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Merge pull request #6715 from nicolaspapp/feat/relative-drawdown

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Add relative drawdown
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Matthias 2022-05-02 21:09:14 +02:00 committed by GitHub
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177
docs/backtesting.md
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@ -287,45 +287,51 @@ A backtesting result will look like that:
| ADA/BTC | 1 | 0.89 | 0.89 | 0.00004434 | 0.44 | 6:00:00 | 1 0 0 100 |
| LTC/BTC | 1 | 0.68 | 0.68 | 0.00003421 | 0.34 | 2:00:00 | 1 0 0 100 |
| TOTAL | 2 | 0.78 | 1.57 | 0.00007855 | 0.78 | 4:00:00 | 2 0 0 100 |
================ SUMMARY METRICS ===============
| Metric | Value |
|------------------------+---------------------|
| Backtesting from | 2019-01-01 00:00:00 |
| Backtesting to | 2019-05-01 00:00:00 |
| Max open trades | 3 |
| | |
| Total/Daily Avg Trades | 429 / 3.575 |
| Starting balance | 0.01000000 BTC |
| Final balance | 0.01762792 BTC |
| Absolute profit | 0.00762792 BTC |
| Total profit % | 76.2% |
| CAGR % | 460.87% |
| Trades per day | 3.575 |
| Avg. stake amount | 0.001 BTC |
| Total trade volume | 0.429 BTC |
| | |
| Best Pair | LSK/BTC 26.26% |
| Worst Pair | ZEC/BTC -10.18% |
| Best Trade | LSK/BTC 4.25% |
| Worst Trade | ZEC/BTC -10.25% |
| Best day | 0.00076 BTC |
| Worst day | -0.00036 BTC |
| Days win/draw/lose | 12 / 82 / 25 |
| Avg. Duration Winners | 4:23:00 |
| Avg. Duration Loser | 6:55:00 |
| Rejected Entry signals | 3089 |
| Entry/Exit Timeouts | 0 / 0 |
| | |
| Min balance | 0.00945123 BTC |
| Max balance | 0.01846651 BTC |
| Drawdown (Account) | 13.33% |
| Drawdown | 0.0015 BTC |
| Drawdown high | 0.0013 BTC |
| Drawdown low | -0.0002 BTC |
| Drawdown Start | 2019-02-15 14:10:00 |
| Drawdown End | 2019-04-11 18:15:00 |
| Market change | -5.88% |
===============================================
================== SUMMARY METRICS ==================
| Metric | Value |
|-----------------------------+---------------------|
| Backtesting from | 2019-01-01 00:00:00 |
| Backtesting to | 2019-05-01 00:00:00 |
| Max open trades | 3 |
| | |
| Total/Daily Avg Trades | 429 / 3.575 |
| Starting balance | 0.01000000 BTC |
| Final balance | 0.01762792 BTC |
| Absolute profit | 0.00762792 BTC |
| Total profit % | 76.2% |
| CAGR % | 460.87% |
| Avg. stake amount | 0.001 BTC |
| Total trade volume | 0.429 BTC |
| | |
| Long / Short | 352 / 77 |
| Total profit Long % | 1250.58% |
| Total profit Short % | -15.02% |
| Absolute profit Long | 0.00838792 BTC |
| Absolute profit Short | -0.00076 BTC |
| | |
| Best Pair | LSK/BTC 26.26% |
| Worst Pair | ZEC/BTC -10.18% |
| Best Trade | LSK/BTC 4.25% |
| Worst Trade | ZEC/BTC -10.25% |
| Best day | 0.00076 BTC |
| Worst day | -0.00036 BTC |
| Days win/draw/lose | 12 / 82 / 25 |
| Avg. Duration Winners | 4:23:00 |
| Avg. Duration Loser | 6:55:00 |
| Rejected Entry signals | 3089 |
| Entry/Exit Timeouts | 0 / 0 |
| | |
| Min balance | 0.00945123 BTC |
| Max balance | 0.01846651 BTC |
| Max % of account underwater | 25.19% |
| Absolute Drawdown (Account) | 13.33% |
| Drawdown | 0.0015 BTC |
| Drawdown high | 0.0013 BTC |
| Drawdown low | -0.0002 BTC |
| Drawdown Start | 2019-02-15 14:10:00 |
| Drawdown End | 2019-04-11 18:15:00 |
| Market change | -5.88% |
=====================================================
```
### Backtesting report table
@ -377,50 +383,51 @@ The last element of the backtest report is the summary metrics table.
It contains some useful key metrics about performance of your strategy on backtesting data.
```
================ SUMMARY METRICS ===============
| Metric | Value |
|------------------------+---------------------|
| Backtesting from | 2019-01-01 00:00:00 |
| Backtesting to | 2019-05-01 00:00:00 |
| Max open trades | 3 |
| | |
| Total/Daily Avg Trades | 429 / 3.575 |
| Starting balance | 0.01000000 BTC |
| Final balance | 0.01762792 BTC |
| Absolute profit | 0.00762792 BTC |
| Total profit % | 76.2% |
| CAGR % | 460.87% |
| Avg. stake amount | 0.001 BTC |
| Total trade volume | 0.429 BTC |
| | |
| Long / Short | 352 / 77 |
| Total profit Long % | 1250.58% |
| Total profit Short % | -15.02% |
| Absolute profit Long | 0.00838792 BTC |
| Absolute profit Short | -0.00076 BTC |
| | |
| Best Pair | LSK/BTC 26.26% |
| Worst Pair | ZEC/BTC -10.18% |
| Best Trade | LSK/BTC 4.25% |
| Worst Trade | ZEC/BTC -10.25% |
| Best day | 0.00076 BTC |
| Worst day | -0.00036 BTC |
| Days win/draw/lose | 12 / 82 / 25 |
| Avg. Duration Winners | 4:23:00 |
| Avg. Duration Loser | 6:55:00 |
| Rejected Entry signals | 3089 |
| Entry/Exit Timeouts | 0 / 0 |
| | |
| Min balance | 0.00945123 BTC |
| Max balance | 0.01846651 BTC |
| Drawdown (Account) | 13.33% |
| Drawdown | 0.0015 BTC |
| Drawdown high | 0.0013 BTC |
| Drawdown low | -0.0002 BTC |
| Drawdown Start | 2019-02-15 14:10:00 |
| Drawdown End | 2019-04-11 18:15:00 |
| Market change | -5.88% |
================================================
================== SUMMARY METRICS ==================
| Metric | Value |
|-----------------------------+---------------------|
| Backtesting from | 2019-01-01 00:00:00 |
| Backtesting to | 2019-05-01 00:00:00 |
| Max open trades | 3 |
| | |
| Total/Daily Avg Trades | 429 / 3.575 |
| Starting balance | 0.01000000 BTC |
| Final balance | 0.01762792 BTC |
| Absolute profit | 0.00762792 BTC |
| Total profit % | 76.2% |
| CAGR % | 460.87% |
| Avg. stake amount | 0.001 BTC |
| Total trade volume | 0.429 BTC |
| | |
| Long / Short | 352 / 77 |
| Total profit Long % | 1250.58% |
| Total profit Short % | -15.02% |
| Absolute profit Long | 0.00838792 BTC |
| Absolute profit Short | -0.00076 BTC |
| | |
| Best Pair | LSK/BTC 26.26% |
| Worst Pair | ZEC/BTC -10.18% |
| Best Trade | LSK/BTC 4.25% |
| Worst Trade | ZEC/BTC -10.25% |
| Best day | 0.00076 BTC |
| Worst day | -0.00036 BTC |
| Days win/draw/lose | 12 / 82 / 25 |
| Avg. Duration Winners | 4:23:00 |
| Avg. Duration Loser | 6:55:00 |
| Rejected Entry signals | 3089 |
| Entry/Exit Timeouts | 0 / 0 |
| | |
| Min balance | 0.00945123 BTC |
| Max balance | 0.01846651 BTC |
| Max % of account underwater | 25.19% |
| Absolute Drawdown (Account) | 13.33% |
| Drawdown | 0.0015 BTC |
| Drawdown high | 0.0013 BTC |
| Drawdown low | -0.0002 BTC |
| Drawdown Start | 2019-02-15 14:10:00 |
| Drawdown End | 2019-04-11 18:15:00 |
| Market change | -5.88% |
=====================================================
```
@ -441,7 +448,9 @@ It contains some useful key metrics about performance of your strategy on backte
- `Rejected Entry signals`: Trade entry signals that could not be acted upon due to `max_open_trades` being reached.
- `Entry/Exit Timeouts`: Entry/exit orders which did not fill (only applicable if custom pricing is used).
- `Min balance` / `Max balance`: Lowest and Highest Wallet balance during the backtest period.
- `Drawdown (Account)`: Maximum Account Drawdown experienced. Calculated as $(Absolute Drawdown) / (DrawdownHigh + startingBalance)$.
- `Max % of account underwater`: Maximum percentage your account has decreased from the top since the simulation started.
Calculated as the maximum of `(Max Balance - Current Balance) / (Max Balance)`.
- `Absolute Drawdown (Account)`: Maximum Account Drawdown experienced. Calculated as `(Absolute Drawdown) / (DrawdownHigh + startingBalance)`.
- `Drawdown`: Maximum, absolute drawdown experienced. Difference between Drawdown High and Subsequent Low point.
- `Drawdown high` / `Drawdown low`: Profit at the beginning and end of the largest drawdown period. A negative low value means initial capital lost.
- `Drawdown Start` / `Drawdown End`: Start and end datetime for this largest drawdown (can also be visualized via the `plot-dataframe` sub-command).

7
docs/hyperopt.md
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@ -116,7 +116,9 @@ optional arguments:
ShortTradeDurHyperOptLoss, OnlyProfitHyperOptLoss,
SharpeHyperOptLoss, SharpeHyperOptLossDaily,
SortinoHyperOptLoss, SortinoHyperOptLossDaily,
CalmarHyperOptLoss, MaxDrawDownHyperOptLoss, ProfitDrawDownHyperOptLoss
CalmarHyperOptLoss, MaxDrawDownHyperOptLoss,
MaxDrawDownRelativeHyperOptLoss,
ProfitDrawDownHyperOptLoss
--disable-param-export
Disable automatic hyperopt parameter export.
--ignore-missing-spaces, --ignore-unparameterized-spaces
@ -563,7 +565,8 @@ Currently, the following loss functions are builtin:
* `SharpeHyperOptLossDaily` - optimizes Sharpe Ratio calculated on **daily** trade returns relative to standard deviation.
* `SortinoHyperOptLoss` - optimizes Sortino Ratio calculated on trade returns relative to **downside** standard deviation.
* `SortinoHyperOptLossDaily` - optimizes Sortino Ratio calculated on **daily** trade returns relative to **downside** standard deviation.
* `MaxDrawDownHyperOptLoss` - Optimizes Maximum drawdown.
* `MaxDrawDownHyperOptLoss` - Optimizes Maximum absolute drawdown.
* `MaxDrawDownRelativeHyperOptLoss` - Optimizes both maximum absolute drawdown while also adjusting for maximum relative drawdown.
* `CalmarHyperOptLoss` - Optimizes Calmar Ratio calculated on trade returns relative to max drawdown.
* `ProfitDrawDownHyperOptLoss` - Optimizes by max Profit & min Drawdown objective. `DRAWDOWN_MULT` variable within the hyperoptloss file can be adjusted to be stricter or more flexible on drawdown purposes.

3
freqtrade/constants.py
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@ -28,7 +28,8 @@ HYPEROPT_LOSS_BUILTIN = ['ShortTradeDurHyperOptLoss', 'OnlyProfitHyperOptLoss',
'SharpeHyperOptLoss', 'SharpeHyperOptLossDaily',
'SortinoHyperOptLoss', 'SortinoHyperOptLossDaily',
'CalmarHyperOptLoss',
'MaxDrawDownHyperOptLoss', 'ProfitDrawDownHyperOptLoss']
'MaxDrawDownHyperOptLoss', 'MaxDrawDownRelativeHyperOptLoss',
'ProfitDrawDownHyperOptLoss']
AVAILABLE_PAIRLISTS = ['StaticPairList', 'VolumePairList',
'AgeFilter', 'OffsetFilter', 'PerformanceFilter',
'PrecisionFilter', 'PriceFilter', 'RangeStabilityFilter',

41
freqtrade/data/metrics.py
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@ -72,18 +72,28 @@ def create_cum_profit(df: pd.DataFrame, trades: pd.DataFrame, col_name: str,
return df
def _calc_drawdown_series(profit_results: pd.DataFrame, *, date_col: str, value_col: str
) -> pd.DataFrame:
def _calc_drawdown_series(profit_results: pd.DataFrame, *, date_col: str, value_col: str,
starting_balance: float) -> pd.DataFrame:
max_drawdown_df = pd.DataFrame()
max_drawdown_df['cumulative'] = profit_results[value_col].cumsum()
max_drawdown_df['high_value'] = max_drawdown_df['cumulative'].cummax()
max_drawdown_df['drawdown'] = max_drawdown_df['cumulative'] - max_drawdown_df['high_value']
max_drawdown_df['date'] = profit_results.loc[:, date_col]
if starting_balance:
cumulative_balance = starting_balance + max_drawdown_df['cumulative']
max_balance = starting_balance + max_drawdown_df['high_value']
max_drawdown_df['drawdown_relative'] = ((max_balance - cumulative_balance) / max_balance)
else:
# NOTE: This is not completely accurate,
# but might good enough if starting_balance is not available
max_drawdown_df['drawdown_relative'] = (
(max_drawdown_df['high_value'] - max_drawdown_df['cumulative'])
/ max_drawdown_df['high_value'])
return max_drawdown_df
def calculate_underwater(trades: pd.DataFrame, *, date_col: str = 'close_date',
value_col: str = 'profit_ratio'
value_col: str = 'profit_ratio', starting_balance: float = 0.0
):
"""
Calculate max drawdown and the corresponding close dates
@ -97,13 +107,18 @@ def calculate_underwater(trades: pd.DataFrame, *, date_col: str = 'close_date',
if len(trades) == 0:
raise ValueError("Trade dataframe empty.")
profit_results = trades.sort_values(date_col).reset_index(drop=True)
max_drawdown_df = _calc_drawdown_series(profit_results, date_col=date_col, value_col=value_col)
max_drawdown_df = _calc_drawdown_series(
profit_results,
date_col=date_col,
value_col=value_col,
starting_balance=starting_balance)
return max_drawdown_df
def calculate_max_drawdown(trades: pd.DataFrame, *, date_col: str = 'close_date',
value_col: str = 'profit_abs', starting_balance: float = 0
value_col: str = 'profit_abs', starting_balance: float = 0,
relative: bool = False
) -> Tuple[float, pd.Timestamp, pd.Timestamp, float, float, float]:
"""
Calculate max drawdown and the corresponding close dates
@ -119,9 +134,15 @@ def calculate_max_drawdown(trades: pd.DataFrame, *, date_col: str = 'close_date'
if len(trades) == 0:
raise ValueError("Trade dataframe empty.")
profit_results = trades.sort_values(date_col).reset_index(drop=True)
max_drawdown_df = _calc_drawdown_series(profit_results, date_col=date_col, value_col=value_col)
max_drawdown_df = _calc_drawdown_series(
profit_results,
date_col=date_col,
value_col=value_col,
starting_balance=starting_balance
)
idxmin = max_drawdown_df['drawdown'].idxmin()
idxmin = max_drawdown_df['drawdown_relative'].idxmax() if relative \
else max_drawdown_df['drawdown'].idxmin()
if idxmin == 0:
raise ValueError("No losing trade, therefore no drawdown.")
high_date = profit_results.loc[max_drawdown_df.iloc[:idxmin]['high_value'].idxmax(), date_col]
@ -129,12 +150,10 @@ def calculate_max_drawdown(trades: pd.DataFrame, *, date_col: str = 'close_date'
high_val = max_drawdown_df.loc[max_drawdown_df.iloc[:idxmin]
['high_value'].idxmax(), 'cumulative']
low_val = max_drawdown_df.loc[idxmin, 'cumulative']
max_drawdown_rel = 0.0
if high_val + starting_balance != 0:
max_drawdown_rel = (high_val - low_val) / (high_val + starting_balance)
max_drawdown_rel = max_drawdown_df.loc[idxmin, 'drawdown_relative']
return (
abs(min(max_drawdown_df['drawdown'])),
abs(max_drawdown_df.loc[idxmin, 'drawdown']),
high_date,
low_date,
high_val,

47
freqtrade/optimize/hyperopt_loss/hyperopt_loss_max_drawdown_relative.py Normal file
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@ -0,0 +1,47 @@
"""
MaxDrawDownRelativeHyperOptLoss
This module defines the alternative HyperOptLoss class which can be used for
Hyperoptimization.
"""
from typing import Dict
from pandas import DataFrame
from freqtrade.data.metrics import calculate_underwater
from freqtrade.optimize.hyperopt import IHyperOptLoss
class MaxDrawDownRelativeHyperOptLoss(IHyperOptLoss):
"""
Defines the loss function for hyperopt.
This implementation optimizes for max draw down and profit
Less max drawdown more profit -> Lower return value
"""
@staticmethod
def hyperopt_loss_function(results: DataFrame, config: Dict,
*args, **kwargs) -> float:
"""
Objective function.
Uses profit ratio weighted max_drawdown when drawdown is available.
Otherwise directly optimizes profit ratio.
"""
total_profit = results['profit_abs'].sum()
try:
drawdown_df = calculate_underwater(
results,
value_col='profit_abs',
starting_balance=config['dry_run_wallet']
)
max_drawdown = abs(min(drawdown_df['drawdown']))
relative_drawdown = max(drawdown_df['drawdown_relative'])
if max_drawdown == 0:
return -total_profit
return -total_profit / max_drawdown / relative_drawdown
except (Exception, ValueError):
return -total_profit

4
freqtrade/optimize/hyperopt_loss_interface.py
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@ -19,11 +19,11 @@ class IHyperOptLoss(ABC):
@staticmethod
@abstractmethod
def hyperopt_loss_function(results: DataFrame, trade_count: int,
def hyperopt_loss_function(*, results: DataFrame, trade_count: int,
min_date: datetime, max_date: datetime,
config: Dict, processed: Dict[str, DataFrame],
backtest_stats: Dict[str, Any],
*args, **kwargs) -> float:
**kwargs) -> float:
"""
Objective function, returns smaller number for better results
"""

37
freqtrade/optimize/optimize_reports.py
View File

@ -498,9 +498,12 @@ def generate_strategy_stats(pairlist: List[str],
(drawdown_abs, drawdown_start, drawdown_end, high_val, low_val,
max_drawdown) = calculate_max_drawdown(
results, value_col='profit_abs', starting_balance=start_balance)
(_, _, _, _, _, max_relative_drawdown) = calculate_max_drawdown(
results, value_col='profit_abs', starting_balance=start_balance, relative=True)
strat_stats.update({
'max_drawdown': max_drawdown_legacy, # Deprecated - do not use
'max_drawdown_account': max_drawdown,
'max_relative_drawdown': max_relative_drawdown,
'max_drawdown_abs': drawdown_abs,
'drawdown_start': drawdown_start.strftime(DATETIME_PRINT_FORMAT),
'drawdown_start_ts': drawdown_start.timestamp() * 1000,
@ -521,6 +524,7 @@ def generate_strategy_stats(pairlist: List[str],
strat_stats.update({
'max_drawdown': 0.0,
'max_drawdown_account': 0.0,
'max_relative_drawdown': 0.0,
'max_drawdown_abs': 0.0,
'max_drawdown_low': 0.0,
'max_drawdown_high': 0.0,
@ -729,6 +733,26 @@ def text_table_add_metrics(strat_results: Dict) -> str:
strat_results['stake_currency'])),
] if strat_results.get('trade_count_short', 0) > 0 else []
drawdown_metrics = []
if 'max_relative_drawdown' in strat_results:
# Compatibility to show old hyperopt results
drawdown_metrics.append(
('Max % of account underwater', f"{strat_results['max_relative_drawdown']:.2%}")
)
drawdown_metrics.extend([
('Absolute Drawdown (Account)', f"{strat_results['max_drawdown_account']:.2%}")
if 'max_drawdown_account' in strat_results else (
'Drawdown', f"{strat_results['max_drawdown']:.2%}"),
('Absolute Drawdown', round_coin_value(strat_results['max_drawdown_abs'],
strat_results['stake_currency'])),
('Drawdown high', round_coin_value(strat_results['max_drawdown_high'],
strat_results['stake_currency'])),
('Drawdown low', round_coin_value(strat_results['max_drawdown_low'],
strat_results['stake_currency'])),
('Drawdown Start', strat_results['drawdown_start']),
('Drawdown End', strat_results['drawdown_end']),
])
# Newly added fields should be ignored if they are missing in strat_results. hyperopt-show
# command stores these results and newer version of freqtrade must be able to handle old
# results with missing new fields.
@ -784,18 +808,7 @@ def text_table_add_metrics(strat_results: Dict) -> str:
('Max balance', round_coin_value(strat_results['csum_max'],
strat_results['stake_currency'])),
# 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'],
strat_results['stake_currency'])),
('Drawdown low', round_coin_value(strat_results['max_drawdown_low'],
strat_results['stake_currency'])),
('Drawdown Start', strat_results['drawdown_start']),
('Drawdown End', strat_results['drawdown_end']),
*drawdown_metrics,
('Market change', f"{strat_results['market_change']:.2%}"),
]

51
freqtrade/plot/plotting.py
View File

@ -159,12 +159,15 @@ def add_profit(fig, row, data: pd.DataFrame, column: str, name: str) -> make_sub
def add_max_drawdown(fig, row, trades: pd.DataFrame, df_comb: pd.DataFrame,
timeframe: str) -> make_subplots:
timeframe: str, starting_balance: float) -> make_subplots:
"""
Add scatter points indicating max drawdown
"""
try:
_, highdate, lowdate, _, _, max_drawdown = calculate_max_drawdown(trades)
_, highdate, lowdate, _, _, max_drawdown = calculate_max_drawdown(
trades,
starting_balance=starting_balance
)
drawdown = go.Scatter(
x=[highdate, lowdate],
@ -189,22 +192,37 @@ def add_max_drawdown(fig, row, trades: pd.DataFrame, df_comb: pd.DataFrame,
return fig
def add_underwater(fig, row, trades: pd.DataFrame) -> make_subplots:
def add_underwater(fig, row, trades: pd.DataFrame, starting_balance: float) -> make_subplots:
"""
Add underwater plot
Add underwater plots
"""
try:
underwater = calculate_underwater(trades, value_col="profit_abs")
underwater = calculate_underwater(
trades,
value_col="profit_abs",
starting_balance=starting_balance
)
underwater = go.Scatter(
underwater_plot = go.Scatter(
x=underwater['date'],
y=underwater['drawdown'],
name="Underwater Plot",
fill='tozeroy',
fillcolor='#cc362b',
line={'color': '#cc362b'},
line={'color': '#cc362b'}
)
fig.add_trace(underwater, row, 1)
underwater_plot_relative = go.Scatter(
x=underwater['date'],
y=(-underwater['drawdown_relative']),
name="Underwater Plot (%)",
fill='tozeroy',
fillcolor='green',
line={'color': 'green'}
)
fig.add_trace(underwater_plot, row, 1)
fig.add_trace(underwater_plot_relative, row + 1, 1)
except ValueError:
logger.warning("No trades found - not plotting underwater plot")
return fig
@ -507,7 +525,8 @@ def generate_candlestick_graph(pair: str, data: pd.DataFrame, trades: pd.DataFra
def generate_profit_graph(pairs: str, data: Dict[str, pd.DataFrame],
trades: pd.DataFrame, timeframe: str, stake_currency: str) -> go.Figure:
trades: pd.DataFrame, timeframe: str, stake_currency: str,
starting_balance: float) -> go.Figure:
# Combine close-values for all pairs, rename columns to "pair"
try:
df_comb = combine_dataframes_with_mean(data, "close")
@ -531,8 +550,8 @@ def generate_profit_graph(pairs: str, data: Dict[str, pd.DataFrame],
name='Avg close price',
)
fig = make_subplots(rows=5, cols=1, shared_xaxes=True,
row_heights=[1, 1, 1, 0.5, 1],
fig = make_subplots(rows=6, cols=1, shared_xaxes=True,
row_heights=[1, 1, 1, 0.5, 0.75, 0.75],
vertical_spacing=0.05,
subplot_titles=[
"AVG Close Price",
@ -540,6 +559,7 @@ def generate_profit_graph(pairs: str, data: Dict[str, pd.DataFrame],
"Profit per pair",
"Parallelism",
"Underwater",
"Relative Drawdown",
])
fig['layout'].update(title="Freqtrade Profit plot")
fig['layout']['yaxis1'].update(title='Price')
@ -547,14 +567,16 @@ def generate_profit_graph(pairs: str, data: Dict[str, pd.DataFrame],
fig['layout']['yaxis3'].update(title=f'Profit {stake_currency}')
fig['layout']['yaxis4'].update(title='Trade count')
fig['layout']['yaxis5'].update(title='Underwater Plot')
fig['layout']['yaxis6'].update(title='Underwater Plot Relative (%)', tickformat=',.2%')
fig['layout']['xaxis']['rangeslider'].update(visible=False)
fig.update_layout(modebar_add=["v1hovermode", "toggleSpikeLines"])
fig.add_trace(avgclose, 1, 1)
fig = add_profit(fig, 2, df_comb, 'cum_profit', 'Profit')
fig = add_max_drawdown(fig, 2, trades, df_comb, timeframe)
fig = add_max_drawdown(fig, 2, trades, df_comb, timeframe, starting_balance)
fig = add_parallelism(fig, 4, trades, timeframe)
fig = add_underwater(fig, 5, trades)
# Two rows consumed
fig = add_underwater(fig, 5, trades, starting_balance)
for pair in pairs:
profit_col = f'cum_profit_{pair}'
@ -670,7 +692,8 @@ def plot_profit(config: Dict[str, Any]) -> None:
# this could be useful to gauge the overall market trend
fig = generate_profit_graph(plot_elements['pairs'], plot_elements['ohlcv'],
trades, config['timeframe'],
config.get('stake_currency', ''))
config.get('stake_currency', ''),
config.get('available_capital', config['dry_run_wallet']))
store_plot_file(fig, filename='freqtrade-profit-plot.html',
directory=config['user_data_dir'] / 'plot',
auto_open=config.get('plot_auto_open', False))

35
tests/data/test_btanalysis.py
View File

@ -376,3 +376,38 @@ def test_calculate_max_drawdown2():
df = DataFrame(zip(values[:5], dates[:5]), columns=['profit', 'open_date'])
with pytest.raises(ValueError, match='No losing trade, therefore no drawdown.'):
calculate_max_drawdown(df, date_col='open_date', value_col='profit')
@pytest.mark.parametrize('profits,relative,highd,lowd,result,result_rel', [
([0.0, -500.0, 500.0, 10000.0, -1000.0], False, 3, 4, 1000.0, 0.090909),
([0.0, -500.0, 500.0, 10000.0, -1000.0], True, 0, 1, 500.0, 0.5),
])
def test_calculate_max_drawdown_abs(profits, relative, highd, lowd, result, result_rel):
"""
Test case from issue https://github.com/freqtrade/freqtrade/issues/6655
[1000, 500, 1000, 11000, 10000] # absolute results
[1000, 50%, 0%, 0%, ~9%] # Relative drawdowns
"""
init_date = Arrow(2020, 1, 1)
dates = [init_date.shift(days=i) for i in range(len(profits))]
df = DataFrame(zip(profits, dates), columns=['profit_abs', 'open_date'])
# sort by profit and reset index
df = df.sort_values('profit_abs').reset_index(drop=True)
df1 = df.copy()
drawdown, hdate, ldate, hval, lval, drawdown_rel = calculate_max_drawdown(
df, date_col='open_date', starting_balance=1000, relative=relative)
# Ensure df has not been altered.
assert df.equals(df1)
assert isinstance(drawdown, float)
assert isinstance(drawdown_rel, float)
assert hdate == init_date.shift(days=highd)
assert ldate == init_date.shift(days=lowd)
# High must be before low
assert hdate < ldate
# High value must be higher than low value
assert hval > lval
assert drawdown == result
assert pytest.approx(drawdown_rel) == result_rel

1
tests/optimize/test_hyperoptloss.py
View File

@ -85,6 +85,7 @@ def test_loss_calculation_has_limited_profit(hyperopt_conf, hyperopt_results) ->
"SharpeHyperOptLoss",
"SharpeHyperOptLossDaily",
"MaxDrawDownHyperOptLoss",
"MaxDrawDownRelativeHyperOptLoss",
"CalmarHyperOptLoss",
"ProfitDrawDownHyperOptLoss",

15
tests/test_plotting.py
View File

@ -332,7 +332,13 @@ def test_generate_profit_graph(testdatadir):
trades = trades[trades['pair'].isin(pairs)]
fig = generate_profit_graph(pairs, data, trades, timeframe="5m", stake_currency='BTC')
fig = generate_profit_graph(
pairs,
data,
trades,
timeframe="5m",
stake_currency='BTC',
starting_balance=0)
assert isinstance(fig, go.Figure)
assert fig.layout.title.text == "Freqtrade Profit plot"
@ -341,7 +347,7 @@ def test_generate_profit_graph(testdatadir):
assert fig.layout.yaxis3.title.text == "Profit BTC"
figure = fig.layout.figure
assert len(figure.data) == 7
assert len(figure.data) == 8
avgclose = find_trace_in_fig_data(figure.data, "Avg close price")
assert isinstance(avgclose, go.Scatter)
@ -356,6 +362,9 @@ def test_generate_profit_graph(testdatadir):
underwater = find_trace_in_fig_data(figure.data, "Underwater Plot")
assert isinstance(underwater, go.Scatter)
underwater_relative = find_trace_in_fig_data(figure.data, "Underwater Plot (%)")
assert isinstance(underwater_relative, go.Scatter)
for pair in pairs:
profit_pair = find_trace_in_fig_data(figure.data, f"Profit {pair}")
assert isinstance(profit_pair, go.Scatter)
@ -363,7 +372,7 @@ def test_generate_profit_graph(testdatadir):
with pytest.raises(OperationalException, match=r"No trades found.*"):
# Pair cannot be empty - so it's an empty dataframe.
generate_profit_graph(pairs, data, trades.loc[trades['pair'].isnull()], timeframe="5m",
stake_currency='BTC')
stake_currency='BTC', starting_balance=0)
def test_start_plot_dataframe(mocker):