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Merge branch 'develop' into add-spice-rack

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6
.github/workflows/ci.yml vendored
View File

@ -24,7 +24,7 @@ jobs:
strategy:
matrix:
os: [ ubuntu-18.04, ubuntu-20.04, ubuntu-22.04 ]
python-version: ["3.8", "3.9", "3.10"]
python-version: ["3.8", "3.9", "3.10.6"]
steps:
- uses: actions/checkout@v3
@ -121,7 +121,7 @@ jobs:
strategy:
matrix:
os: [ macos-latest ]
python-version: ["3.8", "3.9", "3.10"]
python-version: ["3.8", "3.9", "3.10.6"]
steps:
- uses: actions/checkout@v3
@ -205,7 +205,7 @@ jobs:
strategy:
matrix:
os: [ windows-latest ]
python-version: ["3.8", "3.9", "3.10"]
python-version: ["3.8", "3.9", "3.10.6"]
steps:
- uses: actions/checkout@v3

2
.pre-commit-config.yaml
View File

@ -15,7 +15,7 @@ repos:
additional_dependencies:
- types-cachetools==5.2.1
- types-filelock==3.2.7
- types-requests==2.28.9
- types-requests==2.28.10
- types-tabulate==0.8.11
- types-python-dateutil==2.8.19
# stages: [push]

10
docs/data-download.md
View File

@ -25,8 +25,7 @@ usage: freqtrade download-data [-h] [-v] [--logfile FILE] [-V] [-c PATH]
[--include-inactive-pairs]
[--timerange TIMERANGE] [--dl-trades]
[--exchange EXCHANGE]
[-t {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} ...]]
[--erase]
[-t TIMEFRAMES [TIMEFRAMES ...]] [--erase]
[--data-format-ohlcv {json,jsongz,hdf5}]
[--data-format-trades {json,jsongz,hdf5}]
[--trading-mode {spot,margin,futures}]
@ -37,7 +36,8 @@ optional arguments:
-p PAIRS [PAIRS ...], --pairs PAIRS [PAIRS ...]
Limit command to these pairs. Pairs are space-
separated.
--pairs-file FILE File containing a list of pairs to download.
--pairs-file FILE File containing a list of pairs. Takes precedence over
--pairs or pairs configured in the configuration.
--days INT Download data for given number of days.
--new-pairs-days INT Download data of new pairs for given number of days.
Default: `None`.
@ -50,7 +50,7 @@ optional arguments:
as --timeframes/-t.
--exchange EXCHANGE Exchange name (default: `bittrex`). Only valid if no
config is provided.
-t {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} ...], --timeframes {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} ...]
-t TIMEFRAMES [TIMEFRAMES ...], --timeframes TIMEFRAMES [TIMEFRAMES ...]
Specify which tickers to download. Space-separated
list. Default: `1m 5m`.
--erase Clean all existing data for the selected
@ -61,7 +61,7 @@ optional arguments:
--data-format-trades {json,jsongz,hdf5}
Storage format for downloaded trades data. (default:
`jsongz`).
--trading-mode {spot,margin,futures}
--trading-mode {spot,margin,futures}, --tradingmode {spot,margin,futures}
Select Trading mode
--prepend Allow data prepending. (Data-appending is disabled)

3
docs/exchanges.md
View File

@ -57,7 +57,8 @@ This configuration enables kraken, as well as rate-limiting to avoid bans from t
Binance supports [time_in_force](configuration.md#understand-order_time_in_force).
!!! Tip "Stoploss on Exchange"
Binance supports `stoploss_on_exchange` and uses `stop-loss-limit` orders. It provides great advantages, so we recommend to benefit from it by enabling stoploss on exchange..
Binance supports `stoploss_on_exchange` and uses `stop-loss-limit` orders. It provides great advantages, so we recommend to benefit from it by enabling stoploss on exchange.
On futures, Binance supports both `stop-limit` as well as `stop-market` orders. You can use either `"limit"` or `"market"` in the `order_types.stoploss` configuration setting to decide which type to use.
### Binance Blacklist

13
docs/freqai.md
View File

@ -190,19 +190,6 @@ The FreqAI strategy requires the user to include the following lines of code in
# passed to any single indicator)
startup_candle_count: int = 20
def informative_pairs(self):
whitelist_pairs = self.dp.current_whitelist()
corr_pairs = self.config["freqai"]["feature_parameters"]["include_corr_pairlist"]
informative_pairs = []
for tf in self.config["freqai"]["feature_parameters"]["include_timeframes"]:
for pair in whitelist_pairs:
informative_pairs.append((pair, tf))
for pair in corr_pairs:
if pair in whitelist_pairs:
continue # avoid duplication
informative_pairs.append((pair, tf))
return informative_pairs
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# the model will return all labels created by user in `populate_any_indicators`

2
docs/requirements-docs.txt
View File

@ -1,6 +1,6 @@
markdown==3.3.7
mkdocs==1.3.1
mkdocs-material==8.4.2
mkdocs-material==8.4.3
mdx_truly_sane_lists==1.3
pymdown-extensions==9.5
jinja2==3.1.2

3
docs/strategy-customization.md
View File

@ -264,7 +264,8 @@ def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFram
### Exit signal rules
Edit the method `populate_exit_trend()` into your strategy file to update your exit strategy.
Please note that the exit-signal is only used if `use_exit_signal` is set to true in the configuration.
The exit-signal is only used for exits if `use_exit_signal` is set to true in the configuration.
`use_exit_signal` will not influence [signal collision rules](#colliding-signals) - which will still apply and can prevent entries.
It's important to always return the dataframe without removing/modifying the columns `"open", "high", "low", "close", "volume"`, otherwise these fields would contain something unexpected.

4
docs/utils.md
View File

@ -525,12 +525,14 @@ Requires a configuration with specified `pairlists` attribute.
Can be used to generate static pairlists to be used during backtesting / hyperopt.
```
usage: freqtrade test-pairlist [-h] [-v] [-c PATH]
usage: freqtrade test-pairlist [-h] [--userdir PATH] [-v] [-c PATH]
[--quote QUOTE_CURRENCY [QUOTE_CURRENCY ...]]
[-1] [--print-json] [--exchange EXCHANGE]
optional arguments:
-h, --help show this help message and exit
--userdir PATH, --user-data-dir PATH
Path to userdata directory.
-v, --verbose Verbose mode (-vv for more, -vvv to get all messages).
-c PATH, --config PATH
Specify configuration file (default:

4
freqtrade/commands/arguments.py
View File

@ -53,8 +53,8 @@ ARGS_LIST_PAIRS = ["exchange", "print_list", "list_pairs_print_json", "print_one
"print_csv", "base_currencies", "quote_currencies", "list_pairs_all",
"trading_mode"]
ARGS_TEST_PAIRLIST = ["verbosity", "config", "quote_currencies", "print_one_column",
"list_pairs_print_json", "exchange"]
ARGS_TEST_PAIRLIST = ["user_data_dir", "verbosity", "config", "quote_currencies",
"print_one_column", "list_pairs_print_json", "exchange"]
ARGS_CREATE_USERDIR = ["user_data_dir", "reset"]

3
freqtrade/commands/cli_options.py
View File

@ -393,7 +393,8 @@ AVAILABLE_CLI_OPTIONS = {
# Download data
"pairs_file": Arg(
'--pairs-file',
help='File containing a list of pairs to download.',
help='File containing a list of pairs. '
'Takes precedence over --pairs or pairs configured in the configuration.',
metavar='FILE',
),
"days": Arg(

6
freqtrade/data/dataprovider.py
View File

@ -86,7 +86,7 @@ class DataProvider:
"""
_candle_type = CandleType.from_string(
candle_type) if candle_type != '' else self._config['candle_type_def']
saved_pair = (pair, str(timeframe), _candle_type)
saved_pair: PairWithTimeframe = (pair, str(timeframe), _candle_type)
if saved_pair not in self.__cached_pairs_backtesting:
timerange = TimeRange.parse_timerange(None if self._config.get(
'timerange') is None else str(self._config.get('timerange')))
@ -196,7 +196,9 @@ class DataProvider:
Clear pair dataframe cache.
"""
self.__cached_pairs = {}
self.__cached_pairs_backtesting = {}
# Don't reset backtesting pairs -
# otherwise they're reloaded each time during hyperopt due to with analyze_per_epoch
# self.__cached_pairs_backtesting = {}
self.__slice_index = 0
# Exchange functions

7
freqtrade/exchange/binance.py
View File

@ -31,7 +31,7 @@ class Binance(Exchange):
"ccxt_futures_name": "future"
}
_ft_has_futures: Dict = {
"stoploss_order_types": {"limit": "stop"},
"stoploss_order_types": {"limit": "limit", "market": "market"},
"tickers_have_price": False,
}
@ -48,13 +48,12 @@ class Binance(Exchange):
Returns True if adjustment is necessary.
:param side: "buy" or "sell"
"""
ordertype = 'stop' if self.trading_mode == TradingMode.FUTURES else 'stop_loss_limit'
order_types = ('stop_loss_limit', 'stop', 'stop_market')
return (
order.get('stopPrice', None) is None
or (
order['type'] == ordertype
order['type'] in order_types
and (
(side == "sell" and stop_loss > float(order['stopPrice'])) or
(side == "buy" and stop_loss < float(order['stopPrice']))

686
freqtrade/exchange/binance_leverage_tiers.json
View File

@ -81,6 +81,104 @@
}
}
],
"1000LUNC/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"
}
}
],
"1000SHIB/BUSD": [
{
"tier": 1.0,
@ -1109,6 +1207,88 @@
}
}
],
"AMB/BUSD": [
{
"tier": 1.0,
"currency": "BUSD",
"minNotional": 0.0,
"maxNotional": 25000.0,
"maintenanceMarginRate": 0.025,
"maxLeverage": 20.0,
"info": {
"bracket": "1",
"initialLeverage": "20",
"notionalCap": "25000",
"notionalFloor": "0",
"maintMarginRatio": "0.025",
"cum": "0.0"
}
},
{
"tier": 2.0,
"currency": "BUSD",
"minNotional": 25000.0,
"maxNotional": 100000.0,
"maintenanceMarginRate": 0.05,
"maxLeverage": 10.0,
"info": {
"bracket": "2",
"initialLeverage": "10",
"notionalCap": "100000",
"notionalFloor": "25000",
"maintMarginRatio": "0.05",
"cum": "625.0"
}
},
{
"tier": 3.0,
"currency": "BUSD",
"minNotional": 100000.0,
"maxNotional": 250000.0,
"maintenanceMarginRate": 0.1,
"maxLeverage": 5.0,
"info": {
"bracket": "3",
"initialLeverage": "5",
"notionalCap": "250000",
"notionalFloor": "100000",
"maintMarginRatio": "0.1",
"cum": "5625.0"
}
},
{
"tier": 4.0,
"currency": "BUSD",
"minNotional": 250000.0,
"maxNotional": 1000000.0,
"maintenanceMarginRate": 0.125,
"maxLeverage": 2.0,
"info": {
"bracket": "4",
"initialLeverage": "2",
"notionalCap": "1000000",
"notionalFloor": "250000",
"maintMarginRatio": "0.125",
"cum": "11875.0"
}
},
{
"tier": 5.0,
"currency": "BUSD",
"minNotional": 1000000.0,
"maxNotional": 5000000.0,
"maintenanceMarginRate": 0.5,
"maxLeverage": 1.0,
"info": {
"bracket": "5",
"initialLeverage": "1",
"notionalCap": "5000000",
"notionalFloor": "1000000",
"maintMarginRatio": "0.5",
"cum": "386875.0"
}
}
],
"ANC/BUSD": [
{
"tier": 1.0,
@ -3300,13 +3480,13 @@
"tier": 6.0,
"currency": "USDT",
"minNotional": 1000000.0,
"maxNotional": 30000000.0,
"maxNotional": 5000000.0,
"maintenanceMarginRate": 0.5,
"maxLeverage": 1.0,
"info": {
"bracket": "6",
"initialLeverage": "1",
"notionalCap": "30000000",
"notionalCap": "5000000",
"notionalFloor": "1000000",
"maintMarginRatio": "0.5",
"cum": "386950.0"
@ -4880,13 +5060,13 @@
"tier": 6.0,
"currency": "USDT",
"minNotional": 1000000.0,
"maxNotional": 30000000.0,
"maxNotional": 5000000.0,
"maintenanceMarginRate": 0.5,
"maxLeverage": 1.0,
"info": {
"bracket": "6",
"initialLeverage": "1",
"notionalCap": "30000000",
"notionalCap": "5000000",
"notionalFloor": "1000000",
"maintMarginRatio": "0.5",
"cum": "386940.0"
@ -8333,6 +8513,104 @@
}
}
],
"FOOTBALL/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"
}
}
],
"FTM/BUSD": [
{
"tier": 1.0,
@ -12123,6 +12401,104 @@
}
}
],
"LUNA2/USDT": [
{
"tier": 1.0,
"currency": "USDT",
"minNotional": 0.0,
"maxNotional": 5000.0,
"maintenanceMarginRate": 0.015,
"maxLeverage": 25.0,
"info": {
"bracket": "1",
"initialLeverage": "25",
"notionalCap": "5000",
"notionalFloor": "0",
"maintMarginRatio": "0.015",
"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": "50.0"
}
},
{
"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": "675.0"
}
},
{
"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": "5675.0"
}
},
{
"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": "11925.0"
}
},
{
"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": "386925.0"
}
}
],
"MANA/USDT": [
{
"tier": 1.0,
@ -13028,10 +13404,10 @@
"minNotional": 0.0,
"maxNotional": 5000.0,
"maintenanceMarginRate": 0.01,
"maxLeverage": 50.0,
"maxLeverage": 25.0,
"info": {
"bracket": "1",
"initialLeverage": "50",
"initialLeverage": "25",
"notionalCap": "5000",
"notionalFloor": "0",
"maintMarginRatio": "0.01",
@ -13805,6 +14181,88 @@
}
}
],
"PHB/BUSD": [
{
"tier": 1.0,
"currency": "BUSD",
"minNotional": 0.0,
"maxNotional": 25000.0,
"maintenanceMarginRate": 0.025,
"maxLeverage": 20.0,
"info": {
"bracket": "1",
"initialLeverage": "20",
"notionalCap": "25000",
"notionalFloor": "0",
"maintMarginRatio": "0.025",
"cum": "0.0"
}
},
{
"tier": 2.0,
"currency": "BUSD",
"minNotional": 25000.0,
"maxNotional": 100000.0,
"maintenanceMarginRate": 0.05,
"maxLeverage": 10.0,
"info": {
"bracket": "2",
"initialLeverage": "10",
"notionalCap": "100000",
"notionalFloor": "25000",
"maintMarginRatio": "0.05",
"cum": "625.0"
}
},
{
"tier": 3.0,
"currency": "BUSD",
"minNotional": 100000.0,
"maxNotional": 250000.0,
"maintenanceMarginRate": 0.1,
"maxLeverage": 5.0,
"info": {
"bracket": "3",
"initialLeverage": "5",
"notionalCap": "250000",
"notionalFloor": "100000",
"maintMarginRatio": "0.1",
"cum": "5625.0"
}
},
{
"tier": 4.0,
"currency": "BUSD",
"minNotional": 250000.0,
"maxNotional": 1000000.0,
"maintenanceMarginRate": 0.125,
"maxLeverage": 2.0,
"info": {
"bracket": "4",
"initialLeverage": "2",
"notionalCap": "1000000",
"notionalFloor": "250000",
"maintMarginRatio": "0.125",
"cum": "11875.0"
}
},
{
"tier": 5.0,
"currency": "BUSD",
"minNotional": 1000000.0,
"maxNotional": 5000000.0,
"maintenanceMarginRate": 0.5,
"maxLeverage": 1.0,
"info": {
"bracket": "5",
"initialLeverage": "1",
"notionalCap": "5000000",
"notionalFloor": "1000000",
"maintMarginRatio": "0.5",
"cum": "386875.0"
}
}
],
"QTUM/USDT": [
{
"tier": 1.0,
@ -14008,10 +14466,10 @@
"minNotional": 0.0,
"maxNotional": 5000.0,
"maintenanceMarginRate": 0.01,
"maxLeverage": 50.0,
"maxLeverage": 25.0,
"info": {
"bracket": "1",
"initialLeverage": "50",
"initialLeverage": "25",
"notionalCap": "5000",
"notionalFloor": "0",
"maintMarginRatio": "0.01",
@ -14478,13 +14936,13 @@
"tier": 6.0,
"currency": "USDT",
"minNotional": 1000000.0,
"maxNotional": 30000000.0,
"maxNotional": 5000000.0,
"maintenanceMarginRate": 0.5,
"maxLeverage": 1.0,
"info": {
"bracket": "6",
"initialLeverage": "1",
"notionalCap": "30000000",
"notionalCap": "5000000",
"notionalFloor": "1000000",
"maintMarginRatio": "0.5",
"cum": "386950.0"
@ -14576,13 +15034,13 @@
"tier": 6.0,
"currency": "USDT",
"minNotional": 1000000.0,
"maxNotional": 30000000.0,
"maxNotional": 5000000.0,
"maintenanceMarginRate": 0.5,
"maxLeverage": 1.0,
"info": {
"bracket": "6",
"initialLeverage": "1",
"notionalCap": "30000000",
"notionalCap": "5000000",
"notionalFloor": "1000000",
"maintMarginRatio": "0.5",
"cum": "386950.0"
@ -15487,6 +15945,104 @@
}
}
],
"SPELL/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"
}
}
],
"SRM/USDT": [
{
"tier": 1.0,
@ -15585,6 +16141,104 @@
}
}
],
"STG/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"
}
}
],
"STMX/USDT": [
{
"tier": 1.0,
@ -16176,13 +16830,13 @@
"tier": 5.0,
"currency": "BUSD",
"minNotional": 1000000.0,
"maxNotional": 30000000.0,
"maxNotional": 5000000.0,
"maintenanceMarginRate": 0.5,
"maxLeverage": 1.0,
"info": {
"bracket": "5",
"initialLeverage": "1",
"notionalCap": "30000000",
"notionalCap": "5000000",
"notionalFloor": "1000000",
"maintMarginRatio": "0.5",
"cum": "386875.0"
@ -16470,13 +17124,13 @@
"tier": 6.0,
"currency": "USDT",
"minNotional": 1000000.0,
"maxNotional": 30000000.0,
"maxNotional": 5000000.0,
"maintenanceMarginRate": 0.5,
"maxLeverage": 1.0,
"info": {
"bracket": "6",
"initialLeverage": "1",
"notionalCap": "30000000",
"notionalCap": "5000000",
"notionalFloor": "1000000",
"maintMarginRatio": "0.5",
"cum": "386950.0"

2
freqtrade/exchange/exchange.py
View File

@ -2304,7 +2304,7 @@ class Exchange:
updated = tiers.get('updated')
if updated:
updated_dt = parser.parse(updated)
if updated_dt < datetime.now(timezone.utc) - timedelta(days=1):
if updated_dt < datetime.now(timezone.utc) - timedelta(weeks=4):
logger.info("Cached leverage tiers are outdated. Will update.")
return None
return tiers['data']

2
freqtrade/freqai/data_drawer.py
View File

@ -355,7 +355,7 @@ class FreqaiDataDrawer:
for dir in model_folders:
result = pattern.match(str(dir.name))
if result is None:
break
continue
coin = result.group(1)
timestamp = result.group(2)

85
freqtrade/freqai/prediction_models/XGBoostClassifier.py Normal file
View File

@ -0,0 +1,85 @@
import logging
from typing import Any, Dict, Tuple
import numpy as np
import numpy.typing as npt
import pandas as pd
from pandas import DataFrame
from pandas.api.types import is_integer_dtype
from sklearn.preprocessing import LabelEncoder
from xgboost import XGBClassifier
from freqtrade.freqai.base_models.BaseClassifierModel import BaseClassifierModel
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
logger = logging.getLogger(__name__)
class XGBoostClassifier(BaseClassifierModel):
"""
User created prediction model. The class needs to override three necessary
functions, predict(), train(), fit(). The class inherits ModelHandler which
has its own DataHandler where data is held, saved, loaded, and managed.
"""
def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
"""
User sets up the training and test data to fit their desired model here
:params:
:data_dictionary: the dictionary constructed by DataHandler to hold
all the training and test data/labels.
"""
X = data_dictionary["train_features"].to_numpy()
y = data_dictionary["train_labels"].to_numpy()[:, 0]
le = LabelEncoder()
if not is_integer_dtype(y):
y = pd.Series(le.fit_transform(y), dtype="int64")
if self.freqai_info.get('data_split_parameters', {}).get('test_size', 0.1) == 0:
eval_set = None
else:
test_features = data_dictionary["test_features"].to_numpy()
test_labels = data_dictionary["test_labels"].to_numpy()[:, 0]
if not is_integer_dtype(test_labels):
test_labels = pd.Series(le.transform(test_labels), dtype="int64")
eval_set = [(test_features, test_labels)]
train_weights = data_dictionary["train_weights"]
init_model = self.get_init_model(dk.pair)
model = XGBClassifier(**self.model_training_parameters)
model.fit(X=X, y=y, eval_set=eval_set, sample_weight=train_weights,
xgb_model=init_model)
return model
def predict(
self, unfiltered_df: DataFrame, dk: FreqaiDataKitchen, **kwargs
) -> Tuple[DataFrame, npt.NDArray[np.int_]]:
"""
Filter the prediction features data and predict with it.
:param: unfiltered_df: Full dataframe for the current backtest period.
:return:
:pred_df: dataframe containing the predictions
:do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove
data (NaNs) or felt uncertain about data (PCA and DI index)
"""
(pred_df, dk.do_predict) = super().predict(unfiltered_df, dk, **kwargs)
le = LabelEncoder()
label = dk.label_list[0]
labels_before = list(dk.data['labels_std'].keys())
labels_after = le.fit_transform(labels_before).tolist()
pred_df[label] = le.inverse_transform(pred_df[label])
pred_df = pred_df.rename(
columns={labels_after[i]: labels_before[i] for i in range(len(labels_before))})
return (pred_df, dk.do_predict)

7
freqtrade/freqtradebot.py
View File

@ -1072,6 +1072,7 @@ class FreqtradeBot(LoggingMixin):
order_obj = Order.parse_from_ccxt_object(stoploss_order, trade.pair, 'stoploss')
trade.orders.append(order_obj)
trade.stoploss_order_id = str(stoploss_order['id'])
trade.stoploss_last_update = datetime.now(timezone.utc)
return True
except InsufficientFundsError as e:
logger.warning(f"Unable to place stoploss order {e}.")
@ -1145,10 +1146,9 @@ class FreqtradeBot(LoggingMixin):
if self.create_stoploss_order(trade=trade, stop_price=stop_price):
# The above will return False if the placement failed and the trade was force-sold.
# in which case the trade will be closed - which we must check below.
trade.stoploss_last_update = datetime.utcnow()
return False
# If stoploss order is canceled for some reason we add it
# If stoploss order is canceled for some reason we add it again
if (trade.is_open
and stoploss_order
and stoploss_order['status'] in ('canceled', 'cancelled')):
@ -1186,7 +1186,8 @@ class FreqtradeBot(LoggingMixin):
if self.exchange.stoploss_adjust(stoploss_norm, order, side=trade.exit_side):
# we check if the update is necessary
update_beat = self.strategy.order_types.get('stoploss_on_exchange_interval', 60)
if (datetime.utcnow() - trade.stoploss_last_update).total_seconds() >= update_beat:
upd_req = datetime.now(timezone.utc) - timedelta(seconds=update_beat)
if trade.stoploss_last_update_utc and upd_req >= trade.stoploss_last_update_utc:
# cancelling the current stoploss on exchange first
logger.info(f"Cancelling current stoploss on exchange for pair {trade.pair} "
f"(orderid:{order['id']}) in order to add another one ...")

17
freqtrade/optimize/backtesting.py
View File

@ -812,14 +812,6 @@ class Backtesting:
return trade
time_in_force = self.strategy.order_time_in_force['entry']
if not pos_adjust:
# Confirm trade entry:
if not strategy_safe_wrapper(self.strategy.confirm_trade_entry, default_retval=True)(
pair=pair, order_type=order_type, amount=stake_amount, rate=propose_rate,
time_in_force=time_in_force, current_time=current_time,
entry_tag=entry_tag, side=direction):
return trade
if stake_amount and (not min_stake_amount or stake_amount > min_stake_amount):
self.order_id_counter += 1
base_currency = self.exchange.get_pair_base_currency(pair)
@ -834,6 +826,15 @@ class Backtesting:
# Backcalculate actual stake amount.
stake_amount = amount * propose_rate / leverage
if not pos_adjust:
# Confirm trade entry:
if not strategy_safe_wrapper(
self.strategy.confirm_trade_entry, default_retval=True)(
pair=pair, order_type=order_type, amount=amount, rate=propose_rate,
time_in_force=time_in_force, current_time=current_time,
entry_tag=entry_tag, side=direction):
return trade
is_short = (direction == 'short')
# Necessary for Margin trading. Disabled until support is enabled.
# interest_rate = self.exchange.get_interest_rate()

117
freqtrade/optimize/hyperopt.py
View File

@ -290,7 +290,7 @@ class Hyperopt:
# noinspection PyProtectedMember
attr.value = params_dict[attr_name]
def generate_optimizer(self, raw_params: List[Any], iteration=None) -> Dict:
def generate_optimizer(self, raw_params: List[Any]) -> Dict[str, Any]:
"""
Used Optimize function.
Called once per epoch to optimize whatever is configured.
@ -410,9 +410,11 @@ class Hyperopt:
model_queue_size=SKOPT_MODEL_QUEUE_SIZE,
)
def run_optimizer_parallel(self, parallel, asked, i) -> List:
def run_optimizer_parallel(
self, parallel: Parallel, asked: List[List]) -> List[Dict[str, Any]]:
""" Start optimizer in a parallel way """
return parallel(delayed(
wrap_non_picklable_objects(self.generate_optimizer))(v, i) for v in asked)
wrap_non_picklable_objects(self.generate_optimizer))(v) for v in asked)
def _set_random_state(self, random_state: Optional[int]) -> int:
return random_state or random.randint(1, 2**16 - 1)
@ -491,6 +493,53 @@ class Hyperopt:
else:
return self.opt.ask(n_points=n_points), [False for _ in range(n_points)]
def get_progressbar_widgets(self):
if self.print_colorized:
widgets = [
' [Epoch ', progressbar.Counter(), ' of ', str(self.total_epochs),
' (', progressbar.Percentage(), ')] ',
progressbar.Bar(marker=progressbar.AnimatedMarker(
fill='\N{FULL BLOCK}',
fill_wrap=Fore.GREEN + '{}' + Fore.RESET,
marker_wrap=Style.BRIGHT + '{}' + Style.RESET_ALL,
)),
' [', progressbar.ETA(), ', ', progressbar.Timer(), ']',
]
else:
widgets = [
' [Epoch ', progressbar.Counter(), ' of ', str(self.total_epochs),
' (', progressbar.Percentage(), ')] ',
progressbar.Bar(marker=progressbar.AnimatedMarker(
fill='\N{FULL BLOCK}',
)),
' [', progressbar.ETA(), ', ', progressbar.Timer(), ']',
]
return widgets
def evaluate_result(self, val: Dict[str, Any], current: int, is_random: bool):
"""
Evaluate results returned from generate_optimizer
"""
val['current_epoch'] = current
val['is_initial_point'] = current <= INITIAL_POINTS
logger.debug("Optimizer epoch evaluated: %s", val)
is_best = HyperoptTools.is_best_loss(val, self.current_best_loss)
# This value is assigned here and not in the optimization method
# to keep proper order in the list of results. That's because
# evaluations can take different time. Here they are aligned in the
# order they will be shown to the user.
val['is_best'] = is_best
val['is_random'] = is_random
self.print_results(val)
if is_best:
self.current_best_loss = val['loss']
self.current_best_epoch = val
self._save_result(val)
def start(self) -> None:
self.random_state = self._set_random_state(self.config.get('hyperopt_random_state'))
logger.info(f"Using optimizer random state: {self.random_state}")
@ -526,64 +575,40 @@ class Hyperopt:
logger.info(f'Effective number of parallel workers used: {jobs}')
# Define progressbar
if self.print_colorized:
widgets = [
' [Epoch ', progressbar.Counter(), ' of ', str(self.total_epochs),
' (', progressbar.Percentage(), ')] ',
progressbar.Bar(marker=progressbar.AnimatedMarker(
fill='\N{FULL BLOCK}',
fill_wrap=Fore.GREEN + '{}' + Fore.RESET,
marker_wrap=Style.BRIGHT + '{}' + Style.RESET_ALL,
)),
' [', progressbar.ETA(), ', ', progressbar.Timer(), ']',
]
else:
widgets = [
' [Epoch ', progressbar.Counter(), ' of ', str(self.total_epochs),
' (', progressbar.Percentage(), ')] ',
progressbar.Bar(marker=progressbar.AnimatedMarker(
fill='\N{FULL BLOCK}',
)),
' [', progressbar.ETA(), ', ', progressbar.Timer(), ']',
]
widgets = self.get_progressbar_widgets()
with progressbar.ProgressBar(
max_value=self.total_epochs, redirect_stdout=False, redirect_stderr=False,
widgets=widgets
) as pbar:
EVALS = ceil(self.total_epochs / jobs)
for i in range(EVALS):
start = 0
if self.analyze_per_epoch:
# First analysis not in parallel mode when using --analyze-per-epoch.
# This allows dataprovider to load it's informative cache.
asked, is_random = self.get_asked_points(n_points=1)
f_val0 = self.generate_optimizer(asked[0])
self.opt.tell(asked, [f_val0['loss']])
self.evaluate_result(f_val0, 1, is_random[0])
pbar.update(1)
start += 1
evals = ceil((self.total_epochs - start) / jobs)
for i in range(evals):
# Correct the number of epochs to be processed for the last
# iteration (should not exceed self.total_epochs in total)
n_rest = (i + 1) * jobs - self.total_epochs
n_rest = (i + 1) * jobs - (self.total_epochs - start)
current_jobs = jobs - n_rest if n_rest > 0 else jobs
asked, is_random = self.get_asked_points(n_points=current_jobs)
f_val = self.run_optimizer_parallel(parallel, asked, i)
f_val = self.run_optimizer_parallel(parallel, asked)
self.opt.tell(asked, [v['loss'] for v in f_val])
# Calculate progressbar outputs
for j, val in enumerate(f_val):
# Use human-friendly indexes here (starting from 1)
current = i * jobs + j + 1
val['current_epoch'] = current
val['is_initial_point'] = current <= INITIAL_POINTS
current = i * jobs + j + 1 + start
logger.debug(f"Optimizer epoch evaluated: {val}")
is_best = HyperoptTools.is_best_loss(val, self.current_best_loss)
# This value is assigned here and not in the optimization method
# to keep proper order in the list of results. That's because
# evaluations can take different time. Here they are aligned in the
# order they will be shown to the user.
val['is_best'] = is_best
val['is_random'] = is_random[j]
self.print_results(val)
if is_best:
self.current_best_loss = val['loss']
self.current_best_epoch = val
self._save_result(val)
self.evaluate_result(val, current, is_random[j])
pbar.update(current)

9
freqtrade/persistence/trade_model.py
View File

@ -83,7 +83,7 @@ class Order(_DECL_BASE):
@property
def safe_price(self) -> float:
return self.average or self.price
return self.average or self.price or self.stop_price
@property
def safe_filled(self) -> float:
@ -376,6 +376,12 @@ class LocalTrade():
def open_date_utc(self):
return self.open_date.replace(tzinfo=timezone.utc)
@property
def stoploss_last_update_utc(self):
if self.stoploss_last_update:
return self.stoploss_last_update.replace(tzinfo=timezone.utc)
return None
@property
def close_date_utc(self):
return self.close_date.replace(tzinfo=timezone.utc)
@ -560,7 +566,6 @@ class LocalTrade():
self.stop_loss = stop_loss_norm
self.stop_loss_pct = -1 * abs(percent)
self.stoploss_last_update = datetime.utcnow()
def adjust_stop_loss(self, current_price: float, stoploss: float,
initial: bool = False, refresh: bool = False) -> None:

17
freqtrade/strategy/interface.py
View File

@ -684,6 +684,22 @@ class IStrategy(ABC, HyperStrategyMixin):
# END - Intended to be overridden by strategy
###
def __informative_pairs_freqai(self) -> ListPairsWithTimeframes:
"""
Create informative-pairs needed for FreqAI
"""
if self.config.get('freqai', {}).get('enabled', False):
whitelist_pairs = self.dp.current_whitelist()
candle_type = self.config.get('candle_type_def', CandleType.SPOT)
corr_pairs = self.config["freqai"]["feature_parameters"]["include_corr_pairlist"]
informative_pairs = []
for tf in self.config["freqai"]["feature_parameters"]["include_timeframes"]:
for pair in set(whitelist_pairs + corr_pairs):
informative_pairs.append((pair, tf, candle_type))
return informative_pairs
return []
def gather_informative_pairs(self) -> ListPairsWithTimeframes:
"""
Internal method which gathers all informative pairs (user or automatically defined).
@ -708,6 +724,7 @@ class IStrategy(ABC, HyperStrategyMixin):
else:
for pair in self.dp.current_whitelist():
informative_pairs.append((pair, inf_data.timeframe, candle_type))
informative_pairs.extend(self.__informative_pairs_freqai())
return list(set(informative_pairs))
def get_strategy_name(self) -> str:

13
freqtrade/templates/FreqaiExampleStrategy.py
View File

@ -47,19 +47,6 @@ class FreqaiExampleStrategy(IStrategy):
std_dev_multiplier_sell = CategoricalParameter(
[0.1, 0.25, 0.4], space="sell", default=0.2, optimize=True)
def informative_pairs(self):
whitelist_pairs = self.dp.current_whitelist()
corr_pairs = self.config["freqai"]["feature_parameters"]["include_corr_pairlist"]
informative_pairs = []
for tf in self.config["freqai"]["feature_parameters"]["include_timeframes"]:
for pair in whitelist_pairs:
informative_pairs.append((pair, tf))
for pair in corr_pairs:
if pair in whitelist_pairs:
continue # avoid duplication
informative_pairs.append((pair, tf))
return informative_pairs
def populate_any_indicators(
self, pair, df, tf, informative=None, set_generalized_indicators=False
):

14
freqtrade/templates/FreqaiHybridExampleStrategy.py
View File

@ -95,20 +95,6 @@ class FreqaiExampleHybridStrategy(IStrategy):
short_rsi = IntParameter(low=51, high=100, default=70, space='sell', optimize=True, load=True)
exit_short_rsi = IntParameter(low=1, high=50, default=30, space='buy', optimize=True, load=True)
# FreqAI required function, leave as is or add additional informatives to existing structure.
def informative_pairs(self):
whitelist_pairs = self.dp.current_whitelist()
corr_pairs = self.config["freqai"]["feature_parameters"]["include_corr_pairlist"]
informative_pairs = []
for tf in self.config["freqai"]["feature_parameters"]["include_timeframes"]:
for pair in whitelist_pairs:
informative_pairs.append((pair, tf))
for pair in corr_pairs:
if pair in whitelist_pairs:
continue # avoid duplication
informative_pairs.append((pair, tf))
return informative_pairs
# FreqAI required function, user can add or remove indicators, but general structure
# must stay the same.
def populate_any_indicators(

2
mkdocs.yml
View File

@ -49,6 +49,8 @@ theme:
logo: "images/logo.png"
favicon: "images/logo.png"
custom_dir: "docs/overrides"
features:
- search.share
palette:
- scheme: default
primary: "blue grey"

2
requirements-dev.txt
View File

@ -25,6 +25,6 @@ nbconvert==7.0.0
# mypy types
types-cachetools==5.2.1
types-filelock==3.2.7
types-requests==2.28.9
types-requests==2.28.10
types-tabulate==0.8.11
types-python-dateutil==2.8.19

14
requirements.txt
View File

@ -1,18 +1,18 @@
numpy==1.23.2
numpy==1.23.3
pandas==1.4.4
pandas-ta==0.3.14b
ccxt==1.93.3
ccxt==1.93.35
# Pin cryptography for now due to rust build errors with piwheels
cryptography==37.0.4
cryptography==38.0.1
aiohttp==3.8.1
SQLAlchemy==1.4.40
SQLAlchemy==1.4.41
python-telegram-bot==13.14
arrow==1.2.3
cachetools==4.2.2
requests==2.28.1
urllib3==1.26.12
jsonschema==4.15.0
jsonschema==4.16.0
TA-Lib==0.4.24
technical==1.3.0
tabulate==0.8.10
@ -34,10 +34,10 @@ orjson==3.8.0
sdnotify==0.3.2
# API Server
fastapi==0.82.0
fastapi==0.83.0
uvicorn==0.18.3
pyjwt==2.4.0
aiofiles==0.8.0
aiofiles==22.1.0
psutil==5.9.2
# Support for colorized terminal output

9
tests/exchange/test_binance.py
View File

@ -23,7 +23,7 @@ from tests.exchange.test_exchange import ccxt_exceptionhandlers
def test_stoploss_order_binance(default_conf, mocker, limitratio, expected, side, trademode):
api_mock = MagicMock()
order_id = 'test_prod_buy_{}'.format(randint(0, 10 ** 6))
order_type = 'stop_loss_limit' if trademode == TradingMode.SPOT else 'stop'
order_type = 'stop_loss_limit' if trademode == TradingMode.SPOT else 'limit'
api_mock.create_order = MagicMock(return_value={
'id': order_id,
@ -45,12 +45,15 @@ def test_stoploss_order_binance(default_conf, mocker, limitratio, expected, side
amount=1,
stop_price=190,
side=side,
order_types={'stoploss_on_exchange_limit_ratio': 1.05},
order_types={'stoploss': 'limit', 'stoploss_on_exchange_limit_ratio': 1.05},
leverage=1.0
)
api_mock.create_order.reset_mock()
order_types = {} if limitratio is None else {'stoploss_on_exchange_limit_ratio': limitratio}
order_types = {'stoploss': 'limit'}
if limitratio is not None:
order_types.update({'stoploss_on_exchange_limit_ratio': limitratio})
order = exchange.stoploss(
pair='ETH/BTC',
amount=1,

2
tests/exchange/test_ccxt_compat.py
View File

@ -297,7 +297,7 @@ class TestCCXTExchange():
def test_ccxt__async_get_candle_history(self, exchange):
exchange, exchangename = exchange
# For some weired reason, this test returns random lengths for bittrex.
if not exchange._ft_has['ohlcv_has_history'] or exchangename in ('bittrex', 'gateio'):
if not exchange._ft_has['ohlcv_has_history'] or exchangename in ('bittrex'):
return
pair = EXCHANGES[exchangename]['pair']
timeframe = EXCHANGES[exchangename]['timeframe']

2
tests/exchange/test_okx.py
View File

@ -472,7 +472,7 @@ def test_load_leverage_tiers_okx(default_conf, mocker, markets, tmpdir, caplog,
api_mock.fetch_market_leverage_tiers.call_count == 0
# 2 day passes ...
time_machine.move_to(datetime.now() + timedelta(days=2))
time_machine.move_to(datetime.now() + timedelta(weeks=5))
exchange.load_leverage_tiers()
assert log_has(logmsg, caplog)

26
tests/freqai/test_freqai_interface.py
View File

@ -9,6 +9,7 @@ import pytest
from freqtrade.configuration import TimeRange
from freqtrade.data.dataprovider import DataProvider
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
from freqtrade.plugins.pairlistmanager import PairListManager
from tests.conftest import get_patched_exchange, log_has_re
from tests.freqai.conftest import get_patched_freqai_strategy
@ -100,6 +101,7 @@ def test_extract_data_and_train_model_MultiTargets(mocker, freqai_conf, model):
@pytest.mark.parametrize('model', [
'LightGBMClassifier',
'CatboostClassifier',
'XGBoostClassifier',
])
def test_extract_data_and_train_model_Classifiers(mocker, freqai_conf, model):
if is_arm() and model == 'CatboostClassifier':
@ -340,3 +342,27 @@ def test_spice_rack(mocker, default_conf, tmpdir):
assert 'freqai' in freqai_conf
assert strategy.freqai
@pytest.mark.parametrize('timeframes,corr_pairs', [
(['5m'], ['ADA/BTC', 'DASH/BTC']),
(['5m'], ['ADA/BTC', 'DASH/BTC', 'ETH/USDT']),
(['5m', '15m'], ['ADA/BTC', 'DASH/BTC', 'ETH/USDT']),
])
def test_freqai_informative_pairs(mocker, freqai_conf, timeframes, corr_pairs):
freqai_conf['freqai']['feature_parameters'].update({
'include_timeframes': timeframes,
'include_corr_pairlist': corr_pairs,
})
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
exchange = get_patched_exchange(mocker, freqai_conf)
pairlists = PairListManager(exchange, freqai_conf)
strategy.dp = DataProvider(freqai_conf, exchange, pairlists)
pairlist = strategy.dp.current_whitelist()
pairs_a = strategy.informative_pairs()
assert len(pairs_a) == 0
pairs_b = strategy.gather_informative_pairs()
# we expect unique pairs * timeframes
assert len(pairs_b) == len(set(pairlist + corr_pairs)) * len(timeframes)

39
tests/optimize/test_hyperopt.py
View File

@ -922,6 +922,45 @@ def test_in_strategy_auto_hyperopt_with_parallel(mocker, hyperopt_conf, tmpdir,
hyperopt.start()
def test_in_strategy_auto_hyperopt_per_epoch(mocker, hyperopt_conf, tmpdir, fee) -> None:
patch_exchange(mocker)
mocker.patch('freqtrade.exchange.Exchange.get_fee', fee)
(Path(tmpdir) / 'hyperopt_results').mkdir(parents=True)
hyperopt_conf.update({
'strategy': 'HyperoptableStrategy',
'user_data_dir': Path(tmpdir),
'hyperopt_random_state': 42,
'spaces': ['all'],
'epochs': 3,
'analyze_per_epoch': True,
})
go = mocker.patch('freqtrade.optimize.hyperopt.Hyperopt.generate_optimizer',
return_value={
'loss': 0.05,
'results_explanation': 'foo result', 'params': {},
'results_metrics': generate_result_metrics(),
})
hyperopt = Hyperopt(hyperopt_conf)
hyperopt.backtesting.exchange.get_max_leverage = MagicMock(return_value=1.0)
assert isinstance(hyperopt.custom_hyperopt, HyperOptAuto)
assert isinstance(hyperopt.backtesting.strategy.buy_rsi, IntParameter)
assert hyperopt.backtesting.strategy.bot_loop_started is True
assert hyperopt.backtesting.strategy.buy_rsi.in_space is True
assert hyperopt.backtesting.strategy.buy_rsi.value == 35
assert hyperopt.backtesting.strategy.sell_rsi.value == 74
assert hyperopt.backtesting.strategy.protection_cooldown_lookback.value == 30
buy_rsi_range = hyperopt.backtesting.strategy.buy_rsi.range
assert isinstance(buy_rsi_range, range)
# Range from 0 - 50 (inclusive)
assert len(list(buy_rsi_range)) == 51
hyperopt.start()
# backtesting should be called 3 times (once per epoch)
assert go.call_count == 3
def test_SKDecimal():
space = SKDecimal(1, 2, decimals=2)
assert 1.5 in space

13
tests/strategy/strats/freqai_test_multimodel_strat.py
View File

@ -43,19 +43,6 @@ class freqai_test_multimodel_strat(IStrategy):
)
max_roi_time_long = IntParameter(0, 800, default=400, space="sell", optimize=False, load=True)
def informative_pairs(self):
whitelist_pairs = self.dp.current_whitelist()
corr_pairs = self.config["freqai"]["feature_parameters"]["include_corr_pairlist"]
informative_pairs = []
for tf in self.config["freqai"]["feature_parameters"]["include_timeframes"]:
for pair in whitelist_pairs:
informative_pairs.append((pair, tf))
for pair in corr_pairs:
if pair in whitelist_pairs:
continue # avoid duplication
informative_pairs.append((pair, tf))
return informative_pairs
def populate_any_indicators(
self, pair, df, tf, informative=None, set_generalized_indicators=False
):

13
tests/strategy/strats/freqai_test_strat.py
View File

@ -43,19 +43,6 @@ class freqai_test_strat(IStrategy):
)
max_roi_time_long = IntParameter(0, 800, default=400, space="sell", optimize=False, load=True)
def informative_pairs(self):
whitelist_pairs = self.dp.current_whitelist()
corr_pairs = self.config["freqai"]["feature_parameters"]["include_corr_pairlist"]
informative_pairs = []
for tf in self.config["freqai"]["feature_parameters"]["include_timeframes"]:
for pair in whitelist_pairs:
informative_pairs.append((pair, tf))
for pair in corr_pairs:
if pair in whitelist_pairs:
continue # avoid duplication
informative_pairs.append((pair, tf))
return informative_pairs
def populate_any_indicators(
self, pair, df, tf, informative=None, set_generalized_indicators=False
):

11
tests/test_freqtradebot.py
View File

@ -1427,6 +1427,7 @@ def test_handle_stoploss_on_exchange_trailing(
trade.is_open = True
trade.open_order_id = None
trade.stoploss_order_id = 100
trade.stoploss_last_update = arrow.utcnow().shift(minutes=-20).datetime
stoploss_order_hanging = MagicMock(return_value={
'id': 100,
@ -1456,7 +1457,7 @@ def test_handle_stoploss_on_exchange_trailing(
)
cancel_order_mock = MagicMock()
stoploss_order_mock = MagicMock(return_value={'id': 13434334})
stoploss_order_mock = MagicMock(return_value={'id': 'so1'})
mocker.patch('freqtrade.exchange.Binance.cancel_stoploss_order', cancel_order_mock)
mocker.patch('freqtrade.exchange.Binance.stoploss', stoploss_order_mock)
@ -1569,6 +1570,7 @@ def test_handle_stoploss_on_exchange_trailing_error(
assert stoploss.call_count == 1
# Fail creating stoploss order
trade.stoploss_last_update = arrow.utcnow().shift(minutes=-601).datetime
caplog.clear()
cancel_mock = mocker.patch("freqtrade.exchange.Binance.cancel_stoploss_order", MagicMock())
mocker.patch("freqtrade.exchange.Binance.stoploss", side_effect=ExchangeError())
@ -1657,6 +1659,7 @@ def test_handle_stoploss_on_exchange_custom_stop(
trade.is_open = True
trade.open_order_id = None
trade.stoploss_order_id = 100
trade.stoploss_last_update = arrow.utcnow().shift(minutes=-601).datetime
stoploss_order_hanging = MagicMock(return_value={
'id': 100,
@ -1685,7 +1688,7 @@ def test_handle_stoploss_on_exchange_custom_stop(
)
cancel_order_mock = MagicMock()
stoploss_order_mock = MagicMock(return_value={'id': 13434334})
stoploss_order_mock = MagicMock(return_value={'id': 'so1'})
mocker.patch('freqtrade.exchange.Binance.cancel_stoploss_order', cancel_order_mock)
mocker.patch('freqtrade.exchange.Binance.stoploss', stoploss_order_mock)
@ -1727,8 +1730,7 @@ def test_handle_stoploss_on_exchange_custom_stop(
assert freqtrade.handle_trade(trade) is True
def test_tsl_on_exchange_compatible_with_edge(mocker, edge_conf, fee, caplog,
limit_order) -> None:
def test_tsl_on_exchange_compatible_with_edge(mocker, edge_conf, fee, limit_order) -> None:
enter_order = limit_order['buy']
exit_order = limit_order['sell']
@ -1784,6 +1786,7 @@ def test_tsl_on_exchange_compatible_with_edge(mocker, edge_conf, fee, caplog,
trade.is_open = True
trade.open_order_id = None
trade.stoploss_order_id = 100
trade.stoploss_last_update = arrow.utcnow()
stoploss_order_hanging = MagicMock(return_value={
'id': 100,