Merge 506b11162d into 1cec06f808

parallel/README.md

@@ -0,0 +1,150 @@
+1. install parallel:
+
+```unzip pp-1.6.4.4.zip```
+
+```cd pp-1.6.4.4```
+
+```python3.6 setup.py install```
+
+2. Move Generator.py into the parent folder, or your "main freqtrade folder."
+
+```mv Generator.py ../```
+
+3. Move the default strategy over the default strategy in freqtrade/strategies folder.
+
+4. Move backtesting.py over the backtesting.py in freqtrade/optimize folder.
+
+5. Optinlally install modded bittrex.py
+
+6. Install dependencies:
+
+```
+sudo add-apt-repository ppa:jonathonf/python-3.6
+
+sudo apt-get update
+
+sudo apt-get install python3.6 python3.6-venv python3.6-dev build-essential autoconf libtool pkg-config make wget git
+
+```
+
+
+7. Install ta-lib:
+
+```
+wget http://prdownloads.sourceforge.net/ta-lib/ta-lib-0.4.0-src.tar.gz
+
+tar xvzf ta-lib-0.4.0-src.tar.gz
+
+cd ta-lib
+
+./configure --prefix=/usr
+
+make
+
+make install
+
+cd ..
+
+rm -rf ./ta-lib*
+
+```
+
+8. Install freqtrade:
+
+```
+cd ~/freqtrade && pip3.6 install -r requirements.txt && python3.6 setup.py install && pip3.6 install -e .
+```
+
+9. Run generator.py:
+
+```python3.6 generator.py```
+
+10. Wait for results.
+
+11. Implement these results into your default_strategy:
+
+***
+You will need to read the if statements and populate_buy_signal and populate_sell_signal in this file carefully.
+
+Once implemented, remove the if statements in the populate_buy_trend.
+
+***
+
+For ease of use, here is an example:
+
+The if statements that run the random generator are:
+
+```
+    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
+```
+
+
+So of you get MFI as a option runnning generator.py, and it's option in output is 91, look at the if statements above at mfi, the populate_buy_trend will now look like this:
+
+
+```
+    def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:
+        dataframe.loc[
+            (
+            (dataframe['mfi'] < 91)
+            ),
+            'buy'] = 1
+
+        return dataframe
+```
+
+It's as simple as that.

parallel/backtesting.py

@@ -0,0 +1,288 @@
+# pragma pylint: disable=missing-docstring, W0212, too-many-arguments
+
+"""
+This module contains the backtesting logic
+"""
+import logging
+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.misc import file_dump_json
+from freqtrade.persistence import Trade
+
+
+logger = logging.getLogger(__name__)
+
+
+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:
+        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', '.8f')
+        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:
+            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']
+        logger.info('Using stake_currency: %s ...', self.config['stake_currency'])
+        logger.info('Using stake_amount: %s ...', self.config['stake_amount'])
+
+        if self.config.get('live'):
+            logger.info('Downloading data for all pairs in whitelist ...')
+            for pair in pairs:
+                data[pair] = exchange.get_ticker_history(pair, self.ticker_interval)
+        else:
+            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:
+            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)
+        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')
+            }
+        )
+        print(
+            '\n==================================== '
+            'BACKTESTING REPORT'
+            ' ====================================\n'
+            '%s',
+            self._generate_text_table(
+                data,
+                results
+            )
+        )
+
+
+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 configuration
+    config = setup_configuration(args)
+    logger.info('Starting freqtrade in Backtesting mode')
+
+    # Initialize backtesting object
+    backtesting = Backtesting(config)
+    backtesting.start()

parallel/bittrex.py

@@ -0,0 +1,241 @@
+import logging
+from typing import Dict, List, Optional
+
+from bittrex.bittrex import API_V1_1, API_V2_0
+from bittrex.bittrex import Bittrex as _Bittrex
+from requests.exceptions import ContentDecodingError
+
+from freqtrade import OperationalException
+from freqtrade.exchange.interface import Exchange
+
+logger = logging.getLogger(__name__)
+
+_API: _Bittrex = None
+_API_V2: _Bittrex = None
+_EXCHANGE_CONF: dict = {}
+
+
+class Bittrex(Exchange):
+    """
+    Bittrex API wrapper.
+    """
+    # Base URL and API endpoints
+    BASE_URL: str = 'https://www.bittrex.com'
+    PAIR_DETAIL_METHOD: str = BASE_URL + '/Market/Index'
+
+    def __init__(self, config: dict) -> None:
+        global _API, _API_V2, _EXCHANGE_CONF
+
+        _EXCHANGE_CONF.update(config)
+        _API = _Bittrex(
+            api_key=_EXCHANGE_CONF['key'],
+            api_secret=_EXCHANGE_CONF['secret'],
+            calls_per_second=1,
+            api_version=API_V1_1,
+        )
+        _API_V2 = _Bittrex(
+            api_key=_EXCHANGE_CONF['key'],
+            api_secret=_EXCHANGE_CONF['secret'],
+            calls_per_second=1,
+            api_version=API_V2_0,
+        )
+        self.cached_ticker = {}
+
+    @staticmethod
+    def _validate_response(response) -> None:
+        """
+        Validates the given bittrex response
+        and raises a ContentDecodingError if a non-fatal issue happened.
+        """
+        temp_error_messages = [
+            'NO_API_RESPONSE',
+            'MIN_TRADE_REQUIREMENT_NOT_MET',
+        ]
+        if response['message'] in temp_error_messages:
+            raise ContentDecodingError(response['message'])
+
+    @property
+    def fee(self) -> float:
+        # 0.25 %: See https://bittrex.com/fees
+        return 0.0025
+
+    def buy(self, pair: str, rate: float, amount: float) -> str:
+        data = _API.buy_limit(pair.replace('_', '-'), amount, rate)
+        if not data['success']:
+            if 'APIKEY_INVALID' in str(data['message']):
+                print('Api Key...')
+            else:
+                Bittrex._validate_response(data)
+                raise OperationalException('{message} params=({pair}, {rate}, {amount})'.format(
+                    message=data['message'],
+                    pair=pair,
+                    rate=rate,
+                    amount=amount))
+        return data['result']['uuid']
+
+    def sell(self, pair: str, rate: float, amount: float) -> str:
+        data = _API.sell_limit(pair.replace('_', '-'), amount, rate)
+        if not data['success']:
+            if 'APIKEY_INVALID' in str(data['message']):
+                print('Api Key...')
+            else:
+                Bittrex._validate_response(data)
+                raise OperationalException('{message} params=({pair}, {rate}, {amount})'.format(
+                    message=data['message'],
+                    pair=pair,
+                    rate=rate,
+                    amount=amount))
+        return data['result']['uuid']
+
+    def get_balance(self, currency: str) -> float:
+        data = _API.get_balance(currency)
+        if not data['success']:
+            if 'APIKEY_INVALID' in str(data['message']):
+                print('Api Key...')
+            else:
+                Bittrex._validate_response(data)
+                raise OperationalException('{message} params=({currency})'.format(
+                    message=data['message'],
+                    currency=currency))
+        return float(data['result']['Balance'] or 0.0)
+
+    def get_balances(self):
+        data = _API.get_balances()
+        if not data['success']:
+            if 'APIKEY_INVALID' in str(data['message']):
+                print('Api Key...')
+            else:
+                Bittrex._validate_response(data)
+                raise OperationalException('{message}'.format(message=data['message']))
+        return data['result']
+
+    def get_ticker(self, pair: str, refresh: Optional[bool] = True) -> dict:
+        if refresh or pair not in self.cached_ticker.keys():
+            data = _API.get_ticker(pair.replace('_', '-'))
+            if not data['success']:
+                if 'APIKEY_INVALID' in str(data['message']):
+                    print('Api Key...')
+                else:
+                    Bittrex._validate_response(data)
+                    raise OperationalException('{message} params=({pair})'.format(
+                        message=data['message'],
+                        pair=pair))
+            keys = ['Bid', 'Ask', 'Last']
+            if not data.get('result') or\
+                    not all(key in data.get('result', {}) for key in keys) or\
+                    not all(data.get('result', {})[key] is not None for key in keys):
+                raise ContentDecodingError('Invalid response from Bittrex params=({pair})'.format(
+                    pair=pair))
+            # Update the pair
+            self.cached_ticker[pair] = {
+                'bid': float(data['result']['Bid']),
+                'ask': float(data['result']['Ask']),
+                'last': float(data['result']['Last']),
+            }
+        return self.cached_ticker[pair]
+
+    def get_ticker_history(self, pair: str, tick_interval: int) -> List[Dict]:
+        if tick_interval == 1:
+            interval = 'oneMin'
+        elif tick_interval == 5:
+            interval = 'fiveMin'
+        elif tick_interval == 30:
+            interval = 'thirtyMin'
+        elif tick_interval == 60:
+            interval = 'hour'
+        elif tick_interval == 1440:
+            interval = 'Day'
+        else:
+            raise ValueError('Unknown tick_interval: {}'.format(tick_interval))
+
+        data = _API_V2.get_candles(pair.replace('_', '-'), interval)
+
+        # These sanity check are necessary because bittrex cannot keep their API stable.
+        if not data.get('result'):
+            raise ContentDecodingError('Invalid response from Bittrex params=({pair})'.format(
+                pair=pair))
+
+        for prop in ['C', 'V', 'O', 'H', 'L', 'T']:
+            for tick in data['result']:
+                if prop not in tick.keys():
+                    raise ContentDecodingError('Required property {} not present '
+                                               'in response params=({})'.format(prop, pair))
+
+        if not data['success']:
+            if 'APIKEY_INVALID' in str(data['message']):
+                print('Api Key...')
+            else:
+                Bittrex._validate_response(data)
+                raise OperationalException('{message} params=({pair})'.format(
+                    message=data['message'],
+                    pair=pair))
+
+        return data['result']
+
+    def get_order(self, order_id: str) -> Dict:
+        data = _API.get_order(order_id)
+        if not data['success']:
+            Bittrex._validate_response(data)
+            raise OperationalException('{message} params=({order_id})'.format(
+                message=data['message'],
+                order_id=order_id))
+        data = data['result']
+        return {
+            'id': data['OrderUuid'],
+            'type': data['Type'],
+            'pair': data['Exchange'].replace('-', '_'),
+            'opened': data['Opened'],
+            'rate': data['PricePerUnit'],
+            'amount': data['Quantity'],
+            'remaining': data['QuantityRemaining'],
+            'closed': data['Closed'],
+        }
+
+    def cancel_order(self, order_id: str) -> None:
+        data = _API.cancel(order_id)
+        if not data['success']:
+            if 'APIKEY_INVALID' in str(data['message']):
+                print('Api Key...')
+            else:
+                Bittrex._validate_response(data)
+                raise OperationalException('{message} params=({order_id})'.format(
+                    message=data['message'],
+                    order_id=order_id))
+
+    def get_pair_detail_url(self, pair: str) -> str:
+        return self.PAIR_DETAIL_METHOD + '?MarketName={}'.format(pair.replace('_', '-'))
+
+    def get_markets(self) -> List[str]:
+        data = _API.get_markets()
+        if not data['success']:
+            if 'APIKEY_INVALID' in str(data['message']):
+                print('Api Key...')
+            else:
+                Bittrex._validate_response(data)
+                raise OperationalException(data['message'])
+        return [m['MarketName'].replace('-', '_') for m in data['result']]
+
+    def get_market_summaries(self) -> List[Dict]:
+        data = _API.get_market_summaries()
+        if not data['success']:
+            if 'APIKEY_INVALID' in str(data['message']):
+                print('Api Key...')
+            else:
+                Bittrex._validate_response(data)
+                raise OperationalException(data['message'])
+        return data['result']
+
+    def get_wallet_health(self) -> List[Dict]:
+        data = _API_V2.get_wallet_health()
+        if not data['success']:
+            if 'APIKEY_INVALID' in str(data['message']):
+                print('Api Key...')
+            else:
+                Bittrex._validate_response(data)
+                raise OperationalException(data['message'])
+        return [{
+            'Currency': entry['Health']['Currency'],
+            'IsActive': entry['Health']['IsActive'],
+            'LastChecked': entry['Health']['LastChecked'],
+            'Notice': entry['Currency'].get('Notice'),
+        } for entry in data['result']]

parallel/config.json

@@ -0,0 +1,56 @@
+{
+    "max_open_trades": 3,
+    "stake_currency": "BTC",
+    "stake_amount": 0.00075,
+    "fiat_display_currency": "USD",
+    "dry_run": false,
+    "unfilledtimeout": 600,
+    "ticker_interval": 5,
+    "bid_strategy": {
+        "ask_last_balance": 0.0
+    },
+    "minimal_roi": {
+        "35":  0.000,
+        "30":  0.005,
+        "25":  0.006,
+        "20":  0.007,
+        "15":  0.008,
+        "10":  0.009,
+        "5":  0.01,
+        "0":  0.015
+    },
+    "stoploss": -0.10,
+    "exchange": {
+        "name": "bittrex",
+        "key": "your_exchange_key",
+        "secret": "your_exchange_secret",
+        "pair_whitelist": [
+            "BTC_ETH",
+            "BTC_LTC",
+            "BTC_ETC",
+            "BTC_DASH",
+            "BTC_ZEC",
+            "BTC_XLM",
+            "BTC_NXT",
+            "BTC_POWR",
+            "BTC_ADA",
+            "BTC_XMR"
+        ],
+        "pair_blacklist": [
+            "BTC_DOGE"
+        ]
+    },
+    "experimental": {
+        "use_sell_signal": false,
+        "sell_profit_only": false
+    },
+    "telegram": {
+        "enabled": true,
+        "token": "your_telegram_token",
+        "chat_id": "your_telegram_chat_id"
+    },
+    "initial_state": "running",
+    "internals": {
+        "process_throttle_secs": 5
+    }
+}

parallel/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
+

parallel/generator.py

@@ -0,0 +1,102 @@
+#!/usr/bin/env python3
+import math
+import sys
+import os
+import time
+import pp
+import math
+import re
+# tuple of all parallel python servers to connect with
+ppservers = ()
+# ppservers = ("10.0.0.1",)
+
+# Number of jobs to run
+work = 32
+cycles = 100
+
+jobs = []
+current = 0
+
+
+def backtesting(ind):
+    er1 = str(ind)
+    ou1 = str(ind * 1024)
+    import threading
+    from io import StringIO
+    from freqtrade.main import main, set_loggers
+    old_stdout = sys.stdout
+    old_stderr = sys.stderr
+    ind1 = sys.stdout = StringIO()
+    ind2 = sys.stderr = StringIO()
+    dat = threading.Thread(target=main(['backtesting']))
+    dat.start()
+    dat.join()
+    er1 = ind2.getvalue()
+    ou1 = ind1.getvalue()
+    sys.stdout = old_stdout
+    sys.stderr = old_stderr
+    return er1, ou1
+
+if len(sys.argv) > 1:
+    ncpus = int(sys.argv[1])
+    # Creates jobserver with ncpus workers
+    job_server = pp.Server(ncpus, ppservers=ppservers)
+else:
+    # Creates jobserver with automatically detected number of workers
+    job_server = pp.Server(ppservers=ppservers)
+
+print("Starting pp with", job_server.get_ncpus(), "workers")
+
+
+start_time = time.time()
+index = 1
+indey = 1
+print('Please wait... sending jobs to server')
+while cycles > index:
+    cycles += 1
+    while work > indey:
+        jobs.append(job_server.submit(backtesting, (index,)))
+        indey += 1
+    job_server.wait()
+    print('Searching this cycle....')
+    for job in jobs:
+        try:
+            res = job()
+            string = str(res)
+            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         (.*)\\n', string).group(1)
+            total = float(tot)
+            if total and (float(total) > float(current)):
+                current = total
+                print('total better profit paremeters:  ')
+                print(format(total, '.8f'))
+                if params:
+                    print(params)
+                    print('~~~~~~')
+                    print('Only enable the above settings! Not all below!')
+                    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))
+                print("Time elapsed: ", time.time() - start_time, "s")
+                job_server.print_stats()
+        except:
+            pass
+    jobs = []
+    indey = 1
+print('DONE')
+print("Time elapsed: ", time.time() - start_time, "s")
+job_server.print_stats()