# source: https://raw.githubusercontent.com/remiotore/ccxt-freqtrade/44beaeb6a420cd8e9f2e4ea93e11d6cfa192ee03/strategies/TDSequentialStrategy_2.py import talib.abstract as ta from pandas import DataFrame import scipy.signal import freqtrade.vendor.qtpylib.indicators as qtpylib from freqtrade.strategy.interface import IStrategy class Github_remiotore_ccxt_freqtrade__TDSequentialStrategy_2__20260111_210550(IStrategy): """ Strategy based on TD Sequential indicator. source: https://hackernoon.com/how-to-buy-sell-cryptocurrency-with-number-indicator-td-sequential-5af46f0ebce1 Buy trigger: When you see 9 consecutive closes "lower" than the close 4 bars prior. An ideal buy is when the low of bars 6 and 7 in the count are exceeded by the low of bars 8 or 9. Sell trigger: When you see 9 consecutive closes "higher" than the close 4 candles prior. An ideal sell is when the the high of bars 6 and 7 in the count are exceeded by the high of bars 8 or 9. Created by @bmoulkaf """ INTERFACE_VERSION = 2 minimal_roi = {'0': 5} stoploss = -0.1 trailing_stop = False timeframe = '15m' use_sell_signal = True sell_profit_only = False ignore_roi_if_buy_signal = False order_types = { 'buy': 'limit', 'sell': 'limit', 'stoploss': 'limit', 'stoploss_on_exchange': False } startup_candle_count: int = 30 order_time_in_force = { 'buy': 'gtc', 'sell': 'gtc', } def informative_pairs(self): """ Define additional, informative pair/interval combinations to be cached from the exchange. These pair/interval combinations are non-tradeable, unless they are part of the whitelist as well. For more information, please consult the documentation :return: List of tuples in the format (pair, interval) Sample: return [("ETH/USDT", "5m"), ("BTC/USDT", "15m"), ] """ return [] def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Adds several different TA indicators to the given DataFrame Performance Note: For the best performance be frugal on the number of indicators you are using. Let uncomment only the indicator you are using in your strategies or your hyperopt configuration, otherwise you will waste your memory and CPU usage. :param dataframe: Raw data from the exchange and parsed by parse_ticker_dataframe() :param metadata: Additional information, like the currently traded pair :return: a Dataframe with all mandatory indicators for the strategies """ dataframe['rsi'] = ta.RSI(dataframe) dataframe['adx'] = ta.ADX(dataframe) dataframe['mfi'] = ta.MFI(dataframe) macd = ta.MACD(dataframe) dataframe['macd'] = macd['macd'] dataframe['macdsignal'] = macd['macdsignal'] dataframe['macdhist'] = macd['macdhist'] dataframe['sar'] = ta.SAR(dataframe) dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9) stoch_fast = ta.STOCHF(dataframe) dataframe['fastd'] = stoch_fast['fastd'] dataframe['fastk'] = stoch_fast['fastk'] bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) dataframe['bb_lowerband'] = bollinger['lower'] dataframe['bb_middleband'] = bollinger['mid'] dataframe['bb_upperband'] = bollinger['upper'] dataframe["bb_percent"] = ( (dataframe["close"] - dataframe["bb_lowerband"]) / (dataframe["bb_upperband"] - dataframe["bb_lowerband"]) ) dataframe["bb_width"] = ( (dataframe["bb_upperband"] - dataframe["bb_lowerband"]) / dataframe["bb_middleband"] ) dataframe['exceed_high'] = False dataframe['exceed_low'] = False dataframe['seq_buy'] = dataframe['close'] < dataframe['close'].shift(4) dataframe['seq_buy'] = dataframe['seq_buy'] * (dataframe['seq_buy'].groupby( (dataframe['seq_buy'] != dataframe['seq_buy'].shift()).cumsum()).cumcount() + 1) dataframe['seq_sell'] = dataframe['close'] > dataframe['close'].shift(4) dataframe['seq_sell'] = dataframe['seq_sell'] * (dataframe['seq_sell'].groupby( (dataframe['seq_sell'] != dataframe['seq_sell'].shift()).cumsum()).cumcount() + 1) for index, row in dataframe.iterrows(): seq_b = row['seq_buy'] if seq_b == 8: dataframe.loc[index, 'exceed_low'] = (row['low'] < dataframe.loc[index - 2, 'low']) | \ (row['low'] < dataframe.loc[index - 1, 'low']) if seq_b > 8: dataframe.loc[index, 'exceed_low'] = (row['low'] < dataframe.loc[index - 3 - (seq_b - 9), 'low']) | \ (row['low'] < dataframe.loc[index - 2 - (seq_b - 9), 'low']) if seq_b == 9: dataframe.loc[index, 'exceed_low'] = row['exceed_low'] | dataframe.loc[index-1, 'exceed_low'] seq_s = row['seq_sell'] if seq_s == 8: dataframe.loc[index, 'exceed_high'] = (row['high'] > dataframe.loc[index - 2, 'high']) | \ (row['high'] > dataframe.loc[index - 1, 'high']) if seq_s > 8: dataframe.loc[index, 'exceed_high'] = (row['high'] > dataframe.loc[index - 3 - (seq_s - 9), 'high']) | \ (row['high'] > dataframe.loc[index - 2 - (seq_s - 9), 'high']) if seq_s == 9: dataframe.loc[index, 'exceed_high'] = row['exceed_high'] | dataframe.loc[index-1, 'exceed_high'] 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 :param metadata: Additional information, like the currently traded pair :return: DataFrame with buy column """ dataframe["buy"] = 0 dataframe.loc[((dataframe['exceed_low']) & (dataframe['seq_buy'] > 8)) & (dataframe['rsi'] > 10) & # Signal: RSI is greater 25 (dataframe['close'] < dataframe['bb_lowerband']) # Signal: price is less than lower bb , 'buy'] = 1 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 :param metadata: Additional information, like the currently traded pair :return: DataFrame with buy columnNA / NaN values """ dataframe["sell"] = 0 dataframe.loc[((dataframe['exceed_high']) | (dataframe['seq_sell'] > 8)) & (dataframe['rsi'] > 90) & # Signal: RSI is greater 70 (dataframe['close'] > dataframe['bb_upperband']) # Signal: price is greater than mid bb , 'sell'] = 1 return dataframe