def init(self, base_data): base_data = base_data.sort_values("date") base_data[f"ma{self.ma_fast_days}"] = SMAIndicator( base_data["close"], self.ma_fast_days).sma_indicator() base_data[f"ma{self.ma_slow_days}"] = SMAIndicator( base_data["close"], self.ma_slow_days).sma_indicator() base_data["madiff"] = (base_data[f"ma{self.ma_fast_days}"] - base_data[f"ma{self.ma_slow_days}"]) base_data["bool_signal"] = base_data["madiff"].map(lambda x: 1 if x > 0 else -1) base_data["bool_signal_shift1"] = ( base_data["bool_signal"].shift(1).fillna(0)) base_data["bool_signal_shift1"] = base_data[ "bool_signal_shift1"].astype(int) base_data["signal"] = 0 base_data.loc[((base_data["bool_signal"] > 0) & (base_data["bool_signal_shift1"] < 0)), "signal", ] = 1 base_data.loc[((base_data["bool_signal"] < 0) & (base_data["bool_signal_shift1"] > 0)), "signal", ] = -1 return base_data
def AddIndicators(df): # Add Simple Moving Average (SMA) indicators df["sma7"] = SMAIndicator(close=df["Close"], window=7, fillna=True).sma_indicator() df["sma25"] = SMAIndicator(close=df["Close"], window=25, fillna=True).sma_indicator() df["sma99"] = SMAIndicator(close=df["Close"], window=99, fillna=True).sma_indicator() # Add Bollinger Bands indicator indicator_bb = BollingerBands(close=df["Close"], window=20, window_dev=2) df['bb_bbm'] = indicator_bb.bollinger_mavg() df['bb_bbh'] = indicator_bb.bollinger_hband() df['bb_bbl'] = indicator_bb.bollinger_lband() # Add Parabolic Stop and Reverse (Parabolic SAR) indicator indicator_psar = PSARIndicator(high=df["High"], low=df["Low"], close=df["Close"], step=0.02, max_step=2, fillna=True) df['psar'] = indicator_psar.psar() # Add Moving Average Convergence Divergence (MACD) indicator df["MACD"] = macd(close=df["Close"], window_slow=26, window_fast=12, fillna=True) # mazas # Add Relative Strength Index (RSI) indicator df["RSI"] = rsi(close=df["Close"], window=14, fillna=True) # mazas return df
def get_moving_average(i, window: int): """ returns the moving average of indicator i i: pandas series windows: [20, 50, 200] """ sma = SMAIndicator(close=i, window=window) return sma.sma_indicator()
def AddIndicators(df): # Add Simple Moving Average (SMA) indicators df["sma7"] = SMAIndicator(close=df["Close"], window=7, fillna=True).sma_indicator() df["sma25"] = SMAIndicator(close=df["Close"], window=25, fillna=True).sma_indicator() df["sma99"] = SMAIndicator(close=df["Close"], window=99, fillna=True).sma_indicator() return df
def get_trend_indicators(df, threshold=0.5, plot=False): df_trend = df.copy() # add custom trend indicators df_trend["sma7"] = SMAIndicator(close=df["Close"], window=7, fillna=True).sma_indicator() df_trend["sma25"] = SMAIndicator(close=df["Close"], window=25, fillna=True).sma_indicator() df_trend["sma99"] = SMAIndicator(close=df["Close"], window=99, fillna=True).sma_indicator() df_trend = add_trend_ta(df_trend, high="High", low="Low", close="Close") return DropCorrelatedFeatures(df_trend, threshold, plot)
def create_trade_sign(self, stock_price: pd.DataFrame) -> pd.DataFrame: stock_price = stock_price.sort_values("date") stock_price[f"ma{self.ma_days}"] = SMAIndicator( stock_price["close"], self.ma_days).sma_indicator() stock_price["bias"] = ( (stock_price["close"] - stock_price[f"ma{self.ma_days}"]) / stock_price[f"ma{self.ma_days}"]) * 100 stock_price[f"max_last_k_days{self.last_k_days}"] = ( stock_price["close"].shift(1).rolling( window=self.last_k_days).max()) stock_price[f"min_last_k_days{self.last_k_days}"] = ( stock_price["close"].shift(1).rolling( window=self.last_k_days).min()) stock_price["signal"] = 0 stock_price.loc[((stock_price["bias"] < self.bais_lower) & (stock_price["close"] > stock_price[f"max_last_k_days{self.last_k_days}"])), "signal", ] = 1 stock_price.loc[((stock_price["bias"] > self.bais_upper) & (stock_price["close"] < stock_price[f"min_last_k_days{self.last_k_days}"])), "signal", ] = -1 return stock_price
def init(self, base_data): base_data = base_data.sort_values("date") base_data[f"ma{self.ma_days}"] = SMAIndicator( base_data["close"], self.ma_days).sma_indicator() base_data["bias"] = ( (base_data["close"] - base_data[f"ma{self.ma_days}"]) / base_data[f"ma{self.ma_days}"]) * 100 base_data[f"max_last_k_days{self.last_k_days}"] = ( base_data["close"].shift(1).rolling(window=self.last_k_days).max()) base_data[f"min_last_k_days{self.last_k_days}"] = ( base_data["close"].shift(1).rolling(window=self.last_k_days).min()) base_data["signal"] = 0 base_data.loc[((base_data["bias"] < self.bais_lower) & (base_data["close"] > base_data[f"max_last_k_days{self.last_k_days}"])), "signal", ] = 1 base_data.loc[((base_data["bias"] > self.bais_upper) & (base_data["close"] < base_data[f"min_last_k_days{self.last_k_days}"])), "signal", ] = -1 return base_data
def add_price_moving_features(df, windows=[50, 100, 200]): for w in windows: df[f"ma_{w}"] = SMAIndicator(close=df["close"], window=w, fillna=False).sma_indicator() df[f"ema_{w}"] = EMAIndicator(close=df["close"], window=w, fillna=False).ema_indicator() return df
def add_indicators(self,data): warnings.filterwarnings("ignore",category=RuntimeWarning) # SMA n =20 data['ma'] = SMAIndicator(data['Close'],20).sma_indicator() data['rsi'] = RSIIndicator(data['Close'],14).rsi() data['macd'] = MACD(data['Close'],26,12,9).macd() data['macd_signal'] = MACD(data['Close'],26,12,9).macd_signal() data['macd_hist'] = MACD(data['Close'],26,12,9).macd_diff() data['adx'] = ADXIndicator(data['High'],data['Low'],data['Close'],14).adx() data['adx_neg'] = ADXIndicator(data['High'],data['Low'],data['Close'],14).adx_neg() data['adx_pos'] = ADXIndicator(data['High'],data['Low'],data['Close'],14).adx_pos() return data
def create_trade_sign(self, stock_price: pd.DataFrame) -> pd.DataFrame: stock_price = stock_price.sort_values("date") stock_price[f"ma{self.ma_fast_days}"] = SMAIndicator( stock_price["close"], self.ma_fast_days).sma_indicator() stock_price[f"ma{self.ma_slow_days}"] = SMAIndicator( stock_price["close"], self.ma_slow_days).sma_indicator() stock_price["madiff"] = (stock_price[f"ma{self.ma_fast_days}"] - stock_price[f"ma{self.ma_slow_days}"]) stock_price["bool_signal"] = stock_price["madiff"].map( lambda x: 1 if x > 0 else -1) stock_price["bool_signal_shift1"] = ( stock_price["bool_signal"].shift(1).fillna(0)) stock_price["bool_signal_shift1"] = stock_price[ "bool_signal_shift1"].astype(int) stock_price["signal"] = 0 stock_price.loc[((stock_price["bool_signal"] > 0) & (stock_price["bool_signal_shift1"] < 0)), "signal", ] = 1 stock_price.loc[((stock_price["bool_signal"] < 0) & (stock_price["bool_signal_shift1"] > 0)), "signal", ] = -1 return stock_price
def create_trade_sign(self, stock_price: pd.DataFrame) -> pd.DataFrame: stock_price = stock_price.sort_values("date") stock_price[f"ma{self.ma_days}"] = SMAIndicator( stock_price["close"], self.ma_days).sma_indicator() stock_price["bias"] = ( (stock_price["close"] - stock_price[f"ma{self.ma_days}"]) / stock_price[f"ma{self.ma_days}"]) * 100 stock_price = stock_price.dropna() stock_price.index = range(len(stock_price)) stock_price["signal"] = stock_price["bias"].map( lambda x: 1 if x < self.bias_lower else (-1 if x > self.bias_upper else 0)) stock_price["signal"] = stock_price["signal"].fillna(0) return stock_price
def init(self, base_data): base_data = base_data.sort_values("date") base_data[f"ma{self.ma_days}"] = SMAIndicator( base_data["close"], self.ma_days).sma_indicator() base_data["bias"] = ( (base_data["close"] - base_data[f"ma{self.ma_days}"]) / base_data[f"ma{self.ma_days}"]) * 100 base_data = base_data.dropna() base_data.index = range(len(base_data)) base_data["signal"] = base_data["bias"].map( lambda x: 1 if x < self.bias_lower else (-1 if x > self.bias_upper else 0)) base_data["signal"] = base_data["signal"].fillna(0) return base_data
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 getDailyData(self, symbol, since, debug=False): try: print("GET DAILY DATA") print("using key [%d/%d] = %s " % (Market.keyindex, len( Market.apikeys), Market.apikeys[Market.keyindex])) data, meta_data = self.ts[Market.keyindex].get_daily( symbol=symbol, outputsize='full') Market.keyindex = (Market.keyindex + 1) % len(Market.apikeys) self.data = data self.meta_data = meta_data self.data = self.data.sort_index() # map values to standard OHLC format self.data['O'] = self.data['1. open'] self.data['H'] = self.data['2. high'] self.data['L'] = self.data['3. low'] self.data['C'] = self.data['4. close'] self.data['V'] = self.data['5. volume'] if debug: print("===VALUES===") print(self.data) # add trend indicator. self.data = ta.utils.dropna(self.data) self.data['sma20'] = SMAIndicator(close=self.data['C'], n=20, fillna=True).sma_indicator() self.data['sma50'] = SMAIndicator(close=self.data['C'], n=50, fillna=True).sma_indicator() self.data['sma100'] = SMAIndicator(close=self.data['C'], n=100, fillna=True).sma_indicator() self.data['sma200'] = SMAIndicator(close=self.data['C'], n=200, fillna=True).sma_indicator() self.data['ema9'] = EMAIndicator(close=self.data['C'], n=9, fillna=True).ema_indicator() self.data['ema20'] = EMAIndicator(close=self.data['C'], n=20, fillna=True).ema_indicator() self.data = ta.add_all_ta_features(self.data, open="O", high="H", low="L", close="C", volume="V", fillna=True) self.data = self.data.loc[self.data.index >= since] candle_names = talib.get_function_groups()['Pattern Recognition'] if debug: print(candle_names) included_items = ('CDLDOJI', 'CDLHAMMER', 'CDLEVENINGSTAR', 'CDLHANGINGMAN', 'CDLSHOOTINGSTAR') candle_names = [ candle for candle in candle_names if candle in included_items ] for candle in candle_names: self.data[candle] = getattr(talib, candle)(self.data['O'], self.data['H'], self.data['L'], self.data['C']) self.data['action'] = 0 if debug: print("===VALUES===") print(self.data) except: print("ERROR GETTING MARKET DATA") time.sleep(60) self.data = None # max 5 api calls per minutes #time.sleep(60 / (2 * len(Market.apikeys))) #time.sleep(60 / 4) return self.data
df2 = pd.DataFrame() SYMBOL = "'BPCL '" df2 = pd.DataFrame() for i in df_unique_symbols: #SYMBOL = "'ALPSINDUS '" #print(SYMBOL) SYMBOL = f'\'{i}\'' #print(SYMBOL) df = pd.read_sql_query( f'select {low_price} low_price, {open_price} open_price,{macd} MACD,{macd_signal} MACD_SIGNAL,' f'{close_price} close_price , {date_recorded} date_recorded ,{ema_indicator} ema_indicator,' f'{high_price} high_price , {volume1} volume , {SYMBOL} symbol' f' from nifty_500_technical_analysis where nifty_500_technical_analysis.symbol = {SYMBOL}', conn) #40 days SMA calculation indicator = SMAIndicator(close=df["close_price"], n=40, fillna=False) SMA_INDICATOR = "SMA_indicator" # print(indicator) df[SMA_INDICATOR] = indicator.sma_indicator() try: df['sma_line_Crossover'] = np.where( df['close_price'] > df['SMA_indicator'], 1, 0) print(df) df['sma_line_Crossover'] = np.where( df['close_price'] < df['SMA_indicator'], -1, df['sma_line_Crossover']) print(df) df['SMA_buy_sell'] = (2 * (np.sign(df['sma_line_Crossover'] - df['sma_line_Crossover'].shift(1))))
indicator_bb = BollingerBands(close=df['cls'], window=20, window_dev=2) indicator_macd = MACD(close=df['cls'], window_fast=12, window_slow=26, window_sign=9) indicator_rsi14 = RSIIndicator(close=df['cls'], window=14) indicator_cci20 = cci(high=df['hgh'], low=df['low'], close=df['cls'], window=20, constant=0.015) indicator_obv = OnBalanceVolumeIndicator(close=df['cls'], volume=df['vol'], fillna=True) indicator_vol_sma20 = SMAIndicator(close=df['vol'], window=20) indicator_ema03 = EMAIndicator(close=df['cls'], window=3) indicator_ema05 = EMAIndicator(close=df['cls'], window=5) indicator_ema08 = EMAIndicator(close=df['cls'], window=8) indicator_ema10 = EMAIndicator(close=df['cls'], window=10) indicator_ema12 = EMAIndicator(close=df['cls'], window=12) indicator_ema15 = EMAIndicator(close=df['cls'], window=15) indicator_ema30 = EMAIndicator(close=df['cls'], window=30) indicator_ema35 = EMAIndicator(close=df['cls'], window=35) indicator_ema40 = EMAIndicator(close=df['cls'], window=40) indicator_ema45 = EMAIndicator(close=df['cls'], window=45) indicator_ema50 = EMAIndicator(close=df['cls'], window=50) indicator_ema60 = EMAIndicator(close=df['cls'], window=60) # Add Bollinger Band high indicator
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_bb = BollingerBands(close=df["adj_close"], window=20, window_dev=2) # Add Bollinger Bands features df['bb_bbm'] = indicator_bb.bollinger_mavg() df['bb_bbh'] = indicator_bb.bollinger_hband() df['bb_bbl'] = indicator_bb.bollinger_lband() # EMA Indicator indicator_ema_200 = EMAIndicator(close=df["adj_close"], window=200) df['ema_200'] = indicator_ema_200.ema_indicator() indicator_ema_100 = EMAIndicator(close=df["adj_close"], window=100) df['ema_100'] = indicator_ema_100.ema_indicator() indicator_ema_50 = EMAIndicator(close=df["adj_close"], window=50) df['ema_50'] = indicator_ema_50.ema_indicator() # SMA Indicator 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()
def getDailyData(self, symbol, since, debug=False): #try: print("GET IBKR INTRADAY DATA") now = datetime.now() print("CREATE CONTRACT FOR %s" % symbol) contract = IBMarket.conn.createStockContract(symbol) dirname = os.path.dirname(__file__) filename = "data/daily/" + symbol + "_" + now.strftime( "%Y_%m_%d") + "_1_day.csv" if os.path.isfile(filename) == True: os.remove(filename) print("REQUEST HISTORICAL DATA FOR %s SINCE %s" % (symbol, since)) IBMarket.market_data_available[symbol] = False IBMarket.conn.requestHistoricalData(contract, rth=self.rth, resolution="1 day", lookback="12 M", csv_path='data/daily/') print("WAIT FOR DATA...") while IBMarket.market_data_available[symbol] == False: time.sleep(5) IBMarket.market_data_available[symbol] = False print("RENAME CSV FILE TO %s" % filename) os.rename("data/daily/" + symbol + ".csv", filename) print("READ CSV FILE:%s" % filename) self.data = pd.read_csv(filename, index_col='datetime', parse_dates=True) self.data = self.data.sort_index() if debug: print("===VALUES===") print(self.data) print("===CALCULATE ALL TA===") self.data = ta.utils.dropna(self.data) self.data['sma20'] = SMAIndicator(close=self.data['C'], n=20, fillna=True).sma_indicator() self.data['sma50'] = SMAIndicator(close=self.data['C'], n=50, fillna=True).sma_indicator() self.data['sma100'] = SMAIndicator(close=self.data['C'], n=100, fillna=True).sma_indicator() self.data['sma200'] = SMAIndicator(close=self.data['C'], n=200, fillna=True).sma_indicator() self.data['ema9'] = EMAIndicator(close=self.data['C'], n=9, fillna=True).ema_indicator() self.data['ema20'] = EMAIndicator(close=self.data['C'], n=20, fillna=True).ema_indicator() self.data = ta.add_all_ta_features(self.data, open="O", high="H", low="L", close="C", volume="V", fillna=True) self.data = self.data.loc[self.data.index >= since] print("===CALCULATE DOJI===") candle_names = talib.get_function_groups()['Pattern Recognition'] included_items = ('CDLDOJI', 'CDLHAMMER', 'CDLEVENINGSTAR', 'CDLHANGINGMAN', 'CDLSHOOTINGSTAR') candle_names = [ candle for candle in candle_names if candle in included_items ] for candle in candle_names: self.data[candle] = getattr(talib, candle)(self.data['O'], self.data['H'], self.data['L'], self.data['C']) if debug: print("===VALUES===") print(self.data[[ 'C', 'V', 'WAP', 'sma20', 'sma50', 'sma100', 'sma200', 'momentum_rsi', 'trend_cci', 'momentum_stoch_signal', 'trend_adx' ]]) #except: # print("ERROR GETTING MARKET DATA") # time.sleep(60) # self.data = None #30 request / 10 mins -> need to wait 20 secs time.sleep(20) return self.data
def getIntraDayData(self, symbol, since, period, live=False, debug=False): #try: print("GET IBKR INTRADAY DATA") if live == True: lookback = int(period * 1600 / 60 / 24) else: dtime = datetime.now() - datetime.strptime(since, '%Y-%m-%d') #lookback = dtime.total_seconds() / 60 / 60 / 24 lookback = 7 now = datetime.now() print("CREATE CONTRACT FOR %s" % symbol) contract = IBMarket.conn.createStockContract(symbol) dirname = os.path.dirname(__file__) filename = "data/intraday/" + symbol + "_" + now.strftime( "%Y_%m_%d") + "_" + str(period) + "_min.csv" if os.path.isfile(filename) == True: os.remove(filename) print( "REQUEST HISTORICAL DATA FOR %s, LOOKBACK %d DAYS, SINCE %s, RTH:%d" % (symbol, lookback, since, self.rth)) IBMarket.market_data_available[symbol] = False IBMarket.conn.requestHistoricalData(contract, rth=self.rth, resolution=str(period) + " mins", lookback=str(lookback) + " D", csv_path='data/intraday/') print("WAIT FOR DATA...") while IBMarket.market_data_available[symbol] == False: time.sleep(5) IBMarket.market_data_available[symbol] = False print("RENAME CSV FILE") os.rename("data/intraday/" + symbol + ".csv", filename) print("READ CSV DATA") self.data = pd.read_csv(filename, index_col='datetime', parse_dates=True) self.data = self.data.sort_index() self.data = self.data[:-1] if debug: print("===VALUES===") print(self.data) print("===CALCULATE SMA===") # add trend indicator. #self.data['sma20'] = self.data['C'].rolling(20, min_periods=20).mean() #self.data['sma50'] = self.data['C'].rolling(50, min_periods=50).mean() #self.data['sma100'] = self.data['C'].rolling(100, min_periods=100).mean() #self.data['sma200'] = self.data['C'].rolling(200, min_periods=200).mean() print("===CALCULATE ALL TA===") self.data = ta.utils.dropna(self.data) self.data['sma20'] = SMAIndicator(close=self.data['C'], n=20, fillna=True).sma_indicator() self.data['sma50'] = SMAIndicator(close=self.data['C'], n=50, fillna=True).sma_indicator() self.data['sma100'] = SMAIndicator(close=self.data['C'], n=100, fillna=True).sma_indicator() self.data['sma200'] = SMAIndicator(close=self.data['C'], n=200, fillna=True).sma_indicator() self.data['ema9'] = EMAIndicator(close=self.data['C'], n=9, fillna=True).ema_indicator() self.data['ema20'] = EMAIndicator(close=self.data['C'], n=20, fillna=True).ema_indicator() self.data = ta.add_all_ta_features(self.data, open="O", high="H", low="L", close="C", volume="V", fillna=True) self.data['mvwap'] = self.data['volume_vwap'].rolling( 14, min_periods=14).mean() self.data = self.data.loc[self.data.index >= since] print("===CALCULATE DOJI===") candle_names = talib.get_function_groups()['Pattern Recognition'] if debug: print(candle_names) included_items = ('CDLDOJI', 'CDLHAMMER', 'CDLEVENINGSTAR', 'CDLHANGINGMAN', 'CDLSHOOTINGSTAR') candle_names = [ candle for candle in candle_names if candle in included_items ] for candle in candle_names: self.data[candle] = getattr(talib, candle)(self.data['O'], self.data['H'], self.data['L'], self.data['C']) if debug: print("===VALUES===") print(self.data) #except: # print("ERROR GETTING MARKET DATA") # time.sleep(60) # self.data = None return self.data
def add_trend_ta(df: pd.DataFrame, high: str, low: str, close: str, fillna: bool = False, colprefix: str = "") -> 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 Returns: pandas.core.frame.DataFrame: Dataframe with new features. """ # MACD indicator_macd = MACD(close=df[close], n_slow=26, n_fast=12, 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() # SMAs df[f'{colprefix}trend_sma_fast'] = SMAIndicator( close=df[close], n=12, fillna=fillna).sma_indicator() df[f'{colprefix}trend_sma_slow'] = SMAIndicator( close=df[close], n=26, fillna=fillna).sma_indicator() # 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 get_sma(data, days): """ Function to get the EMA of a ticker """ sma = SMAIndicator(close=data, n=days) return sma.sma_indicator()
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}")
def __init__(self, symbols): # data = json.loads(symbols) # df_stock = pd.json_normalize(symbols) # df_stock = pd.read_csv(fn,names = ['sym']).drop_duplicates() df_stock = pd.DataFrame(symbols) ls_stock = df_stock['sym'].to_list() df_stock = df_stock.reset_index() df_stock.columns = ['sort', 'sym'] df_stock.head() # In[3]: start = dt.date.today() + relativedelta(days=-150) end = dt.date.today() + relativedelta(days=-0) ls_tickers = ls_stock ls_df = [] for ticker in ls_tickers: try: df = web.DataReader(ticker, 'yahoo', start, end) except Exception as e: print(str(e)) continue df['sym'] = ticker ls_df.append(df.copy()) df_price = pd.concat(ls_df).reset_index() df_price.columns = [ 'dte', 'hgh', 'low', 'opn', 'cls', 'vol', 'cls_adj', 'sym' ] df_price.sort_values(['sym', 'dte'], inplace=True) df_price = df_price[['dte', 'sym', 'hgh', 'low', 'cls', 'vol']].copy() df_price['curr'] = end df_price['curr'] = pd.to_datetime(df_price['curr']) df_price['dte'] = pd.to_datetime(df_price['dte']) df_price['ndays'] = (df_price['curr'] - df_price['dte']).dt.days df_price['ndays'] = df_price.groupby(['sym'])['ndays'].rank() df_price[df_price['sym'] == 'SPY'].head() # In[4]: ls_df = [] ls_tickers = ls_stock for ticker in ls_tickers: #df = dropna(df_price[df_price['sym']==ticker]) df = df_price[df_price['sym'] == ticker].copy() indicator_bb = BollingerBands(close=df['cls'], window=20, window_dev=2) indicator_macd = MACD(close=df['cls'], window_fast=12, window_slow=26, window_sign=9) indicator_rsi14 = RSIIndicator(close=df['cls'], window=14) indicator_cci20 = cci(high=df['hgh'], low=df['low'], close=df['cls'], window=20, constant=0.015) indicator_obv = OnBalanceVolumeIndicator(close=df['cls'], volume=df['vol'], fillna=True) indicator_vol_sma20 = SMAIndicator(close=df['vol'], window=20) indicator_ema03 = EMAIndicator(close=df['cls'], window=3) indicator_ema05 = EMAIndicator(close=df['cls'], window=5) indicator_ema08 = EMAIndicator(close=df['cls'], window=8) indicator_ema10 = EMAIndicator(close=df['cls'], window=10) indicator_ema12 = EMAIndicator(close=df['cls'], window=12) indicator_ema15 = EMAIndicator(close=df['cls'], window=15) indicator_ema30 = EMAIndicator(close=df['cls'], window=30) indicator_ema35 = EMAIndicator(close=df['cls'], window=35) indicator_ema40 = EMAIndicator(close=df['cls'], window=40) indicator_ema45 = EMAIndicator(close=df['cls'], window=45) indicator_ema50 = EMAIndicator(close=df['cls'], window=50) indicator_ema60 = EMAIndicator(close=df['cls'], window=60) # Add Bollinger Band high indicator df['bb_bbhi'] = indicator_bb.bollinger_hband_indicator() # Add Bollinger Band low indicator df['bb_bbli'] = indicator_bb.bollinger_lband_indicator() #df['macd'] = indicator_macd.macd() df['macd'] = indicator_macd.macd_diff() #df['macd_signal'] = indicator_macd.macd_signal() df['obv'] = indicator_obv.on_balance_volume() df['vol_sma20'] = indicator_vol_sma20.sma_indicator() df['ema03'] = indicator_ema03.ema_indicator() df['ema05'] = indicator_ema05.ema_indicator() df['ema08'] = indicator_ema08.ema_indicator() df['ema10'] = indicator_ema10.ema_indicator() df['ema12'] = indicator_ema12.ema_indicator() df['ema15'] = indicator_ema15.ema_indicator() df['ema30'] = indicator_ema30.ema_indicator() df['ema35'] = indicator_ema35.ema_indicator() df['ema40'] = indicator_ema40.ema_indicator() df['ema45'] = indicator_ema45.ema_indicator() df['ema50'] = indicator_ema50.ema_indicator() df['ema60'] = indicator_ema60.ema_indicator() df['rsi14'] = indicator_rsi14.rsi() df['cci20'] = indicator_cci20 ls_df.append(df.copy()) df = pd.concat(ls_df) df['score_vol_sma20'] = df[['vol', 'vol_sma20']].apply(lambda x: x[0] / x[1], axis=1) df['emash_min'] = df[[ 'ema03', 'ema05', 'ema08', 'ema10', 'ema12', 'ema15' ]].min(axis=1) df['emash_max'] = df[[ 'ema03', 'ema05', 'ema08', 'ema10', 'ema12', 'ema15' ]].max(axis=1) df['emash_avg'] = df[[ 'ema03', 'ema05', 'ema08', 'ema10', 'ema12', 'ema15' ]].mean(axis=1) #df['score_short'] = df[['cls','emash_min','emash_max','emash_min']].apply(lambda x: 100 * (x[0]-x[1])/(x[2]-x[3]),axis=1) df['emalg_min'] = df[[ 'ema30', 'ema35', 'ema40', 'ema45', 'ema50', 'ema60' ]].min(axis=1) df['emalg_max'] = df[[ 'ema30', 'ema35', 'ema40', 'ema45', 'ema50', 'ema60' ]].max(axis=1) df['emalg_avg'] = df[[ 'ema30', 'ema35', 'ema40', 'ema45', 'ema50', 'ema60' ]].mean(axis=1) #df['score_long'] = df[['cls','emalg_min','emalg_max','emalg_min']].apply(lambda x: 100 * (x[0]-x[1])/(x[2]-x[3]),axis=1) df['ema_min'] = df[[ 'ema03', 'ema05', 'ema08', 'ema10', 'ema12', 'ema15', 'ema30', 'ema35', 'ema40', 'ema45', 'ema50', 'ema60' ]].min(axis=1) df['ema_max'] = df[[ 'ema03', 'ema05', 'ema08', 'ema10', 'ema12', 'ema15', 'ema30', 'ema35', 'ema40', 'ema45', 'ema50', 'ema60' ]].max(axis=1) df['score_ovlp_ema'] = df[[ 'emash_min', 'emalg_max', 'ema_max', 'ema_min' ]].apply(lambda x: 100 * (x[0] - x[1]) / (x[2] - x[3]), axis=1) df = pd.merge(df_stock, df, on=['sym'], how='inner').sort_values(['sort', 'ndays']) decimals = pd.Series([1, 0, 0, 2, 0, 0, 2, 0, 0, 0, 0], index=[ 'cls', 'ndays', 'vol', 'score_vol_sma20', 'bb_bbhi', 'bb_bbli', 'macd', 'obv', 'rsi14', 'cci20', 'score_ovlp_ema' ]) cols = [ 'ndays', 'dte', 'sort', 'sym', 'cls', 'vol', 'score_vol_sma20', 'bb_bbhi', 'bb_bbli', 'macd', 'obv', 'rsi14', 'cci20', 'score_ovlp_ema' ] df = df[df['ndays'] <= 10][cols].round(decimals).copy() print(df['score_ovlp_ema'].min(), df['score_ovlp_ema'].max()) df[df['sym'] == 'QQQ'].head(50) self.df = df
def SMA(df,n): sma = SMAIndicator(df['Close'],n) df['SMA-'+str(n)] = sma.sma_indicator() return df