Merge 861fa3d9ea into 586f49cafd

user_data/random/backtesting.py

@@ -0,0 +1,295 @@
+# pragma pylint: disable=missing-docstring, W0212, too-many-arguments
+
+"""
+This module contains the backtesting logic
+"""
+from argparse import Namespace
+from typing import Dict, Tuple, Any, List, Optional
+
+import arrow
+from pandas import DataFrame, Series
+from tabulate import tabulate
+import freqtrade.optimize as optimize
+from freqtrade import exchange
+from freqtrade.analyze import Analyze
+from freqtrade.arguments import Arguments
+from freqtrade.configuration import Configuration
+from freqtrade.exchange import Bittrex
+from freqtrade.logger import Logger
+from freqtrade.misc import file_dump_json
+from freqtrade.persistence import Trade
+import sys
+import os
+import time
+class Backtesting(object):
+    """
+    Backtesting class, this class contains all the logic to run a backtest
+
+    To run a backtest:
+    backtesting = Backtesting(config)
+    backtesting.start()
+    """
+    def __init__(self, config: Dict[str, Any]) -> None:
+
+        # Init the logger
+        self.logging = Logger(name=__name__, level=config['loglevel'])
+        self.logger = self.logging.get_logger()
+        self.config = config
+        self.analyze = None
+        self.ticker_interval = None
+        self.tickerdata_to_dataframe = None
+        self.populate_buy_trend = None
+        self.populate_sell_trend = None
+        self._init()
+
+    def _init(self) -> None:
+        """
+        Init objects required for backtesting
+        :return: None
+        """
+        self.analyze = Analyze(self.config)
+        self.ticker_interval = self.analyze.strategy.ticker_interval
+        self.tickerdata_to_dataframe = self.analyze.tickerdata_to_dataframe
+        self.populate_buy_trend = self.analyze.populate_buy_trend
+        self.populate_sell_trend = self.analyze.populate_sell_trend
+        exchange._API = Bittrex({'key': '', 'secret': ''})
+
+    @staticmethod
+    def get_timeframe(data: Dict[str, DataFrame]) -> Tuple[arrow.Arrow, arrow.Arrow]:
+        """
+        Get the maximum timeframe for the given backtest data
+        :param data: dictionary with preprocessed backtesting data
+        :return: tuple containing min_date, max_date
+        """
+        all_dates = Series([])
+        for pair_data in data.values():
+            all_dates = all_dates.append(pair_data['date'])
+        all_dates.sort_values(inplace=True)
+        return arrow.get(all_dates.iloc[0]), arrow.get(all_dates.iloc[-1])
+
+    def _generate_text_table(self, data: Dict[str, Dict], results: DataFrame) -> str:
+        """
+        Generates and returns a text table for the given backtest data and the results dataframe
+        :return: pretty printed table with tabulate as str
+        """
+        stake_currency = self.config.get('stake_currency')
+
+        floatfmt = ('.8f', '.8f', '.8f', '.8f', '.1f')
+        tabular_data = []
+        headers = ['total profit ' + stake_currency]
+
+        # Append Total
+        tabular_data.append([
+            'TOTAL',
+            results.profit_BTC.sum(),
+        ])
+        return tabulate(tabular_data, headers=headers, floatfmt=floatfmt)
+
+    def _get_sell_trade_entry(
+            self, pair: str, buy_row: DataFrame,
+            partial_ticker: List, trade_count_lock: Dict, args: Dict) -> Optional[Tuple]:
+
+        stake_amount = args['stake_amount']
+        max_open_trades = args.get('max_open_trades', 0)
+        trade = Trade(
+            open_rate=buy_row.close,
+            open_date=buy_row.date,
+            stake_amount=stake_amount,
+            amount=stake_amount / buy_row.open,
+            fee=exchange.get_fee()
+        )
+
+        # calculate win/lose forwards from buy point
+        for sell_row in partial_ticker:
+            if max_open_trades > 0:
+                # Increase trade_count_lock for every iteration
+                trade_count_lock[sell_row.date] = trade_count_lock.get(sell_row.date, 0) + 1
+
+            buy_signal = sell_row.buy
+            if self.analyze.should_sell(trade, sell_row.close, sell_row.date, buy_signal,
+                                        sell_row.sell):
+                return \
+                    sell_row, \
+                    (
+                        pair,
+                        trade.calc_profit_percent(rate=sell_row.close),
+                        trade.calc_profit(rate=sell_row.close),
+                        (sell_row.date - buy_row.date).seconds // 60
+                    ), \
+                    sell_row.date
+        return None
+
+    def backtest(self, args: Dict) -> DataFrame:
+        """
+        Implements backtesting functionality
+
+        NOTE: This method is used by Hyperopt at each iteration. Please keep it optimized.
+        Of course try to not have ugly code. By some accessor are sometime slower than functions.
+        Avoid, logging on this method
+
+        :param args: a dict containing:
+            stake_amount: btc amount to use for each trade
+            processed: a processed dictionary with format {pair, data}
+            max_open_trades: maximum number of concurrent trades (default: 0, disabled)
+            realistic: do we try to simulate realistic trades? (default: True)
+            sell_profit_only: sell if profit only
+            use_sell_signal: act on sell-signal
+        :return: DataFrame
+        """
+        headers = ['date', 'buy', 'open', 'close', 'sell']
+        processed = args['processed']
+        max_open_trades = args.get('max_open_trades', 0)
+        realistic = args.get('realistic', False)
+        record = args.get('record', None)
+        records = []
+        trades = []
+        trade_count_lock = {}
+        for pair, pair_data in processed.items():
+            pair_data['buy'], pair_data['sell'] = 0, 0  # cleanup from previous run
+
+            ticker_data = self.populate_sell_trend(self.populate_buy_trend(pair_data))[headers]
+            ticker = [x for x in ticker_data.itertuples()]
+
+            lock_pair_until = None
+            for index, row in enumerate(ticker):
+                if row.buy == 0 or row.sell == 1:
+                    continue  # skip rows where no buy signal or that would immediately sell off
+
+                if realistic:
+                    if lock_pair_until is not None and row.date <= lock_pair_until:
+                        continue
+                if max_open_trades > 0:
+                    # Check if max_open_trades has already been reached for the given date
+                    if not trade_count_lock.get(row.date, 0) < max_open_trades:
+                        continue
+
+                    trade_count_lock[row.date] = trade_count_lock.get(row.date, 0) + 1
+
+                ret = self._get_sell_trade_entry(pair, row, ticker[index + 1:],
+                                                 trade_count_lock, args)
+
+                if ret:
+                    row2, trade_entry, next_date = ret
+                    lock_pair_until = next_date
+                    trades.append(trade_entry)
+                    if record:
+                        # Note, need to be json.dump friendly
+                        # record a tuple of pair, current_profit_percent,
+                        # entry-date, duration
+                        records.append((pair, trade_entry[1],
+                                        row.date.strftime('%s'),
+                                        row2.date.strftime('%s'),
+                                        row.date, trade_entry[3]))
+        # For now export inside backtest(), maybe change so that backtest()
+        # returns a tuple like: (dataframe, records, logs, etc)
+        if record and record.find('trades') >= 0:
+            self.logger.info('Dumping backtest results')
+            file_dump_json('backtest-result.json', records)
+        labels = ['currency', 'profit_percent', 'profit_BTC', 'duration']
+        return DataFrame.from_records(trades, columns=labels)
+
+    def start(self) -> None:
+        """
+        Run a backtesting end-to-end
+        :return: None
+        """
+        data = {}
+        pairs = self.config['exchange']['pair_whitelist']
+        self.logger.info('Using stake_currency: %s ...', self.config['stake_currency'])
+        self.logger.info('Using stake_amount: %s ...', self.config['stake_amount'])
+
+        if self.config.get('live'):
+            self.logger.info('Downloading data for all pairs in whitelist ...')
+            for pair in pairs:
+                data[pair] = exchange.get_ticker_history(pair, self.ticker_interval)
+        else:
+            self.logger.info('Using local backtesting data (using whitelist in given config) ...')
+
+            timerange = Arguments.parse_timerange(self.config.get('timerange'))
+            data = optimize.load_data(
+                self.config['datadir'],
+                pairs=pairs,
+                ticker_interval=self.ticker_interval,
+                refresh_pairs=self.config.get('refresh_pairs', False),
+                timerange=timerange
+            )
+
+        # Ignore max_open_trades in backtesting, except realistic flag was passed
+        if self.config.get('realistic_simulation', False):
+            max_open_trades = self.config['max_open_trades']
+        else:
+            self.logger.info('Ignoring max_open_trades (realistic_simulation not set) ...')
+            max_open_trades = 0
+
+        preprocessed = self.tickerdata_to_dataframe(data)
+
+        # Print timeframe
+        min_date, max_date = self.get_timeframe(preprocessed)
+        self.logger.info(
+            'Measuring data from %s up to %s (%s days)..',
+            min_date.isoformat(),
+            max_date.isoformat(),
+            (max_date - min_date).days
+        )
+
+        # Execute backtest and print results
+        sell_profit_only = self.config.get('experimental', {}).get('sell_profit_only', False)
+        use_sell_signal = self.config.get('experimental', {}).get('use_sell_signal', False)
+        results = self.backtest(
+            {
+                'stake_amount': self.config.get('stake_amount'),
+                'processed': preprocessed,
+                'max_open_trades': max_open_trades,
+                'realistic': self.config.get('realistic_simulation', False),
+                'sell_profit_only': sell_profit_only,
+                'use_sell_signal': use_sell_signal,
+                'record': self.config.get('export')
+            }
+        )
+
+        self.logging.set_format('%(message)s')
+        self.logger.info(
+            '\n==================================== '
+            'BACKTESTING REPORT'
+            ' ====================================\n'
+            '%s',
+            self._generate_text_table(
+                data,
+                results
+            )
+        )
+        time.sleep(2)
+        os.close(1)
+def setup_configuration(args: Namespace) -> Dict[str, Any]:
+    """
+    Prepare the configuration for the backtesting
+    :param args: Cli args from Arguments()
+    :return: Configuration
+    """
+    configuration = Configuration(args)
+    config = configuration.get_config()
+
+    # Ensure we do not use Exchange credentials
+    config['exchange']['key'] = ''
+    config['exchange']['secret'] = ''
+
+    return config
+
+
+def start(args: Namespace) -> None:
+    """
+    Start Backtesting script
+    :param args: Cli args from Arguments()
+    :return: None
+    """
+
+    # Initialize logger
+    logger = Logger(name=__name__).get_logger()
+    logger.info('Starting freqtrade in Backtesting mode')
+
+    # Initialize configuration
+    config = setup_configuration(args)
+
+    # Initialize backtesting object
+    backtesting = Backtesting(config)
+    backtesting.start()

user_data/random/default_strategy.py

@@ -0,0 +1,342 @@
+
+# --- Do not remove these libs ---
+from freqtrade.strategy.interface import IStrategy
+from typing import Dict, List
+from hyperopt import hp
+from functools import reduce
+from pandas import DataFrame
+# --------------------------------
+
+# Add your lib to import here
+import talib.abstract as ta
+import freqtrade.vendor.qtpylib.indicators as qtpylib
+import numpy # noqa
+
+import random
+
+# Update this variable if you change the class name
+class_name = 'DefaultStrategy'
+
+
+# This class is a sample. Feel free to customize it.
+
+
+
+
+def Select():
+    param = []
+    random_items = []
+    param.append(str('[' + 'uptrend_long_ema' + '[' + 'enabled' + ']'))
+    param.append(str('[' + 'macd_below_zero' +  '][' + 'enabled' + ']'))
+    param.append(str('[' + 'uptrend_short_ema' '][' + 'enabled'+ ']'))
+    param.append(str('[' + 'mfi' '][' + 'enabled'+ ']'))
+    param.append(str('[' + 'fastd' '][' + 'enabled'+ ']'))
+    param.append(str('[' + 'adx' '][' + 'enabled'+ ']'))
+    param.append(str('[' + 'rsi' '][' + 'enabled'+ ']'))
+    param.append(str('[' + 'over_sar' '][' + 'enabled'+ ']'))
+    param.append(str('[' + 'green_candle' '][' + 'enabled'+ ']'))
+    param.append(str('[' + 'uptrend_sma' '][' + 'enabled'+ ']'))
+    param.append(str('[' + 'closebb' '][' + 'enabled'+ ']'))
+    param.append(str('[' + 'temabb' '][' + 'enabled'+ ']'))
+    param.append(str('[' + 'fastdt' '][' + 'enabled'+ ']'))
+    param.append(str('[' + 'ao' '][' + 'enabled'+ ']'))
+    param.append(str('[' + 'ema3' '][' + 'enabled'+ ']'))
+    param.append(str('[' + 'macd' '][' + 'enabled'+ ']'))
+    param.append(str('[' + 'closesar' '][' + 'enabled'+ ']'))
+    param.append(str('[' + 'htsine' '][' + 'enabled'+ ']'))
+    param.append(str('[' + 'has' '][' + 'enabled'+ ']'))
+    param.append(str('[' + 'plusdi' '][' + 'enabled'+ ']'))
+    howmany = random.randint(1,20)
+    random_items = random.choices(population=param, k=howmany)
+    print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~')
+    print('The Parameters Enabled Are As Follows!!!:  ' + str(random_items))
+    print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~')
+    return random_items
+
+
+
+
+class DefaultStrategy(IStrategy):
+    """
+    This is a test strategy to inspire you.
+    More information in https://github.com/gcarq/freqtrade/blob/develop/docs/bot-optimization.md
+
+    You can:
+    - Rename the class name (Do not forget to update class_name)
+    - Add any methods you want to build your strategy
+    - Add any lib you need to build your strategy
+
+    You must keep:
+    - the lib in the section "Do not remove these libs"
+    - the prototype for the methods: minimal_roi, stoploss, populate_indicators, populate_buy_trend,
+    populate_sell_trend, hyperopt_space, buy_strategy_generator
+    """
+
+    # Minimal ROI designed for the strategy.
+    # This attribute will be overridden if the config file contains "minimal_roi"
+    minimal_roi = {
+        "40": 0.0,
+        "30": 0.01,
+        "20": 0.02,
+        "0": 0.04
+    }
+
+    ticker_interval = 5
+
+    # Optimal stoploss designed for the strategy
+    # This attribute will be overridden if the config file contains "stoploss"
+    stoploss = -0.10
+
+    def populate_indicators(self, dataframe: DataFrame) -> 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.
+        """
+
+        # Momentum Indicator
+        # ------------------------------------
+
+        # ADX
+        dataframe['adx'] = ta.ADX(dataframe)
+
+
+        # Awesome oscillator
+        dataframe['ao'] = qtpylib.awesome_oscillator(dataframe)
+
+        # Commodity Channel Index: values Oversold:<-100, Overbought:>100
+        dataframe['cci'] = ta.CCI(dataframe)
+
+        # MACD
+        macd = ta.MACD(dataframe)
+        dataframe['macd'] = macd['macd']
+        dataframe['macdsignal'] = macd['macdsignal']
+        dataframe['macdhist'] = macd['macdhist']
+
+        # MFI
+        dataframe['mfi'] = ta.MFI(dataframe)
+
+        # Minus Directional Indicator / Movement
+        dataframe['minus_dm'] = ta.MINUS_DM(dataframe)
+        dataframe['minus_di'] = ta.MINUS_DI(dataframe)
+
+        # Plus Directional Indicator / Movement
+        dataframe['plus_dm'] = ta.PLUS_DM(dataframe)
+        dataframe['plus_di'] = ta.PLUS_DI(dataframe)
+        dataframe['minus_di'] = ta.MINUS_DI(dataframe)
+
+        # ROC
+        dataframe['roc'] = ta.ROC(dataframe)
+
+        # RSI
+        dataframe['rsi'] = ta.RSI(dataframe)
+
+        # Inverse Fisher transform on RSI, values [-1.0, 1.0] (https://goo.gl/2JGGoy)
+        rsi = 0.1 * (dataframe['rsi'] - 50)
+        dataframe['fisher_rsi'] = (numpy.exp(2 * rsi) - 1) / (numpy.exp(2 * rsi) + 1)
+
+        # Inverse Fisher transform on RSI normalized, value [0.0, 100.0] (https://goo.gl/2JGGoy)
+        dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1)
+
+        # Stoch
+        stoch = ta.STOCH(dataframe)
+        dataframe['slowd'] = stoch['slowd']
+        dataframe['slowk'] = stoch['slowk']
+
+        # Stoch fast
+        stoch_fast = ta.STOCHF(dataframe)
+        dataframe['fastd'] = stoch_fast['fastd']
+        dataframe['fastk'] = stoch_fast['fastk']
+
+        # Stoch RSI
+        stoch_rsi = ta.STOCHRSI(dataframe)
+        dataframe['fastd_rsi'] = stoch_rsi['fastd']
+        dataframe['fastk_rsi'] = stoch_rsi['fastk']
+
+
+        # Overlap Studies
+        # ------------------------------------
+
+        """
+        # Previous Bollinger bands
+        # Because ta.BBANDS implementation is broken with small numbers, it actually
+        # returns middle band for all the three bands. Switch to qtpylib.bollinger_bands
+        # and use middle band instead.
+
+        dataframe['blower'] = ta.BBANDS(dataframe, nbdevup=2, nbdevdn=2)['lowerband']
+        """
+
+        # Bollinger bands
+        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']
+
+
+        # EMA - Exponential Moving Average
+        dataframe['ema3'] = ta.EMA(dataframe, timeperiod=3)
+        dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5)
+        dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
+        dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
+        dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)
+
+        # SAR Parabol
+        dataframe['sar'] = ta.SAR(dataframe)
+
+        # SMA - Simple Moving Average
+        dataframe['sma'] = ta.SMA(dataframe, timeperiod=40)
+
+
+        # TEMA - Triple Exponential Moving Average
+        dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
+
+        # Cycle Indicator
+        # ------------------------------------
+        # Hilbert Transform Indicator - SineWave
+        hilbert = ta.HT_SINE(dataframe)
+        dataframe['htsine'] = hilbert['sine']
+        dataframe['htleadsine'] = hilbert['leadsine']
+
+        # Pattern Recognition - Bullish candlestick patterns
+        # ------------------------------------
+
+        # Hammer: values [0, 100]
+        dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe)
+        # Inverted Hammer: values [0, 100]
+        dataframe['CDLINVERTEDHAMMER'] = ta.CDLINVERTEDHAMMER(dataframe)
+        # Dragonfly Doji: values [0, 100]
+        dataframe['CDLDRAGONFLYDOJI'] = ta.CDLDRAGONFLYDOJI(dataframe)
+        # Piercing Line: values [0, 100]
+        dataframe['CDLPIERCING'] = ta.CDLPIERCING(dataframe) # values [0, 100]
+        # Morningstar: values [0, 100]
+        dataframe['CDLMORNINGSTAR'] = ta.CDLMORNINGSTAR(dataframe) # values [0, 100]
+        # Three White Soldiers: values [0, 100]
+        dataframe['CDL3WHITESOLDIERS'] = ta.CDL3WHITESOLDIERS(dataframe) # values [0, 100]
+
+
+        # Pattern Recognition - Bearish candlestick patterns
+        # ------------------------------------
+
+        # Hanging Man: values [0, 100]
+        dataframe['CDLHANGINGMAN'] = ta.CDLHANGINGMAN(dataframe)
+        # Shooting Star: values [0, 100]
+        dataframe['CDLSHOOTINGSTAR'] = ta.CDLSHOOTINGSTAR(dataframe)
+        # Gravestone Doji: values [0, 100]
+        dataframe['CDLGRAVESTONEDOJI'] = ta.CDLGRAVESTONEDOJI(dataframe)
+        # Dark Cloud Cover: values [0, 100]
+        dataframe['CDLDARKCLOUDCOVER'] = ta.CDLDARKCLOUDCOVER(dataframe)
+        # Evening Doji Star: values [0, 100]
+        dataframe['CDLEVENINGDOJISTAR'] = ta.CDLEVENINGDOJISTAR(dataframe)
+        # Evening Star: values [0, 100]
+        dataframe['CDLEVENINGSTAR'] = ta.CDLEVENINGSTAR(dataframe)
+
+
+        # Pattern Recognition - Bullish/Bearish candlestick patterns
+        # ------------------------------------
+
+        # Three Line Strike: values [0, -100, 100]
+        dataframe['CDL3LINESTRIKE'] = ta.CDL3LINESTRIKE(dataframe)
+        # Spinning Top: values [0, -100, 100]
+        dataframe['CDLSPINNINGTOP'] = ta.CDLSPINNINGTOP(dataframe) # values [0, -100, 100]
+        # Engulfing: values [0, -100, 100]
+        dataframe['CDLENGULFING'] = ta.CDLENGULFING(dataframe) # values [0, -100, 100]
+        # Harami: values [0, -100, 100]
+        dataframe['CDLHARAMI'] = ta.CDLHARAMI(dataframe) # values [0, -100, 100]
+        # Three Outside Up/Down: values [0, -100, 100]
+        dataframe['CDL3OUTSIDE'] = ta.CDL3OUTSIDE(dataframe) # values [0, -100, 100]
+        # Three Inside Up/Down: values [0, -100, 100]
+        dataframe['CDL3INSIDE'] = ta.CDL3INSIDE(dataframe) # values [0, -100, 100]
+
+
+        # Chart type
+        # ------------------------------------
+
+        # Heikinashi stategy
+        heikinashi = qtpylib.heikinashi(dataframe)
+        dataframe['ha_open'] = heikinashi['open']
+        dataframe['ha_close'] = heikinashi['close']
+        dataframe['ha_high'] = heikinashi['high']
+        dataframe['ha_low'] = heikinashi['low']
+
+
+        return dataframe
+
+    params = Select()
+    valm = random.randint(1,100)
+    print('MFI Value  :' + str(valm) + ' XXX')
+    valfast = random.randint(1,100)
+    print('FASTD Value  :' + str(valfast) + ' XXX')
+    valadx = random.randint(1,100)
+    print('ADX Value  :' + str(valadx) + ' XXX')
+    valrsi = random.randint(1,100)
+    print('RSI Value  :' + str(valrsi) + ' XXX')
+    def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:
+
+        conditions = []
+        # GUARDS AND TRENDS
+        if 'uptrend_long_ema' in str(self.params):
+            conditions.append(dataframe['ema50'] > dataframe['ema100'])
+        if 'macd_below_zero' in str(self.params):
+            conditions.append(dataframe['macd'] < 0)
+        if 'uptrend_short_ema' in str(self.params):
+            conditions.append(dataframe['ema5'] > dataframe['ema10'])
+        if 'mfi' in str(self.params):
+
+            conditions.append(dataframe['mfi'] < self.valm)
+        if 'fastd' in str(self.params):
+
+            conditions.append(dataframe['fastd'] < self.valfast)
+        if 'adx' in str(self.params):
+
+            conditions.append(dataframe['adx'] > self.valadx)
+        if 'rsi' in str(self.params):
+
+            conditions.append(dataframe['rsi'] < self.valrsi)
+        if 'over_sar' in str(self.params):
+            conditions.append(dataframe['close'] > dataframe['sar'])
+        if 'green_candle' in str(self.params):
+            conditions.append(dataframe['close'] > dataframe['open'])
+        if 'uptrend_sma' in str(self.params):
+            prevsma = dataframe['sma'].shift(1)
+            conditions.append(dataframe['sma'] > prevsma)
+        if 'closebb' in str(self.params):
+            conditions.append(dataframe['close'] < dataframe['bb_lowerband'])
+        if 'temabb' in str(self.params):
+            conditions.append(dataframe['tema'] < dataframe['bb_lowerband'])
+        if 'fastdt' in str(self.params):
+            conditions.append(qtpylib.crossed_above(dataframe['fastd'], 10.0))
+        if 'ao' in str(self.params):
+            conditions.append(qtpylib.crossed_above(dataframe['ao'], 0.0))
+        if 'ema3' in str(self.params):
+            conditions.append(qtpylib.crossed_above(dataframe['ema3'], dataframe['ema10']))
+        if 'macd' in str(self.params):
+            conditions.append(qtpylib.crossed_above(dataframe['macd'], dataframe['macdsignal']))
+        if 'closesar' in str(self.params):
+            conditions.append(qtpylib.crossed_above(dataframe['close'], dataframe['sar']))
+        if 'htsine' in str(self.params):
+            conditions.append(qtpylib.crossed_above(dataframe['htleadsine'], dataframe['htsine']))
+        if 'has' in str(self.params):
+            conditions.append((qtpylib.crossed_above(dataframe['ha_close'], dataframe['ha_open'])) & (dataframe['ha_low'] == dataframe['ha_open']))
+        if 'plusdi' in str(self.params):
+            conditions.append(qtpylib.crossed_above(dataframe['plus_di'], dataframe['minus_di']))
+
+        dataframe.loc[
+            reduce(lambda x, y: x & y, conditions),
+            'buy'] = 1
+
+        return dataframe
+
+    def populate_sell_trend(self, dataframe: DataFrame) -> DataFrame:
+        """
+        Based on TA indicators, populates the sell signal for the given dataframe
+        :param dataframe: DataFrame
+        :return: DataFrame with buy column
+        """
+        dataframe.loc[
+            (
+            ),
+            'sell'] = 1
+        return dataframe
+

user_data/random/random.py

@@ -0,0 +1,68 @@
+#!/usr/bin/env python3
+import os
+import multiprocessing
+from itertools import zip_longest
+import subprocess
+import re
+PROC_COUNT = multiprocessing.cpu_count() - 1
+cwd = os.getcwd()
+print(cwd)
+global procs
+import time
+limit = 24
+WORK_DIR = os.path.join(
+    os.path.sep,
+    os.path.abspath(os.path.dirname(__file__)),
+    '..', 'freqtrade', 'main.py'
+)
+
+# Spawn workers
+command = [
+    'python3.6',
+    '-u',
+     WORK_DIR,
+    'backtesting',
+]
+global current
+current = 0
+procs = 0
+DEVNULL = open(os.devnull, 'wb')
+
+while True:
+    while procs < 32:
+        try:
+            procs + 1
+            proc = subprocess.Popen(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, bufsize=1, universal_newlines=True)
+            data = proc.communicate()
+            string = str(data)
+            params = re.search(r'~~~~(.*)~~~~', string).group(1)
+            mfi = re.search(r'MFI Value(.*)XXX', string)
+            fastd = re.search(r'FASTD Value(.*)XXX', string)
+            adx = re.search(r'ADX Value(.*)XXX', string)
+            rsi = re.search(r'RSI Value(.*)XXX', string)
+            tot = re.search(r'TOTAL(.*)', string).group(1)
+            total = re.search(r'[-+]?([0-9]*\.[0-9]+|[0-9]+)', tot).group(1)
+            if total and (float(total) > float(current)):
+                current = total
+                print('total better profit paremeters:  ')
+                print(total)
+                if params:
+                    print(params)
+                    print('~~~~~~')
+                    print('Only enable the above settings, not all settings below are used!')
+                    print('~~~~~~')
+                if mfi:
+                    print('~~~MFI~~~')
+                    print(mfi.group(1))
+                if fastd:
+                    print('~~~FASTD~~~')
+                    print(fastd.group(1))
+                if adx:
+                    print('~~~ADX~~~')
+                    print(adx.group(1))
+                if rsi:
+                    print('~~~RSI~~~')
+                    print(rsi.group(1))
+            procs - 1
+        except Exception as e:
+            print(e)