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Merge branch 'develop' into backtest_live_models

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This commit is contained in:
Wagner Costa Santos 2022-09-27 10:27:47 -03:00
commit 3c002ff752
10 changed files with 449 additions and 23 deletions
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8
freqtrade/data/btanalysis.py
View File

@ -284,7 +284,7 @@ def load_backtest_data(filename: Union[Path, str], strategy: Optional[str] = Non
df['enter_tag'] = df['buy_tag'] df['enter_tag'] = df['buy_tag']
df = df.drop(['buy_tag'], axis=1) df = df.drop(['buy_tag'], axis=1)
if 'orders' not in df.columns: if 'orders' not in df.columns:
df.loc[:, 'orders'] = None df['orders'] = None
else: else:
# old format - only with lists. # old format - only with lists.
@ -341,9 +341,9 @@ def trade_list_to_dataframe(trades: List[LocalTrade]) -> pd.DataFrame:
""" """
df = pd.DataFrame.from_records([t.to_json(True) for t in trades], columns=BT_DATA_COLUMNS) df = pd.DataFrame.from_records([t.to_json(True) for t in trades], columns=BT_DATA_COLUMNS)
if len(df) > 0: if len(df) > 0:
df.loc[:, 'close_date'] = pd.to_datetime(df['close_date'], utc=True) df['close_date'] = pd.to_datetime(df['close_date'], utc=True)
df.loc[:, 'open_date'] = pd.to_datetime(df['open_date'], utc=True) df['open_date'] = pd.to_datetime(df['open_date'], utc=True)
df.loc[:, 'close_rate'] = df['close_rate'].astype('float64') df['close_rate'] = df['close_rate'].astype('float64')
return df return df

2
freqtrade/data/history/idatahandler.py
View File

@ -272,7 +272,7 @@ class IDataHandler(ABC):
return res return res
def ohlcv_load(self, pair, timeframe: str, def ohlcv_load(self, pair, timeframe: str,
candle_type: CandleType, candle_type: CandleType, *,
timerange: Optional[TimeRange] = None, timerange: Optional[TimeRange] = None,
fill_missing: bool = True, fill_missing: bool = True,
drop_incomplete: bool = True, drop_incomplete: bool = True,

432
freqtrade/exchange/binance_leverage_tiers.json
View File

@ -4485,6 +4485,120 @@
} }
} }
], ],
"BTCUSDT_221230": [
{
"tier": 1.0,
"currency": "USDT",
"minNotional": 0.0,
"maxNotional": 375000.0,
"maintenanceMarginRate": 0.02,
"maxLeverage": 25.0,
"info": {
"bracket": "1",
"initialLeverage": "25",
"notionalCap": "375000",
"notionalFloor": "0",
"maintMarginRatio": "0.02",
"cum": "0.0"
}
},
{
"tier": 2.0,
"currency": "USDT",
"minNotional": 375000.0,
"maxNotional": 2000000.0,
"maintenanceMarginRate": 0.05,
"maxLeverage": 10.0,
"info": {
"bracket": "2",
"initialLeverage": "10",
"notionalCap": "2000000",
"notionalFloor": "375000",
"maintMarginRatio": "0.05",
"cum": "11250.0"
}
},
{
"tier": 3.0,
"currency": "USDT",
"minNotional": 2000000.0,
"maxNotional": 4000000.0,
"maintenanceMarginRate": 0.1,
"maxLeverage": 5.0,
"info": {
"bracket": "3",
"initialLeverage": "5",
"notionalCap": "4000000",
"notionalFloor": "2000000",
"maintMarginRatio": "0.1",
"cum": "111250.0"
}
},
{
"tier": 4.0,
"currency": "USDT",
"minNotional": 4000000.0,
"maxNotional": 10000000.0,
"maintenanceMarginRate": 0.125,
"maxLeverage": 4.0,
"info": {
"bracket": "4",
"initialLeverage": "4",
"notionalCap": "10000000",
"notionalFloor": "4000000",
"maintMarginRatio": "0.125",
"cum": "211250.0"
}
},
{
"tier": 5.0,
"currency": "USDT",
"minNotional": 10000000.0,
"maxNotional": 20000000.0,
"maintenanceMarginRate": 0.15,
"maxLeverage": 3.0,
"info": {
"bracket": "5",
"initialLeverage": "3",
"notionalCap": "20000000",
"notionalFloor": "10000000",
"maintMarginRatio": "0.15",
"cum": "461250.0"
}
},
{
"tier": 6.0,
"currency": "USDT",
"minNotional": 20000000.0,
"maxNotional": 40000000.0,
"maintenanceMarginRate": 0.25,
"maxLeverage": 2.0,
"info": {
"bracket": "6",
"initialLeverage": "2",
"notionalCap": "40000000",
"notionalFloor": "20000000",
"maintMarginRatio": "0.25",
"cum": "2461250.0"
}
},
{
"tier": 7.0,
"currency": "USDT",
"minNotional": 40000000.0,
"maxNotional": 400000000.0,
"maintenanceMarginRate": 0.5,
"maxLeverage": 1.0,
"info": {
"bracket": "7",
"initialLeverage": "1",
"notionalCap": "400000000",
"notionalFloor": "40000000",
"maintMarginRatio": "0.5",
"cum": "1.246125E7"
}
}
],
"BTS/USDT": [ "BTS/USDT": [
{ {
"tier": 1.0, "tier": 1.0,
@ -5759,6 +5873,104 @@
} }
} }
], ],
"CVX/USDT": [
{
"tier": 1.0,
"currency": "USDT",
"minNotional": 0.0,
"maxNotional": 5000.0,
"maintenanceMarginRate": 0.01,
"maxLeverage": 25.0,
"info": {
"bracket": "1",
"initialLeverage": "25",
"notionalCap": "5000",
"notionalFloor": "0",
"maintMarginRatio": "0.01",
"cum": "0.0"
}
},
{
"tier": 2.0,
"currency": "USDT",
"minNotional": 5000.0,
"maxNotional": 25000.0,
"maintenanceMarginRate": 0.025,
"maxLeverage": 20.0,
"info": {
"bracket": "2",
"initialLeverage": "20",
"notionalCap": "25000",
"notionalFloor": "5000",
"maintMarginRatio": "0.025",
"cum": "75.0"
}
},
{
"tier": 3.0,
"currency": "USDT",
"minNotional": 25000.0,
"maxNotional": 100000.0,
"maintenanceMarginRate": 0.05,
"maxLeverage": 10.0,
"info": {
"bracket": "3",
"initialLeverage": "10",
"notionalCap": "100000",
"notionalFloor": "25000",
"maintMarginRatio": "0.05",
"cum": "700.0"
}
},
{
"tier": 4.0,
"currency": "USDT",
"minNotional": 100000.0,
"maxNotional": 250000.0,
"maintenanceMarginRate": 0.1,
"maxLeverage": 5.0,
"info": {
"bracket": "4",
"initialLeverage": "5",
"notionalCap": "250000",
"notionalFloor": "100000",
"maintMarginRatio": "0.1",
"cum": "5700.0"
}
},
{
"tier": 5.0,
"currency": "USDT",
"minNotional": 250000.0,
"maxNotional": 1000000.0,
"maintenanceMarginRate": 0.125,
"maxLeverage": 2.0,
"info": {
"bracket": "5",
"initialLeverage": "2",
"notionalCap": "1000000",
"notionalFloor": "250000",
"maintMarginRatio": "0.125",
"cum": "11950.0"
}
},
{
"tier": 6.0,
"currency": "USDT",
"minNotional": 1000000.0,
"maxNotional": 5000000.0,
"maintenanceMarginRate": 0.5,
"maxLeverage": 1.0,
"info": {
"bracket": "6",
"initialLeverage": "1",
"notionalCap": "5000000",
"notionalFloor": "1000000",
"maintMarginRatio": "0.5",
"cum": "386950.0"
}
}
],
"DAR/USDT": [ "DAR/USDT": [
{ {
"tier": 1.0, "tier": 1.0,
@ -8105,6 +8317,120 @@
} }
} }
], ],
"ETHUSDT_221230": [
{
"tier": 1.0,
"currency": "USDT",
"minNotional": 0.0,
"maxNotional": 375000.0,
"maintenanceMarginRate": 0.02,
"maxLeverage": 25.0,
"info": {
"bracket": "1",
"initialLeverage": "25",
"notionalCap": "375000",
"notionalFloor": "0",
"maintMarginRatio": "0.02",
"cum": "0.0"
}
},
{
"tier": 2.0,
"currency": "USDT",
"minNotional": 375000.0,
"maxNotional": 2000000.0,
"maintenanceMarginRate": 0.05,
"maxLeverage": 10.0,
"info": {
"bracket": "2",
"initialLeverage": "10",
"notionalCap": "2000000",
"notionalFloor": "375000",
"maintMarginRatio": "0.05",
"cum": "11250.0"
}
},
{
"tier": 3.0,
"currency": "USDT",
"minNotional": 2000000.0,
"maxNotional": 4000000.0,
"maintenanceMarginRate": 0.1,
"maxLeverage": 5.0,
"info": {
"bracket": "3",
"initialLeverage": "5",
"notionalCap": "4000000",
"notionalFloor": "2000000",
"maintMarginRatio": "0.1",
"cum": "111250.0"
}
},
{
"tier": 4.0,
"currency": "USDT",
"minNotional": 4000000.0,
"maxNotional": 10000000.0,
"maintenanceMarginRate": 0.125,
"maxLeverage": 4.0,
"info": {
"bracket": "4",
"initialLeverage": "4",
"notionalCap": "10000000",
"notionalFloor": "4000000",
"maintMarginRatio": "0.125",
"cum": "211250.0"
}
},
{
"tier": 5.0,
"currency": "USDT",
"minNotional": 10000000.0,
"maxNotional": 20000000.0,
"maintenanceMarginRate": 0.15,
"maxLeverage": 3.0,
"info": {
"bracket": "5",
"initialLeverage": "3",
"notionalCap": "20000000",
"notionalFloor": "10000000",
"maintMarginRatio": "0.15",
"cum": "461250.0"
}
},
{
"tier": 6.0,
"currency": "USDT",
"minNotional": 20000000.0,
"maxNotional": 40000000.0,
"maintenanceMarginRate": 0.25,
"maxLeverage": 2.0,
"info": {
"bracket": "6",
"initialLeverage": "2",
"notionalCap": "40000000",
"notionalFloor": "20000000",
"maintMarginRatio": "0.25",
"cum": "2461250.0"
}
},
{
"tier": 7.0,
"currency": "USDT",
"minNotional": 40000000.0,
"maxNotional": 400000000.0,
"maintenanceMarginRate": 0.5,
"maxLeverage": 1.0,
"info": {
"bracket": "7",
"initialLeverage": "1",
"notionalCap": "400000000",
"notionalFloor": "40000000",
"maintMarginRatio": "0.5",
"cum": "1.246125E7"
}
}
],
"FIL/BUSD": [ "FIL/BUSD": [
{ {
"tier": 1.0, "tier": 1.0,
@ -10138,10 +10464,10 @@
"minNotional": 0.0, "minNotional": 0.0,
"maxNotional": 5000.0, "maxNotional": 5000.0,
"maintenanceMarginRate": 0.01, "maintenanceMarginRate": 0.01,
"maxLeverage": 50.0, "maxLeverage": 25.0,
"info": { "info": {
"bracket": "1", "bracket": "1",
"initialLeverage": "50", "initialLeverage": "25",
"notionalCap": "5000", "notionalCap": "5000",
"notionalFloor": "0", "notionalFloor": "0",
"maintMarginRatio": "0.01", "maintMarginRatio": "0.01",
@ -10216,13 +10542,13 @@
"tier": 6.0, "tier": 6.0,
"currency": "USDT", "currency": "USDT",
"minNotional": 1000000.0, "minNotional": 1000000.0,
"maxNotional": 30000000.0, "maxNotional": 5000000.0,
"maintenanceMarginRate": 0.5, "maintenanceMarginRate": 0.5,
"maxLeverage": 1.0, "maxLeverage": 1.0,
"info": { "info": {
"bracket": "6", "bracket": "6",
"initialLeverage": "1", "initialLeverage": "1",
"notionalCap": "30000000", "notionalCap": "5000000",
"notionalFloor": "1000000", "notionalFloor": "1000000",
"maintMarginRatio": "0.5", "maintMarginRatio": "0.5",
"cum": "386950.0" "cum": "386950.0"
@ -11389,6 +11715,104 @@
} }
} }
], ],
"LDO/USDT": [
{
"tier": 1.0,
"currency": "USDT",
"minNotional": 0.0,
"maxNotional": 5000.0,
"maintenanceMarginRate": 0.01,
"maxLeverage": 25.0,
"info": {
"bracket": "1",
"initialLeverage": "25",
"notionalCap": "5000",
"notionalFloor": "0",
"maintMarginRatio": "0.01",
"cum": "0.0"
}
},
{
"tier": 2.0,
"currency": "USDT",
"minNotional": 5000.0,
"maxNotional": 25000.0,
"maintenanceMarginRate": 0.025,
"maxLeverage": 20.0,
"info": {
"bracket": "2",
"initialLeverage": "20",
"notionalCap": "25000",
"notionalFloor": "5000",
"maintMarginRatio": "0.025",
"cum": "75.0"
}
},
{
"tier": 3.0,
"currency": "USDT",
"minNotional": 25000.0,
"maxNotional": 100000.0,
"maintenanceMarginRate": 0.05,
"maxLeverage": 10.0,
"info": {
"bracket": "3",
"initialLeverage": "10",
"notionalCap": "100000",
"notionalFloor": "25000",
"maintMarginRatio": "0.05",
"cum": "700.0"
}
},
{
"tier": 4.0,
"currency": "USDT",
"minNotional": 100000.0,
"maxNotional": 250000.0,
"maintenanceMarginRate": 0.1,
"maxLeverage": 5.0,
"info": {
"bracket": "4",
"initialLeverage": "5",
"notionalCap": "250000",
"notionalFloor": "100000",
"maintMarginRatio": "0.1",
"cum": "5700.0"
}
},
{
"tier": 5.0,
"currency": "USDT",
"minNotional": 250000.0,
"maxNotional": 1000000.0,
"maintenanceMarginRate": 0.125,
"maxLeverage": 2.0,
"info": {
"bracket": "5",
"initialLeverage": "2",
"notionalCap": "1000000",
"notionalFloor": "250000",
"maintMarginRatio": "0.125",
"cum": "11950.0"
}
},
{
"tier": 6.0,
"currency": "USDT",
"minNotional": 1000000.0,
"maxNotional": 5000000.0,
"maintenanceMarginRate": 0.5,
"maxLeverage": 1.0,
"info": {
"bracket": "6",
"initialLeverage": "1",
"notionalCap": "5000000",
"notionalFloor": "1000000",
"maintMarginRatio": "0.5",
"cum": "386950.0"
}
}
],
"LEVER/BUSD": [ "LEVER/BUSD": [
{ {
"tier": 1.0, "tier": 1.0,

4
freqtrade/optimize/backtesting.py
View File

@ -377,10 +377,10 @@ class Backtesting:
for col in HEADERS[5:]: for col in HEADERS[5:]:
tag_col = col in ('enter_tag', 'exit_tag') tag_col = col in ('enter_tag', 'exit_tag')
if col in df_analyzed.columns: if col in df_analyzed.columns:
df_analyzed.loc[:, col] = df_analyzed.loc[:, col].replace( df_analyzed[col] = df_analyzed.loc[:, col].replace(
[nan], [0 if not tag_col else None]).shift(1) [nan], [0 if not tag_col else None]).shift(1)
elif not df_analyzed.empty: elif not df_analyzed.empty:
df_analyzed.loc[:, col] = 0 if not tag_col else None df_analyzed[col] = 0 if not tag_col else None
df_analyzed = df_analyzed.drop(df_analyzed.head(1).index) df_analyzed = df_analyzed.drop(df_analyzed.head(1).index)

6
freqtrade/optimize/optimize_reports.py
View File

@ -173,7 +173,7 @@ def generate_tag_metrics(tag_type: str,
tabular_data = [] tabular_data = []
if tag_type in results.columns: if tag_type in results.columns:
for tag, count in results[tag_type].value_counts().iteritems(): for tag, count in results[tag_type].value_counts().items():
result = results[results[tag_type] == tag] result = results[results[tag_type] == tag]
if skip_nan and result['profit_abs'].isnull().all(): if skip_nan and result['profit_abs'].isnull().all():
continue continue
@ -199,7 +199,7 @@ def generate_exit_reason_stats(max_open_trades: int, results: DataFrame) -> List
""" """
tabular_data = [] tabular_data = []
for reason, count in results['exit_reason'].value_counts().iteritems(): for reason, count in results['exit_reason'].value_counts().items():
result = results.loc[results['exit_reason'] == reason] result = results.loc[results['exit_reason'] == reason]
profit_mean = result['profit_ratio'].mean() profit_mean = result['profit_ratio'].mean()
@ -361,7 +361,7 @@ def generate_daily_stats(results: DataFrame) -> Dict[str, Any]:
winning_days = sum(daily_profit > 0) winning_days = sum(daily_profit > 0)
draw_days = sum(daily_profit == 0) draw_days = sum(daily_profit == 0)
losing_days = sum(daily_profit < 0) losing_days = sum(daily_profit < 0)
daily_profit_list = [(str(idx.date()), val) for idx, val in daily_profit.iteritems()] daily_profit_list = [(str(idx.date()), val) for idx, val in daily_profit.items()]
return { return {
'backtest_best_day': best_rel, 'backtest_best_day': best_rel,

6
requirements.txt
View File

@ -1,5 +1,7 @@
numpy==1.23.3 numpy==1.23.3
pandas==1.5.0 pandas==1.5.0; platform_machine != 'armv7l'
# Piwheels doesn't have 1.5.0 yet.
pandas==1.4.3; platform_machine == 'armv7l'
pandas-ta==0.3.14b pandas-ta==0.3.14b
ccxt==1.93.98 ccxt==1.93.98
@ -21,7 +23,7 @@ jinja2==3.1.2
tables==3.7.0 tables==3.7.0
blosc==1.10.6 blosc==1.10.6
joblib==1.2.0 joblib==1.2.0
pyarrow==9.0.0 pyarrow==9.0.0; platform_machine != 'armv7l'
# find first, C search in arrays # find first, C search in arrays
py_find_1st==1.1.5 py_find_1st==1.1.5

2
setup.py
View File

@ -73,7 +73,7 @@ setup(
'tables', 'tables',
'blosc', 'blosc',
'joblib', 'joblib',
'pyarrow', 'pyarrow; platform_machine != "armv7l"',
'fastapi', 'fastapi',
'uvicorn', 'uvicorn',
'psutil', 'psutil',

2
tests/data/test_btanalysis.py
View File

@ -275,7 +275,7 @@ def test_create_cum_profit1(testdatadir):
filename = testdatadir / "backtest_results/backtest-result_new.json" filename = testdatadir / "backtest_results/backtest-result_new.json"
bt_data = load_backtest_data(filename) bt_data = load_backtest_data(filename)
# Move close-time to "off" the candle, to make sure the logic still works # 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) bt_data['close_date'] = bt_data.loc[:, 'close_date'] + DateOffset(seconds=20)
timerange = TimeRange.parse_timerange("20180110-20180112") timerange = TimeRange.parse_timerange("20180110-20180112")
df = load_pair_history(pair="TRX/BTC", timeframe='5m', df = load_pair_history(pair="TRX/BTC", timeframe='5m',

2
tests/optimize/test_backtesting.py
View File

@ -839,7 +839,7 @@ def test_backtest_trim_no_data_left(default_conf, fee, mocker, testdatadir) -> N
data = history.load_data(datadir=testdatadir, timeframe='5m', pairs=['UNITTEST/BTC'], data = history.load_data(datadir=testdatadir, timeframe='5m', pairs=['UNITTEST/BTC'],
timerange=timerange) timerange=timerange)
df = data['UNITTEST/BTC'] df = data['UNITTEST/BTC']
df.loc[:, 'date'] = df.loc[:, 'date'] - timedelta(days=1) df['date'] = df.loc[:, 'date'] - timedelta(days=1)
# Trimming 100 candles, so after 2nd trimming, no candle is left. # Trimming 100 candles, so after 2nd trimming, no candle is left.
df = df.iloc[:100] df = df.iloc[:100]
data['XRP/USDT'] = df data['XRP/USDT'] = df

8
tests/plugins/test_pairlist.py
View File

@ -622,10 +622,10 @@ def test_VolumePairList_range(mocker, whitelist_conf, shitcoinmarkets, tickers,
# create candles for high volume with all candles high volume, but very low price. # 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 = ohlcv_history.copy()
ohlcv_history_high_volume.loc[:, 'volume'] = 10 ohlcv_history_high_volume['volume'] = 10
ohlcv_history_high_volume.loc[:, 'low'] = ohlcv_history_high_volume.loc[:, 'low'] * 0.01 ohlcv_history_high_volume['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['high'] = ohlcv_history_high_volume.loc[:, 'high'] * 0.01
ohlcv_history_high_volume.loc[:, 'close'] = ohlcv_history_high_volume.loc[:, 'close'] * 0.01 ohlcv_history_high_volume['close'] = ohlcv_history_high_volume.loc[:, 'close'] * 0.01
mocker.patch('freqtrade.exchange.ftx.Ftx.market_is_tradable', return_value=True) mocker.patch('freqtrade.exchange.ftx.Ftx.market_is_tradable', return_value=True)