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Merge branch 'develop' of https://github.com/freqtrade/freqtrade into batched_hyperopt

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orehunt 2020-03-02 08:13:37 +01:00
commit f23d57f18d
46 changed files with 923 additions and 439 deletions
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8
.github/workflows/ci.yml vendored
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@ -23,7 +23,7 @@ jobs:
python-version: [3.7, 3.8]
steps:
- uses: actions/checkout@v1
- uses: actions/checkout@v2
- name: Set up Python
uses: actions/setup-python@v1
@ -118,7 +118,7 @@ jobs:
python-version: [3.7]
steps:
- uses: actions/checkout@v1
- uses: actions/checkout@v2
- name: Set up Python
uses: actions/setup-python@v1
@ -175,7 +175,7 @@ jobs:
docs_check:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v1
- uses: actions/checkout@v2
- name: Documentation syntax
run: |
@ -195,7 +195,7 @@ jobs:
runs-on: ubuntu-18.04
if: (github.event_name == 'push' || github.event_name == 'schedule' || github.event_name == 'release') && github.repository == 'freqtrade/freqtrade'
steps:
- uses: actions/checkout@v1
- uses: actions/checkout@v2
- name: Set up Python
uses: actions/setup-python@v1

2
Dockerfile
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@ -1,4 +1,4 @@
FROM python:3.8.1-slim-buster
FROM python:3.8.2-slim-buster
RUN apt-get update \
&& apt-get -y install curl build-essential libssl-dev \

3
README.md
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@ -25,7 +25,8 @@ hesitate to read the source code and understand the mechanism of this bot.
## Exchange marketplaces supported
- [X] [Bittrex](https://bittrex.com/)
- [X] [Binance](https://www.binance.com/) ([*Note for binance users](#a-note-on-binance))
- [X] [Binance](https://www.binance.com/) ([*Note for binance users](docs/exchanges.md#blacklists))
- [X] [Kraken](https://kraken.com/)
- [ ] [113 others to tests](https://github.com/ccxt/ccxt/). _(We cannot guarantee they will work)_
## Documentation

13
docs/bot-usage.md
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@ -275,7 +275,7 @@ Check the corresponding [Data Downloading](data-download.md) section for more de
## Hyperopt commands
To optimize your strategy, you can use hyperopt parameter hyperoptimization
to find optimal parameter values for your stategy.
to find optimal parameter values for your strategy.
```
usage: freqtrade hyperopt [-h] [-v] [--logfile FILE] [-V] [-c PATH] [-d PATH]
@ -323,7 +323,7 @@ optional arguments:
--print-all Print all results, not only the best ones.
--no-color Disable colorization of hyperopt results. May be
useful if you are redirecting output to a file.
--print-json Print best result detailization in JSON format.
--print-json Print best results in JSON format.
-j JOBS, --job-workers JOBS
The number of concurrently running jobs for
hyperoptimization (hyperopt worker processes). If -1
@ -341,10 +341,11 @@ optional arguments:
class (IHyperOptLoss). Different functions can
generate completely different results, since the
target for optimization is different. Built-in
Hyperopt-loss-functions are: DefaultHyperOptLoss,
OnlyProfitHyperOptLoss, SharpeHyperOptLoss,
SharpeHyperOptLossDaily.(default:
`DefaultHyperOptLoss`).
Hyperopt-loss-functions are:
DefaultHyperOptLoss, OnlyProfitHyperOptLoss,
SharpeHyperOptLoss, SharpeHyperOptLossDaily,
SortinoHyperOptLoss, SortinoHyperOptLossDaily.
(default: `DefaultHyperOptLoss`).
Common arguments:
-v, --verbose Verbose mode (-vv for more, -vvv to get all messages).

26
docs/configuration.md
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@ -340,7 +340,7 @@ This is most of the time the default time in force. It means the order will rema
on exchange till it is canceled by user. It can be fully or partially fulfilled.
If partially fulfilled, the remaining will stay on the exchange till cancelled.
**FOK (Full Or Kill):**
**FOK (Fill Or Kill):**
It means if the order is not executed immediately AND fully then it is canceled by the exchange.
@ -370,16 +370,18 @@ The possible values are: `gtc` (default), `fok` or `ioc`.
Freqtrade is based on [CCXT library](https://github.com/ccxt/ccxt) that supports over 100 cryptocurrency
exchange markets and trading APIs. The complete up-to-date list can be found in the
[CCXT repo homepage](https://github.com/ccxt/ccxt/tree/master/python). However, the bot was tested
with only Bittrex and Binance.
The bot was tested with the following exchanges:
[CCXT repo homepage](https://github.com/ccxt/ccxt/tree/master/python).
However, the bot was tested by the development team with only Bittrex, Binance and Kraken,
so the these are the only officially supported exhanges:
- [Bittrex](https://bittrex.com/): "bittrex"
- [Binance](https://www.binance.com/): "binance"
- [Kraken](https://kraken.com/): "kraken"
Feel free to test other exchanges and submit your PR to improve the bot.
Some exchanges require special configuration, which can be found on the [Exchange-specific Notes](exchanges.md) documentation page.
#### Sample exchange configuration
A exchange configuration for "binance" would look as follows:
@ -532,6 +534,12 @@ It uses configuration from `exchange.pair_whitelist` and `exchange.pair_blacklis
`refresh_period` allows setting the period (in seconds), at which the pairlist will be refreshed. Defaults to 1800s (30 minutes).
`VolumePairList` is based on the ticker data, as reported by the ccxt library:
* The `bidVolume` is the volume (amount) of current best bid in the orderbook.
* The `askVolume` is the volume (amount) of current best ask in the orderbook.
* The `quoteVolume` is the amount of quote (stake) currency traded (bought or sold) in last 24 hours.
```json
"pairlists": [{
"method": "VolumePairList",
@ -626,6 +634,11 @@ In production mode, the bot will engage your money. Be careful, since a wrong
strategy can lose all your money. Be aware of what you are doing when
you run it in production mode.
### Setup your exchange account
You will need to create API Keys (usually you get `key` and `secret`, some exchanges require an additional `password`) from the Exchange website and you'll need to insert this into the appropriate fields in the configuration or when asked by the `freqtrade new-config` command.
API Keys are usually only required for live trading (trading for real money, bot running in "production mode", executing real orders on the exchange) and are not required for the bot running in dry-run (trade simulation) mode. When you setup the bot in dry-run mode, you may fill these fields with empty values.
### To switch your bot in production mode
**Edit your `config.json` file.**
@ -647,9 +660,6 @@ you run it in production mode.
}
```
!!! Note
If you have an exchange API key yet, [see our tutorial](installation.md#setup-your-exchange-account).
You should also make sure to read the [Exchanges](exchanges.md) section of the documentation to be aware of potential configuration details specific to your exchange.
### Using proxy with Freqtrade

7
docs/developer.md
View File

@ -234,7 +234,7 @@ git checkout -b new_release <commitid>
Determine if crucial bugfixes have been made between this commit and the current state, and eventually cherry-pick these.
* Edit `freqtrade/__init__.py` and add the version matching the current date (for example `2019.7` for July 2019). Minor versions can be `2019.7-1` should we need to do a second release that month.
* Edit `freqtrade/__init__.py` and add the version matching the current date (for example `2019.7` for July 2019). Minor versions can be `2019.7.1` should we need to do a second release that month. Version numbers must follow allowed versions from PEP0440 to avoid failures pushing to pypi.
* Commit this part
* push that branch to the remote and create a PR against the master branch
@ -268,11 +268,6 @@ Once the PR against master is merged (best right after merging):
* Use "master" as reference (this step comes after the above PR is merged).
* Use the above changelog as release comment (as codeblock)
### After-release
* Update version in develop by postfixing that with `-dev` (`2019.6 -> 2019.6-dev`).
* Create a PR against develop to update that branch.
## Releases
### pypi

5
docs/exchanges.md
View File

@ -62,6 +62,11 @@ res = [ f"{x['MarketCurrency']}/{x['BaseCurrency']}" for x in ct.publicGetMarket
print(res)
```
## All exchanges
Should you experience constant errors with Nonce (like `InvalidNonce`), it is best to regenerate the API keys. Resetting Nonce is difficult and it's usually easier to regenerate the API keys.
## Random notes for other exchanges
* The Ocean (exchange id: `theocean`) exchange uses Web3 functionality and requires `web3` python package to be installed:

18
docs/hyperopt.md
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@ -31,9 +31,9 @@ This will create a new hyperopt file from a template, which will be located unde
Depending on the space you want to optimize, only some of the below are required:
* fill `buy_strategy_generator` - for buy signal optimization
* fill `indicator_space` - for buy signal optimzation
* fill `indicator_space` - for buy signal optimization
* fill `sell_strategy_generator` - for sell signal optimization
* fill `sell_indicator_space` - for sell signal optimzation
* fill `sell_indicator_space` - for sell signal optimization
!!! Note
`populate_indicators` needs to create all indicators any of thee spaces may use, otherwise hyperopt will not work.
@ -81,11 +81,11 @@ There are two places you need to change in your hyperopt file to add a new buy h
There you have two different types of indicators: 1. `guards` and 2. `triggers`.
1. Guards are conditions like "never buy if ADX < 10", or never buy if current price is over EMA10.
2. Triggers are ones that actually trigger buy in specific moment, like "buy when EMA5 crosses over EMA10" or "buy when close price touches lower bollinger band".
2. Triggers are ones that actually trigger buy in specific moment, like "buy when EMA5 crosses over EMA10" or "buy when close price touches lower Bollinger band".
Hyperoptimization will, for each eval round, pick one trigger and possibly
multiple guards. The constructed strategy will be something like
"*buy exactly when close price touches lower bollinger band, BUT only if
"*buy exactly when close price touches lower Bollinger band, BUT only if
ADX > 10*".
If you have updated the buy strategy, i.e. changed the contents of
@ -172,7 +172,7 @@ So let's write the buy strategy using these values:
Hyperopting will now call this `populate_buy_trend` as many times you ask it (`epochs`)
with different value combinations. It will then use the given historical data and make
buys based on the buy signals generated with the above function and based on the results
it will end with telling you which paramter combination produced the best profits.
it will end with telling you which parameter combination produced the best profits.
The above setup expects to find ADX, RSI and Bollinger Bands in the populated indicators.
When you want to test an indicator that isn't used by the bot currently, remember to
@ -191,8 +191,10 @@ Currently, the following loss functions are builtin:
* `DefaultHyperOptLoss` (default legacy Freqtrade hyperoptimization loss function)
* `OnlyProfitHyperOptLoss` (which takes only amount of profit into consideration)
* `SharpeHyperOptLoss` (optimizes Sharpe Ratio calculated on the trade returns)
* `SharpeHyperOptLossDaily` (optimizes Sharpe Ratio calculated on daily trade returns)
* `SharpeHyperOptLoss` (optimizes Sharpe Ratio calculated on trade returns relative to standard deviation)
* `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)
Creation of a custom loss function is covered in the [Advanced Hyperopt](advanced-hyperopt.md) part of the documentation.
@ -272,7 +274,7 @@ In some situations, you may need to run Hyperopt (and Backtesting) with the
By default, hyperopt emulates the behavior of the Freqtrade Live Run/Dry Run, where only one
open trade is allowed for every traded pair. The total number of trades open for all pairs
is also limited by the `max_open_trades` setting. During Hyperopt/Backtesting this may lead to
some potential trades to be hidden (or masked) by previosly open trades.
some potential trades to be hidden (or masked) by previously open trades.
The `--eps`/`--enable-position-stacking` argument allows emulation of buying the same pair multiple times,
while `--dmmp`/`--disable-max-market-positions` disables applying `max_open_trades`

20
docs/installation.md
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@ -2,6 +2,8 @@
This page explains how to prepare your environment for running the bot.
Please consider using the prebuilt [docker images](docker.md) to get started quickly while trying out freqtrade evaluating how it operates.
## Prerequisite
### Requirements
@ -14,15 +16,7 @@ Click each one for install guide:
* [virtualenv](https://virtualenv.pypa.io/en/stable/installation/) (Recommended)
* [TA-Lib](https://mrjbq7.github.io/ta-lib/install.html) (install instructions below)
### API keys
Before running your bot in production you will need to setup few
external API. In production mode, the bot will require valid Exchange API
credentials. We also recommend a [Telegram bot](telegram-usage.md#setup-your-telegram-bot) (optional but recommended).
### Setup your exchange account
You will need to create API Keys (Usually you get `key` and `secret`) from the Exchange website and insert this into the appropriate fields in the configuration or when asked by the installation script.
We also recommend a [Telegram bot](telegram-usage.md#setup-your-telegram-bot), which is optional but recommended.
## Quick start
@ -65,11 +59,11 @@ usage:
** --install **
With this option, the script will install everything you need to run the bot:
With this option, the script will install the bot and most dependencies:
You will need to have git and python3.6+ installed beforehand for this to work.
* Mandatory software as: `ta-lib`
* Setup your virtualenv
* Configure your `config.json` file
* Setup your virtualenv under `.env/`
This option is a combination of installation tasks, `--reset` and `--config`.
@ -83,7 +77,7 @@ This option will hard reset your branch (only if you are on either `master` or `
** --config **
Use this option to configure the `config.json` configuration file. The script will interactively ask you questions to setup your bot and create your `config.json`.
DEPRECATED - use `freqtrade new-config -c config.json` instead.
------

17
docs/strategy-customization.md
View File

@ -249,6 +249,23 @@ minimal_roi = {
While technically not completely disabled, this would sell once the trade reaches 10000% Profit.
To use times based on candle duration (ticker_interval or timeframe), the following snippet can be handy.
This will allow you to change the ticket_interval for the strategy, and ROI times will still be set as candles (e.g. after 3 candles ...)
``` python
from freqtrade.exchange import timeframe_to_minutes
class AwesomeStrategy(IStrategy):
ticker_interval = "1d"
ticker_interval_mins = timeframe_to_minutes(ticker_interval)
minimal_roi = {
"0": 0.05, # 5% for the first 3 candles
str(ticker_interval_mins * 3)): 0.02, # 2% after 3 candles
str(ticker_interval_mins * 6)): 0.01, # 1% After 6 candles
}
```
### Stoploss
Setting a stoploss is highly recommended to protect your capital from strong moves against you.

6
docs/strategy_analysis_example.md
View File

@ -121,7 +121,6 @@ from freqtrade.data.btanalysis import analyze_trade_parallelism
# Analyze the above
parallel_trades = analyze_trade_parallelism(trades, '5m')
parallel_trades.plot()
```
@ -134,11 +133,14 @@ Freqtrade offers interactive plotting capabilities based on plotly.
from freqtrade.plot.plotting import generate_candlestick_graph
# Limit graph period to keep plotly quick and reactive
# Filter trades to one pair
trades_red = trades.loc[trades['pair'] == pair]
data_red = data['2019-06-01':'2019-06-10']
# Generate candlestick graph
graph = generate_candlestick_graph(pair=pair,
data=data_red,
trades=trades,
trades=trades_red,
indicators1=['sma20', 'ema50', 'ema55'],
indicators2=['rsi', 'macd', 'macdsignal', 'macdhist']
)

3
freqtrade/commands/cli_options.py
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@ -324,7 +324,8 @@ AVAILABLE_CLI_OPTIONS = {
help='Specify the class name of the hyperopt loss function class (IHyperOptLoss). '
'Different functions can generate completely different results, '
'since the target for optimization is different. Built-in Hyperopt-loss-functions are: '
'DefaultHyperOptLoss, OnlyProfitHyperOptLoss, SharpeHyperOptLoss, SharpeHyperOptLossDaily.'
'DefaultHyperOptLoss, OnlyProfitHyperOptLoss, SharpeHyperOptLoss, SharpeHyperOptLossDaily, '
'SortinoHyperOptLoss, SortinoHyperOptLossDaily.'
'(default: `%(default)s`).',
metavar='NAME',
default=constants.DEFAULT_HYPEROPT_LOSS,

79
freqtrade/commands/hyperopt_commands.py
View File

@ -46,18 +46,12 @@ def start_hyperopt_list(args: Dict[str, Any]) -> None:
trials = _hyperopt_filter_trials(trials, filteroptions)
# TODO: fetch the interval for epochs to print from the cli option
epoch_start, epoch_stop = 0, None
if print_colorized:
colorama_init(autoreset=True)
try:
# Human-friendly indexes used here (starting from 1)
for val in trials[epoch_start:epoch_stop]:
Hyperopt.print_results_explanation(val, total_epochs,
not filteroptions['only_best'], print_colorized)
Hyperopt.print_result_table(config, trials, total_epochs,
not filteroptions['only_best'], print_colorized)
except KeyboardInterrupt:
print('User interrupted..')
@ -75,6 +69,12 @@ def start_hyperopt_show(args: Dict[str, Any]) -> None:
config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
print_json = config.get('print_json', False)
no_header = config.get('hyperopt_show_no_header', False)
trials_file = (config['user_data_dir'] /
'hyperopt_results' / 'hyperopt_results.pickle')
n = config.get('hyperopt_show_index', -1)
filteroptions = {
'only_best': config.get('hyperopt_list_best', False),
'only_profitable': config.get('hyperopt_list_profitable', False),
@ -87,10 +87,6 @@ def start_hyperopt_show(args: Dict[str, Any]) -> None:
'filter_min_total_profit': config.get('hyperopt_list_min_total_profit', None),
'filter_max_total_profit': config.get('hyperopt_list_max_total_profit', None)
}
no_header = config.get('hyperopt_show_no_header', False)
trials_file = (config['user_data_dir'] /
'hyperopt_results' / 'hyperopt_results.pickle')
# Previous evaluations
trials = Hyperopt.load_previous_results(trials_file)
@ -99,20 +95,17 @@ def start_hyperopt_show(args: Dict[str, Any]) -> None:
trials = _hyperopt_filter_trials(trials, filteroptions)
trials_epochs = len(trials)
n = config.get('hyperopt_show_index', -1)
if n > trials_epochs:
raise OperationalException(
f"The index of the epoch to show should be less than {trials_epochs + 1}.")
f"The index of the epoch to show should be less than {trials_epochs + 1}.")
if n < -trials_epochs:
raise OperationalException(
f"The index of the epoch to show should be greater than {-trials_epochs - 1}.")
f"The index of the epoch to show should be greater than {-trials_epochs - 1}.")
# Translate epoch index from human-readable format to pythonic
if n > 0:
n -= 1
print_json = config.get('print_json', False)
if trials:
val = trials[n]
Hyperopt.print_epoch_details(val, total_epochs, print_json, no_header,
@ -129,52 +122,52 @@ def _hyperopt_filter_trials(trials: List, filteroptions: dict) -> List:
trials = [x for x in trials if x['results_metrics']['profit'] > 0]
if filteroptions['filter_min_trades'] > 0:
trials = [
x for x in trials
if x['results_metrics']['trade_count'] > filteroptions['filter_min_trades']
]
x for x in trials
if x['results_metrics']['trade_count'] > filteroptions['filter_min_trades']
]
if filteroptions['filter_max_trades'] > 0:
trials = [
x for x in trials
if x['results_metrics']['trade_count'] < filteroptions['filter_max_trades']
]
x for x in trials
if x['results_metrics']['trade_count'] < filteroptions['filter_max_trades']
]
if filteroptions['filter_min_avg_time'] is not None:
trials = [x for x in trials if x['results_metrics']['trade_count'] > 0]
trials = [
x for x in trials
if x['results_metrics']['duration'] > filteroptions['filter_min_avg_time']
]
x for x in trials
if x['results_metrics']['duration'] > filteroptions['filter_min_avg_time']
]
if filteroptions['filter_max_avg_time'] is not None:
trials = [x for x in trials if x['results_metrics']['trade_count'] > 0]
trials = [
x for x in trials
if x['results_metrics']['duration'] < filteroptions['filter_max_avg_time']
]
x for x in trials
if x['results_metrics']['duration'] < filteroptions['filter_max_avg_time']
]
if filteroptions['filter_min_avg_profit'] is not None:
trials = [x for x in trials if x['results_metrics']['trade_count'] > 0]
trials = [
x for x in trials
if x['results_metrics']['avg_profit']
> filteroptions['filter_min_avg_profit']
]
x for x in trials
if x['results_metrics']['avg_profit']
> filteroptions['filter_min_avg_profit']
]
if filteroptions['filter_max_avg_profit'] is not None:
trials = [x for x in trials if x['results_metrics']['trade_count'] > 0]
trials = [
x for x in trials
if x['results_metrics']['avg_profit']
< filteroptions['filter_max_avg_profit']
]
x for x in trials
if x['results_metrics']['avg_profit']
< filteroptions['filter_max_avg_profit']
]
if filteroptions['filter_min_total_profit'] is not None:
trials = [x for x in trials if x['results_metrics']['trade_count'] > 0]
trials = [
x for x in trials
if x['results_metrics']['profit'] > filteroptions['filter_min_total_profit']
]
x for x in trials
if x['results_metrics']['profit'] > filteroptions['filter_min_total_profit']
]
if filteroptions['filter_max_total_profit'] is not None:
trials = [x for x in trials if x['results_metrics']['trade_count'] > 0]
trials = [
x for x in trials
if x['results_metrics']['profit'] < filteroptions['filter_max_total_profit']
]
x for x in trials
if x['results_metrics']['profit'] < filteroptions['filter_max_total_profit']
]
logger.info(f"{len(trials)} " +
("best " if filteroptions['only_best'] else "") +

4
freqtrade/commands/list_commands.py
View File

@ -58,7 +58,7 @@ def _print_objs_tabular(objs: List, print_colorized: bool) -> None:
else yellow + "DUPLICATE NAME" + reset)
} for s in objs]
print(tabulate(objss_to_print, headers='keys', tablefmt='pipe'))
print(tabulate(objss_to_print, headers='keys', tablefmt='psql', stralign='right'))
def start_list_strategies(args: Dict[str, Any]) -> None:
@ -192,7 +192,7 @@ def start_list_markets(args: Dict[str, Any], pairs_only: bool = False) -> None:
else:
# print data as a table, with the human-readable summary
print(f"{summary_str}:")
print(tabulate(tabular_data, headers='keys', tablefmt='pipe'))
print(tabulate(tabular_data, headers='keys', tablefmt='psql', stralign='right'))
elif not (args.get('print_one_column', False) or
args.get('list_pairs_print_json', False) or
args.get('print_csv', False)):

12
freqtrade/configuration/config_validation.py
View File

@ -150,15 +150,3 @@ def _validate_whitelist(conf: Dict[str, Any]) -> None:
if (pl.get('method') == 'StaticPairList'
and not conf.get('exchange', {}).get('pair_whitelist')):
raise OperationalException("StaticPairList requires pair_whitelist to be set.")
if pl.get('method') == 'StaticPairList':
stake = conf['stake_currency']
invalid_pairs = []
for pair in conf['exchange'].get('pair_whitelist'):
if not pair.endswith(f'/{stake}'):
invalid_pairs.append(pair)
if invalid_pairs:
raise OperationalException(
f"Stake-currency '{stake}' not compatible with pair-whitelist. "
f"Please remove the following pairs: {invalid_pairs}")

16
freqtrade/configuration/configuration.py
View File

@ -94,6 +94,8 @@ class Configuration:
# Keep a copy of the original configuration file
config['original_config'] = deepcopy(config)
self._process_logging_options(config)
self._process_runmode(config)
self._process_common_options(config)
@ -144,8 +146,6 @@ class Configuration:
def _process_common_options(self, config: Dict[str, Any]) -> None:
self._process_logging_options(config)
# Set strategy if not specified in config and or if it's non default
if self.args.get("strategy") or not config.get('strategy'):
config.update({'strategy': self.args.get("strategy")})
@ -166,11 +166,6 @@ class Configuration:
if 'sd_notify' in self.args and self.args["sd_notify"]:
config['internals'].update({'sd_notify': True})
self._args_to_config(config,
argname='dry_run',
logstring='Parameter --dry-run detected, '
'overriding dry_run to: {} ...')
def _process_datadir_options(self, config: Dict[str, Any]) -> None:
"""
Extract information for sys.argv and load directory configurations
@ -412,10 +407,15 @@ class Configuration:
def _process_runmode(self, config: Dict[str, Any]) -> None:
self._args_to_config(config,
argname='dry_run',
logstring='Parameter --dry-run detected, '
'overriding dry_run to: {} ...')
if not self.runmode:
# Handle real mode, infer dry/live from config
self.runmode = RunMode.DRY_RUN if config.get('dry_run', True) else RunMode.LIVE
logger.info(f"Runmode set to {self.runmode}.")
logger.info(f"Runmode set to {self.runmode.value}.")
config.update({'runmode': self.runmode})

2
freqtrade/constants.py
View File

@ -422,7 +422,6 @@ CONF_SCHEMA = {
'type': 'array',
'items': {
'type': 'string',
'pattern': '^[0-9A-Z]+/[0-9A-Z]+$'
},
'uniqueItems': True
},
@ -430,7 +429,6 @@ CONF_SCHEMA = {
'type': 'array',
'items': {
'type': 'string',
'pattern': '^[0-9A-Z]+/[0-9A-Z]+$'
},
'uniqueItems': True
},

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

@ -71,6 +71,8 @@ class JsonDataHandler(IDataHandler):
return DataFrame(columns=self._columns)
pairdata = read_json(filename, orient='values')
pairdata.columns = self._columns
pairdata = pairdata.astype(dtype={'open': 'float', 'high': 'float',
'low': 'float', 'close': 'float', 'volume': 'float'})
pairdata['date'] = to_datetime(pairdata['date'],
unit='ms',
utc=True,

58
freqtrade/exchange/exchange.py
View File

@ -66,8 +66,6 @@ class Exchange:
self._config.update(config)
self._cached_ticker: Dict[str, Any] = {}
# Holds last candle refreshed time of each pair
self._pairs_last_refresh_time: Dict[Tuple[str, str], int] = {}
# Timestamp of last markets refresh
@ -228,6 +226,18 @@ class Exchange:
markets = self.markets
return sorted(set([x['quote'] for _, x in markets.items()]))
def get_pair_quote_currency(self, pair: str) -> str:
"""
Return a pair's quote currency
"""
return self.markets.get(pair, {}).get('quote', '')
def get_pair_base_currency(self, pair: str) -> str:
"""
Return a pair's quote currency
"""
return self.markets.get(pair, {}).get('base', '')
def klines(self, pair_interval: Tuple[str, str], copy: bool = True) -> DataFrame:
if pair_interval in self._klines:
return self._klines[pair_interval].copy() if copy else self._klines[pair_interval]
@ -300,7 +310,7 @@ class Exchange:
if not self.markets:
logger.warning('Unable to validate pairs (assuming they are correct).')
return
invalid_pairs = []
for pair in pairs:
# Note: ccxt has BaseCurrency/QuoteCurrency format for pairs
# TODO: add a support for having coins in BTC/USDT format
@ -322,6 +332,13 @@ class Exchange:
logger.warning(f"Pair {pair} is restricted for some users on this exchange."
f"Please check if you are impacted by this restriction "
f"on the exchange and eventually remove {pair} from your whitelist.")
if (self._config['stake_currency'] and
self.get_pair_quote_currency(pair) != self._config['stake_currency']):
invalid_pairs.append(pair)
if invalid_pairs:
raise OperationalException(
f"Stake-currency '{self._config['stake_currency']}' not compatible with "
f"pair-whitelist. Please remove the following pairs: {invalid_pairs}")
def get_valid_pair_combination(self, curr_1: str, curr_2: str) -> str:
"""
@ -591,28 +608,17 @@ class Exchange:
raise OperationalException(e) from e
@retrier
def fetch_ticker(self, pair: str, refresh: Optional[bool] = True) -> dict:
if refresh or pair not in self._cached_ticker.keys():
try:
if pair not in self._api.markets or not self._api.markets[pair].get('active'):
raise DependencyException(f"Pair {pair} not available")
data = self._api.fetch_ticker(pair)
try:
self._cached_ticker[pair] = {
'bid': float(data['bid']),
'ask': float(data['ask']),
}
except KeyError:
logger.debug("Could not cache ticker data for %s", pair)
return data
except (ccxt.NetworkError, ccxt.ExchangeError) as e:
raise TemporaryError(
f'Could not load ticker due to {e.__class__.__name__}. Message: {e}') from e
except ccxt.BaseError as e:
raise OperationalException(e) from e
else:
logger.info("returning cached ticker-data for %s", pair)
return self._cached_ticker[pair]
def fetch_ticker(self, pair: str) -> dict:
try:
if pair not in self._api.markets or not self._api.markets[pair].get('active'):
raise DependencyException(f"Pair {pair} not available")
data = self._api.fetch_ticker(pair)
return data
except (ccxt.NetworkError, ccxt.ExchangeError) as e:
raise TemporaryError(
f'Could not load ticker due to {e.__class__.__name__}. Message: {e}') from e
except ccxt.BaseError as e:
raise OperationalException(e) from e
def get_historic_ohlcv(self, pair: str, timeframe: str,
since_ms: int) -> List:
@ -1018,7 +1024,7 @@ def is_exchange_known_ccxt(exchange_name: str, ccxt_module: CcxtModuleType = Non
def is_exchange_officially_supported(exchange_name: str) -> bool:
return exchange_name in ['bittrex', 'binance']
return exchange_name in ['bittrex', 'binance', 'kraken']
def ccxt_exchanges(ccxt_module: CcxtModuleType = None) -> List[str]:

57
freqtrade/freqtradebot.py
View File

@ -6,11 +6,11 @@ import logging
import traceback
from datetime import datetime
from math import isclose
from os import getpid
from threading import Lock
from typing import Any, Dict, List, Optional, Tuple
import arrow
from cachetools import TTLCache
from requests.exceptions import RequestException
from freqtrade import __version__, constants, persistence
@ -52,9 +52,8 @@ class FreqtradeBot:
# Init objects
self.config = config
self._heartbeat_msg = 0
self.heartbeat_interval = self.config.get('internals', {}).get('heartbeat_interval', 60)
self._sell_rate_cache = TTLCache(maxsize=100, ttl=5)
self._buy_rate_cache = TTLCache(maxsize=100, ttl=5)
self.strategy: IStrategy = StrategyResolver.load_strategy(self.config)
@ -159,11 +158,6 @@ class FreqtradeBot:
self.check_handle_timedout()
Trade.session.flush()
if (self.heartbeat_interval
and (arrow.utcnow().timestamp - self._heartbeat_msg > self.heartbeat_interval)):
logger.info(f"Bot heartbeat. PID={getpid()}")
self._heartbeat_msg = arrow.utcnow().timestamp
def _refresh_whitelist(self, trades: List[Trade] = []) -> List[str]:
"""
Refresh whitelist from pairlist or edge and extend it with trades.
@ -234,11 +228,20 @@ class FreqtradeBot:
return trades_created
def get_buy_rate(self, pair: str, refresh: bool, tick: Dict = None) -> float:
def get_buy_rate(self, pair: str, refresh: bool) -> float:
"""
Calculates bid target between current ask price and last price
:param pair: Pair to get rate for
:param refresh: allow cached data
:return: float: Price
"""
if not refresh:
rate = self._buy_rate_cache.get(pair)
# Check if cache has been invalidated
if rate:
logger.info(f"Using cached buy rate for {pair}.")
return rate
config_bid_strategy = self.config.get('bid_strategy', {})
if 'use_order_book' in config_bid_strategy and\
config_bid_strategy.get('use_order_book', False):
@ -251,11 +254,8 @@ class FreqtradeBot:
logger.info('...top %s order book buy rate %0.8f', order_book_top, order_book_rate)
used_rate = order_book_rate
else:
if not tick:
logger.info('Using Last Ask / Last Price')
ticker = self.exchange.fetch_ticker(pair, refresh)
else:
ticker = tick
logger.info('Using Last Ask / Last Price')
ticker = self.exchange.fetch_ticker(pair)
if ticker['ask'] < ticker['last']:
ticker_rate = ticker['ask']
else:
@ -263,6 +263,8 @@ class FreqtradeBot:
ticker_rate = ticker['ask'] + balance * (ticker['last'] - ticker['ask'])
used_rate = ticker_rate
self._buy_rate_cache[pair] = used_rate
return used_rate
def get_trade_stake_amount(self, pair: str) -> float:
@ -566,7 +568,7 @@ class FreqtradeBot:
"""
Sends rpc notification when a buy cancel occured.
"""
current_rate = self.get_buy_rate(trade.pair, True)
current_rate = self.get_buy_rate(trade.pair, False)
msg = {
'type': RPCMessageType.BUY_CANCEL_NOTIFICATION,
@ -621,8 +623,17 @@ class FreqtradeBot:
The orderbook portion is only used for rpc messaging, which would otherwise fail
for BitMex (has no bid/ask in fetch_ticker)
or remain static in any other case since it's not updating.
:param pair: Pair to get rate for
:param refresh: allow cached data
:return: Bid rate
"""
if not refresh:
rate = self._sell_rate_cache.get(pair)
# Check if cache has been invalidated
if rate:
logger.info(f"Using cached sell rate for {pair}.")
return rate
config_ask_strategy = self.config.get('ask_strategy', {})
if config_ask_strategy.get('use_order_book', False):
logger.debug('Using order book to get sell rate')
@ -631,7 +642,8 @@ class FreqtradeBot:
rate = order_book['bids'][0][0]
else:
rate = self.exchange.fetch_ticker(pair, refresh)['bid']
rate = self.exchange.fetch_ticker(pair)['bid']
self._sell_rate_cache[pair] = rate
return rate
def handle_trade(self, trade: Trade) -> bool:
@ -948,8 +960,8 @@ class FreqtradeBot:
"""
# Update wallets to ensure amounts tied up in a stoploss is now free!
self.wallets.update()
wallet_amount = self.wallets.get_free(pair.split('/')[0])
trade_base_currency = self.exchange.get_pair_base_currency(pair)
wallet_amount = self.wallets.get_free(trade_base_currency)
logger.debug(f"{pair} - Wallet: {wallet_amount} - Trade-amount: {amount}")
if wallet_amount >= amount:
return amount
@ -1058,7 +1070,7 @@ class FreqtradeBot:
"""
profit_rate = trade.close_rate if trade.close_rate else trade.close_rate_requested
profit_trade = trade.calc_profit(rate=profit_rate)
current_rate = self.get_sell_rate(trade.pair, True)
current_rate = self.get_sell_rate(trade.pair, False)
profit_percent = trade.calc_profit_ratio(profit_rate)
gain = "profit" if profit_percent > 0 else "loss"
@ -1135,12 +1147,13 @@ class FreqtradeBot:
if trade.fee_open == 0 or order['status'] == 'open':
return order_amount
trade_base_currency = self.exchange.get_pair_base_currency(trade.pair)
# use fee from order-dict if possible
if ('fee' in order and order['fee'] is not None and
(order['fee'].keys() >= {'currency', 'cost'})):
if (order['fee']['currency'] is not None and
order['fee']['cost'] is not None and
trade.pair.startswith(order['fee']['currency'])):
trade_base_currency == order['fee']['currency']):
new_amount = order_amount - order['fee']['cost']
logger.info("Applying fee on amount for %s (from %s to %s) from Order",
trade, order['amount'], new_amount)
@ -1162,7 +1175,7 @@ class FreqtradeBot:
# only applies if fee is in quote currency!
if (exectrade['fee']['currency'] is not None and
exectrade['fee']['cost'] is not None and
trade.pair.startswith(exectrade['fee']['currency'])):
trade_base_currency == exectrade['fee']['currency']):
fee_abs += exectrade['fee']['cost']
if not isclose(amount, order_amount, abs_tol=constants.MATH_CLOSE_PREC):

47
freqtrade/optimize/backtesting.py
View File

@ -423,28 +423,37 @@ class Backtesting:
strategy if len(self.strategylist) > 1 else None)
print(f"Result for strategy {strategy}")
print(' BACKTESTING REPORT '.center(133, '='))
print(generate_text_table(data,
stake_currency=self.config['stake_currency'],
max_open_trades=self.config['max_open_trades'],
results=results))
table = generate_text_table(data, stake_currency=self.config['stake_currency'],
max_open_trades=self.config['max_open_trades'],
results=results)
if isinstance(table, str):
print(' BACKTESTING REPORT '.center(len(table.splitlines()[0]), '='))
print(table)
print(' SELL REASON STATS '.center(133, '='))
print(generate_text_table_sell_reason(data,
stake_currency=self.config['stake_currency'],
max_open_trades=self.config['max_open_trades'],
results=results))
table = generate_text_table_sell_reason(data,
stake_currency=self.config['stake_currency'],
max_open_trades=self.config['max_open_trades'],
results=results)
if isinstance(table, str):
print(' SELL REASON STATS '.center(len(table.splitlines()[0]), '='))
print(table)
print(' LEFT OPEN TRADES REPORT '.center(133, '='))
print(generate_text_table(data,
stake_currency=self.config['stake_currency'],
max_open_trades=self.config['max_open_trades'],
results=results.loc[results.open_at_end], skip_nan=True))
table = generate_text_table(data,
stake_currency=self.config['stake_currency'],
max_open_trades=self.config['max_open_trades'],
results=results.loc[results.open_at_end], skip_nan=True)
if isinstance(table, str):
print(' LEFT OPEN TRADES REPORT '.center(len(table.splitlines()[0]), '='))
print(table)
if isinstance(table, str):
print('=' * len(table.splitlines()[0]))
print()
if len(all_results) > 1:
# Print Strategy summary table
print(' STRATEGY SUMMARY '.center(133, '='))
print(generate_text_table_strategy(self.config['stake_currency'],
self.config['max_open_trades'],
all_results=all_results))
table = generate_text_table_strategy(self.config['stake_currency'],
self.config['max_open_trades'],
all_results=all_results)
print(' STRATEGY SUMMARY '.center(len(table.splitlines()[0]), '='))
print(table)
print('=' * len(table.splitlines()[0]))
print('\nFor more details, please look at the detail tables above')

69
freqtrade/optimize/hyperopt.py
View File

@ -11,6 +11,7 @@ import sys
import warnings
from collections import OrderedDict, deque
from math import factorial, log, inf
from numpy import iinfo, int32
from operator import itemgetter
from pathlib import Path
from pprint import pprint
@ -19,6 +20,12 @@ from typing import Any, Dict, List, Optional
import rapidjson
from colorama import Fore, Style
from colorama import init as colorama_init
from joblib import (Parallel, cpu_count, delayed, dump, load, wrap_non_picklable_objects)
from joblib import parallel_backend
from multiprocessing import Manager
from queue import Queue
from pandas import DataFrame, json_normalize, isna
from tabulate import tabulate
from freqtrade.data.converter import trim_dataframe
from freqtrade.data.history import get_timerange
@ -30,13 +37,6 @@ import freqtrade.optimize.hyperopt_backend as backend
from freqtrade.optimize.hyperopt_interface import IHyperOpt # noqa: F401
from freqtrade.optimize.hyperopt_loss_interface import IHyperOptLoss # noqa: F401
from freqtrade.resolvers.hyperopt_resolver import (HyperOptLossResolver, HyperOptResolver)
from joblib import (Parallel, cpu_count, delayed, dump, load, wrap_non_picklable_objects)
from joblib import parallel_backend
from multiprocessing import Manager
from queue import Queue
from pandas import DataFrame
from numpy import iinfo, int32
# Suppress scikit-learn FutureWarnings from skopt
with warnings.catch_warnings():
@ -364,6 +364,61 @@ class Hyperopt:
f"{results['current_epoch']:5d}/{total_epochs}: " +
f"{results['results_explanation']} " + f"Objective: {results['loss']:.5f}")
@staticmethod
def print_result_table(config: dict, results: list, total_epochs: int, highlight_best: bool,
print_colorized: bool) -> None:
"""
Log result table
"""
if not results:
return
trials = json_normalize(results, max_level=1)
trials['Best'] = ''
trials = trials[[
'Best', 'current_epoch', 'results_metrics.trade_count', 'results_metrics.avg_profit',
'results_metrics.total_profit', 'results_metrics.profit', 'results_metrics.duration',
'loss', 'is_initial_point', 'is_best'
]]
trials.columns = [
'Best', 'Epoch', 'Trades', 'Avg profit', 'Total profit', 'Profit', 'Avg duration',
'Objective', 'is_initial_point', 'is_best'
]
trials['is_profit'] = False
trials.loc[trials['is_initial_point'], 'Best'] = '*'
trials.loc[trials['is_best'], 'Best'] = 'Best'
trials['Objective'] = trials['Objective'].astype(str)
trials.loc[trials['Total profit'] > 0, 'is_profit'] = True
trials['Trades'] = trials['Trades'].astype(str)
trials['Epoch'] = trials['Epoch'].apply(lambda x: "{}/{}".format(x, total_epochs))
trials['Avg profit'] = trials['Avg profit'].apply(lambda x: '{:,.2f}%'.format(x)
if not isna(x) else x)
trials['Profit'] = trials['Profit'].apply(lambda x: '{:,.2f}%'.format(x)
if not isna(x) else x)
trials['Total profit'] = trials['Total profit'].apply(
lambda x: '{: 11.8f} '.format(x) + config['stake_currency'] if not isna(x) else x)
trials['Avg duration'] = trials['Avg duration'].apply(lambda x: '{:,.1f}m'.format(x)
if not isna(x) else x)
if print_colorized:
for i in range(len(trials)):
if trials.loc[i]['is_profit']:
for z in range(len(trials.loc[i]) - 3):
trials.iat[i, z] = "{}{}{}".format(Fore.GREEN, str(trials.loc[i][z]),
Fore.RESET)
if trials.loc[i]['is_best'] and highlight_best:
for z in range(len(trials.loc[i]) - 3):
trials.iat[i, z] = "{}{}{}".format(Style.BRIGHT, str(trials.loc[i][z]),
Style.RESET_ALL)
trials = trials.drop(columns=['is_initial_point', 'is_best', 'is_profit'])
print(
tabulate(trials.to_dict(orient='list'),
headers='keys',
tablefmt='psql',
stralign="right"))
def has_space(self, space: str) -> bool:
"""
Tell if the space value is contained in the configuration

49
freqtrade/optimize/hyperopt_loss_sortino.py Normal file
View File

@ -0,0 +1,49 @@
"""
SortinoHyperOptLoss
This module defines the alternative HyperOptLoss class which can be used for
Hyperoptimization.
"""
from datetime import datetime
from pandas import DataFrame
import numpy as np
from freqtrade.optimize.hyperopt import IHyperOptLoss
class SortinoHyperOptLoss(IHyperOptLoss):
"""
Defines the loss function for hyperopt.
This implementation uses the Sortino Ratio calculation.
"""
@staticmethod
def hyperopt_loss_function(results: DataFrame, trade_count: int,
min_date: datetime, max_date: datetime,
*args, **kwargs) -> float:
"""
Objective function, returns smaller number for more optimal results.
Uses Sortino Ratio calculation.
"""
total_profit = results["profit_percent"]
days_period = (max_date - min_date).days
# adding slippage of 0.1% per trade
total_profit = total_profit - 0.0005
expected_returns_mean = total_profit.sum() / days_period
results['downside_returns'] = 0
results.loc[total_profit < 0, 'downside_returns'] = results['profit_percent']
down_stdev = np.std(results['downside_returns'])
if np.std(total_profit) != 0.0:
sortino_ratio = expected_returns_mean / down_stdev * np.sqrt(365)
else:
# Define high (negative) sortino ratio to be clear that this is NOT optimal.
sortino_ratio = -20.
# print(expected_returns_mean, down_stdev, sortino_ratio)
return -sortino_ratio

70
freqtrade/optimize/hyperopt_loss_sortino_daily.py Normal file
View File

@ -0,0 +1,70 @@
"""
SortinoHyperOptLossDaily
This module defines the alternative HyperOptLoss class which can be used for
Hyperoptimization.
"""
import math
from datetime import datetime
from pandas import DataFrame, date_range
from freqtrade.optimize.hyperopt import IHyperOptLoss
class SortinoHyperOptLossDaily(IHyperOptLoss):
"""
Defines the loss function for hyperopt.
This implementation uses the Sortino Ratio calculation.
"""
@staticmethod
def hyperopt_loss_function(results: DataFrame, trade_count: int,
min_date: datetime, max_date: datetime,
*args, **kwargs) -> float:
"""
Objective function, returns smaller number for more optimal results.
Uses Sortino Ratio calculation.
Sortino Ratio calculated as described in
http://www.redrockcapital.com/Sortino__A__Sharper__Ratio_Red_Rock_Capital.pdf
"""
resample_freq = '1D'
slippage_per_trade_ratio = 0.0005
days_in_year = 365
minimum_acceptable_return = 0.0
# apply slippage per trade to profit_percent
results.loc[:, 'profit_percent_after_slippage'] = \
results['profit_percent'] - slippage_per_trade_ratio
# create the index within the min_date and end max_date
t_index = date_range(start=min_date, end=max_date, freq=resample_freq,
normalize=True)
sum_daily = (
results.resample(resample_freq, on='close_time').agg(
{"profit_percent_after_slippage": sum}).reindex(t_index).fillna(0)
)
total_profit = sum_daily["profit_percent_after_slippage"] - minimum_acceptable_return
expected_returns_mean = total_profit.mean()
sum_daily['downside_returns'] = 0
sum_daily.loc[total_profit < 0, 'downside_returns'] = total_profit
total_downside = sum_daily['downside_returns']
# Here total_downside contains min(0, P - MAR) values,
# where P = sum_daily["profit_percent_after_slippage"]
down_stdev = math.sqrt((total_downside**2).sum() / len(total_downside))
if (down_stdev != 0.):
sortino_ratio = expected_returns_mean / down_stdev * math.sqrt(days_in_year)
else:
# Define high (negative) sortino ratio to be clear that this is NOT optimal.
sortino_ratio = -20.
# print(t_index, sum_daily, total_profit)
# print(minimum_acceptable_return, expected_returns_mean, down_stdev, sortino_ratio)
return -sortino_ratio

8
freqtrade/optimize/optimize_reports.py
View File

@ -66,7 +66,7 @@ def generate_text_table(data: Dict[str, Dict], stake_currency: str, max_open_tra
])
# Ignore type as floatfmt does allow tuples but mypy does not know that
return tabulate(tabular_data, headers=headers,
floatfmt=floatfmt, tablefmt="pipe") # type: ignore
floatfmt=floatfmt, tablefmt="orgtbl", stralign="right") # type: ignore
def generate_text_table_sell_reason(
@ -112,7 +112,7 @@ def generate_text_table_sell_reason(
profit_percent_tot,
]
)
return tabulate(tabular_data, headers=headers, tablefmt="pipe")
return tabulate(tabular_data, headers=headers, tablefmt="orgtbl", stralign="right")
def generate_text_table_strategy(stake_currency: str, max_open_trades: str,
@ -146,7 +146,7 @@ def generate_text_table_strategy(stake_currency: str, max_open_trades: str,
])
# Ignore type as floatfmt does allow tuples but mypy does not know that
return tabulate(tabular_data, headers=headers,
floatfmt=floatfmt, tablefmt="pipe") # type: ignore
floatfmt=floatfmt, tablefmt="orgtbl", stralign="right") # type: ignore
def generate_edge_table(results: dict) -> str:
@ -172,4 +172,4 @@ def generate_edge_table(results: dict) -> str:
# Ignore type as floatfmt does allow tuples but mypy does not know that
return tabulate(tabular_data, headers=headers,
floatfmt=floatfmt, tablefmt="pipe") # type: ignore
floatfmt=floatfmt, tablefmt="orgtbl", stralign="right") # type: ignore

3
freqtrade/pairlist/IPairList.py
View File

@ -99,7 +99,8 @@ class IPairList(ABC):
logger.warning(f"Pair {pair} is not compatible with exchange "
f"{self._exchange.name}. Removing it from whitelist..")
continue
if not pair.endswith(self._config['stake_currency']):
if self._exchange.get_pair_quote_currency(pair) != self._config['stake_currency']:
logger.warning(f"Pair {pair} is not compatible with your stake currency "
f"{self._config['stake_currency']}. Removing it from whitelist..")
continue

4
freqtrade/pairlist/VolumePairList.py
View File

@ -91,9 +91,9 @@ class VolumePairList(IPairList):
if self._pairlist_pos == 0:
# If VolumePairList is the first in the list, use fresh pairlist
# check length so that we make sure that '/' is actually in the string
# Check if pair quote currency equals to the stake currency.
filtered_tickers = [v for k, v in tickers.items()
if (len(k.split('/')) == 2 and k.split('/')[1] == base_currency
if (self._exchange.get_pair_quote_currency(k) == base_currency
and v[key] is not None)]
else:
# If other pairlist is in front, use the incomming pairlist.

6
freqtrade/rpc/rpc.py
View File

@ -460,9 +460,9 @@ class RPC:
if self._freqtrade.state != State.RUNNING:
raise RPCException('trader is not running')
# Check pair is in stake currency
# Check if pair quote currency equals to the stake currency.
stake_currency = self._freqtrade.config.get('stake_currency')
if not pair.endswith(stake_currency):
if not self._freqtrade.exchange.get_pair_quote_currency(pair) == stake_currency:
raise RPCException(
f'Wrong pair selected. Please pairs with stake {stake_currency} pairs only')
# check if valid pair
@ -517,7 +517,7 @@ class RPC:
if add:
stake_currency = self._freqtrade.config.get('stake_currency')
for pair in add:
if (pair.endswith(stake_currency)
if (self._freqtrade.exchange.get_pair_quote_currency(pair) == stake_currency
and pair not in self._freqtrade.pairlists.blacklist):
self._freqtrade.pairlists.blacklist.append(pair)

124
freqtrade/templates/sample_strategy.py
View File

@ -124,24 +124,70 @@ class SampleStrategy(IStrategy):
# Momentum Indicators
# ------------------------------------
# RSI
dataframe['rsi'] = ta.RSI(dataframe)
# ADX
dataframe['adx'] = ta.ADX(dataframe)
# # Plus Directional Indicator / Movement
# dataframe['plus_dm'] = ta.PLUS_DM(dataframe)
# dataframe['plus_di'] = ta.PLUS_DI(dataframe)
# # Minus Directional Indicator / Movement
# dataframe['minus_dm'] = ta.MINUS_DM(dataframe)
# dataframe['minus_di'] = ta.MINUS_DI(dataframe)
# # Aroon, Aroon Oscillator
# aroon = ta.AROON(dataframe)
# dataframe['aroonup'] = aroon['aroonup']
# dataframe['aroondown'] = aroon['aroondown']
# dataframe['aroonosc'] = ta.AROONOSC(dataframe)
# # Awesome oscillator
# # Awesome Oscillator
# dataframe['ao'] = qtpylib.awesome_oscillator(dataframe)
# # Commodity Channel Index: values Oversold:<-100, Overbought:>100
# # Keltner Channel
# keltner = qtpylib.keltner_channel(dataframe)
# dataframe["kc_upperband"] = keltner["upper"]
# dataframe["kc_lowerband"] = keltner["lower"]
# dataframe["kc_middleband"] = keltner["mid"]
# dataframe["kc_percent"] = (
# (dataframe["close"] - dataframe["kc_lowerband"]) /
# (dataframe["kc_upperband"] - dataframe["kc_lowerband"])
# )
# dataframe["kc_width"] = (
# (dataframe["kc_upperband"] - dataframe["kc_lowerband"]) / dataframe["kc_middleband"]
# )
# # Ultimate Oscillator
# dataframe['uo'] = ta.ULTOSC(dataframe)
# # Commodity Channel Index: values [Oversold:-100, Overbought:100]
# dataframe['cci'] = ta.CCI(dataframe)
# RSI
dataframe['rsi'] = ta.RSI(dataframe)
# # Inverse Fisher transform on RSI: values [-1.0, 1.0] (https://goo.gl/2JGGoy)
# rsi = 0.1 * (dataframe['rsi'] - 50)
# dataframe['fisher_rsi'] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1)
# # Inverse Fisher transform on RSI normalized: values [0.0, 100.0] (https://goo.gl/2JGGoy)
# dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1)
# # Stochastic Slow
# stoch = ta.STOCH(dataframe)
# dataframe['slowd'] = stoch['slowd']
# dataframe['slowk'] = stoch['slowk']
# Stochastic Fast
stoch_fast = ta.STOCHF(dataframe)
dataframe['fastd'] = stoch_fast['fastd']
dataframe['fastk'] = stoch_fast['fastk']
# # Stochastic RSI
# stoch_rsi = ta.STOCHRSI(dataframe)
# dataframe['fastd_rsi'] = stoch_rsi['fastd']
# dataframe['fastk_rsi'] = stoch_rsi['fastk']
# MACD
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
@ -151,60 +197,58 @@ class SampleStrategy(IStrategy):
# MFI
dataframe['mfi'] = ta.MFI(dataframe)
# # Minus Directional Indicator / Movement
# dataframe['minus_dm'] = ta.MINUS_DM(dataframe)
# dataframe['minus_di'] = ta.MINUS_DI(dataframe)
# # Plus Directional Indicator / Movement
# dataframe['plus_dm'] = ta.PLUS_DM(dataframe)
# dataframe['plus_di'] = ta.PLUS_DI(dataframe)
# dataframe['minus_di'] = ta.MINUS_DI(dataframe)
# # ROC
# dataframe['roc'] = ta.ROC(dataframe)
# # Inverse Fisher transform on RSI, values [-1.0, 1.0] (https://goo.gl/2JGGoy)
# rsi = 0.1 * (dataframe['rsi'] - 50)
# dataframe['fisher_rsi'] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1)
# # Inverse Fisher transform on RSI normalized, value [0.0, 100.0] (https://goo.gl/2JGGoy)
# dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1)
# # Stoch
# stoch = ta.STOCH(dataframe)
# dataframe['slowd'] = stoch['slowd']
# dataframe['slowk'] = stoch['slowk']
# Stoch fast
stoch_fast = ta.STOCHF(dataframe)
dataframe['fastd'] = stoch_fast['fastd']
dataframe['fastk'] = stoch_fast['fastk']
# # Stoch RSI
# stoch_rsi = ta.STOCHRSI(dataframe)
# dataframe['fastd_rsi'] = stoch_rsi['fastd']
# dataframe['fastk_rsi'] = stoch_rsi['fastk']
# Overlap Studies
# ------------------------------------
# Bollinger bands
# Bollinger Bands
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
dataframe['bb_lowerband'] = bollinger['lower']
dataframe['bb_middleband'] = bollinger['mid']
dataframe['bb_upperband'] = bollinger['upper']
dataframe["bb_percent"] = (
(dataframe["close"] - dataframe["bb_lowerband"]) /
(dataframe["bb_upperband"] - dataframe["bb_lowerband"])
)
dataframe["bb_width"] = (
(dataframe["bb_upperband"] - dataframe["bb_lowerband"]) / dataframe["bb_middleband"]
)
# Bollinger Bands - Weighted (EMA based instead of SMA)
# weighted_bollinger = qtpylib.weighted_bollinger_bands(
# qtpylib.typical_price(dataframe), window=20, stds=2
# )
# dataframe["wbb_upperband"] = weighted_bollinger["upper"]
# dataframe["wbb_lowerband"] = weighted_bollinger["lower"]
# dataframe["wbb_middleband"] = weighted_bollinger["mid"]
# dataframe["wbb_percent"] = (
# (dataframe["close"] - dataframe["wbb_lowerband"]) /
# (dataframe["wbb_upperband"] - dataframe["wbb_lowerband"])
# )
# dataframe["wbb_width"] = (
# (dataframe["wbb_upperband"] - dataframe["wbb_lowerband"]) /
# dataframe["wbb_middleband"]
# )
# # EMA - Exponential Moving Average
# dataframe['ema3'] = ta.EMA(dataframe, timeperiod=3)
# dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5)
# dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
# dataframe['ema21'] = ta.EMA(dataframe, timeperiod=21)
# dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
# dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)
# # SMA - Simple Moving Average
# dataframe['sma'] = ta.SMA(dataframe, timeperiod=40)
# dataframe['sma3'] = ta.SMA(dataframe, timeperiod=3)
# dataframe['sma5'] = ta.SMA(dataframe, timeperiod=5)
# dataframe['sma10'] = ta.SMA(dataframe, timeperiod=10)
# dataframe['sma21'] = ta.SMA(dataframe, timeperiod=21)
# dataframe['sma50'] = ta.SMA(dataframe, timeperiod=50)
# dataframe['sma100'] = ta.SMA(dataframe, timeperiod=100)
# SAR Parabol
# Parabolic SAR
dataframe['sar'] = ta.SAR(dataframe)
# TEMA - Triple Exponential Moving Average
@ -264,7 +308,7 @@ class SampleStrategy(IStrategy):
# # Chart type
# # ------------------------------------
# # Heikinashi stategy
# # Heikin Ashi Strategy
# heikinashi = qtpylib.heikinashi(dataframe)
# dataframe['ha_open'] = heikinashi['open']
# dataframe['ha_close'] = heikinashi['close']

6
freqtrade/templates/strategy_analysis_example.ipynb
View File

@ -190,7 +190,6 @@
"# Analyze the above\n",
"parallel_trades = analyze_trade_parallelism(trades, '5m')\n",
"\n",
"\n",
"parallel_trades.plot()"
]
},
@ -212,11 +211,14 @@
"from freqtrade.plot.plotting import generate_candlestick_graph\n",
"# Limit graph period to keep plotly quick and reactive\n",
"\n",
"# Filter trades to one pair\n",
"trades_red = trades.loc[trades['pair'] == pair]\n",
"\n",
"data_red = data['2019-06-01':'2019-06-10']\n",
"# Generate candlestick graph\n",
"graph = generate_candlestick_graph(pair=pair,\n",
" data=data_red,\n",
" trades=trades,\n",
" trades=trades_red,\n",
" indicators1=['sma20', 'ema50', 'ema55'],\n",
" indicators2=['rsi', 'macd', 'macdsignal', 'macdhist']\n",
" )\n",

123
freqtrade/templates/subtemplates/indicators_full.j2
View File

@ -2,24 +2,70 @@
# Momentum Indicators
# ------------------------------------
# RSI
dataframe['rsi'] = ta.RSI(dataframe)
# ADX
dataframe['adx'] = ta.ADX(dataframe)
# # Plus Directional Indicator / Movement
# dataframe['plus_dm'] = ta.PLUS_DM(dataframe)
# dataframe['plus_di'] = ta.PLUS_DI(dataframe)
# # Minus Directional Indicator / Movement
# dataframe['minus_dm'] = ta.MINUS_DM(dataframe)
# dataframe['minus_di'] = ta.MINUS_DI(dataframe)
# # Aroon, Aroon Oscillator
# aroon = ta.AROON(dataframe)
# dataframe['aroonup'] = aroon['aroonup']
# dataframe['aroondown'] = aroon['aroondown']
# dataframe['aroonosc'] = ta.AROONOSC(dataframe)
# # Awesome oscillator
# # Awesome Oscillator
# dataframe['ao'] = qtpylib.awesome_oscillator(dataframe)
# # Commodity Channel Index: values Oversold:<-100, Overbought:>100
# # Keltner Channel
# keltner = qtpylib.keltner_channel(dataframe)
# dataframe["kc_upperband"] = keltner["upper"]
# dataframe["kc_lowerband"] = keltner["lower"]
# dataframe["kc_middleband"] = keltner["mid"]
# dataframe["kc_percent"] = (
# (dataframe["close"] - dataframe["kc_lowerband"]) /
# (dataframe["kc_upperband"] - dataframe["kc_lowerband"])
# )
# dataframe["kc_width"] = (
# (dataframe["kc_upperband"] - dataframe["kc_lowerband"]) / dataframe["kc_middleband"]
# )
# # Ultimate Oscillator
# dataframe['uo'] = ta.ULTOSC(dataframe)
# # Commodity Channel Index: values [Oversold:-100, Overbought:100]
# dataframe['cci'] = ta.CCI(dataframe)
# RSI
dataframe['rsi'] = ta.RSI(dataframe)
# # Inverse Fisher transform on RSI: values [-1.0, 1.0] (https://goo.gl/2JGGoy)
# rsi = 0.1 * (dataframe['rsi'] - 50)
# dataframe['fisher_rsi'] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1)
# # Inverse Fisher transform on RSI normalized: values [0.0, 100.0] (https://goo.gl/2JGGoy)
# dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1)
# # Stochastic Slow
# stoch = ta.STOCH(dataframe)
# dataframe['slowd'] = stoch['slowd']
# dataframe['slowk'] = stoch['slowk']
# Stochastic Fast
stoch_fast = ta.STOCHF(dataframe)
dataframe['fastd'] = stoch_fast['fastd']
dataframe['fastk'] = stoch_fast['fastk']
# # Stochastic RSI
# stoch_rsi = ta.STOCHRSI(dataframe)
# dataframe['fastd_rsi'] = stoch_rsi['fastd']
# dataframe['fastk_rsi'] = stoch_rsi['fastk']
# MACD
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
@ -29,60 +75,57 @@ dataframe['macdhist'] = macd['macdhist']
# MFI
dataframe['mfi'] = ta.MFI(dataframe)
# # Minus Directional Indicator / Movement
# dataframe['minus_dm'] = ta.MINUS_DM(dataframe)
# dataframe['minus_di'] = ta.MINUS_DI(dataframe)
# # Plus Directional Indicator / Movement
# dataframe['plus_dm'] = ta.PLUS_DM(dataframe)
# dataframe['plus_di'] = ta.PLUS_DI(dataframe)
# dataframe['minus_di'] = ta.MINUS_DI(dataframe)
# # ROC
# dataframe['roc'] = ta.ROC(dataframe)
# # Inverse Fisher transform on RSI, values [-1.0, 1.0] (https://goo.gl/2JGGoy)
# rsi = 0.1 * (dataframe['rsi'] - 50)
# dataframe['fisher_rsi'] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1)
# # Inverse Fisher transform on RSI normalized, value [0.0, 100.0] (https://goo.gl/2JGGoy)
# dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1)
# # Stoch
# stoch = ta.STOCH(dataframe)
# dataframe['slowd'] = stoch['slowd']
# dataframe['slowk'] = stoch['slowk']
# Stoch fast
stoch_fast = ta.STOCHF(dataframe)
dataframe['fastd'] = stoch_fast['fastd']
dataframe['fastk'] = stoch_fast['fastk']
# # Stoch RSI
# stoch_rsi = ta.STOCHRSI(dataframe)
# dataframe['fastd_rsi'] = stoch_rsi['fastd']
# dataframe['fastk_rsi'] = stoch_rsi['fastk']
# Overlap Studies
# ------------------------------------
# Bollinger bands
# Bollinger Bands
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
dataframe['bb_lowerband'] = bollinger['lower']
dataframe['bb_middleband'] = bollinger['mid']
dataframe['bb_upperband'] = bollinger['upper']
dataframe["bb_percent"] = (
(dataframe["close"] - dataframe["bb_lowerband"]) /
(dataframe["bb_upperband"] - dataframe["bb_lowerband"])
)
dataframe["bb_width"] = (
(dataframe["bb_upperband"] - dataframe["bb_lowerband"]) / dataframe["bb_middleband"]
)
# Bollinger Bands - Weighted (EMA based instead of SMA)
# weighted_bollinger = qtpylib.weighted_bollinger_bands(
# qtpylib.typical_price(dataframe), window=20, stds=2
# )
# dataframe["wbb_upperband"] = weighted_bollinger["upper"]
# dataframe["wbb_lowerband"] = weighted_bollinger["lower"]
# dataframe["wbb_middleband"] = weighted_bollinger["mid"]
# dataframe["wbb_percent"] = (
# (dataframe["close"] - dataframe["wbb_lowerband"]) /
# (dataframe["wbb_upperband"] - dataframe["wbb_lowerband"])
# )
# dataframe["wbb_width"] = (
# (dataframe["wbb_upperband"] - dataframe["wbb_lowerband"]) / dataframe["wbb_middleband"]
# )
# # EMA - Exponential Moving Average
# dataframe['ema3'] = ta.EMA(dataframe, timeperiod=3)
# dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5)
# dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
# dataframe['ema21'] = ta.EMA(dataframe, timeperiod=21)
# dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
# dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)
# # SMA - Simple Moving Average
# dataframe['sma'] = ta.SMA(dataframe, timeperiod=40)
# dataframe['sma3'] = ta.SMA(dataframe, timeperiod=3)
# dataframe['sma5'] = ta.SMA(dataframe, timeperiod=5)
# dataframe['sma10'] = ta.SMA(dataframe, timeperiod=10)
# dataframe['sma21'] = ta.SMA(dataframe, timeperiod=21)
# dataframe['sma50'] = ta.SMA(dataframe, timeperiod=50)
# dataframe['sma100'] = ta.SMA(dataframe, timeperiod=100)
# SAR Parabol
# Parabolic SAR
dataframe['sar'] = ta.SAR(dataframe)
# TEMA - Triple Exponential Moving Average
@ -142,7 +185,7 @@ dataframe['htleadsine'] = hilbert['leadsine']
# # Chart type
# # ------------------------------------
# # Heikinashi stategy
# # Heikin Ashi Strategy
# heikinashi = qtpylib.heikinashi(dataframe)
# dataframe['ha_open'] = heikinashi['open']
# dataframe['ha_close'] = heikinashi['close']

2
freqtrade/wallets.py
View File

@ -74,7 +74,7 @@ class Wallets:
)
for trade in open_trades:
curr = trade.pair.split('/')[0]
curr = self._exchange.get_pair_base_currency(trade.pair)
_wallets[curr] = Wallet(
curr,
trade.amount,

62
freqtrade/worker.py
View File

@ -4,6 +4,7 @@ Main Freqtrade worker class.
import logging
import time
import traceback
from os import getpid
from typing import Any, Callable, Dict, Optional
import sdnotify
@ -26,12 +27,15 @@ class Worker:
"""
Init all variables and objects the bot needs to work
"""
logger.info('Starting worker %s', __version__)
logger.info(f"Starting worker {__version__}")
self._args = args
self._config = config
self._init(False)
self.last_throttle_start_time: float = 0
self._heartbeat_msg: float = 0
# Tell systemd that we completed initialization phase
if self._sd_notify:
logger.debug("sd_notify: READY=1")
@ -48,10 +52,10 @@ class Worker:
# Init the instance of the bot
self.freqtrade = FreqtradeBot(self._config)
self._throttle_secs = self._config.get('internals', {}).get(
'process_throttle_secs',
constants.PROCESS_THROTTLE_SECS
)
internals_config = self._config.get('internals', {})
self._throttle_secs = internals_config.get('process_throttle_secs',
constants.PROCESS_THROTTLE_SECS)
self._heartbeat_interval = internals_config.get('heartbeat_interval', 60)
self._sd_notify = sdnotify.SystemdNotifier() if \
self._config.get('internals', {}).get('sd_notify', False) else None
@ -63,31 +67,33 @@ class Worker:
if state == State.RELOAD_CONF:
self._reconfigure()
def _worker(self, old_state: Optional[State], throttle_secs: Optional[float] = None) -> State:
def _worker(self, old_state: Optional[State]) -> State:
"""
Trading routine that must be run at each loop
The main routine that runs each throttling iteration and handles the states.
:param old_state: the previous service state from the previous call
:return: current service state
"""
state = self.freqtrade.state
if throttle_secs is None:
throttle_secs = self._throttle_secs
# Log state transition
if state != old_state:
self.freqtrade.notify_status(f'{state.name.lower()}')
logger.info('Changing state to: %s', state.name)
logger.info(f"Changing state to: {state.name}")
if state == State.RUNNING:
self.freqtrade.startup()
# Reset heartbeat timestamp to log the heartbeat message at
# first throttling iteration when the state changes
self._heartbeat_msg = 0
if state == State.STOPPED:
# Ping systemd watchdog before sleeping in the stopped state
if self._sd_notify:
logger.debug("sd_notify: WATCHDOG=1\\nSTATUS=State: STOPPED.")
self._sd_notify.notify("WATCHDOG=1\nSTATUS=State: STOPPED.")
time.sleep(throttle_secs)
self._throttle(func=self._process_stopped, throttle_secs=self._throttle_secs)
elif state == State.RUNNING:
# Ping systemd watchdog before throttling
@ -95,28 +101,40 @@ class Worker:
logger.debug("sd_notify: WATCHDOG=1\\nSTATUS=State: RUNNING.")
self._sd_notify.notify("WATCHDOG=1\nSTATUS=State: RUNNING.")
self._throttle(func=self._process, min_secs=throttle_secs)
self._throttle(func=self._process_running, throttle_secs=self._throttle_secs)
if self._heartbeat_interval:
now = time.time()
if (now - self._heartbeat_msg) > self._heartbeat_interval:
logger.info(f"Bot heartbeat. PID={getpid()}, "
f"version='{__version__}', state='{state.name}'")
self._heartbeat_msg = now
return state
def _throttle(self, func: Callable[..., Any], min_secs: float, *args, **kwargs) -> Any:
def _throttle(self, func: Callable[..., Any], throttle_secs: float, *args, **kwargs) -> Any:
"""
Throttles the given callable that it
takes at least `min_secs` to finish execution.
:param func: Any callable
:param min_secs: minimum execution time in seconds
:return: Any
:param throttle_secs: throttling interation execution time limit in seconds
:return: Any (result of execution of func)
"""
start = time.time()
self.last_throttle_start_time = time.time()
logger.debug("========================================")
result = func(*args, **kwargs)
end = time.time()
duration = max(min_secs - (end - start), 0.0)
logger.debug('Throttling %s for %.2f seconds', func.__name__, duration)
time.sleep(duration)
time_passed = time.time() - self.last_throttle_start_time
sleep_duration = max(throttle_secs - time_passed, 0.0)
logger.debug(f"Throttling with '{func.__name__}()': sleep for {sleep_duration:.2f} s, "
f"last iteration took {time_passed:.2f} s.")
time.sleep(sleep_duration)
return result
def _process(self) -> None:
logger.debug("========================================")
def _process_stopped(self) -> None:
# Maybe do here something in the future...
pass
def _process_running(self) -> None:
try:
self.freqtrade.process()
except TemporaryError as error:

4
mkdocs.yml
View File

@ -1,8 +1,8 @@
site_name: Freqtrade
nav:
- About: index.md
- Installation: installation.md
- Home: index.md
- Installation Docker: docker.md
- Installation: installation.md
- Configuration: configuration.md
- Strategy Customization: strategy-customization.md
- Stoploss: stoploss.md

4
requirements-common.txt
View File

@ -1,11 +1,11 @@
# requirements without requirements installable via conda
# mainly used for Raspberry pi installs
ccxt==1.22.61
ccxt==1.22.95
SQLAlchemy==1.3.13
python-telegram-bot==12.4.2
arrow==0.15.5
cachetools==4.0.0
requests==2.22.0
requests==2.23.0
urllib3==1.25.8
wrapt==1.12.0
jsonschema==3.2.0

2
requirements-hyperopt.txt
View File

@ -4,6 +4,6 @@
# Required for hyperopt
scipy==1.4.1
scikit-learn==0.22.1
scikit-optimize==0.7.2
scikit-optimize==0.7.4
filelock==3.0.12
joblib==0.14.1

2
requirements-plot.txt
View File

@ -1,5 +1,5 @@
# Include all requirements to run the bot.
-r requirements.txt
plotly==4.5.0
plotly==4.5.1

55
tests/commands/test_commands.py
View File

@ -217,8 +217,9 @@ def test_list_markets(mocker, markets, capsys):
]
start_list_markets(get_args(args), False)
captured = capsys.readouterr()
assert ("Exchange Bittrex has 9 active markets: "
"BLK/BTC, ETH/BTC, ETH/USDT, LTC/BTC, LTC/USD, NEO/BTC, TKN/BTC, XLTCUSDT, XRP/BTC.\n"
assert ("Exchange Bittrex has 10 active markets: "
"BLK/BTC, ETH/BTC, ETH/USDT, LTC/BTC, LTC/ETH, LTC/USD, NEO/BTC, "
"TKN/BTC, XLTCUSDT, XRP/BTC.\n"
in captured.out)
patch_exchange(mocker, api_mock=api_mock, id="binance")
@ -231,7 +232,7 @@ def test_list_markets(mocker, markets, capsys):
pargs['config'] = None
start_list_markets(pargs, False)
captured = capsys.readouterr()
assert re.match("\nExchange Binance has 9 active markets:\n",
assert re.match("\nExchange Binance has 10 active markets:\n",
captured.out)
patch_exchange(mocker, api_mock=api_mock, id="bittrex")
@ -243,8 +244,8 @@ def test_list_markets(mocker, markets, capsys):
]
start_list_markets(get_args(args), False)
captured = capsys.readouterr()
assert ("Exchange Bittrex has 11 markets: "
"BLK/BTC, BTT/BTC, ETH/BTC, ETH/USDT, LTC/BTC, LTC/USD, LTC/USDT, NEO/BTC, "
assert ("Exchange Bittrex has 12 markets: "
"BLK/BTC, BTT/BTC, ETH/BTC, ETH/USDT, LTC/BTC, LTC/ETH, LTC/USD, LTC/USDT, NEO/BTC, "
"TKN/BTC, XLTCUSDT, XRP/BTC.\n"
in captured.out)
@ -256,8 +257,8 @@ def test_list_markets(mocker, markets, capsys):
]
start_list_markets(get_args(args), True)
captured = capsys.readouterr()
assert ("Exchange Bittrex has 8 active pairs: "
"BLK/BTC, ETH/BTC, ETH/USDT, LTC/BTC, LTC/USD, NEO/BTC, TKN/BTC, XRP/BTC.\n"
assert ("Exchange Bittrex has 9 active pairs: "
"BLK/BTC, ETH/BTC, ETH/USDT, LTC/BTC, LTC/ETH, LTC/USD, NEO/BTC, TKN/BTC, XRP/BTC.\n"
in captured.out)
# Test list-pairs subcommand with --all: all pairs
@ -268,8 +269,8 @@ def test_list_markets(mocker, markets, capsys):
]
start_list_markets(get_args(args), True)
captured = capsys.readouterr()
assert ("Exchange Bittrex has 10 pairs: "
"BLK/BTC, BTT/BTC, ETH/BTC, ETH/USDT, LTC/BTC, LTC/USD, LTC/USDT, NEO/BTC, "
assert ("Exchange Bittrex has 11 pairs: "
"BLK/BTC, BTT/BTC, ETH/BTC, ETH/USDT, LTC/BTC, LTC/ETH, LTC/USD, LTC/USDT, NEO/BTC, "
"TKN/BTC, XRP/BTC.\n"
in captured.out)
@ -282,8 +283,8 @@ def test_list_markets(mocker, markets, capsys):
]
start_list_markets(get_args(args), False)
captured = capsys.readouterr()
assert ("Exchange Bittrex has 5 active markets with ETH, LTC as base currencies: "
"ETH/BTC, ETH/USDT, LTC/BTC, LTC/USD, XLTCUSDT.\n"
assert ("Exchange Bittrex has 6 active markets with ETH, LTC as base currencies: "
"ETH/BTC, ETH/USDT, LTC/BTC, LTC/ETH, LTC/USD, XLTCUSDT.\n"
in captured.out)
# active markets, base=LTC
@ -295,8 +296,8 @@ def test_list_markets(mocker, markets, capsys):
]
start_list_markets(get_args(args), False)
captured = capsys.readouterr()
assert ("Exchange Bittrex has 3 active markets with LTC as base currency: "
"LTC/BTC, LTC/USD, XLTCUSDT.\n"
assert ("Exchange Bittrex has 4 active markets with LTC as base currency: "
"LTC/BTC, LTC/ETH, LTC/USD, XLTCUSDT.\n"
in captured.out)
# active markets, quote=USDT, USD
@ -384,7 +385,7 @@ def test_list_markets(mocker, markets, capsys):
]
start_list_markets(get_args(args), False)
captured = capsys.readouterr()
assert ("Exchange Bittrex has 9 active markets:\n"
assert ("Exchange Bittrex has 10 active markets:\n"
in captured.out)
# Test tabular output, no markets found
@ -407,7 +408,7 @@ def test_list_markets(mocker, markets, capsys):
]
start_list_markets(get_args(args), False)
captured = capsys.readouterr()
assert ('["BLK/BTC","ETH/BTC","ETH/USDT","LTC/BTC","LTC/USD","NEO/BTC",'
assert ('["BLK/BTC","ETH/BTC","ETH/USDT","LTC/BTC","LTC/ETH","LTC/USD","NEO/BTC",'
'"TKN/BTC","XLTCUSDT","XRP/BTC"]'
in captured.out)
@ -446,11 +447,9 @@ def test_create_datadir_failed(caplog):
def test_create_datadir(caplog, mocker):
# Ensure that caplog is empty before starting ...
# Should prevent random failures.
caplog.clear()
# Added assert here to analyze random test-failures ...
assert len(caplog.record_tuples) == 0
# Capture caplog length here trying to avoid random test failure
len_caplog_before = len(caplog.record_tuples)
cud = mocker.patch("freqtrade.commands.deploy_commands.create_userdata_dir", MagicMock())
csf = mocker.patch("freqtrade.commands.deploy_commands.copy_sample_files", MagicMock())
@ -463,7 +462,7 @@ def test_create_datadir(caplog, mocker):
assert cud.call_count == 1
assert csf.call_count == 1
assert len(caplog.record_tuples) == 0
assert len(caplog.record_tuples) == len_caplog_before
def test_start_new_strategy(mocker, caplog):
@ -778,6 +777,20 @@ def test_hyperopt_list(mocker, capsys, hyperopt_results):
assert all(x not in captured.out
for x in [" 1/12", " 3/12", " 4/12", " 5/12", " 6/12", " 7/12", " 8/12", " 9/12",
" 11/12", " 12/12"])
args = [
"hyperopt-list",
"--profitable"
]
pargs = get_args(args)
pargs['config'] = None
start_hyperopt_list(pargs)
captured = capsys.readouterr()
assert all(x in captured.out
for x in [" 2/12", " 10/12", "Best result:", "Buy hyperspace params",
"Sell hyperspace params", "ROI table", "Stoploss"])
assert all(x not in captured.out
for x in [" 1/12", " 3/12", " 4/12", " 5/12", " 6/12", " 7/12", " 8/12", " 9/12",
" 11/12", " 12/12"])
args = [
"hyperopt-list",
"--no-details",

29
tests/conftest.py
View File

@ -575,7 +575,34 @@ def get_markets():
}
},
'info': {},
}
},
'LTC/ETH': {
'id': 'LTCETH',
'symbol': 'LTC/ETH',
'base': 'LTC',
'quote': 'ETH',
'active': True,
'precision': {
'base': 8,
'quote': 8,
'amount': 3,
'price': 5
},
'limits': {
'amount': {
'min': 0.001,
'max': 10000000.0
},
'price': {
'min': 1e-05,
'max': 1000.0
},
'cost': {
'min': 0.01,
'max': None
}
},
},
}

113
tests/exchange/test_exchange.py
View File

@ -400,13 +400,40 @@ def test_validate_stake_currency_error(default_conf, mocker, caplog):
def test_get_quote_currencies(default_conf, mocker):
ex = get_patched_exchange(mocker, default_conf)
assert set(ex.get_quote_currencies()) == set(['USD', 'BTC', 'USDT'])
assert set(ex.get_quote_currencies()) == set(['USD', 'ETH', 'BTC', 'USDT'])
@pytest.mark.parametrize('pair,expected', [
('XRP/BTC', 'BTC'),
('LTC/USD', 'USD'),
('ETH/USDT', 'USDT'),
('XLTCUSDT', 'USDT'),
('XRP/NOCURRENCY', ''),
])
def test_get_pair_quote_currency(default_conf, mocker, pair, expected):
ex = get_patched_exchange(mocker, default_conf)
assert ex.get_pair_quote_currency(pair) == expected
@pytest.mark.parametrize('pair,expected', [
('XRP/BTC', 'XRP'),
('LTC/USD', 'LTC'),
('ETH/USDT', 'ETH'),
('XLTCUSDT', 'LTC'),
('XRP/NOCURRENCY', ''),
])
def test_get_pair_base_currency(default_conf, mocker, pair, expected):
ex = get_patched_exchange(mocker, default_conf)
assert ex.get_pair_base_currency(pair) == expected
def test_validate_pairs(default_conf, mocker): # test exchange.validate_pairs directly
api_mock = MagicMock()
type(api_mock).markets = PropertyMock(return_value={
'ETH/BTC': {}, 'LTC/BTC': {}, 'XRP/BTC': {}, 'NEO/BTC': {}
'ETH/BTC': {'quote': 'BTC'},
'LTC/BTC': {'quote': 'BTC'},
'XRP/BTC': {'quote': 'BTC'},
'NEO/BTC': {'quote': 'BTC'},
})
id_mock = PropertyMock(return_value='test_exchange')
type(api_mock).id = id_mock
@ -454,9 +481,9 @@ def test_validate_pairs_exception(default_conf, mocker, caplog):
def test_validate_pairs_restricted(default_conf, mocker, caplog):
api_mock = MagicMock()
type(api_mock).markets = PropertyMock(return_value={
'ETH/BTC': {}, 'LTC/BTC': {},
'XRP/BTC': {'info': {'IsRestricted': True}},
'NEO/BTC': {'info': 'TestString'}, # info can also be a string ...
'ETH/BTC': {'quote': 'BTC'}, 'LTC/BTC': {'quote': 'BTC'},
'XRP/BTC': {'quote': 'BTC', 'info': {'IsRestricted': True}},
'NEO/BTC': {'quote': 'BTC', 'info': 'TestString'}, # info can also be a string ...
})
mocker.patch('freqtrade.exchange.Exchange._init_ccxt', MagicMock(return_value=api_mock))
mocker.patch('freqtrade.exchange.Exchange.validate_timeframes')
@ -469,6 +496,54 @@ def test_validate_pairs_restricted(default_conf, mocker, caplog):
f"on the exchange and eventually remove XRP/BTC from your whitelist.", caplog)
def test_validate_pairs_stakecompatibility(default_conf, mocker, caplog):
api_mock = MagicMock()
type(api_mock).markets = PropertyMock(return_value={
'ETH/BTC': {'quote': 'BTC'}, 'LTC/BTC': {'quote': 'BTC'},
'XRP/BTC': {'quote': 'BTC'}, 'NEO/BTC': {'quote': 'BTC'},
'HELLO-WORLD': {'quote': 'BTC'},
})
mocker.patch('freqtrade.exchange.Exchange._init_ccxt', MagicMock(return_value=api_mock))
mocker.patch('freqtrade.exchange.Exchange.validate_timeframes')
mocker.patch('freqtrade.exchange.Exchange._load_async_markets')
mocker.patch('freqtrade.exchange.Exchange.validate_stakecurrency')
Exchange(default_conf)
def test_validate_pairs_stakecompatibility_downloaddata(default_conf, mocker, caplog):
api_mock = MagicMock()
default_conf['stake_currency'] = ''
type(api_mock).markets = PropertyMock(return_value={
'ETH/BTC': {'quote': 'BTC'}, 'LTC/BTC': {'quote': 'BTC'},
'XRP/BTC': {'quote': 'BTC'}, 'NEO/BTC': {'quote': 'BTC'},
'HELLO-WORLD': {'quote': 'BTC'},
})
mocker.patch('freqtrade.exchange.Exchange._init_ccxt', MagicMock(return_value=api_mock))
mocker.patch('freqtrade.exchange.Exchange.validate_timeframes')
mocker.patch('freqtrade.exchange.Exchange._load_async_markets')
mocker.patch('freqtrade.exchange.Exchange.validate_stakecurrency')
Exchange(default_conf)
def test_validate_pairs_stakecompatibility_fail(default_conf, mocker, caplog):
default_conf['exchange']['pair_whitelist'].append('HELLO-WORLD')
api_mock = MagicMock()
type(api_mock).markets = PropertyMock(return_value={
'ETH/BTC': {'quote': 'BTC'}, 'LTC/BTC': {'quote': 'BTC'},
'XRP/BTC': {'quote': 'BTC'}, 'NEO/BTC': {'quote': 'BTC'},
'HELLO-WORLD': {'quote': 'USDT'},
})
mocker.patch('freqtrade.exchange.Exchange._init_ccxt', MagicMock(return_value=api_mock))
mocker.patch('freqtrade.exchange.Exchange.validate_timeframes')
mocker.patch('freqtrade.exchange.Exchange._load_async_markets')
mocker.patch('freqtrade.exchange.Exchange.validate_stakecurrency')
with pytest.raises(OperationalException, match=r"Stake-currency 'BTC' not compatible with.*"):
Exchange(default_conf)
@pytest.mark.parametrize("timeframe", [
('5m'), ("1m"), ("15m"), ("1h")
])
@ -1121,25 +1196,16 @@ def test_fetch_ticker(default_conf, mocker, exchange_name):
assert ticker['bid'] == 0.5
assert ticker['ask'] == 1
assert 'ETH/BTC' in exchange._cached_ticker
assert exchange._cached_ticker['ETH/BTC']['bid'] == 0.5
assert exchange._cached_ticker['ETH/BTC']['ask'] == 1
# Test caching
api_mock.fetch_ticker = MagicMock()
exchange.fetch_ticker(pair='ETH/BTC', refresh=False)
assert api_mock.fetch_ticker.call_count == 0
ccxt_exceptionhandlers(mocker, default_conf, api_mock, exchange_name,
"fetch_ticker", "fetch_ticker",
pair='ETH/BTC', refresh=True)
pair='ETH/BTC')
api_mock.fetch_ticker = MagicMock(return_value={})
exchange = get_patched_exchange(mocker, default_conf, api_mock, id=exchange_name)
exchange.fetch_ticker(pair='ETH/BTC', refresh=True)
exchange.fetch_ticker(pair='ETH/BTC')
with pytest.raises(DependencyException, match=r'Pair XRP/ETH not available'):
exchange.fetch_ticker(pair='XRP/ETH', refresh=True)
exchange.fetch_ticker(pair='XRP/ETH')
@pytest.mark.parametrize("exchange_name", EXCHANGES)
@ -1828,6 +1894,7 @@ def test_get_valid_pair_combination(default_conf, mocker, markets):
# 'ETH/BTC': 'active': True
# 'ETH/USDT': 'active': True
# 'LTC/BTC': 'active': False
# 'LTC/ETH': 'active': True
# 'LTC/USD': 'active': True
# 'LTC/USDT': 'active': True
# 'NEO/BTC': 'active': False
@ -1836,26 +1903,26 @@ def test_get_valid_pair_combination(default_conf, mocker, markets):
# 'XRP/BTC': 'active': False
# all markets
([], [], False, False,
['BLK/BTC', 'BTT/BTC', 'ETH/BTC', 'ETH/USDT', 'LTC/BTC', 'LTC/USD',
['BLK/BTC', 'BTT/BTC', 'ETH/BTC', 'ETH/USDT', 'LTC/BTC', 'LTC/ETH', 'LTC/USD',
'LTC/USDT', 'NEO/BTC', 'TKN/BTC', 'XLTCUSDT', 'XRP/BTC']),
# active markets
([], [], False, True,
['BLK/BTC', 'ETH/BTC', 'ETH/USDT', 'LTC/BTC', 'LTC/USD', 'NEO/BTC',
['BLK/BTC', 'ETH/BTC', 'ETH/USDT', 'LTC/BTC', 'LTC/ETH', 'LTC/USD', 'NEO/BTC',
'TKN/BTC', 'XLTCUSDT', 'XRP/BTC']),
# all pairs
([], [], True, False,
['BLK/BTC', 'BTT/BTC', 'ETH/BTC', 'ETH/USDT', 'LTC/BTC', 'LTC/USD',
['BLK/BTC', 'BTT/BTC', 'ETH/BTC', 'ETH/USDT', 'LTC/BTC', 'LTC/ETH', 'LTC/USD',
'LTC/USDT', 'NEO/BTC', 'TKN/BTC', 'XRP/BTC']),
# active pairs
([], [], True, True,
['BLK/BTC', 'ETH/BTC', 'ETH/USDT', 'LTC/BTC', 'LTC/USD', 'NEO/BTC',
['BLK/BTC', 'ETH/BTC', 'ETH/USDT', 'LTC/BTC', 'LTC/ETH', 'LTC/USD', 'NEO/BTC',
'TKN/BTC', 'XRP/BTC']),
# all markets, base=ETH, LTC
(['ETH', 'LTC'], [], False, False,
['ETH/BTC', 'ETH/USDT', 'LTC/BTC', 'LTC/USD', 'LTC/USDT', 'XLTCUSDT']),
['ETH/BTC', 'ETH/USDT', 'LTC/BTC', 'LTC/ETH', 'LTC/USD', 'LTC/USDT', 'XLTCUSDT']),
# all markets, base=LTC
(['LTC'], [], False, False,
['LTC/BTC', 'LTC/USD', 'LTC/USDT', 'XLTCUSDT']),
['LTC/BTC', 'LTC/ETH', 'LTC/USD', 'LTC/USDT', 'XLTCUSDT']),
# all markets, quote=USDT
([], ['USDT'], False, False,
['ETH/USDT', 'LTC/USDT', 'XLTCUSDT']),

36
tests/optimize/test_hyperopt.py
View File

@ -330,6 +330,42 @@ def test_sharpe_loss_daily_prefers_higher_profits(default_conf, hyperopt_results
assert under > correct
def test_sortino_loss_prefers_higher_profits(default_conf, hyperopt_results) -> None:
results_over = hyperopt_results.copy()
results_over['profit_percent'] = hyperopt_results['profit_percent'] * 2
results_under = hyperopt_results.copy()
results_under['profit_percent'] = hyperopt_results['profit_percent'] / 2
default_conf.update({'hyperopt_loss': 'SortinoHyperOptLoss'})
hl = HyperOptLossResolver.load_hyperoptloss(default_conf)
correct = hl.hyperopt_loss_function(hyperopt_results, len(hyperopt_results),
datetime(2019, 1, 1), datetime(2019, 5, 1))
over = hl.hyperopt_loss_function(results_over, len(hyperopt_results),
datetime(2019, 1, 1), datetime(2019, 5, 1))
under = hl.hyperopt_loss_function(results_under, len(hyperopt_results),
datetime(2019, 1, 1), datetime(2019, 5, 1))
assert over < correct
assert under > correct
def test_sortino_loss_daily_prefers_higher_profits(default_conf, hyperopt_results) -> None:
results_over = hyperopt_results.copy()
results_over['profit_percent'] = hyperopt_results['profit_percent'] * 2
results_under = hyperopt_results.copy()
results_under['profit_percent'] = hyperopt_results['profit_percent'] / 2
default_conf.update({'hyperopt_loss': 'SortinoHyperOptLossDaily'})
hl = HyperOptLossResolver.load_hyperoptloss(default_conf)
correct = hl.hyperopt_loss_function(hyperopt_results, len(hyperopt_results),
datetime(2019, 1, 1), datetime(2019, 5, 1))
over = hl.hyperopt_loss_function(results_over, len(hyperopt_results),
datetime(2019, 1, 1), datetime(2019, 5, 1))
under = hl.hyperopt_loss_function(results_under, len(hyperopt_results),
datetime(2019, 1, 1), datetime(2019, 5, 1))
assert over < correct
assert under > correct
def test_onlyprofit_loss_prefers_higher_profits(default_conf, hyperopt_results) -> None:
results_over = hyperopt_results.copy()
results_over['profit_percent'] = hyperopt_results['profit_percent'] * 2

43
tests/optimize/test_optimize_reports.py
View File

@ -22,14 +22,14 @@ def test_generate_text_table(default_conf, mocker):
)
result_str = (
'| Pair | Buys | Avg Profit % | Cum Profit % | Tot Profit BTC |'
' Tot Profit % | Avg Duration | Wins | Draws | Losses |\n'
'|:--------|-------:|---------------:|---------------:|-----------------:|'
'---------------:|:---------------|-------:|--------:|---------:|\n'
'| Pair | Buys | Avg Profit % | Cum Profit % | Tot Profit BTC |'
' Tot Profit % | Avg Duration | Wins | Draws | Losses |\n'
'|---------+--------+----------------+----------------+------------------+'
'----------------+----------------+--------+---------+----------|\n'
'| ETH/BTC | 2 | 15.00 | 30.00 | 0.60000000 |'
' 15.00 | 0:20:00 | 2 | 0 | 0 |\n'
'| TOTAL | 2 | 15.00 | 30.00 | 0.60000000 |'
' 15.00 | 0:20:00 | 2 | 0 | 0 |'
' 15.00 | 0:20:00 | 2 | 0 | 0 |\n'
'| TOTAL | 2 | 15.00 | 30.00 | 0.60000000 |'
' 15.00 | 0:20:00 | 2 | 0 | 0 |'
)
assert generate_text_table(data={'ETH/BTC': {}},
stake_currency='BTC', max_open_trades=2,
@ -52,13 +52,13 @@ def test_generate_text_table_sell_reason(default_conf, mocker):
)
result_str = (
'| Sell Reason | Sells | Wins | Draws | Losses |'
'| Sell Reason | Sells | Wins | Draws | Losses |'
' Avg Profit % | Cum Profit % | Tot Profit BTC | Tot Profit % |\n'
'|:--------------|--------:|-------:|--------:|---------:|'
'---------------:|---------------:|-----------------:|---------------:|\n'
'| roi | 2 | 2 | 0 | 0 |'
'|---------------+---------+--------+---------+----------+'
'----------------+----------------+------------------+----------------|\n'
'| roi | 2 | 2 | 0 | 0 |'
' 15 | 30 | 0.6 | 15 |\n'
'| stop_loss | 1 | 0 | 0 | 1 |'
'| stop_loss | 1 | 0 | 0 | 1 |'
' -10 | -10 | -0.2 | -5 |'
)
assert generate_text_table_sell_reason(
@ -95,14 +95,14 @@ def test_generate_text_table_strategy(default_conf, mocker):
)
result_str = (
'| Strategy | Buys | Avg Profit % | Cum Profit % | Tot'
' Profit BTC | Tot Profit % | Avg Duration | Wins | Draws | Losses |\n'
'|:--------------|-------:|---------------:|---------------:|------'
'-----------:|---------------:|:---------------|-------:|--------:|---------:|\n'
'| TestStrategy1 | 3 | 20.00 | 60.00 | '
' 1.10000000 | 30.00 | 0:17:00 | 3 | 0 | 0 |\n'
'| TestStrategy2 | 3 | 30.00 | 90.00 | '
' 1.30000000 | 45.00 | 0:20:00 | 3 | 0 | 0 |'
'| Strategy | Buys | Avg Profit % | Cum Profit % | Tot'
' Profit BTC | Tot Profit % | Avg Duration | Wins | Draws | Losses |\n'
'|---------------+--------+----------------+----------------+------------------+'
'----------------+----------------+--------+---------+----------|\n'
'| TestStrategy1 | 3 | 20.00 | 60.00 | 1.10000000 |'
' 30.00 | 0:17:00 | 3 | 0 | 0 |\n'
'| TestStrategy2 | 3 | 30.00 | 90.00 | 1.30000000 |'
' 45.00 | 0:20:00 | 3 | 0 | 0 |'
)
assert generate_text_table_strategy('BTC', 2, all_results=results) == result_str
@ -111,8 +111,7 @@ def test_generate_edge_table(edge_conf, mocker):
results = {}
results['ETH/BTC'] = PairInfo(-0.01, 0.60, 2, 1, 3, 10, 60)
assert generate_edge_table(results).count(':|') == 7
assert generate_edge_table(results).count('+') == 7
assert generate_edge_table(results).count('| ETH/BTC |') == 1
assert generate_edge_table(results).count(
'| Risk Reward Ratio | Required Risk Reward | Expectancy |') == 1

14
tests/rpc/test_rpc.py
View File

@ -65,10 +65,8 @@ def test_rpc_trade_status(default_conf, ticker, fee, mocker) -> None:
'open_order': '(limit buy rem=0.00000000)'
} == results[0]
mocker.patch('freqtrade.exchange.Exchange.fetch_ticker',
mocker.patch('freqtrade.freqtradebot.FreqtradeBot.get_sell_rate',
MagicMock(side_effect=DependencyException(f"Pair 'ETH/BTC' not available")))
# invalidate ticker cache
rpc._freqtrade.exchange._cached_ticker = {}
results = rpc._rpc_trade_status()
assert isnan(results[0]['current_profit'])
assert isnan(results[0]['current_rate'])
@ -134,10 +132,8 @@ def test_rpc_status_table(default_conf, ticker, fee, mocker) -> None:
assert 'ETH/BTC' in result[0][1]
assert '-0.59% (-0.09)' == result[0][3]
mocker.patch('freqtrade.exchange.Exchange.fetch_ticker',
mocker.patch('freqtrade.freqtradebot.FreqtradeBot.get_sell_rate',
MagicMock(side_effect=DependencyException(f"Pair 'ETH/BTC' not available")))
# invalidate ticker cache
rpc._freqtrade.exchange._cached_ticker = {}
result, headers = rpc._rpc_status_table(default_conf['stake_currency'], 'USD')
assert 'instantly' == result[0][2]
assert 'ETH/BTC' in result[0][1]
@ -260,10 +256,8 @@ def test_rpc_trade_statistics(default_conf, ticker, ticker_sell_up, fee,
assert prec_satoshi(stats['best_rate'], 6.2)
# Test non-available pair
mocker.patch('freqtrade.exchange.Exchange.fetch_ticker',
mocker.patch('freqtrade.freqtradebot.FreqtradeBot.get_sell_rate',
MagicMock(side_effect=DependencyException(f"Pair 'ETH/BTC' not available")))
# invalidate ticker cache
rpc._freqtrade.exchange._cached_ticker = {}
stats = rpc._rpc_trade_statistics(stake_currency, fiat_display_currency)
assert stats['trade_count'] == 2
assert stats['first_trade_date'] == 'just now'
@ -687,7 +681,7 @@ def test_rpcforcebuy(mocker, default_conf, ticker, fee, limit_buy_order) -> None
# Test buy pair not with stakes
with pytest.raises(RPCException, match=r'Wrong pair selected. Please pairs with stake.*'):
rpc._rpc_forcebuy('XRP/ETH', 0.0001)
rpc._rpc_forcebuy('LTC/ETH', 0.0001)
pair = 'XRP/BTC'
# Test not buying

14
tests/test_configuration.py
View File

@ -34,13 +34,6 @@ def all_conf():
return conf
def test_load_config_invalid_pair(default_conf) -> None:
default_conf['exchange']['pair_whitelist'].append('ETH-BTC')
with pytest.raises(ValidationError, match=r'.*does not match.*'):
validate_config_schema(default_conf)
def test_load_config_missing_attributes(default_conf) -> None:
conf = deepcopy(default_conf)
conf.pop('exchange')
@ -326,6 +319,7 @@ def test_load_dry_run(default_conf, mocker, config_value, expected, arglist) ->
validated_conf = configuration.load_config()
assert validated_conf['dry_run'] is expected
assert validated_conf['runmode'] == (RunMode.DRY_RUN if expected else RunMode.LIVE)
def test_load_custom_strategy(default_conf, mocker) -> None:
@ -810,12 +804,6 @@ def test_validate_whitelist(default_conf):
validate_config_consistency(conf)
conf = deepcopy(default_conf)
conf['stake_currency'] = 'USDT'
with pytest.raises(OperationalException,
match=r"Stake-currency 'USDT' not compatible with pair-whitelist.*"):
validate_config_consistency(conf)
def test_load_config_test_comments() -> None:
"""

53
tests/test_freqtradebot.py
View File

@ -782,7 +782,7 @@ def test_process_exchange_failures(default_conf, ticker, mocker) -> None:
worker = Worker(args=None, config=default_conf)
patch_get_signal(worker.freqtrade)
worker._process()
worker._process_running()
assert sleep_mock.has_calls()
@ -799,7 +799,7 @@ def test_process_operational_exception(default_conf, ticker, mocker) -> None:
assert worker.freqtrade.state == State.RUNNING
worker._process()
worker._process_running()
assert worker.freqtrade.state == State.STOPPED
assert 'OperationalException' in msg_mock.call_args_list[-1][0][0]['status']
@ -915,13 +915,21 @@ def test_process_informative_pairs_added(default_conf, ticker, mocker) -> None:
(5, 10, 1.0, 5), # last bigger than ask
(5, 10, 0.5, 5), # last bigger than ask
])
def test_get_buy_rate(mocker, default_conf, ask, last, last_ab, expected) -> None:
def test_get_buy_rate(mocker, default_conf, caplog, ask, last, last_ab, expected) -> None:
default_conf['bid_strategy']['ask_last_balance'] = last_ab
freqtrade = get_patched_freqtradebot(mocker, default_conf)
mocker.patch('freqtrade.exchange.Exchange.fetch_ticker',
MagicMock(return_value={'ask': ask, 'last': last}))
assert freqtrade.get_buy_rate('ETH/BTC', True) == expected
assert not log_has("Using cached buy rate for ETH/BTC.", caplog)
assert freqtrade.get_buy_rate('ETH/BTC', False) == expected
assert log_has("Using cached buy rate for ETH/BTC.", caplog)
# Running a 2nd time with Refresh on!
caplog.clear()
assert freqtrade.get_buy_rate('ETH/BTC', True) == expected
assert not log_has("Using cached buy rate for ETH/BTC.", caplog)
def test_execute_buy(mocker, default_conf, fee, limit_buy_order) -> None:
@ -2192,6 +2200,7 @@ def test_handle_timedout_limit_buy(mocker, default_conf, limit_buy_order) -> Non
Trade.session = MagicMock()
trade = MagicMock()
trade.pair = 'LTC/ETH'
limit_buy_order['remaining'] = limit_buy_order['amount']
assert freqtrade.handle_timedout_limit_buy(trade, limit_buy_order)
assert cancel_order_mock.call_count == 1
@ -2215,6 +2224,7 @@ def test_handle_timedout_limit_buy_corder_empty(mocker, default_conf, limit_buy_
Trade.session = MagicMock()
trade = MagicMock()
trade.pair = 'LTC/ETH'
limit_buy_order['remaining'] = limit_buy_order['amount']
assert freqtrade.handle_timedout_limit_buy(trade, limit_buy_order)
assert cancel_order_mock.call_count == 1
@ -3614,7 +3624,7 @@ def test_order_book_ask_strategy(default_conf, limit_buy_order, limit_sell_order
assert freqtrade.handle_trade(trade) is True
def test_get_sell_rate(default_conf, mocker, ticker, order_book_l2) -> None:
def test_get_sell_rate(default_conf, mocker, caplog, ticker, order_book_l2) -> None:
mocker.patch.multiple(
'freqtrade.exchange.Exchange',
@ -3626,8 +3636,15 @@ def test_get_sell_rate(default_conf, mocker, ticker, order_book_l2) -> None:
# Test regular mode
ft = get_patched_freqtradebot(mocker, default_conf)
rate = ft.get_sell_rate(pair, True)
assert not log_has("Using cached sell rate for ETH/BTC.", caplog)
assert isinstance(rate, float)
assert rate == 0.00001098
# Use caching
rate = ft.get_sell_rate(pair, False)
assert rate == 0.00001098
assert log_has("Using cached sell rate for ETH/BTC.", caplog)
caplog.clear()
# Test orderbook mode
default_conf['ask_strategy']['use_order_book'] = True
@ -3635,8 +3652,12 @@ def test_get_sell_rate(default_conf, mocker, ticker, order_book_l2) -> None:
default_conf['ask_strategy']['order_book_max'] = 2
ft = get_patched_freqtradebot(mocker, default_conf)
rate = ft.get_sell_rate(pair, True)
assert not log_has("Using cached sell rate for ETH/BTC.", caplog)
assert isinstance(rate, float)
assert rate == 0.043936
rate = ft.get_sell_rate(pair, False)
assert rate == 0.043936
assert log_has("Using cached sell rate for ETH/BTC.", caplog)
def test_startup_state(default_conf, mocker):
@ -3665,30 +3686,6 @@ def test_startup_trade_reinit(default_conf, edge_conf, mocker):
assert reinit_mock.call_count == 0
def test_process_i_am_alive(default_conf, mocker, caplog):
patch_RPCManager(mocker)
patch_exchange(mocker)
mocker.patch('freqtrade.exchange.Exchange.exchange_has', MagicMock(return_value=True))
ftbot = get_patched_freqtradebot(mocker, default_conf)
message = r"Bot heartbeat\. PID=.*"
ftbot.process()
assert log_has_re(message, caplog)
assert ftbot._heartbeat_msg != 0
caplog.clear()
# Message is not shown before interval is up
ftbot.process()
assert not log_has_re(message, caplog)
caplog.clear()
# Set clock - 70 seconds
ftbot._heartbeat_msg -= 70
ftbot.process()
assert log_has_re(message, caplog)
@pytest.mark.usefixtures("init_persistence")
def test_sync_wallet_dry_run(mocker, default_conf, ticker, fee, limit_buy_order, caplog):
default_conf['dry_run'] = True

62
tests/test_worker.py
View File

@ -5,7 +5,7 @@ from unittest.mock import MagicMock, PropertyMock
from freqtrade.data.dataprovider import DataProvider
from freqtrade.state import State
from freqtrade.worker import Worker
from tests.conftest import get_patched_worker, log_has
from tests.conftest import get_patched_worker, log_has, log_has_re
def test_worker_state(mocker, default_conf, markets) -> None:
@ -38,15 +38,13 @@ def test_worker_running(mocker, default_conf, caplog) -> None:
def test_worker_stopped(mocker, default_conf, caplog) -> None:
mock_throttle = MagicMock()
mocker.patch('freqtrade.worker.Worker._throttle', mock_throttle)
mock_sleep = mocker.patch('time.sleep', return_value=None)
worker = get_patched_worker(mocker, default_conf)
worker.freqtrade.state = State.STOPPED
state = worker._worker(old_state=State.RUNNING)
assert state is State.STOPPED
assert log_has('Changing state to: STOPPED', caplog)
assert mock_throttle.call_count == 0
assert mock_sleep.call_count == 1
assert mock_throttle.call_count == 1
def test_throttle(mocker, default_conf, caplog) -> None:
@ -57,14 +55,14 @@ def test_throttle(mocker, default_conf, caplog) -> None:
worker = get_patched_worker(mocker, default_conf)
start = time.time()
result = worker._throttle(throttled_func, min_secs=0.1)
result = worker._throttle(throttled_func, throttle_secs=0.1)
end = time.time()
assert result == 42
assert end - start > 0.1
assert log_has('Throttling throttled_func for 0.10 seconds', caplog)
assert log_has_re(r"Throttling with 'throttled_func\(\)': sleep for 0\.10 s.*", caplog)
result = worker._throttle(throttled_func, min_secs=-1)
result = worker._throttle(throttled_func, throttle_secs=-1)
assert result == 42
@ -74,8 +72,54 @@ def test_throttle_with_assets(mocker, default_conf) -> None:
worker = get_patched_worker(mocker, default_conf)
result = worker._throttle(throttled_func, min_secs=0.1, nb_assets=666)
result = worker._throttle(throttled_func, throttle_secs=0.1, nb_assets=666)
assert result == 666
result = worker._throttle(throttled_func, min_secs=0.1)
result = worker._throttle(throttled_func, throttle_secs=0.1)
assert result == -1
def test_worker_heartbeat_running(default_conf, mocker, caplog):
message = r"Bot heartbeat\. PID=.*state='RUNNING'"
mock_throttle = MagicMock()
mocker.patch('freqtrade.worker.Worker._throttle', mock_throttle)
worker = get_patched_worker(mocker, default_conf)
worker.freqtrade.state = State.RUNNING
worker._worker(old_state=State.STOPPED)
assert log_has_re(message, caplog)
caplog.clear()
# Message is not shown before interval is up
worker._worker(old_state=State.RUNNING)
assert not log_has_re(message, caplog)
caplog.clear()
# Set clock - 70 seconds
worker._heartbeat_msg -= 70
worker._worker(old_state=State.RUNNING)
assert log_has_re(message, caplog)
def test_worker_heartbeat_stopped(default_conf, mocker, caplog):
message = r"Bot heartbeat\. PID=.*state='STOPPED'"
mock_throttle = MagicMock()
mocker.patch('freqtrade.worker.Worker._throttle', mock_throttle)
worker = get_patched_worker(mocker, default_conf)
worker.freqtrade.state = State.STOPPED
worker._worker(old_state=State.RUNNING)
assert log_has_re(message, caplog)
caplog.clear()
# Message is not shown before interval is up
worker._worker(old_state=State.STOPPED)
assert not log_has_re(message, caplog)
caplog.clear()
# Set clock - 70 seconds
worker._heartbeat_msg -= 70
worker._worker(old_state=State.STOPPED)
assert log_has_re(message, caplog)