Merge pull request #361 from kryofly/backtest-export

Backtest export

docs/backtesting.md

@@ -51,6 +51,11 @@ python3 ./freqtrade/main.py backtesting --realistic-simulation --live
 python3 ./freqtrade/main.py backtesting --datadir freqtrade/tests/testdata-20180101
 ```
 
+**Exporting trades to file**
+```bash
+freqtrade backtesting --export trades
+```
+
 **Running backtest with smaller testset**  
 Use the `--timerange` argument to change how much of the testset
 you want to use. The last N ticks/timeframes will be used.

freqtrade/misc.py

@@ -191,6 +191,14 @@ def build_subcommands(parser: argparse.ArgumentParser) -> None:
         action='store_true',
         dest='refresh_pairs',
     )
+    backtesting_cmd.add_argument(
+        '--export',
+        help='Export backtest results, argument are: trades\
+              Example --export trades',
+        type=str,
+        default=None,
+        dest='export',
+    )
     backtesting_cmd.add_argument(
         '--timerange',
         help='Specify what timerange of data to use.',

freqtrade/optimize/__init__.py

@@ -8,6 +8,7 @@ from pandas import DataFrame
 from freqtrade.exchange import get_ticker_history
 from freqtrade.optimize.hyperopt_conf import hyperopt_optimize_conf
 from freqtrade.analyze import populate_indicators, parse_ticker_dataframe
+from freqtrade import misc
 
 logger = logging.getLogger(__name__)
 
@@ -149,7 +150,6 @@ def download_backtesting_testdata(datadir: str, pair: str, interval: int = 5) ->
     logger.debug("New End: {}".format(data[-1:][0]['T']))
     data = sorted(data, key=lambda data: data['T'])
 
-    with open(filename, "wt") as fp:
+    misc.file_dump_json(filename, data)
-        json.dump(data, fp)
 
     return True

freqtrade/optimize/backtesting.py

@@ -66,17 +66,60 @@ def generate_text_table(
     return tabulate(tabular_data, headers=headers, floatfmt=floatfmt)
 
 
-def backtest(stake_amount: float, processed: Dict[str, DataFrame],
+def get_trade_entry(pair, row, ticker, trade_count_lock, args):
-             max_open_trades: int = 0, realistic: bool = True, sell_profit_only: bool = False,
+    stake_amount = args['stake_amount']
-             stoploss: int = -1.00, use_sell_signal: bool = False) -> DataFrame:
+    max_open_trades = args.get('max_open_trades', 0)
+    sell_profit_only = args.get('sell_profit_only', False)
+    stoploss = args.get('stoploss', -1)
+    use_sell_signal = args.get('use_sell_signal', False)
+    trade = Trade(open_rate=row.close,
+                  open_date=row.date,
+                  stake_amount=stake_amount,
+                  amount=stake_amount / row.open,
+                  fee=exchange.get_fee()
+                  )
+
+    # calculate win/lose forwards from buy point
+    sell_subset = ticker[row.Index + 1:][['close', 'date', 'sell']]
+    for row2 in sell_subset.itertuples(index=True):
+        if max_open_trades > 0:
+            # Increase trade_count_lock for every iteration
+            trade_count_lock[row2.date] = trade_count_lock.get(row2.date, 0) + 1
+
+        current_profit_percent = trade.calc_profit_percent(rate=row2.close)
+        if (sell_profit_only and current_profit_percent < 0):
+            continue
+        if min_roi_reached(trade, row2.close, row2.date) or \
+            (row2.sell == 1 and use_sell_signal) or \
+                current_profit_percent <= stoploss:
+            current_profit_btc = trade.calc_profit(rate=row2.close)
+            return row2, (pair,
+                          current_profit_percent,
+                          current_profit_btc,
+                          row2.Index - row.Index,
+                          current_profit_btc > 0,
+                          current_profit_btc < 0
+                          )
+
+
+def backtest(args) -> DataFrame:
     """
     Implements backtesting functionality
-    :param stake_amount: btc amount to use for each trade
+    :param args: a dict containing:
-    :param processed: a processed dictionary with format {pair, data}
+        stake_amount: btc amount to use for each trade
-    :param max_open_trades: maximum number of concurrent trades (default: 0, disabled)
+        processed: a processed dictionary with format {pair, data}
-    :param realistic: do we try to simulate realistic trades? (default: True)
+        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
+        stoploss: use stoploss
     :return: DataFrame
     """
+    processed = args['processed']
+    max_open_trades = args.get('max_open_trades', 0)
+    realistic = args.get('realistic', True)
+    record = args.get('record', None)
+    records = []
     trades = []
     trade_count_lock: dict = {}
     exchange._API = Bittrex({'key': '', 'secret': ''})
@@ -99,41 +142,25 @@ def backtest(stake_amount: float, processed: Dict[str, DataFrame],
                 # Increase lock
                 trade_count_lock[row.date] = trade_count_lock.get(row.date, 0) + 1
 
-            trade = Trade(
+            ret = get_trade_entry(pair, row, ticker,
-                open_rate=row.close,
+                                  trade_count_lock, args)
-                open_date=row.date,
+            if ret:
-                stake_amount=stake_amount,
+                row2, trade_entry = ret
-                amount=stake_amount / row.open,
+                lock_pair_until = row2.Index
-                fee=exchange.get_fee()
+                trades.append(trade_entry)
-            )
+                if record:
-
+                    # Note, need to be json.dump friendly
-            # calculate win/lose forwards from buy point
+                    # record a tuple of pair, current_profit_percent,
-            sell_subset = ticker[row.Index + 1:][['close', 'date', 'sell']]
+                    # entry-date, duration
-            for row2 in sell_subset.itertuples(index=True):
+                    records.append((pair, trade_entry[1],
-                if max_open_trades > 0:
+                                    row.date.strftime('%s'),
-                    # Increase trade_count_lock for every iteration
+                                    row2.date.strftime('%s'),
-                    trade_count_lock[row2.date] = trade_count_lock.get(row2.date, 0) + 1
+                                    row.Index, trade_entry[3]))
-
+    # For now export inside backtest(), maybe change so that backtest()
-                current_profit_percent = trade.calc_profit_percent(rate=row2.close)
+    # returns a tuple like: (dataframe, records, logs, etc)
-                if (sell_profit_only and current_profit_percent < 0):
+    if record and record.find('trades') >= 0:
-                    continue
+        logger.info('Dumping backtest results')
-                if min_roi_reached(trade, row2.close, row2.date) or \
+        misc.file_dump_json('backtest-result.json', records)
-                    (row2.sell == 1 and use_sell_signal) or \
-                        current_profit_percent <= stoploss:
-                    current_profit_btc = trade.calc_profit(rate=row2.close)
-                    lock_pair_until = row2.Index
-
-                    trades.append(
-                        (
-                            pair,
-                            current_profit_percent,
-                            current_profit_btc,
-                            row2.Index - row.Index,
-                            current_profit_btc > 0,
-                            current_profit_btc < 0
-                        )
-                    )
-                    break
     labels = ['currency', 'profit_percent', 'profit_BTC', 'duration', 'profit', 'loss']
     return DataFrame.from_records(trades, columns=labels)
 
@@ -180,17 +207,18 @@ def start(args):
     # Print timeframe
     min_date, max_date = get_timeframe(preprocessed)
     logger.info('Measuring data from %s up to %s ...', min_date.isoformat(), max_date.isoformat())
-
     # Execute backtest and print results
-    results = backtest(
+    sell_profit_only = config.get('experimental', {}).get('sell_profit_only', False)
-        stake_amount=config['stake_amount'],
+    use_sell_signal = config.get('experimental', {}).get('use_sell_signal', False)
-        processed=preprocessed,
+    results = backtest({'stake_amount': config['stake_amount'],
-        max_open_trades=max_open_trades,
+                        'processed': preprocessed,
-        realistic=args.realistic_simulation,
+                        'max_open_trades': max_open_trades,
-        sell_profit_only=config.get('experimental', {}).get('sell_profit_only', False),
+                        'realistic': args.realistic_simulation,
-        stoploss=config.get('stoploss'),
+                        'sell_profit_only': sell_profit_only,
-        use_sell_signal=config.get('experimental', {}).get('use_sell_signal', False)
+                        'use_sell_signal': use_sell_signal,
-    )
+                        'stoploss': config.get('stoploss'),
+                        'record': args.export
+                        })
     logger.info(
         '\n==================================== BACKTESTING REPORT ====================================\n%s',  # noqa
         generate_text_table(data, results, config['stake_currency'], args.ticker_interval)

freqtrade/optimize/hyperopt.py

@@ -164,7 +164,9 @@ def optimizer(params):
     from freqtrade.optimize import backtesting
     backtesting.populate_buy_trend = buy_strategy_generator(params)
 
-    results = backtest(OPTIMIZE_CONFIG['stake_amount'], PROCESSED, stoploss=params['stoploss'])
+    results = backtest({'stake_amount': OPTIMIZE_CONFIG['stake_amount'],
+                        'processed': PROCESSED,
+                        'stoploss': params['stoploss']})
     result_explanation = format_results(results)
 
     total_profit = results.profit_percent.sum()

freqtrade/tests/optimize/test_backtesting.py

@@ -51,8 +51,10 @@ def test_backtest(default_conf, mocker):
 
     data = optimize.load_data(None, ticker_interval=5, pairs=['BTC_ETH'])
     data = trim_dictlist(data, -200)
-    results = backtest(default_conf['stake_amount'],
+    results = backtest({'stake_amount': default_conf['stake_amount'],
-                       optimize.preprocess(data), 10, True)
+                        'processed': optimize.preprocess(data),
+                        'max_open_trades': 10,
+                        'realistic': True})
     assert not results.empty
 
 
@@ -63,8 +65,10 @@ def test_backtest_1min_ticker_interval(default_conf, mocker):
     # Run a backtesting for an exiting 5min ticker_interval
     data = optimize.load_data(None, ticker_interval=1, pairs=['BTC_UNITEST'])
     data = trim_dictlist(data, -200)
-    results = backtest(default_conf['stake_amount'],
+    results = backtest({'stake_amount': default_conf['stake_amount'],
-                       optimize.preprocess(data), 1, True)
+                        'processed': optimize.preprocess(data),
+                        'max_open_trades': 1,
+                        'realistic': True})
     assert not results.empty
 
 
@@ -115,7 +119,10 @@ def simple_backtest(config, contour, num_results):
     data = load_data_test(contour)
     processed = optimize.preprocess(data)
     assert isinstance(processed, dict)
-    results = backtest(config['stake_amount'], processed, 1, True)
+    results = backtest({'stake_amount': config['stake_amount'],
+                        'processed': processed,
+                        'max_open_trades': 1,
+                        'realistic': True})
     # results :: <class 'pandas.core.frame.DataFrame'>
     assert len(results) == num_results
 
@@ -128,8 +135,10 @@ def test_backtest2(default_conf, mocker):
     mocker.patch.dict('freqtrade.main._CONF', default_conf)
     data = optimize.load_data(None, ticker_interval=5, pairs=['BTC_ETH'])
     data = trim_dictlist(data, -200)
-    results = backtest(default_conf['stake_amount'],
+    results = backtest({'stake_amount': default_conf['stake_amount'],
-                       optimize.preprocess(data), 10, True)
+                        'processed': optimize.preprocess(data),
+                        'max_open_trades': 10,
+                        'realistic': True})
     assert not results.empty
 
 
@@ -169,6 +178,7 @@ def test_backtest_start(default_conf, mocker, caplog):
     args.level = 10
     args.live = False
     args.datadir = None
+    args.export = None
     args.timerange = '-100'  # needed due to MagicMock malleability
     backtesting.start(args)
     # check the logs, that will contain the backtest result

freqtrade/tests/test_misc.py

@@ -5,10 +5,11 @@ import time
 from copy import deepcopy
 
 import pytest
+from unittest.mock import MagicMock
 from jsonschema import ValidationError
 
 from freqtrade.misc import (common_args_parser, load_config, parse_args,
-                            throttle, parse_timerange)
+                            throttle, file_dump_json, parse_timerange)
 
 
 def test_throttle():
@@ -133,6 +134,14 @@ def test_parse_args_hyperopt_custom(mocker):
     assert call_args.func is not None
 
 
+def test_file_dump_json(default_conf, mocker):
+    file_open = mocker.patch('freqtrade.misc.open', MagicMock())
+    json_dump = mocker.patch('json.dump', MagicMock())
+    file_dump_json('somefile', [1, 2, 3])
+    assert file_open.call_count == 1
+    assert json_dump.call_count == 1
+
+
 def test_parse_timerange_incorrect():
     assert ((None, 'line'), None, -200) == parse_timerange('-200')
     assert (('line', None), 200, None) == parse_timerange('200-')