macd()
nse_technical["trend_macd_signal"] = indicator_macd.macd_signal()
nse_technical["trend_macd_diff"] = indicator_macd.macd_diff()
'''
Simple Moving Average
'''
nse_technical["trend_sma_fast"] = SMAIndicator(
close=nse_technical['close'], window=12,
fillna=False).sma_indicator()
nse_technical["trend_sma_slow"] = SMAIndicator(
close=nse_technical['close'], window=26,
fillna=False).sma_indicator()
'''
Exponential Moving Average
'''
nse_technical["trend_ema_fast"] = EMAIndicator(
close=nse_technical['close'], window=12,
fillna=False).ema_indicator()
nse_technical["trend_ema_slow"] = EMAIndicator(
close=nse_technical['close'], window=26,
fillna=False).ema_indicator()
'''
Ichimoku Indicator
'''
indicator_ichi = IchimokuIndicator(
high=nse_technical['high'],
low=nse_technical['low'],
window1=9,
window2=26,
window3=52,
visual=False,
fillna=False,
)
nse_technical[
"trend_ichimoku_conv"] = indicator_ichi.ichimoku_conversion_line(
)
nse_technical[
"trend_ichimoku_base"] = indicator_ichi.ichimoku_base_line()
nse_technical["trend_ichimoku_a"] = indicator_ichi.ichimoku_a()
nse_technical["trend_ichimoku_b"] = indicator_ichi.ichimoku_b()
indicator_ichi_visual = IchimokuIndicator(
high=nse_technical['high'],
low=nse_technical['low'],
window1=9,
window2=26,
window3=52,
visual=True,
fillna=False,
)
nse_technical[
"trend_visual_ichimoku_a"] = indicator_ichi_visual.ichimoku_a(
)
nse_technical[
"trend_visual_ichimoku_b"] = indicator_ichi_visual.ichimoku_b(
)
'''
Bollinger Band
'''
indicator_bb = BollingerBands(close=nse_technical['close'],
window=20,
window_dev=2,
fillna=False)
nse_technical["volatility_bbm"] = indicator_bb.bollinger_mavg()
nse_technical["volatility_bbh"] = indicator_bb.bollinger_hband()
nse_technical["volatility_bbl"] = indicator_bb.bollinger_lband()
nse_technical["volatility_bbw"] = indicator_bb.bollinger_wband()
nse_technical["volatility_bbp"] = indicator_bb.bollinger_pband()
nse_technical[
"volatility_bbhi"] = indicator_bb.bollinger_hband_indicator()
nse_technical[
"volatility_bbli"] = indicator_bb.bollinger_lband_indicator()
'''
Accumulation Distribution Index
'''
nse_technical["volume_adi"] = AccDistIndexIndicator(
high=nse_technical['high'],
low=nse_technical['low'],
close=nse_technical['close'],
volume=nse_technical['total_traded_quantity'],
fillna=False).acc_dist_index()
'''
Money Flow Index
'''
nse_technical["volume_mfi"] = MFIIndicator(
high=nse_technical['high'],
low=nse_technical['low'],
close=nse_technical['close'],
volume=nse_technical['total_traded_quantity'],
window=14,
fillna=False,
).money_flow_index()
'''
Relative Strength Index (RSI)
'''
nse_technical["momentum_rsi"] = RSIIndicator(
close=nse_technical['close'], window=14, fillna=False).rsi()
'''
Stoch RSI (StochRSI)
'''
indicator_srsi = StochRSIIndicator(close=nse_technical['close'],
window=14,
smooth1=3,
smooth2=3,
fillna=False)
nse_technical["momentum_stoch_rsi"] = indicator_srsi.stochrsi()
nse_technical["momentum_stoch_rsi_k"] = indicator_srsi.stochrsi_k()
nse_technical["momentum_stoch_rsi_d"] = indicator_srsi.stochrsi_d()
nse_technical.replace({np.nan: None}, inplace=True)
nse_technical.replace([np.inf, -np.inf], None, inplace=True)
list_to_create = []
list_to_update = []
for index in range(len(nse_history_data) - 1, -1, -1):
data = nse_history_data[index]
if data.technicals:
break
technical = NSETechnical(
nse_historical_data=data,
trend_macd=nse_technical['trend_macd'][index],
trend_macd_signal=nse_technical['trend_macd_signal']
[index],
trend_macd_diff=nse_technical['trend_macd_diff'][index],
trend_sma_fast=nse_technical['trend_sma_fast'][index],
trend_sma_slow=nse_technical['trend_sma_slow'][index],
trend_ema_fast=nse_technical['trend_ema_fast'][index],
trend_ema_slow=nse_technical['trend_ema_slow'][index],
trend_ichimoku_conv=nse_technical['trend_ichimoku_conv']
[index],
trend_ichimoku_base=nse_technical['trend_ichimoku_base']
[index],
trend_ichimoku_a=nse_technical['trend_ichimoku_a'][index],
trend_ichimoku_b=nse_technical['trend_ichimoku_b'][index],
trend_visual_ichimoku_a=nse_technical[
'trend_visual_ichimoku_a'][index],
trend_visual_ichimoku_b=nse_technical[
'trend_visual_ichimoku_b'][index],
volatility_bbm=nse_technical['volatility_bbm'][index],
volatility_bbh=nse_technical['volatility_bbh'][index],
volatility_bbl=nse_technical['volatility_bbl'][index],
volatility_bbw=nse_technical['volatility_bbw'][index],
volatility_bbp=nse_technical['volatility_bbp'][index],
volatility_bbhi=nse_technical['volatility_bbhi'][index],
volatility_bbli=nse_technical['volatility_bbli'][index],
volume_adi=nse_technical['volume_adi'][index],
volume_mfi=nse_technical['volume_mfi'][index],
momentum_rsi=nse_technical['momentum_rsi'][index],
momentum_stoch_rsi=nse_technical['momentum_stoch_rsi']
[index],
momentum_stoch_rsi_k=nse_technical['momentum_stoch_rsi_k']
[index],
momentum_stoch_rsi_d=nse_technical['momentum_stoch_rsi_d']
[index])
data.technicals = True
list_to_update.append(data)
list_to_create.append(technical)
NSETechnical.objects.bulk_create(list_to_create)
NSEHistoricalData.objects.bulk_update(list_to_update,
['technicals'])
print(f"Technicals updated for {symbol}")
start = datetime(2000, 9, 1)
ETH = pdr.DataReader(symbol,'yahoo',start,end)
df = pd.DataFrame(data=ETH)
kama_indicator = KAMAIndicator(close = df["Close"], window = 10, pow1 = 2, pow2 = 30, fillna = False)
df['kama'] = kama_indicator.kama()
ppo_indicator = PercentagePriceOscillator(close = df["Close"], window_slow = 20, window_fast = 10, window_sign = 9, fillna = False)
df['ppo'] = ppo_indicator.ppo()
roc_indicator = ROCIndicator(close = df["Close"], window = 12, fillna = False)
df['roc'] = roc_indicator.roc()
macd_indicator = MACD(close = df["Close"], window_slow = 20, window_fast = 12, window_sign = 9, fillna = False)
df['macd'] = macd_indicator.macd()
rsi_indicator = RSIIndicator(close = df["Close"], window = 14, fillna = False)
df['rsi'] = rsi_indicator.rsi()
aroon_indicator = AroonIndicator(close = df["Close"], window = 20, fillna = False)
df['aroon'] = aroon_indicator.aroon_indicator()
boll_indicator = BollingerBands(close = df["Close"], window = 20, window_dev = 2, fillna = False)
df['boll_mavg'] = boll_indicator.bollinger_mavg()
df.rename(columns = {"Close": "price"}, inplace=True)
prices = df['price'].to_numpy()
df['day_of_month'] = df.index.day
df['day_of_week'] = df.index.dayofweek
#%%画K线主图
datacent = go.Datacent()
if datacent.qihuo_connectSer():
qihuocount = list(range(len(cf.cfqihuo)))
df = datacent.qihuoK(cflen)
df = tdx_tools.SuperTrend(
df,
period=cf.st_period_fast,
multiplier=cf.st_mult_fast,
)
df = tdx_tools.StochRSI(df, m=14, p=7)
dfmacd = MACD(df['close'], n_slow=10, n_fast=5, n_sign=89)
df["macd"] = dfmacd.macd()
df['diff'] = dfmacd.macd_diff()
df['macd_signal'] = dfmacd.macd_signal()
df = df.iloc[100:]
weekday_quotes = [
tuple([i] + list(quote[1:]))
for i, quote in enumerate(df.values, )
]
fig = plt.figure(figsize=(1000 / 72, 500 / 72),
facecolor='#CCCCCC',
edgecolor='#CCCCCC')
df = df.reset_index()
df = actionOrder(df)