# source: https://raw.githubusercontent.com/botum/trendline-indicator/cb92456deebb3fcc6422094296bc1813909195ec/user_data/strategies/trendOHCL001.py # pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement import talib.abstract as ta from pandas import DataFrame import freqtrade.vendor.qtpylib.indicators as qtpylib from freqtrade.strategy.interface import IStrategy from user_data.indicators.trendlines import * # from user_data.indicators.sure import * class_name = 'DefaultStrategy' # pair = 'ETH/BTC' class github_botum_trendline_indicator__trendOHCL001__20180814_005404(IStrategy): """ author@: Bruno Sarlo Strategy for buying and selling the trendlines """ # Minimal ROI designed for the strategy minimal_roi = { # "40": 0.00001, # "30": 0.002, # "60": 0.005, # "30": 0.02, "0": 0.02 } # Optimal stoploss designed for the strategy stoploss = -0.02 # Optimal ticker interval for the strategy ticker_interval = "5m" def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Indicator for trends """ # dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) # dataframe['rsi_sup_trend'], rsi_sup_trends = get_trends_serie(self, dataframe['rsi'].fillna(100), # interval=self.ticker_interval, # type='sup', tolerance=0.0000001, min_tests=4, # angle_max = 180, # angle_min = -180, # thresh_up = 0.5, thresh_down = -0.5, # chart=True, pair=pair) # Bollinger bands # bollinger = indicators.bollinger_bands(dataframe, field='close', period=20, stdv=1.5) # create the BB expansion indicator # dataframe['bb_exp'] = (dataframe['bb_upper'] - dataframe['bb_lower']) / dataframe['bb_upper'] # macd = ta.MACD(dataframe) # dataframe['macd'] = macd['macd'] # dataframe['macdsignal'] = macd['macdsignal'] # dataframe['macdhist'] = macd['macdhist'] # dataframe['cci'] = ta.CCI(dataframe) # # dataframe['mfi'] = ta.MFI(dataframe) dataframe = get_trends_lightbuoy_OHCL(dataframe, interval=self.ticker_interval, pivot_type='fractals', pressision=0.00001, su_min_tests=4, re_min_tests=1, body_min_tests=1, ticker_gap = 5, fake=0.0001, nearby=0.001, angle_max = 100, angle_min = 80, thresh_up = 0.01, thresh_down = -0.01, chart=False, pair=metadata['pair']) # dataframe, su, re = get_sure_zigzag_OHCL(self, dataframe, # intervals=[self.ticker_interval], quantile=0.03, # up_thresh=0.005, down_thresh=0.005) # print (dataframe.sup_trend) # plot_trends(dataframe, interval=self.ticker_interval, pair=pair) # print ('pair: ', pair) # print ('close: ', dataframe.iloc[-1].close) # print ('bb expan: ', dataframe.iloc[-1].bb_exp * 100, '%') # print ('resistence: ', (dataframe.iloc[-1].res_trend / dataframe.iloc[-1].sup_trend) * 100) # print ('stoploss: ', dataframe.iloc[-1].sup_trend * (1 - (dataframe.iloc[-1].res_trend / dataframe.iloc[-1].sup_trend)) * 100 / dataframe.iloc[-1].close, '%') return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Based on TA indicators, populates the buy signal for the given dataframe :param dataframe: DataFrame :return: DataFrame with buy column """ dataframe.loc[ ( ((dataframe['close'] * 10000000 > 1)) & (dataframe['close'] == dataframe.s1_trend) # | # (dataframe['close'] > dataframe.re_trend) # (dataframe['close'] <= dataframe.sup_trend) # & # (dataframe['volume'].shift(1) < dataframe['volume']/10) # & # (dataframe['rsi'] < 25) # & # (dataframe.r1_trend >= dataframe.s1_trend*1.03) # (dataframe['close']==dataframe['sup_trend']) # 0 ), 'buy'] = 1 # UNCOMMENT TO PLOT self.plot_dataframe(dataframe, metadata['pair'], 'buy', ['s1_trend,r1_trend', '', 'rsi']) return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Based on TA indicators, populates the sell signal for the given dataframe :param dataframe: DataFrame :return: DataFrame with buy column """ dataframe.loc[ ( (dataframe['close'] >= dataframe['r1_trend']) | (dataframe['close'] <= dataframe['s1_trend'] * 0.99) # | # (dataframe['close'] <= dataframe['sup_trend'] * (1 - dataframe.iloc[-1].bb_exp)) # 0 ), 'sell'] = 1 # UNCOMMENT TO PLOT self.plot_dataframe(dataframe, metadata['pair'], 'sell', ['s1_trend,r1_trend', '', 'rsi']) return dataframe def plot_dataframe(self, data, pair, signal: str, indicators: list): """ plots our dataframe everytime new data arrive and a tick is closed :param indicators: list of indicators :param data: :return: """ import plotly.graph_objs as go from plotly import tools from plotly.offline import plot as plt def generate_row(fig, row, raw_indicators, data) -> tools.make_subplots: """ Generator all the indicator selected by the user for a specific row """ if raw_indicators is None or raw_indicators == "": return fig for indicator in raw_indicators.split(','): if indicator in data: scattergl = go.Scattergl( x=data['date'], y=data[indicator], name=indicator ) fig.append_trace(scattergl, row, 1) return fig rows = len(indicators) if rows < 3: rows = 3 # Define the graph fig = tools.make_subplots( rows=rows, cols=1, shared_xaxes=True, row_width=[1, 1, 4], vertical_spacing=0.0001, ) fig['layout'].update(title=pair) fig['layout']['yaxis1'].update(title='Price') fig['layout']['yaxis2'].update(title='Volume') fig['layout']['yaxis3'].update(title='Other') if rows > 3: for x in range(3, rows): fig['layout']['yaxis{}'.format(x)].update(title='Other {}'.format(x)) # Common information candles = go.Candlestick( x=data.date, open=data.open, high=data.high, low=data.low, close=data.close, name='Price' ) fig.append_trace(candles, 1, 1) df_buy = data[data['buy'] == 1] buys = go.Scattergl( x=df_buy.date, y=df_buy.close, mode='markers', name='buy', marker=dict( symbol='triangle-up-dot', size=9, line=dict(width=1), color='green', ) ) fig.append_trace(buys, 1, 1) # Row 2 volume = go.Bar( x=data['date'], y=data['volume'], name='Volume' ) fig.append_trace(volume, 2, 1) row = 0 for indicator in indicators: row = row + 1 # #print(row) generate_row(fig, row, indicator, data) from pathlib import Path plt(fig, auto_open=False, filename=str( Path('user_data').joinpath( "{}_{}_{}_analyze_{}_{}.html".format(__class__.__name__, pair.replace('/', '-'), signal, self.ticker_interval, data['date'].iloc[-1]))))