def acceptCandle(self, candle): ''' accepts a candle and updates current state of strategy to determine buy/sell ''' self.addCandle(candle) macd = MACD(self.candleDf['C']) # print(macd.macd().iloc[-1], macd.macd_signal().iloc[-1], macd.macd_diff().iloc[-1], macd.macd().iloc[-1] - macd.macd_signal().iloc()[-1]) if macd.macd_diff().iloc[-1] >= 0 and macd.macd_diff().iloc[-2] < 0: self.buy(0.05 * self.capital) elif macd.macd_diff().iloc[-1] <= 0 and macd.macd_diff().iloc[-2] > 0: self.sell()
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 init(self, base_data): base_data = base_data.sort_values("date") macd = MACD(close=base_data["close"], n_slow=26, n_fast=12, n_sign=9) base_data["macd_diff"] = macd.macd_diff() base_data["macd_signal"] = macd.macd_signal() base_data["fs_dif"] = base_data["macd_diff"] - base_data["macd_signal"] base_data["bool_signal"] = base_data["fs_dif"].map( lambda x: 1 if x > 0 else -1 ) base_data["bool_signal_shift1"] = base_data["bool_signal"].shift(1) 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 base_data.index = range(len(base_data)) return base_data
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 acceptCandle(self, candle): ''' accepts a candle and updates current state of strategy to determine buy/sell ''' self.addCandle(candle) macd = MACD(self.candleDf['C'], 20, 6, 5) rsi = RSIIndicator(self.candleDf['C'], 8) prevRsi = rsi.rsi().iloc[-2] currentRsi = rsi.rsi().iloc[-1] # print(macd.macd().iloc[-1], macd.macd_signal().iloc[-1], macd.macd_diff().iloc[-1], macd.macd().iloc[-1] - macd.macd_signal().iloc()[-1]) if macd.macd_diff().iloc[-1] >= 0 and macd.macd_diff().iloc[-2] < 0\ and currentRsi >= self.oversold and prevRsi < self.oversold: self.buy(0.05 * self.capital) elif macd.macd_diff().iloc[-1] <= 0 and macd.macd_diff().iloc[-2] > 0\ and currentRsi <= self.overbought and prevRsi > self.overbought: self.sell()
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 create_trade_sign(self, stock_price: pd.DataFrame) -> pd.DataFrame: stock_price = stock_price.sort_values("date") macd = MACD(close=stock_price["close"], n_slow=26, n_fast=12, n_sign=9) stock_price["macd_diff"] = macd.macd_diff() stock_price["macd_signal"] = macd.macd_signal() stock_price["fs_dif"] = (stock_price["macd_diff"] - stock_price["macd_signal"]) stock_price["bool_signal"] = stock_price["fs_dif"].map( lambda x: 1 if x > 0 else -1) stock_price["bool_signal_shift1"] = stock_price["bool_signal"].shift(1) 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 stock_price.index = range(len(stock_price)) return stock_price
def create_trade_sign(self, stock_price: pd.DataFrame) -> pd.DataFrame: stock_price = stock_price.sort_values("date") macd = MACD(close=stock_price["close"], n_slow=26, n_fast=12, n_sign=9) stock_price["DIF"] = macd.macd_diff() stock_price["MACD"] = macd.macd_signal() stock_price["OSC"] = stock_price["DIF"] - stock_price["MACD"] stock_price["OSC_signal"] = stock_price["OSC"].map(lambda x: 1 if x > 0 else -1) stock_price["OSC_signal_yesterday"] = stock_price["OSC_signal"].shift( 1) stock_price["signal"] = 0 stock_price.loc[((stock_price["OSC_signal"] > 0) & (stock_price["OSC_signal_yesterday"] < 0)), "signal", ] = 1 # 下而上穿過 stock_price.loc[((stock_price["OSC_signal"] < 0) & (stock_price["OSC_signal_yesterday"] > 0)), "signal", ] = -1 # 上而下穿過 stock_price.index = range(len(stock_price)) return stock_price
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(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()
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()
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 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
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())
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
#%%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)
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()
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 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