Backtest-reports should calculate total gains based on starting capital
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Matthias
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@ -252,7 +252,10 @@ A backtesting result will look like that:
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| Max open trades | 3 |
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| Total trades | 429 |
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| Total Profit % | 152.41% |
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| Starting capital | 0.01000000 BTC |
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| End capital | 0.01762792 BTC |
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| Absolute profit | 0.00762792 BTC |
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| Total Profit % | 76.2% |
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| Trades per day | 3.575 |
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| Best Pair | LSK/BTC 26.26% |
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@ -261,6 +264,7 @@ A backtesting result will look like that:
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| Worst Trade | ZEC/BTC -10.25% |
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| Best day | 25.27% |
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| Worst day | -30.67% |
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| Days win/draw/lose | 12 / 82 / 25 |
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| Avg. Duration Winners | 4:23:00 |
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| Avg. Duration Loser | 6:55:00 |
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@ -328,7 +332,10 @@ It contains some useful key metrics about performance of your strategy on backte
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| Max open trades | 3 |
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| Total trades | 429 |
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| Total Profit % | 152.41% |
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| Starting capital | 0.01000000 BTC |
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| End capital | 0.01762792 BTC |
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| Absolute profit | 0.00762792 BTC |
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| Total Profit % | 76.2% |
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| Trades per day | 3.575 |
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| Best Pair | LSK/BTC 26.26% |
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@ -337,6 +344,7 @@ It contains some useful key metrics about performance of your strategy on backte
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| Worst Trade | ZEC/BTC -10.25% |
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| Best day | 25.27% |
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| Worst day | -30.67% |
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| Days win/draw/lose | 12 / 82 / 25 |
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| Avg. Duration Winners | 4:23:00 |
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| Avg. Duration Loser | 6:55:00 |
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@ -351,11 +359,15 @@ It contains some useful key metrics about performance of your strategy on backte
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- `Backtesting from` / `Backtesting to`: Backtesting range (usually defined with the `--timerange` option).
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- `Max open trades`: Setting of `max_open_trades` (or `--max-open-trades`) - or number of pairs in the pairlist (whatever is lower).
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- `Total trades`: Identical to the total trades of the backtest output table.
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- `Total Profit %`: Total profit. Aligned to the `TOTAL` row's `Tot Profit %` from the first table.
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- `Starting capital`: Start capital - as given by dry-run-wallet (config or command line).
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- `End capital`: Final capital - starting capital + absolute profit.
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- `Absolute profit`: Profit made in stake currency.
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- `Total Profit %`: Total profit. Aligned to the `TOTAL` row's `Tot Profit %` from the first table. Calculated as `(End capital − Starting capital) / Starting capital`.
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- `Trades per day`: Total trades divided by the backtesting duration in days (this will give you information about how many trades to expect from the strategy).
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- `Best Pair` / `Worst Pair`: Best and worst performing pair, and it's corresponding `Cum Profit %`.
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- `Best Trade` / `Worst Trade`: Biggest winning trade and biggest losing trade
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- `Best day` / `Worst day`: Best and worst day based on daily profit.
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- `Days win/draw/lose`: Winning / Losing days (draws are usually days without closed trade).
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- `Avg. Duration Winners` / `Avg. Duration Loser`: Average durations for winning and losing trades.
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- `Max Drawdown`: Maximum drawdown experienced. For example, the value of 50% means that from highest to subsequent lowest point, a 50% drop was experienced).
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- `Drawdown Start` / `Drawdown End`: Start and end datetime for this largest drawdown (can also be visualized via the `plot-dataframe` sub-command).
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@ -56,12 +56,13 @@ def _get_line_header(first_column: str, stake_currency: str) -> List[str]:
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'Wins', 'Draws', 'Losses']
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def _generate_result_line(result: DataFrame, max_open_trades: int, first_column: str) -> Dict:
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def _generate_result_line(result: DataFrame, starting_balance: int, first_column: str) -> Dict:
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"""
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Generate one result dict, with "first_column" as key.
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"""
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profit_sum = result['profit_ratio'].sum()
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profit_total = profit_sum / max_open_trades
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# (end-capital - starting capital) / starting capital
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profit_total = result['profit_abs'].sum() / starting_balance
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return {
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'key': first_column,
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@ -88,13 +89,13 @@ def _generate_result_line(result: DataFrame, max_open_trades: int, first_column:
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}
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def generate_pair_metrics(data: Dict[str, Dict], stake_currency: str, max_open_trades: int,
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def generate_pair_metrics(data: Dict[str, Dict], stake_currency: str, starting_balance: int,
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results: DataFrame, skip_nan: bool = False) -> List[Dict]:
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"""
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Generates and returns a list for the given backtest data and the results dataframe
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:param data: Dict of <pair: dataframe> containing data that was used during backtesting.
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:param stake_currency: stake-currency - used to correctly name headers
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:param max_open_trades: Maximum allowed open trades
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:param starting_balance: Starting balance
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:param results: Dataframe containing the backtest results
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:param skip_nan: Print "left open" open trades
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:return: List of Dicts containing the metrics per pair
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@ -107,10 +108,10 @@ def generate_pair_metrics(data: Dict[str, Dict], stake_currency: str, max_open_t
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if skip_nan and result['profit_abs'].isnull().all():
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continue
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tabular_data.append(_generate_result_line(result, max_open_trades, pair))
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tabular_data.append(_generate_result_line(result, starting_balance, pair))
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# Append Total
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tabular_data.append(_generate_result_line(results, max_open_trades, 'TOTAL'))
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tabular_data.append(_generate_result_line(results, starting_balance, 'TOTAL'))
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return tabular_data
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@ -159,7 +160,7 @@ def generate_strategy_metrics(all_results: Dict) -> List[Dict]:
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tabular_data = []
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for strategy, results in all_results.items():
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tabular_data.append(_generate_result_line(
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results['results'], results['config']['max_open_trades'], strategy)
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results['results'], results['config']['dry_run_wallet'], strategy)
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)
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return tabular_data
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@ -246,15 +247,16 @@ def generate_backtest_stats(btdata: Dict[str, DataFrame],
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continue
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config = content['config']
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max_open_trades = min(config['max_open_trades'], len(btdata.keys()))
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starting_balance = config['dry_run_wallet']
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stake_currency = config['stake_currency']
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pair_results = generate_pair_metrics(btdata, stake_currency=stake_currency,
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max_open_trades=max_open_trades,
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starting_balance=starting_balance,
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results=results, skip_nan=False)
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sell_reason_stats = generate_sell_reason_stats(max_open_trades=max_open_trades,
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results=results)
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left_open_results = generate_pair_metrics(btdata, stake_currency=stake_currency,
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max_open_trades=max_open_trades,
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starting_balance=starting_balance,
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results=results.loc[results['is_open']],
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skip_nan=True)
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daily_stats = generate_daily_stats(results)
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@ -276,7 +278,7 @@ def generate_backtest_stats(btdata: Dict[str, DataFrame],
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'left_open_trades': left_open_results,
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'total_trades': len(results),
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'profit_mean': results['profit_ratio'].mean() if len(results) > 0 else 0,
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'profit_total': results['profit_ratio'].sum() / max_open_trades,
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'profit_total': results['profit_abs'].sum() / starting_balance,
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'profit_total_abs': results['profit_abs'].sum(),
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'backtest_start': min_date.datetime,
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'backtest_start_ts': min_date.int_timestamp * 1000,
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@ -292,6 +294,9 @@ def generate_backtest_stats(btdata: Dict[str, DataFrame],
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'pairlist': list(btdata.keys()),
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'stake_amount': config['stake_amount'],
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'stake_currency': config['stake_currency'],
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'starting_balance': starting_balance,
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'dry_run_wallet': starting_balance,
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'final_balance': content['final_balance'],
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'max_open_trades': max_open_trades,
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'max_open_trades_setting': (config['max_open_trades']
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if config['max_open_trades'] != float('inf') else -1),
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@ -431,6 +436,13 @@ def text_table_add_metrics(strat_results: Dict) -> str:
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('Max open trades', strat_results['max_open_trades']),
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('', ''), # Empty line to improve readability
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('Total trades', strat_results['total_trades']),
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('Starting capital', round_coin_value(strat_results['starting_balance'],
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strat_results['stake_currency'])),
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('End capital', round_coin_value(strat_results['final_balance'],
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strat_results['stake_currency'])),
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('Absolute profit ', round_coin_value(strat_results['profit_total_abs'],
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strat_results['stake_currency'])),
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('Total Profit %', f"{round(strat_results['profit_total'] * 100, 2)}%"),
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('Trades per day', strat_results['trades_per_day']),
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('', ''), # Empty line to improve readability
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@ -261,6 +261,7 @@ def get_default_conf(testdatadir):
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"20": 0.02,
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"0": 0.04
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},
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"dry_run_wallet": 1000,
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"stoploss": -0.10,
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"unfilledtimeout": {
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"buy": 10,
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@ -48,7 +48,7 @@ def test_text_table_bt_results():
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)
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pair_results = generate_pair_metrics(data={'ETH/BTC': {}}, stake_currency='BTC',
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max_open_trades=2, results=results)
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starting_balance=4, results=results)
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assert text_table_bt_results(pair_results, stake_currency='BTC') == result_str
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@ -78,6 +78,7 @@ def test_generate_backtest_stats(default_conf, testdatadir):
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}),
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'config': default_conf,
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'locks': [],
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'final_balance': 1000.02,
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'backtest_start_time': Arrow.utcnow().int_timestamp,
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'backtest_end_time': Arrow.utcnow().int_timestamp,
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}
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@ -189,7 +190,7 @@ def test_generate_pair_metrics():
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)
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pair_results = generate_pair_metrics(data={'ETH/BTC': {}}, stake_currency='BTC',
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max_open_trades=2, results=results)
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starting_balance=2, results=results)
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assert isinstance(pair_results, list)
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assert len(pair_results) == 2
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assert pair_results[-1]['key'] == 'TOTAL'
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@ -291,6 +292,7 @@ def test_generate_sell_reason_stats():
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def test_text_table_strategy(default_conf):
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default_conf['max_open_trades'] = 2
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default_conf['dry_run_wallet'] = 3
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results = {}
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results['TestStrategy1'] = {'results': pd.DataFrame(
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{
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@ -323,9 +325,9 @@ def test_text_table_strategy(default_conf):
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'|---------------+--------+----------------+----------------+------------------+'
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'----------------+----------------+--------+---------+----------|\n'
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'| TestStrategy1 | 3 | 20.00 | 60.00 | 1.10000000 |'
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' 30.00 | 0:17:00 | 3 | 0 | 0 |\n'
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' 36.67 | 0:17:00 | 3 | 0 | 0 |\n'
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'| TestStrategy2 | 3 | 30.00 | 90.00 | 1.30000000 |'
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' 45.00 | 0:20:00 | 3 | 0 | 0 |'
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' 43.33 | 0:20:00 | 3 | 0 | 0 |'
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)
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strategy_results = generate_strategy_metrics(all_results=results)
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