Merge branch 'freqtrade:develop' into dca
This commit is contained in:
Reigo Reinmets
GitHub
commit
8e424f7c73
2
.github/workflows/ci.yml
vendored
2
.github/workflows/ci.yml
vendored
@ -196,7 +196,7 @@ jobs:
|
||||
uses: rjstone/discord-webhook-notify@v1
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||||
if: failure() && ( github.event_name != 'pull_request' || github.event.pull_request.head.repo.fork == false)
|
||||
with:
|
||||
severity: error
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||||
severity: info
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||||
details: Test Succeeded!
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||||
webhookUrl: ${{ secrets.DISCORD_WEBHOOK }}
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||||
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||||
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build_helpers/TA_Lib-0.4.23-cp310-cp310-win_amd64.whl
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build_helpers/TA_Lib-0.4.23-cp37-cp37m-win_amd64.whl
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build_helpers/TA_Lib-0.4.23-cp38-cp38-win_amd64.whl
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build_helpers/TA_Lib-0.4.23-cp38-cp38-win_amd64.whl
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build_helpers/TA_Lib-0.4.23-cp39-cp39-win_amd64.whl
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@ -6,16 +6,16 @@ python -m pip install --upgrade pip wheel
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$pyv = python -c "import sys; print(f'{sys.version_info.major}.{sys.version_info.minor}')"
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if ($pyv -eq '3.7') {
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||||
pip install build_helpers\TA_Lib-0.4.22-cp37-cp37m-win_amd64.whl
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pip install build_helpers\TA_Lib-0.4.23-cp37-cp37m-win_amd64.whl
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||||
}
|
||||
if ($pyv -eq '3.8') {
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pip install build_helpers\TA_Lib-0.4.22-cp38-cp38-win_amd64.whl
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pip install build_helpers\TA_Lib-0.4.23-cp38-cp38-win_amd64.whl
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}
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||||
if ($pyv -eq '3.9') {
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pip install build_helpers\TA_Lib-0.4.22-cp39-cp39-win_amd64.whl
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pip install build_helpers\TA_Lib-0.4.23-cp39-cp39-win_amd64.whl
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}
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if ($pyv -eq '3.10') {
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pip install build_helpers\TA_Lib-0.4.22-cp310-cp310-win_amd64.whl
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pip install build_helpers\TA_Lib-0.4.23-cp310-cp310-win_amd64.whl
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}
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pip install -r requirements-dev.txt
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pip install -e .
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@ -18,6 +18,7 @@
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"sell_profit_only": false,
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"sell_profit_offset": 0.0,
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"ignore_roi_if_buy_signal": false,
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"ignore_buying_expired_candle_after": 300,
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"minimal_roi": {
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"40": 0.0,
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"30": 0.01,
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@ -312,7 +312,7 @@ A backtesting result will look like that:
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| | |
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| Min balance | 0.00945123 BTC |
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| Max balance | 0.01846651 BTC |
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| Drawdown | 50.63% |
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| Drawdown (Account) | 13.33% |
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| Drawdown | 0.0015 BTC |
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| Drawdown high | 0.0013 BTC |
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| Drawdown low | -0.0002 BTC |
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@ -399,7 +399,7 @@ It contains some useful key metrics about performance of your strategy on backte
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| | |
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| Min balance | 0.00945123 BTC |
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| Max balance | 0.01846651 BTC |
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| Drawdown | 50.63% |
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| Drawdown (Account) | 13.33% |
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| Drawdown | 0.0015 BTC |
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| Drawdown high | 0.0013 BTC |
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| Drawdown low | -0.0002 BTC |
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@ -426,7 +426,8 @@ It contains some useful key metrics about performance of your strategy on backte
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- `Avg. Duration Winners` / `Avg. Duration Loser`: Average durations for winning and losing trades.
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- `Rejected Buy signals`: Buy signals that could not be acted upon due to max_open_trades being reached.
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- `Min balance` / `Max balance`: Lowest and Highest Wallet balance during the backtest period.
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- `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 (Account)`: Maximum Account Drawdown experienced. Calculated as $(Absolute Drawdown) / (DrawdownHigh + startingBalance)$.
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- `Drawdown`: Maximum, absolute drawdown experienced. Difference between Drawdown High and Subsequent Low point.
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- `Drawdown high` / `Drawdown low`: Profit at the beginning and end of the largest drawdown period. A negative low value means initial capital lost.
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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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- `Market change`: Change of the market during the backtest period. Calculated as average of all pairs changes from the first to the last candle using the "close" column.
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@ -15,8 +15,8 @@ This command line option was deprecated in 2019.7-dev (develop branch) and remov
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### The **--dynamic-whitelist** command line option
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This command line option was deprecated in 2018 and removed freqtrade 2019.6-dev (develop branch)
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and in freqtrade 2019.7.
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This command line option was deprecated in 2018 and removed freqtrade 2019.6-dev (develop branch) and in freqtrade 2019.7.
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Please refer to [pairlists](plugins.md#pairlists-and-pairlist-handlers) instead.
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### the `--live` command line option
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@ -222,9 +222,9 @@ should be rewritten to
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```python
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frames = [dataframe]
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for val in self.buy_ema_short.range:
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frames.append({
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frames.append(DataFrame({
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f'ema_short_{val}': ta.EMA(dataframe, timeperiod=val)
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})
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}))
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# Append columns to existing dataframe
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merged_frame = pd.concat(frames, axis=1)
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@ -23,7 +23,7 @@ git clone https://github.com/freqtrade/freqtrade.git
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Install ta-lib according to the [ta-lib documentation](https://github.com/mrjbq7/ta-lib#windows).
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As compiling from source on windows has heavy dependencies (requires a partial visual studio installation), there is also a repository of unofficial pre-compiled windows Wheels [here](https://www.lfd.uci.edu/~gohlke/pythonlibs/#ta-lib), which need to be downloaded and installed using `pip install TA_Lib‑0.4.22‑cp38‑cp38‑win_amd64.whl` (make sure to use the version matching your python version).
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As compiling from source on windows has heavy dependencies (requires a partial visual studio installation), there is also a repository of unofficial pre-compiled windows Wheels [here](https://www.lfd.uci.edu/~gohlke/pythonlibs/#ta-lib), which need to be downloaded and installed using `pip install TA_Lib-0.4.23-cp38-cp38-win_amd64.whl` (make sure to use the version matching your python version).
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Freqtrade provides these dependencies for the latest 3 Python versions (3.7, 3.8, 3.9 and 3.10) and for 64bit Windows.
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Other versions must be downloaded from the above link.
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@ -9,21 +9,13 @@ import numpy as np
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import pandas as pd
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from freqtrade.constants import LAST_BT_RESULT_FN
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from freqtrade.exceptions import OperationalException
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from freqtrade.misc import json_load
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from freqtrade.persistence import LocalTrade, Trade, init_db
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logger = logging.getLogger(__name__)
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# Old format - maybe remove?
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BT_DATA_COLUMNS_OLD = ["pair", "profit_percent", "open_date", "close_date", "index",
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"trade_duration", "open_rate", "close_rate", "open_at_end", "sell_reason"]
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# Mid-term format, created by BacktestResult Named Tuple
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BT_DATA_COLUMNS_MID = ['pair', 'profit_percent', 'open_date', 'close_date', 'trade_duration',
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'open_rate', 'close_rate', 'open_at_end', 'sell_reason', 'fee_open',
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'fee_close', 'amount', 'profit_abs', 'profit_ratio']
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# Newest format
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BT_DATA_COLUMNS = ['pair', 'stake_amount', 'amount', 'open_date', 'close_date',
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'open_rate', 'close_rate',
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@ -167,23 +159,9 @@ def load_backtest_data(filename: Union[Path, str], strategy: Optional[str] = Non
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)
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else:
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# old format - only with lists.
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df = pd.DataFrame(data, columns=BT_DATA_COLUMNS_OLD)
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if not df.empty:
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df['open_date'] = pd.to_datetime(df['open_date'],
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unit='s',
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utc=True,
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infer_datetime_format=True
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)
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df['close_date'] = pd.to_datetime(df['close_date'],
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unit='s',
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utc=True,
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infer_datetime_format=True
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||||
)
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# Create compatibility with new format
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df['profit_abs'] = df['close_rate'] - df['open_rate']
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raise OperationalException(
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"Backtest-results with only trades data are no longer supported.")
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if not df.empty:
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if 'profit_ratio' not in df.columns:
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df['profit_ratio'] = df['profit_percent']
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df = df.sort_values("open_date").reset_index(drop=True)
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return df
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@ -392,15 +370,17 @@ def calculate_underwater(trades: pd.DataFrame, *, date_col: str = 'close_date',
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def calculate_max_drawdown(trades: pd.DataFrame, *, date_col: str = 'close_date',
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value_col: str = 'profit_ratio'
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) -> Tuple[float, pd.Timestamp, pd.Timestamp, float, float]:
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value_col: str = 'profit_abs', starting_balance: float = 0
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) -> Tuple[float, pd.Timestamp, pd.Timestamp, float, float, float]:
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"""
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Calculate max drawdown and the corresponding close dates
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:param trades: DataFrame containing trades (requires columns close_date and profit_ratio)
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:param date_col: Column in DataFrame to use for dates (defaults to 'close_date')
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:param value_col: Column in DataFrame to use for values (defaults to 'profit_ratio')
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:return: Tuple (float, highdate, lowdate, highvalue, lowvalue) with absolute max drawdown,
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high and low time and high and low value.
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:param value_col: Column in DataFrame to use for values (defaults to 'profit_abs')
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:param starting_balance: Portfolio starting balance - properly calculate relative drawdown.
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:return: Tuple (float, highdate, lowdate, highvalue, lowvalue, relative_drawdown)
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with absolute max drawdown, high and low time and high and low value,
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and the relative account drawdown
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:raise: ValueError if trade-dataframe was found empty.
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"""
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if len(trades) == 0:
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@ -416,7 +396,18 @@ def calculate_max_drawdown(trades: pd.DataFrame, *, date_col: str = 'close_date'
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high_val = max_drawdown_df.loc[max_drawdown_df.iloc[:idxmin]
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['high_value'].idxmax(), 'cumulative']
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low_val = max_drawdown_df.loc[idxmin, 'cumulative']
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return abs(min(max_drawdown_df['drawdown'])), high_date, low_date, high_val, low_val
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max_drawdown_rel = 0.0
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if high_val + starting_balance != 0:
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max_drawdown_rel = (high_val - low_val) / (high_val + starting_balance)
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return (
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abs(min(max_drawdown_df['drawdown'])),
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high_date,
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low_date,
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high_val,
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low_val,
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max_drawdown_rel
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)
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def calculate_csum(trades: pd.DataFrame, starting_balance: float = 0) -> Tuple[float, float]:
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@ -67,6 +67,8 @@ class Exchange:
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"ohlcv_params": {},
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"ohlcv_candle_limit": 500,
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"ohlcv_partial_candle": True,
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# Check https://github.com/ccxt/ccxt/issues/10767 for removal of ohlcv_volume_currency
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"ohlcv_volume_currency": "base", # "base" or "quote"
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"trades_pagination": "time", # Possible are "time" or "id"
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"trades_pagination_arg": "since",
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"l2_limit_range": None,
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@ -656,7 +658,8 @@ class Exchange:
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max_slippage_val = rate * ((1 + slippage) if side == 'buy' else (1 - slippage))
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||||
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||||
remaining_amount = amount
|
||||
filled_amount = 0
|
||||
filled_amount = 0.0
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||||
book_entry_price = 0.0
|
||||
for book_entry in ob[ob_type]:
|
||||
book_entry_price = book_entry[0]
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book_entry_coin_volume = book_entry[1]
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@ -19,6 +19,7 @@ class Ftx(Exchange):
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_ft_has: Dict = {
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"stoploss_on_exchange": True,
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"ohlcv_candle_limit": 1500,
|
||||
"ohlcv_volume_currency": "quote",
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||||
}
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||||
|
||||
def market_is_tradable(self, market: Dict[str, Any]) -> bool:
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@ -21,6 +21,7 @@ class Gateio(Exchange):
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||||
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||||
_ft_has: Dict = {
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||||
"ohlcv_candle_limit": 1000,
|
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"ohlcv_volume_currency": "quote",
|
||||
}
|
||||
|
||||
_headers = {'X-Gate-Channel-Id': 'freqtrade'}
|
||||
|
||||
@ -14,5 +14,5 @@ class Okex(Exchange):
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"""
|
||||
|
||||
_ft_has: Dict = {
|
||||
"ohlcv_candle_limit": 100,
|
||||
"ohlcv_candle_limit": 300,
|
||||
}
|
||||
|
||||
@ -270,8 +270,8 @@ class Backtesting:
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||||
df_analyzed = self.strategy.advise_sell(
|
||||
self.strategy.advise_buy(pair_data, {'pair': pair}), {'pair': pair}).copy()
|
||||
# Trim startup period from analyzed dataframe
|
||||
df_analyzed = trim_dataframe(df_analyzed, self.timerange,
|
||||
startup_candles=self.required_startup)
|
||||
df_analyzed = processed[pair] = pair_data = trim_dataframe(
|
||||
df_analyzed, self.timerange, startup_candles=self.required_startup)
|
||||
# To avoid using data from future, we use buy/sell signals shifted
|
||||
# from the previous candle
|
||||
df_analyzed.loc[:, 'buy'] = df_analyzed.loc[:, 'buy'].shift(1)
|
||||
@ -287,9 +287,6 @@ class Backtesting:
|
||||
# Convert from Pandas to list for performance reasons
|
||||
# (Looping Pandas is slow.)
|
||||
data[pair] = df_analyzed[headers].values.tolist()
|
||||
|
||||
# Do not hold on to old data to reduce memory usage
|
||||
processed[pair] = pair_data = None
|
||||
return data
|
||||
|
||||
def _get_close_rate(self, sell_row: Tuple, trade: LocalTrade, sell: SellCheckTuple,
|
||||
@ -445,7 +442,9 @@ class Backtesting:
|
||||
return self._get_sell_trade_entry_for_candle(trade, sell_row)
|
||||
detail_data.loc[:, 'buy'] = sell_row[BUY_IDX]
|
||||
detail_data.loc[:, 'sell'] = sell_row[SELL_IDX]
|
||||
headers = ['date', 'buy', 'open', 'close', 'sell', 'low', 'high']
|
||||
detail_data.loc[:, 'buy_tag'] = sell_row[BUY_TAG_IDX]
|
||||
detail_data.loc[:, 'exit_tag'] = sell_row[EXIT_TAG_IDX]
|
||||
headers = ['date', 'buy', 'open', 'close', 'sell', 'low', 'high', 'buy_tag', 'exit_tag']
|
||||
for det_row in detail_data[headers].values.tolist():
|
||||
res = self._get_sell_trade_entry_for_candle(trade, det_row)
|
||||
if res:
|
||||
|
||||
@ -76,6 +76,7 @@ class Hyperopt:
|
||||
self.config = config
|
||||
|
||||
self.backtesting = Backtesting(self.config)
|
||||
self.pairlist = self.backtesting.pairlists.whitelist
|
||||
|
||||
if not self.config.get('hyperopt'):
|
||||
self.custom_hyperopt = HyperOptAuto(self.config)
|
||||
@ -332,7 +333,7 @@ class Hyperopt:
|
||||
params_details = self._get_params_details(params_dict)
|
||||
|
||||
strat_stats = generate_strategy_stats(
|
||||
processed, self.backtesting.strategy.get_strategy_name(),
|
||||
self.pairlist, self.backtesting.strategy.get_strategy_name(),
|
||||
backtesting_results, min_date, max_date, market_change=0
|
||||
)
|
||||
results_explanation = HyperoptTools.format_results_explanation_string(
|
||||
|
||||
@ -47,10 +47,9 @@ class CalmarHyperOptLoss(IHyperOptLoss):
|
||||
|
||||
# calculate max drawdown
|
||||
try:
|
||||
_, _, _, high_val, low_val = calculate_max_drawdown(
|
||||
_, _, _, _, _, max_drawdown = calculate_max_drawdown(
|
||||
results, value_col="profit_abs"
|
||||
)
|
||||
max_drawdown = (high_val - low_val) / high_val
|
||||
except ValueError:
|
||||
max_drawdown = 0
|
||||
|
||||
|
||||
@ -299,8 +299,7 @@ class HyperoptTools():
|
||||
f"Objective: {results['loss']:.5f}")
|
||||
|
||||
@staticmethod
|
||||
def prepare_trials_columns(trials: pd.DataFrame, legacy_mode: bool,
|
||||
has_drawdown: bool) -> pd.DataFrame:
|
||||
def prepare_trials_columns(trials: pd.DataFrame, has_drawdown: bool) -> pd.DataFrame:
|
||||
trials['Best'] = ''
|
||||
|
||||
if 'results_metrics.winsdrawslosses' not in trials.columns:
|
||||
@ -309,33 +308,26 @@ class HyperoptTools():
|
||||
|
||||
if not has_drawdown:
|
||||
# Ensure compatibility with older versions of hyperopt results
|
||||
trials['results_metrics.max_drawdown_abs'] = None
|
||||
trials['results_metrics.max_drawdown'] = None
|
||||
trials['results_metrics.max_drawdown_account'] = None
|
||||
|
||||
if not legacy_mode:
|
||||
# New mode, using backtest result for metrics
|
||||
trials['results_metrics.winsdrawslosses'] = trials.apply(
|
||||
lambda x: f"{x['results_metrics.wins']} {x['results_metrics.draws']:>4} "
|
||||
f"{x['results_metrics.losses']:>4}", axis=1)
|
||||
trials = trials[['Best', 'current_epoch', 'results_metrics.total_trades',
|
||||
'results_metrics.winsdrawslosses',
|
||||
'results_metrics.profit_mean', 'results_metrics.profit_total_abs',
|
||||
'results_metrics.profit_total', 'results_metrics.holding_avg',
|
||||
'results_metrics.max_drawdown', 'results_metrics.max_drawdown_abs',
|
||||
'loss', 'is_initial_point', 'is_best']]
|
||||
# New mode, using backtest result for metrics
|
||||
trials['results_metrics.winsdrawslosses'] = trials.apply(
|
||||
lambda x: f"{x['results_metrics.wins']} {x['results_metrics.draws']:>4} "
|
||||
f"{x['results_metrics.losses']:>4}", axis=1)
|
||||
|
||||
else:
|
||||
# Legacy mode
|
||||
trials = trials[['Best', 'current_epoch', 'results_metrics.trade_count',
|
||||
'results_metrics.winsdrawslosses', 'results_metrics.avg_profit',
|
||||
'results_metrics.total_profit', 'results_metrics.profit',
|
||||
'results_metrics.duration', 'results_metrics.max_drawdown',
|
||||
'results_metrics.max_drawdown_abs', 'loss', 'is_initial_point',
|
||||
'is_best']]
|
||||
trials = trials[['Best', 'current_epoch', 'results_metrics.total_trades',
|
||||
'results_metrics.winsdrawslosses',
|
||||
'results_metrics.profit_mean', 'results_metrics.profit_total_abs',
|
||||
'results_metrics.profit_total', 'results_metrics.holding_avg',
|
||||
'results_metrics.max_drawdown',
|
||||
'results_metrics.max_drawdown_account', 'results_metrics.max_drawdown_abs',
|
||||
'loss', 'is_initial_point', 'is_best']]
|
||||
|
||||
trials.columns = ['Best', 'Epoch', 'Trades', ' Win Draw Loss', 'Avg profit',
|
||||
'Total profit', 'Profit', 'Avg duration', 'Max Drawdown',
|
||||
'max_drawdown_abs', 'Objective', 'is_initial_point', 'is_best']
|
||||
trials.columns = [
|
||||
'Best', 'Epoch', 'Trades', ' Win Draw Loss', 'Avg profit',
|
||||
'Total profit', 'Profit', 'Avg duration', 'max_drawdown', 'max_drawdown_account',
|
||||
'max_drawdown_abs', 'Objective', 'is_initial_point', 'is_best'
|
||||
]
|
||||
|
||||
return trials
|
||||
|
||||
@ -351,10 +343,9 @@ class HyperoptTools():
|
||||
tabulate.PRESERVE_WHITESPACE = True
|
||||
trials = json_normalize(results, max_level=1)
|
||||
|
||||
legacy_mode = 'results_metrics.total_trades' not in trials
|
||||
has_drawdown = 'results_metrics.max_drawdown_abs' in trials.columns
|
||||
has_account_drawdown = 'results_metrics.max_drawdown_account' in trials.columns
|
||||
|
||||
trials = HyperoptTools.prepare_trials_columns(trials, legacy_mode, has_drawdown)
|
||||
trials = HyperoptTools.prepare_trials_columns(trials, has_account_drawdown)
|
||||
|
||||
trials['is_profit'] = False
|
||||
trials.loc[trials['is_initial_point'], 'Best'] = '* '
|
||||
@ -362,12 +353,12 @@ class HyperoptTools():
|
||||
trials.loc[trials['is_initial_point'] & trials['is_best'], 'Best'] = '* Best'
|
||||
trials.loc[trials['Total profit'] > 0, 'is_profit'] = True
|
||||
trials['Trades'] = trials['Trades'].astype(str)
|
||||
perc_multi = 1 if legacy_mode else 100
|
||||
# perc_multi = 1 if legacy_mode else 100
|
||||
trials['Epoch'] = trials['Epoch'].apply(
|
||||
lambda x: '{}/{}'.format(str(x).rjust(len(str(total_epochs)), ' '), total_epochs)
|
||||
)
|
||||
trials['Avg profit'] = trials['Avg profit'].apply(
|
||||
lambda x: f'{x * perc_multi:,.2f}%'.rjust(7, ' ') if not isna(x) else "--".rjust(7, ' ')
|
||||
lambda x: f'{x:,.2%}'.rjust(7, ' ') if not isna(x) else "--".rjust(7, ' ')
|
||||
)
|
||||
trials['Avg duration'] = trials['Avg duration'].apply(
|
||||
lambda x: f'{x:,.1f} m'.rjust(7, ' ') if isinstance(x, float) else f"{x}"
|
||||
@ -379,24 +370,25 @@ class HyperoptTools():
|
||||
|
||||
stake_currency = config['stake_currency']
|
||||
|
||||
if has_drawdown:
|
||||
trials['Max Drawdown'] = trials.apply(
|
||||
lambda x: '{} {}'.format(
|
||||
round_coin_value(x['max_drawdown_abs'], stake_currency),
|
||||
'({:,.2f}%)'.format(x['Max Drawdown'] * perc_multi).rjust(10, ' ')
|
||||
).rjust(25 + len(stake_currency))
|
||||
if x['Max Drawdown'] != 0.0 else '--'.rjust(25 + len(stake_currency)),
|
||||
axis=1
|
||||
)
|
||||
else:
|
||||
trials = trials.drop(columns=['Max Drawdown'])
|
||||
trials[f"Max Drawdown{' (Acct)' if has_account_drawdown else ''}"] = trials.apply(
|
||||
lambda x: "{} {}".format(
|
||||
round_coin_value(x['max_drawdown_abs'], stake_currency),
|
||||
(f"({x['max_drawdown_account']:,.2%})"
|
||||
if has_account_drawdown
|
||||
else f"({x['max_drawdown']:,.2%})"
|
||||
).rjust(10, ' ')
|
||||
).rjust(25 + len(stake_currency))
|
||||
if x['max_drawdown'] != 0.0 or x['max_drawdown_account'] != 0.0
|
||||
else '--'.rjust(25 + len(stake_currency)),
|
||||
axis=1
|
||||
)
|
||||
|
||||
trials = trials.drop(columns=['max_drawdown_abs'])
|
||||
trials = trials.drop(columns=['max_drawdown_abs', 'max_drawdown', 'max_drawdown_account'])
|
||||
|
||||
trials['Profit'] = trials.apply(
|
||||
lambda x: '{} {}'.format(
|
||||
round_coin_value(x['Total profit'], stake_currency),
|
||||
'({:,.2f}%)'.format(x['Profit'] * perc_multi).rjust(10, ' ')
|
||||
f"({x['Profit']:,.2%})".rjust(10, ' ')
|
||||
).rjust(25+len(stake_currency))
|
||||
if x['Total profit'] != 0.0 else '--'.rjust(25+len(stake_currency)),
|
||||
axis=1
|
||||
|
||||
@ -1,4 +1,5 @@
|
||||
import logging
|
||||
from copy import deepcopy
|
||||
from datetime import datetime, timedelta, timezone
|
||||
from pathlib import Path
|
||||
from typing import Any, Dict, List, Union
|
||||
@ -98,11 +99,11 @@ def _generate_result_line(result: DataFrame, starting_balance: int, first_column
|
||||
}
|
||||
|
||||
|
||||
def generate_pair_metrics(data: Dict[str, Dict], stake_currency: str, starting_balance: int,
|
||||
def generate_pair_metrics(pairlist: List[str], stake_currency: str, starting_balance: int,
|
||||
results: DataFrame, skip_nan: bool = False) -> List[Dict]:
|
||||
"""
|
||||
Generates and returns a list for the given backtest data and the results dataframe
|
||||
:param data: Dict of <pair: dataframe> containing data that was used during backtesting.
|
||||
:param pairlist: Pairlist used
|
||||
:param stake_currency: stake-currency - used to correctly name headers
|
||||
:param starting_balance: Starting balance
|
||||
:param results: Dataframe containing the backtest results
|
||||
@ -112,7 +113,7 @@ def generate_pair_metrics(data: Dict[str, Dict], stake_currency: str, starting_b
|
||||
|
||||
tabular_data = []
|
||||
|
||||
for pair in data:
|
||||
for pair in pairlist:
|
||||
result = results[results['pair'] == pair]
|
||||
if skip_nan and result['profit_abs'].isnull().all():
|
||||
continue
|
||||
@ -194,29 +195,21 @@ def generate_sell_reason_stats(max_open_trades: int, results: DataFrame) -> List
|
||||
return tabular_data
|
||||
|
||||
|
||||
def generate_strategy_comparison(all_results: Dict) -> List[Dict]:
|
||||
def generate_strategy_comparison(bt_stats: Dict) -> List[Dict]:
|
||||
"""
|
||||
Generate summary per strategy
|
||||
:param all_results: Dict of <Strategyname: DataFrame> containing results for all strategies
|
||||
:param bt_stats: Dict of <Strategyname: DataFrame> containing results for all strategies
|
||||
:return: List of Dicts containing the metrics per Strategy
|
||||
"""
|
||||
|
||||
tabular_data = []
|
||||
for strategy, results in all_results.items():
|
||||
tabular_data.append(_generate_result_line(
|
||||
results['results'], results['config']['dry_run_wallet'], strategy)
|
||||
)
|
||||
try:
|
||||
max_drawdown_per, _, _, _, _ = calculate_max_drawdown(results['results'],
|
||||
value_col='profit_ratio')
|
||||
max_drawdown_abs, _, _, _, _ = calculate_max_drawdown(results['results'],
|
||||
value_col='profit_abs')
|
||||
except ValueError:
|
||||
max_drawdown_per = 0
|
||||
max_drawdown_abs = 0
|
||||
tabular_data[-1]['max_drawdown_per'] = round(max_drawdown_per * 100, 2)
|
||||
tabular_data[-1]['max_drawdown_abs'] = \
|
||||
round_coin_value(max_drawdown_abs, results['config']['stake_currency'], False)
|
||||
for strategy, result in bt_stats.items():
|
||||
tabular_data.append(deepcopy(result['results_per_pair'][-1]))
|
||||
# Update "key" to strategy (results_per_pair has it as "Total").
|
||||
tabular_data[-1]['key'] = strategy
|
||||
tabular_data[-1]['max_drawdown_account'] = result['max_drawdown_account']
|
||||
tabular_data[-1]['max_drawdown_abs'] = round_coin_value(
|
||||
result['max_drawdown_abs'], result['stake_currency'], False)
|
||||
return tabular_data
|
||||
|
||||
|
||||
@ -352,14 +345,14 @@ def generate_daily_stats(results: DataFrame) -> Dict[str, Any]:
|
||||
}
|
||||
|
||||
|
||||
def generate_strategy_stats(btdata: Dict[str, DataFrame],
|
||||
def generate_strategy_stats(pairlist: List[str],
|
||||
strategy: str,
|
||||
content: Dict[str, Any],
|
||||
min_date: datetime, max_date: datetime,
|
||||
market_change: float
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
:param btdata: Backtest data
|
||||
:param pairlist: List of pairs to backtest
|
||||
:param strategy: Strategy name
|
||||
:param content: Backtest result data in the format:
|
||||
{'results: results, 'config: config}}.
|
||||
@ -372,11 +365,11 @@ def generate_strategy_stats(btdata: Dict[str, DataFrame],
|
||||
if not isinstance(results, DataFrame):
|
||||
return {}
|
||||
config = content['config']
|
||||
max_open_trades = min(config['max_open_trades'], len(btdata.keys()))
|
||||
max_open_trades = min(config['max_open_trades'], len(pairlist))
|
||||
starting_balance = config['dry_run_wallet']
|
||||
stake_currency = config['stake_currency']
|
||||
|
||||
pair_results = generate_pair_metrics(btdata, stake_currency=stake_currency,
|
||||
pair_results = generate_pair_metrics(pairlist, stake_currency=stake_currency,
|
||||
starting_balance=starting_balance,
|
||||
results=results, skip_nan=False)
|
||||
|
||||
@ -385,7 +378,7 @@ def generate_strategy_stats(btdata: Dict[str, DataFrame],
|
||||
|
||||
sell_reason_stats = generate_sell_reason_stats(max_open_trades=max_open_trades,
|
||||
results=results)
|
||||
left_open_results = generate_pair_metrics(btdata, stake_currency=stake_currency,
|
||||
left_open_results = generate_pair_metrics(pairlist, stake_currency=stake_currency,
|
||||
starting_balance=starting_balance,
|
||||
results=results.loc[results['is_open']],
|
||||
skip_nan=True)
|
||||
@ -429,7 +422,7 @@ def generate_strategy_stats(btdata: Dict[str, DataFrame],
|
||||
|
||||
'trades_per_day': round(len(results) / backtest_days, 2),
|
||||
'market_change': market_change,
|
||||
'pairlist': list(btdata.keys()),
|
||||
'pairlist': pairlist,
|
||||
'stake_amount': config['stake_amount'],
|
||||
'stake_currency': config['stake_currency'],
|
||||
'stake_currency_decimals': decimals_per_coin(config['stake_currency']),
|
||||
@ -462,12 +455,14 @@ def generate_strategy_stats(btdata: Dict[str, DataFrame],
|
||||
}
|
||||
|
||||
try:
|
||||
max_drawdown, _, _, _, _ = calculate_max_drawdown(
|
||||
max_drawdown_legacy, _, _, _, _, _ = calculate_max_drawdown(
|
||||
results, value_col='profit_ratio')
|
||||
drawdown_abs, drawdown_start, drawdown_end, high_val, low_val = calculate_max_drawdown(
|
||||
results, value_col='profit_abs')
|
||||
(drawdown_abs, drawdown_start, drawdown_end, high_val, low_val,
|
||||
max_drawdown) = calculate_max_drawdown(
|
||||
results, value_col='profit_abs', starting_balance=starting_balance)
|
||||
strat_stats.update({
|
||||
'max_drawdown': max_drawdown,
|
||||
'max_drawdown': max_drawdown_legacy, # Deprecated - do not use
|
||||
'max_drawdown_account': max_drawdown,
|
||||
'max_drawdown_abs': drawdown_abs,
|
||||
'drawdown_start': drawdown_start.strftime(DATETIME_PRINT_FORMAT),
|
||||
'drawdown_start_ts': drawdown_start.timestamp() * 1000,
|
||||
@ -487,6 +482,7 @@ def generate_strategy_stats(btdata: Dict[str, DataFrame],
|
||||
except ValueError:
|
||||
strat_stats.update({
|
||||
'max_drawdown': 0.0,
|
||||
'max_drawdown_account': 0.0,
|
||||
'max_drawdown_abs': 0.0,
|
||||
'max_drawdown_low': 0.0,
|
||||
'max_drawdown_high': 0.0,
|
||||
@ -515,13 +511,13 @@ def generate_backtest_stats(btdata: Dict[str, DataFrame],
|
||||
"""
|
||||
result: Dict[str, Any] = {'strategy': {}}
|
||||
market_change = calculate_market_change(btdata, 'close')
|
||||
|
||||
pairlist = list(btdata.keys())
|
||||
for strategy, content in all_results.items():
|
||||
strat_stats = generate_strategy_stats(btdata, strategy, content,
|
||||
strat_stats = generate_strategy_stats(pairlist, strategy, content,
|
||||
min_date, max_date, market_change=market_change)
|
||||
result['strategy'][strategy] = strat_stats
|
||||
|
||||
strategy_results = generate_strategy_comparison(all_results=all_results)
|
||||
strategy_results = generate_strategy_comparison(bt_stats=result['strategy'])
|
||||
|
||||
result['strategy_comparison'] = strategy_results
|
||||
|
||||
@ -646,7 +642,12 @@ def text_table_strategy(strategy_results, stake_currency: str) -> str:
|
||||
headers.append('Drawdown')
|
||||
|
||||
# Align drawdown string on the center two space separator.
|
||||
drawdown = [f'{t["max_drawdown_per"]:.2f}' for t in strategy_results]
|
||||
if 'max_drawdown_account' in strategy_results[0]:
|
||||
drawdown = [f'{t["max_drawdown_account"] * 100:.2f}' for t in strategy_results]
|
||||
else:
|
||||
# Support for prior backtest results
|
||||
drawdown = [f'{t["max_drawdown_per"]:.2f}' for t in strategy_results]
|
||||
|
||||
dd_pad_abs = max([len(t['max_drawdown_abs']) for t in strategy_results])
|
||||
dd_pad_per = max([len(dd) for dd in drawdown])
|
||||
drawdown = [f'{t["max_drawdown_abs"]:>{dd_pad_abs}} {stake_currency} {dd:>{dd_pad_per}}%'
|
||||
@ -716,7 +717,10 @@ def text_table_add_metrics(strat_results: Dict) -> str:
|
||||
('Max balance', round_coin_value(strat_results['csum_max'],
|
||||
strat_results['stake_currency'])),
|
||||
|
||||
('Drawdown', f"{strat_results['max_drawdown']:.2%}"),
|
||||
# Compatibility to show old hyperopt results
|
||||
('Drawdown (Account)', f"{strat_results['max_drawdown_account']:.2%}")
|
||||
if 'max_drawdown_account' in strat_results else (
|
||||
'Drawdown', f"{strat_results['max_drawdown']:.2%}"),
|
||||
('Drawdown', round_coin_value(strat_results['max_drawdown_abs'],
|
||||
strat_results['stake_currency'])),
|
||||
('Drawdown high', round_coin_value(strat_results['max_drawdown_high'],
|
||||
|
||||
@ -161,7 +161,7 @@ def add_max_drawdown(fig, row, trades: pd.DataFrame, df_comb: pd.DataFrame,
|
||||
Add scatter points indicating max drawdown
|
||||
"""
|
||||
try:
|
||||
max_drawdown, highdate, lowdate, _, _ = calculate_max_drawdown(trades)
|
||||
_, highdate, lowdate, _, _, max_drawdown = calculate_max_drawdown(trades)
|
||||
|
||||
drawdown = go.Scatter(
|
||||
x=[highdate, lowdate],
|
||||
|
||||
@ -4,7 +4,6 @@ Volume PairList provider
|
||||
Provides dynamic pair list based on trade volumes
|
||||
"""
|
||||
import logging
|
||||
from functools import partial
|
||||
from typing import Any, Dict, List
|
||||
|
||||
import arrow
|
||||
@ -120,10 +119,17 @@ class VolumePairList(IPairList):
|
||||
else:
|
||||
# Use fresh pairlist
|
||||
# Check if pair quote currency equals to the stake currency.
|
||||
_pairlist = [k for k in self._exchange.get_markets(
|
||||
quote_currencies=[self._stake_currency],
|
||||
pairs_only=True, active_only=True).keys()]
|
||||
# No point in testing for blacklisted pairs...
|
||||
_pairlist = self.verify_blacklist(_pairlist, logger.info)
|
||||
|
||||
filtered_tickers = [
|
||||
v for k, v in tickers.items()
|
||||
if (self._exchange.get_pair_quote_currency(k) == self._stake_currency
|
||||
and (self._use_range or v[self._sort_key] is not None))]
|
||||
and (self._use_range or v[self._sort_key] is not None)
|
||||
and v['symbol'] in _pairlist)]
|
||||
pairlist = [s['symbol'] for s in filtered_tickers]
|
||||
|
||||
pairlist = self.filter_pairlist(pairlist, tickers)
|
||||
@ -178,12 +184,16 @@ class VolumePairList(IPairList):
|
||||
] if (p['symbol'], self._lookback_timeframe) in candles else None
|
||||
# in case of candle data calculate typical price and quoteVolume for candle
|
||||
if pair_candles is not None and not pair_candles.empty:
|
||||
pair_candles['typical_price'] = (pair_candles['high'] + pair_candles['low']
|
||||
+ pair_candles['close']) / 3
|
||||
pair_candles['quoteVolume'] = (
|
||||
pair_candles['volume'] * pair_candles['typical_price']
|
||||
)
|
||||
if self._exchange._ft_has["ohlcv_volume_currency"] == "base":
|
||||
pair_candles['typical_price'] = (pair_candles['high'] + pair_candles['low']
|
||||
+ pair_candles['close']) / 3
|
||||
|
||||
pair_candles['quoteVolume'] = (
|
||||
pair_candles['volume'] * pair_candles['typical_price']
|
||||
)
|
||||
else:
|
||||
# Exchange ohlcv data is in quote volume already.
|
||||
pair_candles['quoteVolume'] = pair_candles['volume']
|
||||
# ensure that a rolling sum over the lookback_period is built
|
||||
# if pair_candles contains more candles than lookback_period
|
||||
quoteVolume = (pair_candles['quoteVolume']
|
||||
@ -204,7 +214,7 @@ class VolumePairList(IPairList):
|
||||
|
||||
# Validate whitelist to only have active market pairs
|
||||
pairs = self._whitelist_for_active_markets([s['symbol'] for s in sorted_tickers])
|
||||
pairs = self.verify_blacklist(pairs, partial(self.log_once, logmethod=logger.info))
|
||||
pairs = self.verify_blacklist(pairs, logmethod=logger.info)
|
||||
# Limit pairlist to the requested number of pairs
|
||||
pairs = pairs[:self._number_pairs]
|
||||
|
||||
|
||||
@ -55,7 +55,8 @@ class MaxDrawdown(IProtection):
|
||||
|
||||
# Drawdown is always positive
|
||||
try:
|
||||
drawdown, _, _, _, _ = calculate_max_drawdown(trades_df, value_col='close_profit')
|
||||
# TODO: This should use absolute profit calculation, considering account balance.
|
||||
drawdown, _, _, _, _, _ = calculate_max_drawdown(trades_df, value_col='close_profit')
|
||||
except ValueError:
|
||||
return False, None, None
|
||||
|
||||
|
||||
@ -7,5 +7,4 @@ scikit-learn==1.0.2
|
||||
scikit-optimize==0.9.0
|
||||
filelock==3.4.2
|
||||
joblib==1.1.0
|
||||
psutil==5.8.0
|
||||
progressbar2==3.55.0
|
||||
|
||||
@ -3,7 +3,7 @@ numpy==1.22.0; python_version > '3.7'
|
||||
pandas==1.3.5
|
||||
pandas-ta==0.3.14b
|
||||
|
||||
ccxt==1.66.20
|
||||
ccxt==1.66.32
|
||||
# Pin cryptography for now due to rust build errors with piwheels
|
||||
cryptography==36.0.1
|
||||
aiohttp==3.8.1
|
||||
@ -14,7 +14,7 @@ cachetools==4.2.2
|
||||
requests==2.26.0
|
||||
urllib3==1.26.7
|
||||
jsonschema==4.3.3
|
||||
TA-Lib==0.4.22
|
||||
TA-Lib==0.4.23
|
||||
technical==1.3.0
|
||||
tabulate==0.8.9
|
||||
pycoingecko==2.2.0
|
||||
@ -36,7 +36,7 @@ fastapi==0.70.1
|
||||
uvicorn==0.16.0
|
||||
pyjwt==2.3.0
|
||||
aiofiles==0.8.0
|
||||
psutil==5.8.0
|
||||
psutil==5.9.0
|
||||
|
||||
# Support for colorized terminal output
|
||||
colorama==0.4.4
|
||||
|
||||
2
setup.py
2
setup.py
@ -43,7 +43,7 @@ setup(
|
||||
],
|
||||
install_requires=[
|
||||
# from requirements.txt
|
||||
'ccxt>=1.60.11',
|
||||
'ccxt>=1.66.32',
|
||||
'SQLAlchemy',
|
||||
'python-telegram-bot>=13.4',
|
||||
'arrow>=0.17.0',
|
||||
|
||||
@ -2020,7 +2020,7 @@ def saved_hyperopt_results():
|
||||
'params_dict': {
|
||||
'mfi-value': 15, 'fastd-value': 20, 'adx-value': 25, 'rsi-value': 28, 'mfi-enabled': False, 'fastd-enabled': True, 'adx-enabled': True, 'rsi-enabled': True, 'trigger': 'macd_cross_signal', 'sell-mfi-value': 88, 'sell-fastd-value': 97, 'sell-adx-value': 51, 'sell-rsi-value': 67, 'sell-mfi-enabled': False, 'sell-fastd-enabled': False, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-bb_upper', 'roi_t1': 1190, 'roi_t2': 541, 'roi_t3': 408, 'roi_p1': 0.026035863879169705, 'roi_p2': 0.12508730043628782, 'roi_p3': 0.27766427921605896, 'stoploss': -0.2562930402099556}, # noqa: E501
|
||||
'params_details': {'buy': {'mfi-value': 15, 'fastd-value': 20, 'adx-value': 25, 'rsi-value': 28, 'mfi-enabled': False, 'fastd-enabled': True, 'adx-enabled': True, 'rsi-enabled': True, 'trigger': 'macd_cross_signal'}, 'sell': {'sell-mfi-value': 88, 'sell-fastd-value': 97, 'sell-adx-value': 51, 'sell-rsi-value': 67, 'sell-mfi-enabled': False, 'sell-fastd-enabled': False, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-bb_upper'}, 'roi': {0: 0.4287874435315165, 408: 0.15112316431545753, 949: 0.026035863879169705, 2139: 0}, 'stoploss': {'stoploss': -0.2562930402099556}}, # noqa: E501
|
||||
'results_metrics': {'total_trades': 2, 'wins': 0, 'draws': 0, 'losses': 2, 'profit_mean': -0.01254995, 'profit_median': -0.012222, 'profit_total': -0.00125625, 'profit_total_abs': -2.50999, 'holding_avg': timedelta(minutes=3930.0), 'stake_currency': 'BTC', 'strategy_name': 'SampleStrategy'}, # noqa: E501
|
||||
'results_metrics': {'total_trades': 2, 'wins': 0, 'draws': 0, 'losses': 2, 'profit_mean': -0.01254995, 'profit_median': -0.012222, 'profit_total': -0.00125625, 'profit_total_abs': -2.50999, 'max_drawdown': 0.23, 'max_drawdown_abs': -0.00125625, 'holding_avg': timedelta(minutes=3930.0), 'stake_currency': 'BTC', 'strategy_name': 'SampleStrategy'}, # noqa: E501
|
||||
'results_explanation': ' 2 trades. Avg profit -1.25%. Total profit -0.00125625 BTC ( -2.51Σ%). Avg duration 3930.0 min.', # noqa: E501
|
||||
'total_profit': -0.00125625,
|
||||
'current_epoch': 1,
|
||||
@ -2036,7 +2036,7 @@ def saved_hyperopt_results():
|
||||
'sell': {'sell-mfi-value': 96, 'sell-fastd-value': 68, 'sell-adx-value': 63, 'sell-rsi-value': 81, 'sell-mfi-enabled': False, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-sar_reversal'}, # noqa: E501
|
||||
'roi': {0: 0.4449309386008759, 140: 0.11955965746663, 823: 0.06403981740598495, 1157: 0}, # noqa: E501
|
||||
'stoploss': {'stoploss': -0.338070047333259}},
|
||||
'results_metrics': {'total_trades': 1, 'wins': 0, 'draws': 0, 'losses': 1, 'profit_mean': 0.012357, 'profit_median': -0.012222, 'profit_total': 6.185e-05, 'profit_total_abs': 0.12357, 'holding_avg': timedelta(minutes=1200.0)}, # noqa: E501
|
||||
'results_metrics': {'total_trades': 1, 'wins': 0, 'draws': 0, 'losses': 1, 'profit_mean': 0.012357, 'profit_median': -0.012222, 'profit_total': 6.185e-05, 'profit_total_abs': 0.12357, 'max_drawdown': 0.23, 'max_drawdown_abs': -0.00125625, 'holding_avg': timedelta(minutes=1200.0)}, # noqa: E501
|
||||
'results_explanation': ' 1 trades. Avg profit 0.12%. Total profit 0.00006185 BTC ( 0.12Σ%). Avg duration 1200.0 min.', # noqa: E501
|
||||
'total_profit': 6.185e-05,
|
||||
'current_epoch': 2,
|
||||
@ -2046,7 +2046,7 @@ def saved_hyperopt_results():
|
||||
'loss': 14.241196856510731,
|
||||
'params_dict': {'mfi-value': 25, 'fastd-value': 16, 'adx-value': 29, 'rsi-value': 20, 'mfi-enabled': False, 'fastd-enabled': False, 'adx-enabled': False, 'rsi-enabled': False, 'trigger': 'macd_cross_signal', 'sell-mfi-value': 98, 'sell-fastd-value': 72, 'sell-adx-value': 51, 'sell-rsi-value': 82, 'sell-mfi-enabled': True, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-macd_cross_signal', 'roi_t1': 889, 'roi_t2': 533, 'roi_t3': 263, 'roi_p1': 0.04759065393663096, 'roi_p2': 0.1488819964638463, 'roi_p3': 0.4102801822104605, 'stoploss': -0.05394588767607611}, # noqa: E501
|
||||
'params_details': {'buy': {'mfi-value': 25, 'fastd-value': 16, 'adx-value': 29, 'rsi-value': 20, 'mfi-enabled': False, 'fastd-enabled': False, 'adx-enabled': False, 'rsi-enabled': False, 'trigger': 'macd_cross_signal'}, 'sell': {'sell-mfi-value': 98, 'sell-fastd-value': 72, 'sell-adx-value': 51, 'sell-rsi-value': 82, 'sell-mfi-enabled': True, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-macd_cross_signal'}, 'roi': {0: 0.6067528326109377, 263: 0.19647265040047726, 796: 0.04759065393663096, 1685: 0}, 'stoploss': {'stoploss': -0.05394588767607611}}, # noqa: E501
|
||||
'results_metrics': {'total_trades': 621, 'wins': 320, 'draws': 0, 'losses': 301, 'profit_mean': -0.043883302093397747, 'profit_median': -0.012222, 'profit_total': -0.13639474, 'profit_total_abs': -272.515306, 'holding_avg': timedelta(minutes=1691.207729468599)}, # noqa: E501
|
||||
'results_metrics': {'total_trades': 621, 'wins': 320, 'draws': 0, 'losses': 301, 'profit_mean': -0.043883302093397747, 'profit_median': -0.012222, 'profit_total': -0.13639474, 'profit_total_abs': -272.515306, 'max_drawdown': 0.25, 'max_drawdown_abs': -272.515306, 'holding_avg': timedelta(minutes=1691.207729468599)}, # noqa: E501
|
||||
'results_explanation': ' 621 trades. Avg profit -0.44%. Total profit -0.13639474 BTC (-272.52Σ%). Avg duration 1691.2 min.', # noqa: E501
|
||||
'total_profit': -0.13639474,
|
||||
'current_epoch': 3,
|
||||
@ -2063,7 +2063,7 @@ def saved_hyperopt_results():
|
||||
'loss': 0.22195522184191518,
|
||||
'params_dict': {'mfi-value': 17, 'fastd-value': 21, 'adx-value': 38, 'rsi-value': 33, 'mfi-enabled': True, 'fastd-enabled': False, 'adx-enabled': True, 'rsi-enabled': False, 'trigger': 'macd_cross_signal', 'sell-mfi-value': 87, 'sell-fastd-value': 82, 'sell-adx-value': 78, 'sell-rsi-value': 69, 'sell-mfi-enabled': True, 'sell-fastd-enabled': False, 'sell-adx-enabled': True, 'sell-rsi-enabled': False, 'sell-trigger': 'sell-macd_cross_signal', 'roi_t1': 1269, 'roi_t2': 601, 'roi_t3': 444, 'roi_p1': 0.07280999507931168, 'roi_p2': 0.08946698095898986, 'roi_p3': 0.1454876733325284, 'stoploss': -0.18181041180901014}, # noqa: E501
|
||||
'params_details': {'buy': {'mfi-value': 17, 'fastd-value': 21, 'adx-value': 38, 'rsi-value': 33, 'mfi-enabled': True, 'fastd-enabled': False, 'adx-enabled': True, 'rsi-enabled': False, 'trigger': 'macd_cross_signal'}, 'sell': {'sell-mfi-value': 87, 'sell-fastd-value': 82, 'sell-adx-value': 78, 'sell-rsi-value': 69, 'sell-mfi-enabled': True, 'sell-fastd-enabled': False, 'sell-adx-enabled': True, 'sell-rsi-enabled': False, 'sell-trigger': 'sell-macd_cross_signal'}, 'roi': {0: 0.3077646493708299, 444: 0.16227697603830155, 1045: 0.07280999507931168, 2314: 0}, 'stoploss': {'stoploss': -0.18181041180901014}}, # noqa: E501
|
||||
'results_metrics': {'total_trades': 14, 'wins': 6, 'draws': 0, 'losses': 8, 'profit_mean': -0.003539515, 'profit_median': -0.012222, 'profit_total': -0.002480140000000001, 'profit_total_abs': -4.955321, 'holding_avg': timedelta(minutes=3402.8571428571427)}, # noqa: E501
|
||||
'results_metrics': {'total_trades': 14, 'wins': 6, 'draws': 0, 'losses': 8, 'profit_mean': -0.003539515, 'profit_median': -0.012222, 'profit_total': -0.002480140000000001, 'profit_total_abs': -4.955321, 'max_drawdown': 0.34, 'max_drawdown_abs': -4.955321, 'holding_avg': timedelta(minutes=3402.8571428571427)}, # noqa: E501
|
||||
'results_explanation': ' 14 trades. Avg profit -0.35%. Total profit -0.00248014 BTC ( -4.96Σ%). Avg duration 3402.9 min.', # noqa: E501
|
||||
'total_profit': -0.002480140000000001,
|
||||
'current_epoch': 5,
|
||||
@ -2073,7 +2073,7 @@ def saved_hyperopt_results():
|
||||
'loss': 0.545315889154162,
|
||||
'params_dict': {'mfi-value': 22, 'fastd-value': 43, 'adx-value': 46, 'rsi-value': 20, 'mfi-enabled': False, 'fastd-enabled': False, 'adx-enabled': True, 'rsi-enabled': True, 'trigger': 'bb_lower', 'sell-mfi-value': 87, 'sell-fastd-value': 65, 'sell-adx-value': 94, 'sell-rsi-value': 63, 'sell-mfi-enabled': False, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-macd_cross_signal', 'roi_t1': 319, 'roi_t2': 556, 'roi_t3': 216, 'roi_p1': 0.06251955472249589, 'roi_p2': 0.11659519602202795, 'roi_p3': 0.0953744132197762, 'stoploss': -0.024551752215582423}, # noqa: E501
|
||||
'params_details': {'buy': {'mfi-value': 22, 'fastd-value': 43, 'adx-value': 46, 'rsi-value': 20, 'mfi-enabled': False, 'fastd-enabled': False, 'adx-enabled': True, 'rsi-enabled': True, 'trigger': 'bb_lower'}, 'sell': {'sell-mfi-value': 87, 'sell-fastd-value': 65, 'sell-adx-value': 94, 'sell-rsi-value': 63, 'sell-mfi-enabled': False, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-macd_cross_signal'}, 'roi': {0: 0.2744891639643, 216: 0.17911475074452382, 772: 0.06251955472249589, 1091: 0}, 'stoploss': {'stoploss': -0.024551752215582423}}, # noqa: E501
|
||||
'results_metrics': {'total_trades': 39, 'wins': 20, 'draws': 0, 'losses': 19, 'profit_mean': -0.0021400679487179478, 'profit_median': -0.012222, 'profit_total': -0.0041773, 'profit_total_abs': -8.346264999999997, 'holding_avg': timedelta(minutes=636.9230769230769)}, # noqa: E501
|
||||
'results_metrics': {'total_trades': 39, 'wins': 20, 'draws': 0, 'losses': 19, 'profit_mean': -0.0021400679487179478, 'profit_median': -0.012222, 'profit_total': -0.0041773, 'profit_total_abs': -8.346264999999997, 'max_drawdown': 0.45, 'max_drawdown_abs': -4.955321, 'holding_avg': timedelta(minutes=636.9230769230769)}, # noqa: E501
|
||||
'results_explanation': ' 39 trades. Avg profit -0.21%. Total profit -0.00417730 BTC ( -8.35Σ%). Avg duration 636.9 min.', # noqa: E501
|
||||
'total_profit': -0.0041773,
|
||||
'current_epoch': 6,
|
||||
@ -2085,7 +2085,7 @@ def saved_hyperopt_results():
|
||||
'params_details': {
|
||||
'buy': {'mfi-value': 13, 'fastd-value': 41, 'adx-value': 21, 'rsi-value': 29, 'mfi-enabled': False, 'fastd-enabled': True, 'adx-enabled': False, 'rsi-enabled': False, 'trigger': 'bb_lower'}, 'sell': {'sell-mfi-value': 99, 'sell-fastd-value': 60, 'sell-adx-value': 81, 'sell-rsi-value': 69, 'sell-mfi-enabled': True, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': False, 'sell-trigger': 'sell-macd_cross_signal'}, 'roi': {0: 0.4837436938134452, 145: 0.10853310701097472, 765: 0.0586919200378493, 1536: 0}, # noqa: E501
|
||||
'stoploss': {'stoploss': -0.14613268022709905}}, # noqa: E501
|
||||
'results_metrics': {'total_trades': 318, 'wins': 100, 'draws': 0, 'losses': 218, 'profit_mean': -0.0039833954716981146, 'profit_median': -0.012222, 'profit_total': -0.06339929, 'profit_total_abs': -126.67197600000004, 'holding_avg': timedelta(minutes=3140.377358490566)}, # noqa: E501
|
||||
'results_metrics': {'total_trades': 318, 'wins': 100, 'draws': 0, 'losses': 218, 'profit_mean': -0.0039833954716981146, 'profit_median': -0.012222, 'profit_total': -0.06339929, 'profit_total_abs': -126.67197600000004, 'max_drawdown': 0.50, 'max_drawdown_abs': -200.955321, 'holding_avg': timedelta(minutes=3140.377358490566)}, # noqa: E501
|
||||
'results_explanation': ' 318 trades. Avg profit -0.40%. Total profit -0.06339929 BTC (-126.67Σ%). Avg duration 3140.4 min.', # noqa: E501
|
||||
'total_profit': -0.06339929,
|
||||
'current_epoch': 7,
|
||||
@ -2095,7 +2095,7 @@ def saved_hyperopt_results():
|
||||
'loss': 20.0, # noqa: E501
|
||||
'params_dict': {'mfi-value': 24, 'fastd-value': 43, 'adx-value': 33, 'rsi-value': 20, 'mfi-enabled': False, 'fastd-enabled': True, 'adx-enabled': True, 'rsi-enabled': True, 'trigger': 'sar_reversal', 'sell-mfi-value': 89, 'sell-fastd-value': 74, 'sell-adx-value': 70, 'sell-rsi-value': 70, 'sell-mfi-enabled': False, 'sell-fastd-enabled': False, 'sell-adx-enabled': False, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-sar_reversal', 'roi_t1': 1149, 'roi_t2': 375, 'roi_t3': 289, 'roi_p1': 0.05571820757172588, 'roi_p2': 0.0606240398618907, 'roi_p3': 0.1729012220156157, 'stoploss': -0.1588514289110401}, # noqa: E501
|
||||
'params_details': {'buy': {'mfi-value': 24, 'fastd-value': 43, 'adx-value': 33, 'rsi-value': 20, 'mfi-enabled': False, 'fastd-enabled': True, 'adx-enabled': True, 'rsi-enabled': True, 'trigger': 'sar_reversal'}, 'sell': {'sell-mfi-value': 89, 'sell-fastd-value': 74, 'sell-adx-value': 70, 'sell-rsi-value': 70, 'sell-mfi-enabled': False, 'sell-fastd-enabled': False, 'sell-adx-enabled': False, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-sar_reversal'}, 'roi': {0: 0.2892434694492323, 289: 0.11634224743361658, 664: 0.05571820757172588, 1813: 0}, 'stoploss': {'stoploss': -0.1588514289110401}}, # noqa: E501
|
||||
'results_metrics': {'total_trades': 1, 'wins': 0, 'draws': 1, 'losses': 0, 'profit_mean': 0.0, 'profit_median': 0.0, 'profit_total': 0.0, 'profit_total_abs': 0.0, 'holding_avg': timedelta(minutes=5340.0)}, # noqa: E501
|
||||
'results_metrics': {'total_trades': 1, 'wins': 0, 'draws': 1, 'losses': 0, 'profit_mean': 0.0, 'profit_median': 0.0, 'profit_total': 0.0, 'profit_total_abs': 0.0, 'max_drawdown': 0.0, 'max_drawdown_abs': 0.52, 'holding_avg': timedelta(minutes=5340.0)}, # noqa: E501
|
||||
'results_explanation': ' 1 trades. Avg profit 0.00%. Total profit 0.00000000 BTC ( 0.00Σ%). Avg duration 5340.0 min.', # noqa: E501
|
||||
'total_profit': 0.0,
|
||||
'current_epoch': 8,
|
||||
@ -2105,7 +2105,7 @@ def saved_hyperopt_results():
|
||||
'loss': 2.4731817780991223,
|
||||
'params_dict': {'mfi-value': 22, 'fastd-value': 20, 'adx-value': 29, 'rsi-value': 40, 'mfi-enabled': False, 'fastd-enabled': False, 'adx-enabled': False, 'rsi-enabled': False, 'trigger': 'sar_reversal', 'sell-mfi-value': 97, 'sell-fastd-value': 65, 'sell-adx-value': 81, 'sell-rsi-value': 64, 'sell-mfi-enabled': True, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-bb_upper', 'roi_t1': 1012, 'roi_t2': 584, 'roi_t3': 422, 'roi_p1': 0.036764323603472565, 'roi_p2': 0.10335480573205287, 'roi_p3': 0.10322347377503042, 'stoploss': -0.2780610808108503}, # noqa: E501
|
||||
'params_details': {'buy': {'mfi-value': 22, 'fastd-value': 20, 'adx-value': 29, 'rsi-value': 40, 'mfi-enabled': False, 'fastd-enabled': False, 'adx-enabled': False, 'rsi-enabled': False, 'trigger': 'sar_reversal'}, 'sell': {'sell-mfi-value': 97, 'sell-fastd-value': 65, 'sell-adx-value': 81, 'sell-rsi-value': 64, 'sell-mfi-enabled': True, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-bb_upper'}, 'roi': {0: 0.2433426031105559, 422: 0.14011912933552545, 1006: 0.036764323603472565, 2018: 0}, 'stoploss': {'stoploss': -0.2780610808108503}}, # noqa: E501
|
||||
'results_metrics': {'total_trades': 229, 'wins': 150, 'draws': 0, 'losses': 79, 'profit_mean': -0.0038433433624454144, 'profit_median': -0.012222, 'profit_total': -0.044050070000000004, 'profit_total_abs': -88.01256299999999, 'holding_avg': timedelta(minutes=6505.676855895196)}, # noqa: E501
|
||||
'results_metrics': {'total_trades': 229, 'wins': 150, 'draws': 0, 'losses': 79, 'profit_mean': -0.0038433433624454144, 'profit_median': -0.012222, 'profit_total': -0.044050070000000004, 'profit_total_abs': -88.01256299999999, 'max_drawdown': 0.41, 'max_drawdown_abs': -150.955321, 'holding_avg': timedelta(minutes=6505.676855895196)}, # noqa: E501
|
||||
'results_explanation': ' 229 trades. Avg profit -0.38%. Total profit -0.04405007 BTC ( -88.01Σ%). Avg duration 6505.7 min.', # noqa: E501
|
||||
'total_profit': -0.044050070000000004, # noqa: E501
|
||||
'current_epoch': 9,
|
||||
@ -2115,7 +2115,7 @@ def saved_hyperopt_results():
|
||||
'loss': -0.2604606005845212, # noqa: E501
|
||||
'params_dict': {'mfi-value': 23, 'fastd-value': 24, 'adx-value': 22, 'rsi-value': 24, 'mfi-enabled': False, 'fastd-enabled': False, 'adx-enabled': False, 'rsi-enabled': True, 'trigger': 'macd_cross_signal', 'sell-mfi-value': 97, 'sell-fastd-value': 70, 'sell-adx-value': 64, 'sell-rsi-value': 80, 'sell-mfi-enabled': False, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-sar_reversal', 'roi_t1': 792, 'roi_t2': 464, 'roi_t3': 215, 'roi_p1': 0.04594053535385903, 'roi_p2': 0.09623192684243963, 'roi_p3': 0.04428219070850663, 'stoploss': -0.16992287161634415}, # noqa: E501
|
||||
'params_details': {'buy': {'mfi-value': 23, 'fastd-value': 24, 'adx-value': 22, 'rsi-value': 24, 'mfi-enabled': False, 'fastd-enabled': False, 'adx-enabled': False, 'rsi-enabled': True, 'trigger': 'macd_cross_signal'}, 'sell': {'sell-mfi-value': 97, 'sell-fastd-value': 70, 'sell-adx-value': 64, 'sell-rsi-value': 80, 'sell-mfi-enabled': False, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-sar_reversal'}, 'roi': {0: 0.18645465290480528, 215: 0.14217246219629864, 679: 0.04594053535385903, 1471: 0}, 'stoploss': {'stoploss': -0.16992287161634415}}, # noqa: E501
|
||||
'results_metrics': {'total_trades': 4, 'wins': 0, 'draws': 0, 'losses': 4, 'profit_mean': 0.001080385, 'profit_median': -0.012222, 'profit_total': 0.00021629, 'profit_total_abs': 0.432154, 'holding_avg': timedelta(minutes=2850.0)}, # noqa: E501
|
||||
'results_metrics': {'total_trades': 4, 'wins': 0, 'draws': 0, 'losses': 4, 'profit_mean': 0.001080385, 'profit_median': -0.012222, 'profit_total': 0.00021629, 'profit_total_abs': 0.432154, 'max_drawdown': 0.13, 'max_drawdown_abs': -4.955321, 'holding_avg': timedelta(minutes=2850.0)}, # noqa: E501
|
||||
'results_explanation': ' 4 trades. Avg profit 0.11%. Total profit 0.00021629 BTC ( 0.43Σ%). Avg duration 2850.0 min.', # noqa: E501
|
||||
'total_profit': 0.00021629,
|
||||
'current_epoch': 10,
|
||||
@ -2126,7 +2126,7 @@ def saved_hyperopt_results():
|
||||
'params_dict': {'mfi-value': 20, 'fastd-value': 32, 'adx-value': 49, 'rsi-value': 23, 'mfi-enabled': True, 'fastd-enabled': True, 'adx-enabled': False, 'rsi-enabled': False, 'trigger': 'bb_lower', 'sell-mfi-value': 75, 'sell-fastd-value': 56, 'sell-adx-value': 61, 'sell-rsi-value': 62, 'sell-mfi-enabled': False, 'sell-fastd-enabled': False, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-macd_cross_signal', 'roi_t1': 579, 'roi_t2': 614, 'roi_t3': 273, 'roi_p1': 0.05307643172744114, 'roi_p2': 0.1352282078262871, 'roi_p3': 0.1913307406325751, 'stoploss': -0.25728526022513887}, # noqa: E501
|
||||
'params_details': {'buy': {'mfi-value': 20, 'fastd-value': 32, 'adx-value': 49, 'rsi-value': 23, 'mfi-enabled': True, 'fastd-enabled': True, 'adx-enabled': False, 'rsi-enabled': False, 'trigger': 'bb_lower'}, 'sell': {'sell-mfi-value': 75, 'sell-fastd-value': 56, 'sell-adx-value': 61, 'sell-rsi-value': 62, 'sell-mfi-enabled': False, 'sell-fastd-enabled': False, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-macd_cross_signal'}, 'roi': {0: 0.3796353801863034, 273: 0.18830463955372825, 887: 0.05307643172744114, 1466: 0}, 'stoploss': {'stoploss': -0.25728526022513887}}, # noqa: E501
|
||||
# New Hyperopt mode!
|
||||
'results_metrics': {'total_trades': 117, 'wins': 67, 'draws': 0, 'losses': 50, 'profit_mean': -0.012698609145299145, 'profit_median': -0.012222, 'profit_total': -0.07436117, 'profit_total_abs': -148.573727, 'holding_avg': timedelta(minutes=4282.5641025641025)}, # noqa: E501
|
||||
'results_metrics': {'total_trades': 117, 'wins': 67, 'draws': 0, 'losses': 50, 'profit_mean': -0.012698609145299145, 'profit_median': -0.012222, 'profit_total': -0.07436117, 'profit_total_abs': -148.573727, 'max_drawdown': 0.52, 'max_drawdown_abs': -224.955321, 'holding_avg': timedelta(minutes=4282.5641025641025)}, # noqa: E501
|
||||
'results_explanation': ' 117 trades. Avg profit -1.27%. Total profit -0.07436117 BTC (-148.57Σ%). Avg duration 4282.6 min.', # noqa: E501
|
||||
'total_profit': -0.07436117,
|
||||
'current_epoch': 11,
|
||||
@ -2136,7 +2136,7 @@ def saved_hyperopt_results():
|
||||
'loss': 100000,
|
||||
'params_dict': {'mfi-value': 10, 'fastd-value': 36, 'adx-value': 31, 'rsi-value': 22, 'mfi-enabled': True, 'fastd-enabled': True, 'adx-enabled': True, 'rsi-enabled': False, 'trigger': 'sar_reversal', 'sell-mfi-value': 80, 'sell-fastd-value': 71, 'sell-adx-value': 60, 'sell-rsi-value': 85, 'sell-mfi-enabled': False, 'sell-fastd-enabled': False, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-bb_upper', 'roi_t1': 1156, 'roi_t2': 581, 'roi_t3': 408, 'roi_p1': 0.06860454019988212, 'roi_p2': 0.12473718444931989, 'roi_p3': 0.2896360635226823, 'stoploss': -0.30889015124682806}, # noqa: E501
|
||||
'params_details': {'buy': {'mfi-value': 10, 'fastd-value': 36, 'adx-value': 31, 'rsi-value': 22, 'mfi-enabled': True, 'fastd-enabled': True, 'adx-enabled': True, 'rsi-enabled': False, 'trigger': 'sar_reversal'}, 'sell': {'sell-mfi-value': 80, 'sell-fastd-value': 71, 'sell-adx-value': 60, 'sell-rsi-value': 85, 'sell-mfi-enabled': False, 'sell-fastd-enabled': False, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-bb_upper'}, 'roi': {0: 0.4829777881718843, 408: 0.19334172464920202, 989: 0.06860454019988212, 2145: 0}, 'stoploss': {'stoploss': -0.30889015124682806}}, # noqa: E501
|
||||
'results_metrics': {'total_trades': 0, 'wins': 0, 'draws': 0, 'losses': 0, 'profit_mean': None, 'profit_median': None, 'profit_total': 0, 'profit_total_abs': 0.0, 'holding_avg': timedelta()}, # noqa: E501
|
||||
'results_metrics': {'total_trades': 0, 'wins': 0, 'draws': 0, 'losses': 0, 'profit_mean': None, 'profit_median': None, 'profit_total': 0, 'profit_total_abs': 0.0, 'max_drawdown': 0.0, 'max_drawdown_abs': 0.0, 'holding_avg': timedelta()}, # noqa: E501
|
||||
'results_explanation': ' 0 trades. Avg profit nan%. Total profit 0.00000000 BTC ( 0.00Σ%). Avg duration nan min.', # noqa: E501
|
||||
'total_profit': 0,
|
||||
'current_epoch': 12,
|
||||
|
||||
@ -8,14 +8,14 @@ from pandas import DataFrame, DateOffset, Timestamp, to_datetime
|
||||
|
||||
from freqtrade.configuration import TimeRange
|
||||
from freqtrade.constants import LAST_BT_RESULT_FN
|
||||
from freqtrade.data.btanalysis import (BT_DATA_COLUMNS, BT_DATA_COLUMNS_MID, BT_DATA_COLUMNS_OLD,
|
||||
analyze_trade_parallelism, calculate_csum,
|
||||
from freqtrade.data.btanalysis import (BT_DATA_COLUMNS, analyze_trade_parallelism, calculate_csum,
|
||||
calculate_market_change, calculate_max_drawdown,
|
||||
calculate_underwater, combine_dataframes_with_mean,
|
||||
create_cum_profit, extract_trades_of_period,
|
||||
get_latest_backtest_filename, get_latest_hyperopt_file,
|
||||
load_backtest_data, load_trades, load_trades_from_db)
|
||||
from freqtrade.data.history import load_data, load_pair_history
|
||||
from freqtrade.exceptions import OperationalException
|
||||
from tests.conftest import create_mock_trades
|
||||
from tests.conftest_trades import MOCK_TRADE_COUNT
|
||||
|
||||
@ -51,20 +51,14 @@ def test_get_latest_hyperopt_file(testdatadir, mocker):
|
||||
assert res == testdatadir.parent / "hyperopt_results.pickle"
|
||||
|
||||
|
||||
def test_load_backtest_data_old_format(testdatadir):
|
||||
def test_load_backtest_data_old_format(testdatadir, mocker):
|
||||
|
||||
filename = testdatadir / "backtest-result_test.json"
|
||||
bt_data = load_backtest_data(filename)
|
||||
assert isinstance(bt_data, DataFrame)
|
||||
assert list(bt_data.columns) == BT_DATA_COLUMNS_OLD + ['profit_abs', 'profit_ratio']
|
||||
assert len(bt_data) == 179
|
||||
filename = testdatadir / "backtest-result_test222.json"
|
||||
mocker.patch('freqtrade.data.btanalysis.load_backtest_stats', return_value=[])
|
||||
|
||||
# Test loading from string (must yield same result)
|
||||
bt_data2 = load_backtest_data(str(filename))
|
||||
assert bt_data.equals(bt_data2)
|
||||
|
||||
with pytest.raises(ValueError, match=r"File .* does not exist\."):
|
||||
load_backtest_data(str("filename") + "nofile")
|
||||
with pytest.raises(OperationalException,
|
||||
match=r"Backtest-results with only trades data are no longer supported."):
|
||||
load_backtest_data(filename)
|
||||
|
||||
|
||||
def test_load_backtest_data_new_format(testdatadir):
|
||||
@ -72,7 +66,7 @@ def test_load_backtest_data_new_format(testdatadir):
|
||||
filename = testdatadir / "backtest-result_new.json"
|
||||
bt_data = load_backtest_data(filename)
|
||||
assert isinstance(bt_data, DataFrame)
|
||||
assert set(bt_data.columns) == set(BT_DATA_COLUMNS_MID)
|
||||
assert set(bt_data.columns) == set(BT_DATA_COLUMNS + ['close_timestamp', 'open_timestamp'])
|
||||
assert len(bt_data) == 179
|
||||
|
||||
# Test loading from string (must yield same result)
|
||||
@ -96,7 +90,7 @@ def test_load_backtest_data_multi(testdatadir):
|
||||
for strategy in ('StrategyTestV2', 'TestStrategy'):
|
||||
bt_data = load_backtest_data(filename, strategy=strategy)
|
||||
assert isinstance(bt_data, DataFrame)
|
||||
assert set(bt_data.columns) == set(BT_DATA_COLUMNS_MID)
|
||||
assert set(bt_data.columns) == set(BT_DATA_COLUMNS + ['close_timestamp', 'open_timestamp'])
|
||||
assert len(bt_data) == 179
|
||||
|
||||
# Test loading from string (must yield same result)
|
||||
@ -167,8 +161,8 @@ def test_extract_trades_of_period(testdatadir):
|
||||
assert trades1.iloc[-1].close_date == Arrow(2017, 11, 14, 15, 25, 0).datetime
|
||||
|
||||
|
||||
def test_analyze_trade_parallelism(default_conf, mocker, testdatadir):
|
||||
filename = testdatadir / "backtest-result_test.json"
|
||||
def test_analyze_trade_parallelism(testdatadir):
|
||||
filename = testdatadir / "backtest-result_new.json"
|
||||
bt_data = load_backtest_data(filename)
|
||||
|
||||
res = analyze_trade_parallelism(bt_data, "5m")
|
||||
@ -242,7 +236,7 @@ def test_combine_dataframes_with_mean_no_data(testdatadir):
|
||||
|
||||
|
||||
def test_create_cum_profit(testdatadir):
|
||||
filename = testdatadir / "backtest-result_test.json"
|
||||
filename = testdatadir / "backtest-result_new.json"
|
||||
bt_data = load_backtest_data(filename)
|
||||
timerange = TimeRange.parse_timerange("20180110-20180112")
|
||||
|
||||
@ -258,7 +252,7 @@ def test_create_cum_profit(testdatadir):
|
||||
|
||||
|
||||
def test_create_cum_profit1(testdatadir):
|
||||
filename = testdatadir / "backtest-result_test.json"
|
||||
filename = testdatadir / "backtest-result_new.json"
|
||||
bt_data = load_backtest_data(filename)
|
||||
# Move close-time to "off" the candle, to make sure the logic still works
|
||||
bt_data.loc[:, 'close_date'] = bt_data.loc[:, 'close_date'] + DateOffset(seconds=20)
|
||||
@ -280,30 +274,31 @@ def test_create_cum_profit1(testdatadir):
|
||||
|
||||
|
||||
def test_calculate_max_drawdown(testdatadir):
|
||||
filename = testdatadir / "backtest-result_test.json"
|
||||
filename = testdatadir / "backtest-result_new.json"
|
||||
bt_data = load_backtest_data(filename)
|
||||
drawdown, hdate, lowdate, hval, lval = calculate_max_drawdown(bt_data)
|
||||
_, hdate, lowdate, hval, lval, drawdown = calculate_max_drawdown(
|
||||
bt_data, value_col="profit_abs")
|
||||
assert isinstance(drawdown, float)
|
||||
assert pytest.approx(drawdown) == 0.21142322
|
||||
assert pytest.approx(drawdown) == 0.12071099
|
||||
assert isinstance(hdate, Timestamp)
|
||||
assert isinstance(lowdate, Timestamp)
|
||||
assert isinstance(hval, float)
|
||||
assert isinstance(lval, float)
|
||||
assert hdate == Timestamp('2018-01-24 14:25:00', tz='UTC')
|
||||
assert lowdate == Timestamp('2018-01-30 04:45:00', tz='UTC')
|
||||
assert hdate == Timestamp('2018-01-25 01:30:00', tz='UTC')
|
||||
assert lowdate == Timestamp('2018-01-25 03:50:00', tz='UTC')
|
||||
|
||||
underwater = calculate_underwater(bt_data)
|
||||
assert isinstance(underwater, DataFrame)
|
||||
|
||||
with pytest.raises(ValueError, match='Trade dataframe empty.'):
|
||||
drawdown, hdate, lowdate, hval, lval = calculate_max_drawdown(DataFrame())
|
||||
calculate_max_drawdown(DataFrame())
|
||||
|
||||
with pytest.raises(ValueError, match='Trade dataframe empty.'):
|
||||
calculate_underwater(DataFrame())
|
||||
|
||||
|
||||
def test_calculate_csum(testdatadir):
|
||||
filename = testdatadir / "backtest-result_test.json"
|
||||
filename = testdatadir / "backtest-result_new.json"
|
||||
bt_data = load_backtest_data(filename)
|
||||
csum_min, csum_max = calculate_csum(bt_data)
|
||||
|
||||
@ -331,12 +326,13 @@ def test_calculate_max_drawdown2():
|
||||
# sort by profit and reset index
|
||||
df = df.sort_values('profit').reset_index(drop=True)
|
||||
df1 = df.copy()
|
||||
drawdown, hdate, ldate, hval, lval = calculate_max_drawdown(
|
||||
drawdown, hdate, ldate, hval, lval, drawdown_rel = calculate_max_drawdown(
|
||||
df, date_col='open_date', value_col='profit')
|
||||
# Ensure df has not been altered.
|
||||
assert df.equals(df1)
|
||||
|
||||
assert isinstance(drawdown, float)
|
||||
assert isinstance(drawdown_rel, float)
|
||||
# High must be before low
|
||||
assert hdate < ldate
|
||||
# High value must be higher than low value
|
||||
|
||||
@ -52,6 +52,12 @@ EXCHANGES = {
|
||||
'hasQuoteVolume': True,
|
||||
'timeframe': '5m',
|
||||
},
|
||||
'bitvavo': {
|
||||
'pair': 'BTC/EUR',
|
||||
'stake_currency': 'EUR',
|
||||
'hasQuoteVolume': True,
|
||||
'timeframe': '5m',
|
||||
},
|
||||
}
|
||||
|
||||
|
||||
@ -68,6 +74,8 @@ def exchange_conf():
|
||||
@pytest.fixture(params=EXCHANGES, scope="class")
|
||||
def exchange(request, exchange_conf):
|
||||
exchange_conf['exchange']['name'] = request.param
|
||||
exchange_conf['stake_currency'] = EXCHANGES[request.param].get(
|
||||
'stake_currency', exchange_conf['stake_currency'])
|
||||
exchange = ExchangeResolver.load_exchange(request.param, exchange_conf, validate=True)
|
||||
|
||||
yield exchange, request.param
|
||||
|
||||
@ -1,5 +1,5 @@
|
||||
# pragma pylint: disable=missing-docstring,W0212,C0103
|
||||
from datetime import datetime
|
||||
from datetime import datetime, timedelta
|
||||
from pathlib import Path
|
||||
from unittest.mock import ANY, MagicMock
|
||||
|
||||
@ -22,6 +22,29 @@ from tests.conftest import (get_args, log_has, log_has_re, patch_exchange,
|
||||
patched_configuration_load_config_file)
|
||||
|
||||
|
||||
def generate_result_metrics():
|
||||
return {
|
||||
'trade_count': 1,
|
||||
'total_trades': 1,
|
||||
'avg_profit': 0.1,
|
||||
'total_profit': 0.001,
|
||||
'profit': 0.01,
|
||||
'duration': 20.0,
|
||||
'wins': 1,
|
||||
'draws': 0,
|
||||
'losses': 0,
|
||||
'profit_mean': 0.01,
|
||||
'profit_total_abs': 0.001,
|
||||
'profit_total': 0.01,
|
||||
'holding_avg': timedelta(minutes=20),
|
||||
'max_drawdown': 0.001,
|
||||
'max_drawdown_abs': 0.001,
|
||||
'loss': 0.001,
|
||||
'is_initial_point': 0.001,
|
||||
'is_best': 1,
|
||||
}
|
||||
|
||||
|
||||
def test_setup_hyperopt_configuration_without_arguments(mocker, default_conf, caplog) -> None:
|
||||
patched_configuration_load_config_file(mocker, default_conf)
|
||||
|
||||
@ -168,8 +191,8 @@ def test_start_no_hyperopt_allowed(mocker, hyperopt_conf, caplog) -> None:
|
||||
start_hyperopt(pargs)
|
||||
|
||||
|
||||
def test_start_no_data(mocker, hyperopt_conf) -> None:
|
||||
hyperopt_conf['user_data_dir'] = Path("tests")
|
||||
def test_start_no_data(mocker, hyperopt_conf, tmpdir) -> None:
|
||||
hyperopt_conf['user_data_dir'] = Path(tmpdir)
|
||||
patched_configuration_load_config_file(mocker, hyperopt_conf)
|
||||
mocker.patch('freqtrade.data.history.load_pair_history', MagicMock(return_value=pd.DataFrame))
|
||||
mocker.patch(
|
||||
@ -178,7 +201,6 @@ def test_start_no_data(mocker, hyperopt_conf) -> None:
|
||||
)
|
||||
|
||||
patch_exchange(mocker)
|
||||
# TODO: migrate to strategy-based hyperopt
|
||||
args = [
|
||||
'hyperopt',
|
||||
'--config', 'config.json',
|
||||
@ -222,14 +244,7 @@ def test_log_results_if_loss_improves(hyperopt, capsys) -> None:
|
||||
hyperopt.print_results(
|
||||
{
|
||||
'loss': 1,
|
||||
'results_metrics':
|
||||
{
|
||||
'trade_count': 1,
|
||||
'avg_profit': 0.1,
|
||||
'total_profit': 0.001,
|
||||
'profit': 1.0,
|
||||
'duration': 20.0
|
||||
},
|
||||
'results_metrics': generate_result_metrics(),
|
||||
'total_profit': 0,
|
||||
'current_epoch': 2, # This starts from 1 (in a human-friendly manner)
|
||||
'is_initial_point': False,
|
||||
@ -238,7 +253,7 @@ def test_log_results_if_loss_improves(hyperopt, capsys) -> None:
|
||||
)
|
||||
out, err = capsys.readouterr()
|
||||
assert all(x in out
|
||||
for x in ["Best", "2/2", " 1", "0.10%", "0.00100000 BTC (1.00%)", "20.0 m"])
|
||||
for x in ["Best", "2/2", " 1", "0.10%", "0.00100000 BTC (1.00%)", "00:20:00"])
|
||||
|
||||
|
||||
def test_no_log_if_loss_does_not_improve(hyperopt, caplog) -> None:
|
||||
@ -295,14 +310,7 @@ def test_start_calls_optimizer(mocker, hyperopt_conf, capsys) -> None:
|
||||
MagicMock(return_value=[{
|
||||
'loss': 1, 'results_explanation': 'foo result',
|
||||
'params': {'buy': {}, 'sell': {}, 'roi': {}, 'stoploss': 0.0},
|
||||
'results_metrics':
|
||||
{
|
||||
'trade_count': 1,
|
||||
'avg_profit': 0.1,
|
||||
'total_profit': 0.001,
|
||||
'profit': 1.0,
|
||||
'duration': 20.0
|
||||
},
|
||||
'results_metrics': generate_result_metrics(),
|
||||
}])
|
||||
)
|
||||
patch_exchange(mocker)
|
||||
@ -359,7 +367,7 @@ def test_hyperopt_format_results(hyperopt):
|
||||
'backtest_start_time': 1619718665,
|
||||
'backtest_end_time': 1619718665,
|
||||
}
|
||||
results_metrics = generate_strategy_stats({'XRP/BTC': None}, '', bt_result,
|
||||
results_metrics = generate_strategy_stats(['XRP/BTC'], '', bt_result,
|
||||
Arrow(2017, 11, 14, 19, 32, 00),
|
||||
Arrow(2017, 12, 14, 19, 32, 00), market_change=0)
|
||||
|
||||
@ -528,14 +536,7 @@ def test_print_json_spaces_all(mocker, hyperopt_conf, capsys) -> None:
|
||||
'roi': {}, 'stoploss': {'stoploss': None},
|
||||
'trailing': {'trailing_stop': None}
|
||||
},
|
||||
'results_metrics':
|
||||
{
|
||||
'trade_count': 1,
|
||||
'avg_profit': 0.1,
|
||||
'total_profit': 0.001,
|
||||
'profit': 1.0,
|
||||
'duration': 20.0
|
||||
}
|
||||
'results_metrics': generate_result_metrics(),
|
||||
}])
|
||||
)
|
||||
patch_exchange(mocker)
|
||||
@ -584,14 +585,7 @@ def test_print_json_spaces_default(mocker, hyperopt_conf, capsys) -> None:
|
||||
'sell': {'sell-mfi-value': None},
|
||||
'roi': {}, 'stoploss': {'stoploss': None}
|
||||
},
|
||||
'results_metrics':
|
||||
{
|
||||
'trade_count': 1,
|
||||
'avg_profit': 0.1,
|
||||
'total_profit': 0.001,
|
||||
'profit': 1.0,
|
||||
'duration': 20.0
|
||||
}
|
||||
'results_metrics': generate_result_metrics(),
|
||||
}])
|
||||
)
|
||||
patch_exchange(mocker)
|
||||
@ -629,14 +623,7 @@ def test_print_json_spaces_roi_stoploss(mocker, hyperopt_conf, capsys) -> None:
|
||||
MagicMock(return_value=[{
|
||||
'loss': 1, 'results_explanation': 'foo result', 'params': {},
|
||||
'params_details': {'roi': {}, 'stoploss': {'stoploss': None}},
|
||||
'results_metrics':
|
||||
{
|
||||
'trade_count': 1,
|
||||
'avg_profit': 0.1,
|
||||
'total_profit': 0.001,
|
||||
'profit': 1.0,
|
||||
'duration': 20.0
|
||||
}
|
||||
'results_metrics': generate_result_metrics(),
|
||||
}])
|
||||
)
|
||||
patch_exchange(mocker)
|
||||
@ -676,14 +663,7 @@ def test_simplified_interface_roi_stoploss(mocker, hyperopt_conf, capsys) -> Non
|
||||
'freqtrade.optimize.hyperopt.Hyperopt.run_optimizer_parallel',
|
||||
MagicMock(return_value=[{
|
||||
'loss': 1, 'results_explanation': 'foo result', 'params': {'stoploss': 0.0},
|
||||
'results_metrics':
|
||||
{
|
||||
'trade_count': 1,
|
||||
'avg_profit': 0.1,
|
||||
'total_profit': 0.001,
|
||||
'profit': 1.0,
|
||||
'duration': 20.0
|
||||
}
|
||||
'results_metrics': generate_result_metrics(),
|
||||
}])
|
||||
)
|
||||
patch_exchange(mocker)
|
||||
@ -756,14 +736,7 @@ def test_simplified_interface_buy(mocker, hyperopt_conf, capsys) -> None:
|
||||
'freqtrade.optimize.hyperopt.Hyperopt.run_optimizer_parallel',
|
||||
MagicMock(return_value=[{
|
||||
'loss': 1, 'results_explanation': 'foo result', 'params': {},
|
||||
'results_metrics':
|
||||
{
|
||||
'trade_count': 1,
|
||||
'avg_profit': 0.1,
|
||||
'total_profit': 0.001,
|
||||
'profit': 1.0,
|
||||
'duration': 20.0
|
||||
}
|
||||
'results_metrics': generate_result_metrics(),
|
||||
}])
|
||||
)
|
||||
patch_exchange(mocker)
|
||||
@ -805,14 +778,7 @@ def test_simplified_interface_sell(mocker, hyperopt_conf, capsys) -> None:
|
||||
'freqtrade.optimize.hyperopt.Hyperopt.run_optimizer_parallel',
|
||||
MagicMock(return_value=[{
|
||||
'loss': 1, 'results_explanation': 'foo result', 'params': {},
|
||||
'results_metrics':
|
||||
{
|
||||
'trade_count': 1,
|
||||
'avg_profit': 0.1,
|
||||
'total_profit': 0.001,
|
||||
'profit': 1.0,
|
||||
'duration': 20.0
|
||||
}
|
||||
'results_metrics': generate_result_metrics(),
|
||||
}])
|
||||
)
|
||||
patch_exchange(mocker)
|
||||
|
||||
@ -1,4 +1,3 @@
|
||||
import datetime
|
||||
import re
|
||||
from datetime import timedelta
|
||||
from pathlib import Path
|
||||
@ -49,7 +48,7 @@ def test_text_table_bt_results():
|
||||
' 0:20:00 | 2 0 1 66.7 |'
|
||||
)
|
||||
|
||||
pair_results = generate_pair_metrics(data={'ETH/BTC': {}}, stake_currency='BTC',
|
||||
pair_results = generate_pair_metrics(['ETH/BTC'], stake_currency='BTC',
|
||||
starting_balance=4, results=results)
|
||||
assert text_table_bt_results(pair_results, stake_currency='BTC') == result_str
|
||||
|
||||
@ -103,7 +102,7 @@ def test_generate_backtest_stats(default_conf, testdatadir, tmpdir):
|
||||
assert strat_stats['backtest_end'] == max_date.strftime(DATETIME_PRINT_FORMAT)
|
||||
assert strat_stats['total_trades'] == len(results['DefStrat']['results'])
|
||||
# Above sample had no loosing trade
|
||||
assert strat_stats['max_drawdown'] == 0.0
|
||||
assert strat_stats['max_drawdown_account'] == 0.0
|
||||
|
||||
# Retry with losing trade
|
||||
results = {'DefStrat': {
|
||||
@ -143,7 +142,7 @@ def test_generate_backtest_stats(default_conf, testdatadir, tmpdir):
|
||||
assert 'strategy_comparison' in stats
|
||||
strat_stats = stats['strategy']['DefStrat']
|
||||
|
||||
assert strat_stats['max_drawdown'] == 0.013803
|
||||
assert pytest.approx(strat_stats['max_drawdown_account']) == 1.399999e-08
|
||||
assert strat_stats['drawdown_start'] == '2017-11-14 22:10:00'
|
||||
assert strat_stats['drawdown_end'] == '2017-11-14 22:43:00'
|
||||
assert strat_stats['drawdown_end_ts'] == 1510699380000
|
||||
@ -165,7 +164,7 @@ def test_generate_backtest_stats(default_conf, testdatadir, tmpdir):
|
||||
filename1 = Path(tmpdir / last_fn)
|
||||
assert filename1.is_file()
|
||||
content = filename1.read_text()
|
||||
assert 'max_drawdown' in content
|
||||
assert 'max_drawdown_account' in content
|
||||
assert 'strategy' in content
|
||||
assert 'pairlist' in content
|
||||
|
||||
@ -208,7 +207,7 @@ def test_generate_pair_metrics():
|
||||
}
|
||||
)
|
||||
|
||||
pair_results = generate_pair_metrics(data={'ETH/BTC': {}}, stake_currency='BTC',
|
||||
pair_results = generate_pair_metrics(['ETH/BTC'], stake_currency='BTC',
|
||||
starting_balance=2, results=results)
|
||||
assert isinstance(pair_results, list)
|
||||
assert len(pair_results) == 2
|
||||
@ -227,9 +226,9 @@ def test_generate_daily_stats(testdatadir):
|
||||
assert isinstance(res, dict)
|
||||
assert round(res['backtest_best_day'], 4) == 0.1796
|
||||
assert round(res['backtest_worst_day'], 4) == -0.1468
|
||||
assert res['winning_days'] == 14
|
||||
assert res['draw_days'] == 4
|
||||
assert res['losing_days'] == 3
|
||||
assert res['winning_days'] == 19
|
||||
assert res['draw_days'] == 0
|
||||
assert res['losing_days'] == 2
|
||||
|
||||
# Select empty dataframe!
|
||||
res = generate_daily_stats(bt_data.loc[bt_data['open_date'] == '2000-01-01', :])
|
||||
@ -324,51 +323,25 @@ def test_generate_sell_reason_stats():
|
||||
assert stop_result['profit_mean_pct'] == round(stop_result['profit_mean'] * 100, 2)
|
||||
|
||||
|
||||
def test_text_table_strategy(default_conf):
|
||||
default_conf['max_open_trades'] = 2
|
||||
default_conf['dry_run_wallet'] = 3
|
||||
results = {}
|
||||
date = datetime.datetime(year=2020, month=1, day=1, hour=12, minute=30)
|
||||
delta = datetime.timedelta(days=1)
|
||||
results['TestStrategy1'] = {'results': pd.DataFrame(
|
||||
{
|
||||
'pair': ['ETH/BTC', 'ETH/BTC', 'ETH/BTC'],
|
||||
'close_date': [date, date + delta, date + delta * 2],
|
||||
'profit_ratio': [0.1, 0.2, 0.3],
|
||||
'profit_abs': [0.2, 0.4, 0.5],
|
||||
'trade_duration': [10, 30, 10],
|
||||
'wins': [2, 0, 0],
|
||||
'draws': [0, 0, 0],
|
||||
'losses': [0, 0, 1],
|
||||
'sell_reason': [SellType.ROI, SellType.ROI, SellType.STOP_LOSS]
|
||||
}
|
||||
), 'config': default_conf}
|
||||
results['TestStrategy2'] = {'results': pd.DataFrame(
|
||||
{
|
||||
'pair': ['LTC/BTC', 'LTC/BTC', 'LTC/BTC'],
|
||||
'close_date': [date, date + delta, date + delta * 2],
|
||||
'profit_ratio': [0.4, 0.2, 0.3],
|
||||
'profit_abs': [0.4, 0.4, 0.5],
|
||||
'trade_duration': [15, 30, 15],
|
||||
'wins': [4, 1, 0],
|
||||
'draws': [0, 0, 0],
|
||||
'losses': [0, 0, 1],
|
||||
'sell_reason': [SellType.ROI, SellType.ROI, SellType.STOP_LOSS]
|
||||
}
|
||||
), 'config': default_conf}
|
||||
def test_text_table_strategy(testdatadir):
|
||||
filename = testdatadir / "backtest-result_multistrat.json"
|
||||
bt_res_data = load_backtest_stats(filename)
|
||||
|
||||
bt_res_data_comparison = bt_res_data.pop('strategy_comparison')
|
||||
|
||||
result_str = (
|
||||
'| Strategy | Buys | Avg Profit % | Cum Profit % | Tot Profit BTC |'
|
||||
'| Strategy | Buys | Avg Profit % | Cum Profit % | Tot Profit BTC |'
|
||||
' Tot Profit % | Avg Duration | Win Draw Loss Win% | Drawdown |\n'
|
||||
'|---------------+--------+----------------+----------------+------------------+'
|
||||
'|----------------+--------+----------------+----------------+------------------+'
|
||||
'----------------+----------------+-------------------------+-----------------------|\n'
|
||||
'| TestStrategy1 | 3 | 20.00 | 60.00 | 1.10000000 |'
|
||||
' 36.67 | 0:17:00 | 3 0 0 100 | 0.00000000 BTC 0.00% |\n'
|
||||
'| TestStrategy2 | 3 | 30.00 | 90.00 | 1.30000000 |'
|
||||
' 43.33 | 0:20:00 | 3 0 0 100 | 0.00000000 BTC 0.00% |'
|
||||
'| StrategyTestV2 | 179 | 0.08 | 14.39 | 0.02608550 |'
|
||||
' 260.85 | 3:40:00 | 170 0 9 95.0 | 0.00308222 BTC 8.67% |\n'
|
||||
'| TestStrategy | 179 | 0.08 | 14.39 | 0.02608550 |'
|
||||
' 260.85 | 3:40:00 | 170 0 9 95.0 | 0.00308222 BTC 8.67% |'
|
||||
)
|
||||
|
||||
strategy_results = generate_strategy_comparison(all_results=results)
|
||||
strategy_results = generate_strategy_comparison(bt_stats=bt_res_data['strategy'])
|
||||
assert strategy_results == bt_res_data_comparison
|
||||
assert text_table_strategy(strategy_results, 'BTC') == result_str
|
||||
|
||||
|
||||
|
||||
@ -565,36 +565,41 @@ def test_VolumePairList_whitelist_gen(mocker, whitelist_conf, shitcoinmarkets, t
|
||||
assert log_has_re(r'^Removed .* from whitelist, because volatility.*$', caplog)
|
||||
|
||||
|
||||
@pytest.mark.parametrize("pairlists,base_currency,volumefilter_result", [
|
||||
@pytest.mark.parametrize("pairlists,base_currency,exchange,volumefilter_result", [
|
||||
# default refresh of 1800 to small for daily candle lookback
|
||||
([{"method": "VolumePairList", "number_assets": 5, "sort_key": "quoteVolume",
|
||||
"lookback_days": 1}],
|
||||
"BTC", "default_refresh_too_short"), # OperationalException expected
|
||||
"BTC", "binance", "default_refresh_too_short"), # OperationalException expected
|
||||
# ambigous configuration with lookback days and period
|
||||
([{"method": "VolumePairList", "number_assets": 5, "sort_key": "quoteVolume",
|
||||
"lookback_days": 1, "lookback_period": 1}],
|
||||
"BTC", "lookback_days_and_period"), # OperationalException expected
|
||||
"BTC", "binance", "lookback_days_and_period"), # OperationalException expected
|
||||
# negative lookback period
|
||||
([{"method": "VolumePairList", "number_assets": 5, "sort_key": "quoteVolume",
|
||||
"lookback_timeframe": "1d", "lookback_period": -1}],
|
||||
"BTC", "lookback_period_negative"), # OperationalException expected
|
||||
"BTC", "binance", "lookback_period_negative"), # OperationalException expected
|
||||
# lookback range exceedes exchange limit
|
||||
([{"method": "VolumePairList", "number_assets": 5, "sort_key": "quoteVolume",
|
||||
"lookback_timeframe": "1m", "lookback_period": 2000, "refresh_period": 3600}],
|
||||
"BTC", 'lookback_exceeds_exchange_request_size'), # OperationalException expected
|
||||
"BTC", "binance", "lookback_exceeds_exchange_request_size"), # OperationalException expected
|
||||
# expecing pairs as given
|
||||
([{"method": "VolumePairList", "number_assets": 5, "sort_key": "quoteVolume",
|
||||
"lookback_timeframe": "1d", "lookback_period": 1, "refresh_period": 86400}],
|
||||
"BTC", ['HOT/BTC', 'LTC/BTC', 'ETH/BTC', 'TKN/BTC', 'XRP/BTC']),
|
||||
"BTC", "binance", ['LTC/BTC', 'ETH/BTC', 'TKN/BTC', 'XRP/BTC', 'HOT/BTC']),
|
||||
# expecting pairs from default tickers, because 1h candles are not available
|
||||
([{"method": "VolumePairList", "number_assets": 5, "sort_key": "quoteVolume",
|
||||
"lookback_timeframe": "1h", "lookback_period": 2, "refresh_period": 3600}],
|
||||
"BTC", ['ETH/BTC', 'TKN/BTC', 'LTC/BTC', 'HOT/BTC', 'FUEL/BTC']),
|
||||
"BTC", "binance", ['ETH/BTC', 'TKN/BTC', 'LTC/BTC', 'HOT/BTC', 'FUEL/BTC']),
|
||||
# ftx data is already in Quote currency, therefore won't require conversion
|
||||
([{"method": "VolumePairList", "number_assets": 5, "sort_key": "quoteVolume",
|
||||
"lookback_timeframe": "1d", "lookback_period": 1, "refresh_period": 86400}],
|
||||
"BTC", "ftx", ['HOT/BTC', 'LTC/BTC', 'ETH/BTC', 'TKN/BTC', 'XRP/BTC']),
|
||||
])
|
||||
def test_VolumePairList_range(mocker, whitelist_conf, shitcoinmarkets, tickers, ohlcv_history,
|
||||
pairlists, base_currency, volumefilter_result, caplog) -> None:
|
||||
pairlists, base_currency, exchange, volumefilter_result) -> None:
|
||||
whitelist_conf['pairlists'] = pairlists
|
||||
whitelist_conf['stake_currency'] = base_currency
|
||||
whitelist_conf['exchange']['name'] = exchange
|
||||
|
||||
ohlcv_history_high_vola = ohlcv_history.copy()
|
||||
ohlcv_history_high_vola.loc[ohlcv_history_high_vola.index == 1, 'close'] = 0.00090
|
||||
@ -603,9 +608,14 @@ def test_VolumePairList_range(mocker, whitelist_conf, shitcoinmarkets, tickers,
|
||||
ohlcv_history_medium_volume = ohlcv_history.copy()
|
||||
ohlcv_history_medium_volume.loc[ohlcv_history_medium_volume.index == 2, 'volume'] = 5
|
||||
|
||||
# create candles for high volume with all candles high volume
|
||||
# create candles for high volume with all candles high volume, but very low price.
|
||||
ohlcv_history_high_volume = ohlcv_history.copy()
|
||||
ohlcv_history_high_volume.loc[:, 'volume'] = 10
|
||||
ohlcv_history_high_volume.loc[:, 'low'] = ohlcv_history_high_volume.loc[:, 'low'] * 0.01
|
||||
ohlcv_history_high_volume.loc[:, 'high'] = ohlcv_history_high_volume.loc[:, 'high'] * 0.01
|
||||
ohlcv_history_high_volume.loc[:, 'close'] = ohlcv_history_high_volume.loc[:, 'close'] * 0.01
|
||||
|
||||
mocker.patch('freqtrade.exchange.ftx.Ftx.market_is_tradable', return_value=True)
|
||||
|
||||
ohlcv_data = {
|
||||
('ETH/BTC', '1d'): ohlcv_history,
|
||||
|
||||
@ -45,7 +45,7 @@ def test_init_plotscript(default_conf, mocker, testdatadir):
|
||||
default_conf['trade_source'] = "file"
|
||||
default_conf['timeframe'] = "5m"
|
||||
default_conf["datadir"] = testdatadir
|
||||
default_conf['exportfilename'] = testdatadir / "backtest-result_test.json"
|
||||
default_conf['exportfilename'] = testdatadir / "backtest-result_new.json"
|
||||
supported_markets = ["TRX/BTC", "ADA/BTC"]
|
||||
ret = init_plotscript(default_conf, supported_markets)
|
||||
assert "ohlcv" in ret
|
||||
@ -157,7 +157,7 @@ def test_plot_trades(testdatadir, caplog):
|
||||
assert fig == fig1
|
||||
assert log_has("No trades found.", caplog)
|
||||
pair = "ADA/BTC"
|
||||
filename = testdatadir / "backtest-result_test.json"
|
||||
filename = testdatadir / "backtest-result_new.json"
|
||||
trades = load_backtest_data(filename)
|
||||
trades = trades.loc[trades['pair'] == pair]
|
||||
|
||||
@ -294,7 +294,7 @@ def test_generate_plot_file(mocker, caplog):
|
||||
|
||||
|
||||
def test_add_profit(testdatadir):
|
||||
filename = testdatadir / "backtest-result_test.json"
|
||||
filename = testdatadir / "backtest-result_new.json"
|
||||
bt_data = load_backtest_data(filename)
|
||||
timerange = TimeRange.parse_timerange("20180110-20180112")
|
||||
|
||||
@ -314,7 +314,7 @@ def test_add_profit(testdatadir):
|
||||
|
||||
|
||||
def test_generate_profit_graph(testdatadir):
|
||||
filename = testdatadir / "backtest-result_test.json"
|
||||
filename = testdatadir / "backtest-result_new.json"
|
||||
trades = load_backtest_data(filename)
|
||||
timerange = TimeRange.parse_timerange("20180110-20180112")
|
||||
pairs = ["TRX/BTC", "XLM/BTC"]
|
||||
@ -343,7 +343,7 @@ def test_generate_profit_graph(testdatadir):
|
||||
|
||||
profit = find_trace_in_fig_data(figure.data, "Profit")
|
||||
assert isinstance(profit, go.Scatter)
|
||||
drawdown = find_trace_in_fig_data(figure.data, "Max drawdown 10.45%")
|
||||
drawdown = find_trace_in_fig_data(figure.data, "Max drawdown 35.69%")
|
||||
assert isinstance(drawdown, go.Scatter)
|
||||
parallel = find_trace_in_fig_data(figure.data, "Parallel trades")
|
||||
assert isinstance(parallel, go.Scatter)
|
||||
@ -381,7 +381,7 @@ def test_load_and_plot_trades(default_conf, mocker, caplog, testdatadir):
|
||||
|
||||
default_conf['trade_source'] = 'file'
|
||||
default_conf["datadir"] = testdatadir
|
||||
default_conf['exportfilename'] = testdatadir / "backtest-result_test.json"
|
||||
default_conf['exportfilename'] = testdatadir / "backtest-result_new.json"
|
||||
default_conf['indicators1'] = ["sma5", "ema10"]
|
||||
default_conf['indicators2'] = ["macd"]
|
||||
default_conf['pairs'] = ["ETH/BTC", "LTC/BTC"]
|
||||
@ -452,7 +452,7 @@ def test_plot_profit(default_conf, mocker, testdatadir):
|
||||
match=r"No trades found, cannot generate Profit-plot.*"):
|
||||
plot_profit(default_conf)
|
||||
|
||||
default_conf['exportfilename'] = testdatadir / "backtest-result_test.json"
|
||||
default_conf['exportfilename'] = testdatadir / "backtest-result_new.json"
|
||||
|
||||
plot_profit(default_conf)
|
||||
|
||||
|
||||
File diff suppressed because one or more lines are too long
2
tests/testdata/backtest-result_new.json
vendored
2
tests/testdata/backtest-result_new.json
vendored
File diff suppressed because one or more lines are too long
1
tests/testdata/backtest-result_test.json
vendored
1
tests/testdata/backtest-result_test.json
vendored
File diff suppressed because one or more lines are too long
Loading…
Reference in New Issue
Block a user