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Merge pull request #5977 from freqtrade/new_release 

New release 2021.11
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.github/ISSUE_TEMPLATE/bug_report.md vendored
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@ -9,7 +9,7 @@ assignees: ''
<!--
Have you searched for similar issues before posting it?
If you have discovered a bug in the bot, please [search our issue tracker](https://github.com/freqtrade/freqtrade/issues?q=is%3Aissue).
If you have discovered a bug in the bot, please [search the issue tracker](https://github.com/freqtrade/freqtrade/issues?q=is%3Aissue).
If it hasn't been reported, please create a new issue.
Please do not use bug reports to request new features.

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.github/ISSUE_TEMPLATE/question.md vendored
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@ -22,4 +22,4 @@ Please do not use the question template to report bugs or to request new feature
## Your question
*Ask the question you have not been able to find an answer in our [Documentation](https://www.freqtrade.io/en/latest/)*
*Ask the question you have not been able to find an answer in the [Documentation](https://www.freqtrade.io/en/latest/)*

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CONTRIBUTING.md
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@ -56,6 +56,13 @@ To help with that, we encourage you to install the git pre-commit
hook that will warn you when you try to commit code that fails these checks.
Guide for installing them is [here](http://flake8.pycqa.org/en/latest/user/using-hooks.html).
##### Additional styles applied
* Have docstrings on all public methods
* Use double-quotes for docstrings
* Multiline docstrings should be indented to the level of the first quote
* Doc-strings should follow the reST format (`:param xxx: ...`, `:return: ...`, `:raises KeyError: ... `)
### 3. Test if all type-hints are correct
#### Run mypy

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Dockerfile
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@ -1,4 +1,4 @@
FROM python:3.9.7-slim-buster as base
FROM python:3.9.9-slim-bullseye as base
# Setup env
ENV LANG C.UTF-8

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README.md
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@ -28,9 +28,10 @@ Please read the [exchange specific notes](docs/exchanges.md) to learn about even
- [X] [Binance](https://www.binance.com/) ([*Note for binance users](docs/exchanges.md#binance-blacklist))
- [X] [Bittrex](https://bittrex.com/)
- [X] [Kraken](https://kraken.com/)
- [X] [FTX](https://ftx.com)
- [X] [Gate.io](https://www.gate.io/ref/6266643)
- [X] [Kraken](https://kraken.com/)
- [X] [OKEX](https://www.okex.com/)
- [ ] [potentially many others](https://github.com/ccxt/ccxt/). _(We cannot guarantee they will work)_
### Community tested
@ -44,7 +45,7 @@ Exchanges confirmed working by the community:
We invite you to read the bot documentation to ensure you understand how the bot is working.
Please find the complete documentation on our [website](https://www.freqtrade.io).
Please find the complete documentation on the [freqtrade website](https://www.freqtrade.io).
## Features
@ -121,7 +122,7 @@ optional arguments:
### Telegram RPC commands
Telegram is not mandatory. However, this is a great way to control your bot. More details and the full command list on our [documentation](https://www.freqtrade.io/en/latest/telegram-usage/)
Telegram is not mandatory. However, this is a great way to control your bot. More details and the full command list on the [documentation](https://www.freqtrade.io/en/latest/telegram-usage/)
- `/start`: Starts the trader.
- `/stop`: Stops the trader.
@ -152,10 +153,10 @@ For any questions not covered by the documentation or for further information ab
### [Bugs / Issues](https://github.com/freqtrade/freqtrade/issues?q=is%3Aissue)
If you discover a bug in the bot, please
[search our issue tracker](https://github.com/freqtrade/freqtrade/issues?q=is%3Aissue)
[search the issue tracker](https://github.com/freqtrade/freqtrade/issues?q=is%3Aissue)
first. If it hasn't been reported, please
[create a new issue](https://github.com/freqtrade/freqtrade/issues/new/choose) and
ensure you follow the template guide so that our team can assist you as
ensure you follow the template guide so that the team can assist you as
quickly as possible.
### [Feature Requests](https://github.com/freqtrade/freqtrade/labels/enhancement)
@ -169,13 +170,13 @@ in the bug reports.
### [Pull Requests](https://github.com/freqtrade/freqtrade/pulls)
Feel like our bot is missing a feature? We welcome your pull requests!
Feel like the bot is missing a feature? We welcome your pull requests!
Please read our
Please read the
[Contributing document](https://github.com/freqtrade/freqtrade/blob/develop/CONTRIBUTING.md)
to understand the requirements before sending your pull-requests.
Coding is not a necessity to contribute - maybe start with improving our documentation?
Coding is not a necessity to contribute - maybe start with improving the documentation?
Issues labeled [good first issue](https://github.com/freqtrade/freqtrade/labels/good%20first%20issue) can be good first contributions, and will help get you familiar with the codebase.
**Note** before starting any major new feature work, *please open an issue describing what you are planning to do* or talk to us on [discord](https://discord.gg/p7nuUNVfP7) (please use the #dev channel for this). This will ensure that interested parties can give valuable feedback on the feature, and let others know that you are working on it.

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config_examples/config_full.example.json
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@ -28,6 +28,7 @@
"unfilledtimeout": {
"buy": 10,
"sell": 30,
"exit_timeout_count": 0,
"unit": "minutes"
},
"bid_strategy": {

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docker/Dockerfile.armhf
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@ -1,4 +1,4 @@
FROM python:3.7.10-slim-buster as base
FROM python:3.9.9-slim-bullseye as base
# Setup env
ENV LANG C.UTF-8

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docs/backtesting.md
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@ -459,7 +459,7 @@ The output will show a table containing the realized absolute Profit (in stake c
### Further backtest-result analysis
To further analyze your backtest results, you can [export the trades](#exporting-trades-to-file).
You can then load the trades to perform further analysis as shown in our [data analysis](data-analysis.md#backtesting) backtesting section.
You can then load the trades to perform further analysis as shown in the [data analysis](data-analysis.md#backtesting) backtesting section.
## Assumptions made by backtesting
@ -478,6 +478,7 @@ Since backtesting lacks some detailed information about what happens within a ca
- Low happens before high for stoploss, protecting capital first
- Trailing stoploss
- Trailing Stoploss is only adjusted if it's below the candle's low (otherwise it would be triggered)
- On trade entry candles that trigger trailing stoploss, the "minimum offset" (`stop_positive_offset`) is assumed (instead of high) - and the stop is calculated from this point
- High happens first - adjusting stoploss
- Low uses the adjusted stoploss (so sells with large high-low difference are backtested correctly)
- ROI applies before trailing-stop, ensuring profits are "top-capped" at ROI if both ROI and trailing stop applies

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docs/configuration.md
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@ -37,6 +37,15 @@ Using this scheme, all configuration settings will also be available as environm
Please note that Environment variables will overwrite corresponding settings in your configuration, but command line Arguments will always win.
Common example:
```
FREQTRADE__TELEGRAM__CHAT_ID=<telegramchatid>
FREQTRADE__TELEGRAM__TOKEN=<telegramToken>
FREQTRADE__EXCHANGE__KEY=<yourExchangeKey>
FREQTRADE__EXCHANGE__SECRET=<yourExchangeSecret>
```
!!! Note
Environment variables detected are logged at startup - so if you can't find why a value is not what you think it should be based on the configuration, make sure it's not loaded from an environment variable.
@ -93,6 +102,7 @@ Mandatory parameters are marked as **Required**, which means that they are requi
| `unfilledtimeout.buy` | **Required.** How long (in minutes or seconds) the bot will wait for an unfilled buy order to complete, after which the order will be cancelled and repeated at current (new) price, as long as there is a signal. [Strategy Override](#parameters-in-the-strategy).<br> **Datatype:** Integer
| `unfilledtimeout.sell` | **Required.** How long (in minutes or seconds) the bot will wait for an unfilled sell order to complete, after which the order will be cancelled and repeated at current (new) price, as long as there is a signal. [Strategy Override](#parameters-in-the-strategy).<br> **Datatype:** Integer
| `unfilledtimeout.unit` | Unit to use in unfilledtimeout setting. Note: If you set unfilledtimeout.unit to "seconds", "internals.process_throttle_secs" must be inferior or equal to timeout [Strategy Override](#parameters-in-the-strategy). <br> *Defaults to `minutes`.* <br> **Datatype:** String
| `unfilledtimeout.exit_timeout_count` | How many times can exit orders time out. Once this number of timeouts is reached, an emergency sell is triggered. 0 to disable and allow unlimited order cancels. [Strategy Override](#parameters-in-the-strategy).<br>*Defaults to `0`.* <br> **Datatype:** Integer
| `bid_strategy.price_side` | Select the side of the spread the bot should look at to get the buy rate. [More information below](#buy-price-side).<br> *Defaults to `bid`.* <br> **Datatype:** String (either `ask` or `bid`).
| `bid_strategy.ask_last_balance` | **Required.** Interpolate the bidding price. More information [below](#buy-price-without-orderbook-enabled).
| `bid_strategy.use_order_book` | Enable buying using the rates in [Order Book Bids](#buy-price-with-orderbook-enabled). <br> **Datatype:** Boolean
@ -192,9 +202,8 @@ There are several methods to configure how much of the stake currency the bot wi
#### Minimum trade stake
The minimum stake amount will depend on exchange and pair and is usually listed in the exchange support pages.
Assuming the minimum tradable amount for XRP/USD is 20 XRP (given by the exchange), and the price is 0.6$.
The minimum stake amount to buy this pair is, therefore, `20 * 0.6 ~= 12`.
Assuming the minimum tradable amount for XRP/USD is 20 XRP (given by the exchange), and the price is 0.6$, the minimum stake amount to buy this pair is `20 * 0.6 ~= 12`.
This exchange has also a limit on USD - where all orders must be > 10$ - which however does not apply in this case.
To guarantee safe execution, freqtrade will not allow buying with a stake-amount of 10.1$, instead, it'll make sure that there's enough space to place a stoploss below the pair (+ an offset, defined by `amount_reserve_percent`, which defaults to 5%).
@ -204,7 +213,7 @@ With a reserve of 5%, the minimum stake amount would be ~12.6$ (`12 * (1 + 0.05)
To limit this calculation in case of large stoploss values, the calculated minimum stake-limit will never be more than 50% above the real limit.
!!! Warning
Since the limits on exchanges are usually stable and are not updated often, some pairs can show pretty high minimum limits, simply because the price increased a lot since the last limit adjustment by the exchange.
Since the limits on exchanges are usually stable and are not updated often, some pairs can show pretty high minimum limits, simply because the price increased a lot since the last limit adjustment by the exchange. Freqtrade adjusts the stake-amount to this value, unless it's > 30% more than the calculated/desired stake-amount - in which case the trade is rejected.
#### Tradable balance

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docs/developer.md
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@ -26,6 +26,8 @@ Alternatively (e.g. if your system is not supported by the setup.sh script), fol
This will install all required tools for development, including `pytest`, `flake8`, `mypy`, and `coveralls`.
Before opening a pull request, please familiarize yourself with our [Contributing Guidelines](https://github.com/freqtrade/freqtrade/blob/develop/CONTRIBUTING.md).
### Devcontainer setup
The fastest and easiest way to get started is to use [VSCode](https://code.visualstudio.com/) with the Remote container extension.
@ -250,7 +252,23 @@ Most exchanges supported by CCXT should work out of the box.
To quickly test the public endpoints of an exchange, add a configuration for your exchange to `test_ccxt_compat.py` and run these tests with `pytest --longrun tests/exchange/test_ccxt_compat.py`.
Completing these tests successfully a good basis point (it's a requirement, actually), however these won't guarantee correct exchange functioning, as this only tests public endpoints, but no private endpoint (like generate order or similar).
Also try to use `freqtrade download-data` for an extended timerange and verify that the data downloaded correctly (no holes, the specified timerange was actually downloaded).
Also try to use `freqtrade download-data` for an extended timerange (multiple months) and verify that the data downloaded correctly (no holes, the specified timerange was actually downloaded).
These are prerequisites to have an exchange listed as either Supported or Community tested (listed on the homepage).
The below are "extras", which will make an exchange better (feature-complete) - but are not absolutely necessary for either of the 2 categories.
Additional tests / steps to complete:
* Verify data provided by `fetch_ohlcv()` - and eventually adjust `ohlcv_candle_limit` for this exchange
* Check L2 orderbook limit range (API documentation) - and eventually set as necessary
* Check if balance shows correctly (*)
* Create market order (*)
* Create limit order (*)
* Complete trade (buy + sell) (*)
* Compare result calculation between exchange and bot
* Ensure fees are applied correctly (check the database against the exchange)
(*) Requires API keys and Balance on the exchange.
### Stoploss On Exchange

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docs/exchanges.md
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@ -46,7 +46,7 @@ In case of problems related to rate-limits (usually DDOS Exceptions in your logs
```
This configuration enables kraken, as well as rate-limiting to avoid bans from the exchange.
`"rateLimit": 3100` defines a wait-event of 0.2s between each call. This can also be completely disabled by setting `"enableRateLimit"` to false.
`"rateLimit": 3100` defines a wait-event of 3.1s between each call. This can also be completely disabled by setting `"enableRateLimit"` to false.
!!! Note
Optimal settings for rate-limiting depend on the exchange and the size of the whitelist, so an ideal parameter will vary on many other settings.
@ -182,6 +182,23 @@ Kucoin supports [time_in_force](configuration.md#understand-order_time_in_force)
For Kucoin, please add `"KCS/<STAKE>"` to your blacklist to avoid issues.
Accounts having KCS accounts use this to pay for fees - if your first trade happens to be on `KCS`, further trades will consume this position and make the initial KCS trade unsellable as the expected amount is not there anymore.
## OKEX
OKEX requires a passphrase for each api key, you will therefore need to add this key into the configuration so your exchange section looks as follows:
```json
"exchange": {
"name": "okex",
"key": "your_exchange_key",
"secret": "your_exchange_secret",
"password": "your_exchange_api_key_password",
// ...
}
```
!!! Warning
OKEX only provides 100 candles per api call. Therefore, the strategy will only have a pretty low amount of data available in backtesting mode.
## 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.

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docs/faq.md
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@ -42,7 +42,7 @@ position for a trade. Be patient!
### I have made 12 trades already, why is my total profit negative?
I understand your disappointment but unfortunately 12 trades is just
not enough to say anything. If you run backtesting, you can see that our
not enough to say anything. If you run backtesting, you can see that the
current algorithm does leave you on the plus side, but that is after
thousands of trades and even there, you will be left with losses on
specific coins that you have traded tens if not hundreds of times. We
@ -54,6 +54,21 @@ you can't say much from few trades.
Yes. You can edit your config and use the `/reload_config` command to reload the configuration. The bot will stop, reload the configuration and strategy and will restart with the new configuration and strategy.
### Why does my bot not sell everything it bought?
This is called "coin dust" and can happen on all exchanges.
It happens because many exchanges subtract fees from the "receiving currency" - so you buy 100 COIN - but you only get 99.9 COIN.
As COIN is trading in full lot sizes (1COIN steps), you cannot sell 0.9 COIN (or 99.9 COIN) - but you need to round down to 99 COIN.
This is not a bot-problem, but will also happen while manual trading.
While freqtrade can handle this (it'll sell 99 COIN), fees are often below the minimum tradable lot-size (you can only trade full COIN, not 0.9 COIN).
Leaving the dust (0.9 COIN) on the exchange makes usually sense, as the next time freqtrade buys COIN, it'll eat into the remaining small balance, this time selling everything it bought, and therefore slowly declining the dust balance (although it most likely will never reach exactly 0).
Where possible (e.g. on binance), the use of the exchange's dedicated fee currency will fix this.
On binance, it's sufficient to have BNB in your account, and have "Pay fees in BNB" enabled in your profile. Your BNB balance will slowly decline (as it's used to pay fees) - but you'll no longer encounter dust (Freqtrade will include the fees in the profit calculations).
Other exchanges don't offer such possibilities, where it's simply something you'll have to accept or move to a different exchange.
### I want to use incomplete candles
Freqtrade will not provide incomplete candles to strategies. Using incomplete candles will lead to repainting and consequently to strategies with "ghost" buys, which are impossible to both backtest, and verify after they happened.
@ -78,6 +93,18 @@ If this happens for all pairs in the pairlist, this might indicate a recent exch
Irrespectively of the reason, Freqtrade will fill up these candles with "empty" candles, where open, high, low and close are set to the previous candle close - and volume is empty. In a chart, this will look like a `_` - and is aligned with how exchanges usually represent 0 volume candles.
### I'm getting "Outdated history for pair xxx" in the log
The bot is trying to tell you that it got an outdated last candle (not the last complete candle).
As a consequence, Freqtrade will not enter a trade for this pair - as trading on old information is usually not what is desired.
This warning can point to one of the below problems:
* Exchange downtime -> Check your exchange status page / blog / twitter feed for details.
* Wrong system time -> Ensure your system-time is correct.
* Barely traded pair -> Check the pair on the exchange webpage, look at the timeframe your strategy uses. If the pair does not have any volume in some candles (usually visualized with a "volume 0" bar, and a "_" as candle), this pair did not have any trades in this timeframe. These pairs should ideally be avoided, as they can cause problems with order-filling.
* API problem -> API returns wrong data (this only here for completeness, and should not happen with supported exchanges).
### I'm getting the "RESTRICTED_MARKET" message in the log
Currently known to happen for US Bittrex users.

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docs/hyperopt.md
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@ -116,7 +116,7 @@ optional arguments:
ShortTradeDurHyperOptLoss, OnlyProfitHyperOptLoss,
SharpeHyperOptLoss, SharpeHyperOptLossDaily,
SortinoHyperOptLoss, SortinoHyperOptLossDaily,
MaxDrawDownHyperOptLoss
CalmarHyperOptLoss, MaxDrawDownHyperOptLoss
--disable-param-export
Disable automatic hyperopt parameter export.
--ignore-missing-spaces, --ignore-unparameterized-spaces
@ -524,6 +524,7 @@ Currently, the following loss functions are builtin:
* `SortinoHyperOptLoss` - optimizes Sortino Ratio calculated on trade returns relative to **downside** standard deviation.
* `SortinoHyperOptLossDaily` - optimizes Sortino Ratio calculated on **daily** trade returns relative to **downside** standard deviation.
* `MaxDrawDownHyperOptLoss` - Optimizes Maximum drawdown.
* `CalmarHyperOptLoss` - Optimizes Calmar Ratio calculated on trade returns relative to max drawdown.
Creation of a custom loss function is covered in the [Advanced Hyperopt](advanced-hyperopt.md) part of the documentation.

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docs/includes/pairlists.md
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@ -198,7 +198,7 @@ Not defining this parameter (or setting it to 0) will use all-time performance.
The optional `min_profit` parameter defines the minimum profit a pair must have to be considered.
Pairs below this level will be filtered out.
Using this parameter without `minutes` is highly discouraged, as it can lead to an empty pairlist without without a way to recover.
Using this parameter without `minutes` is highly discouraged, as it can lead to an empty pairlist without a way to recover.
```json
"pairlists": [
@ -211,6 +211,8 @@ Using this parameter without `minutes` is highly discouraged, as it can lead to
],
```
As this Filter uses past performance of the bot, it'll have some startup-period - and should only be used after the bot has a few 100 trades in the database.
!!! Warning "Backtesting"
`PerformanceFilter` does not support backtesting mode.
@ -290,7 +292,7 @@ If the trading range over the last 10 days is <1% or >99%, remove the pair from
#### VolatilityFilter
Volatility is the degree of historical variation of a pairs over time, is is measured by the standard deviation of logarithmic daily returns. Returns are assumed to be normally distributed, although actual distribution might be different. In a normal distribution, 68% of observations fall within one standard deviation and 95% of observations fall within two standard deviations. Assuming a volatility of 0.05 means that the expected returns for 20 out of 30 days is expected to be less than 5% (one standard deviation). Volatility is a positive ratio of the expected deviation of return and can be greater than 1.00. Please refer to the wikipedia definition of [`volatility`](https://en.wikipedia.org/wiki/Volatility_(finance)).
Volatility is the degree of historical variation of a pairs over time, it is measured by the standard deviation of logarithmic daily returns. Returns are assumed to be normally distributed, although actual distribution might be different. In a normal distribution, 68% of observations fall within one standard deviation and 95% of observations fall within two standard deviations. Assuming a volatility of 0.05 means that the expected returns for 20 out of 30 days is expected to be less than 5% (one standard deviation). Volatility is a positive ratio of the expected deviation of return and can be greater than 1.00. Please refer to the wikipedia definition of [`volatility`](https://en.wikipedia.org/wiki/Volatility_(finance)).
This filter removes pairs if the average volatility over a `lookback_days` days is below `min_volatility` or above `max_volatility`. Since this is a filter that requires additional data, the results are cached for `refresh_period`.
@ -344,5 +346,5 @@ The below example blacklists `BNB/BTC`, uses `VolumePairList` with `20` assets,
"refresh_period": 86400
},
{"method": "ShuffleFilter", "seed": 42}
],
],
```

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docs/index.md
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@ -36,11 +36,12 @@ Freqtrade is a crypto-currency algorithmic trading software developed in python
Please read the [exchange specific notes](exchanges.md) to learn about eventual, special configurations needed for each exchange.
- [X] [Binance](https://www.binance.com/) ([*Note for binance users](docs/exchanges.md#binance-blacklist))
- [X] [Binance](https://www.binance.com/) ([*Note for binance users](exchanges.md#binance-blacklist))
- [X] [Bittrex](https://bittrex.com/)
- [X] [FTX](https://ftx.com)
- [X] [Kraken](https://kraken.com/)
- [X] [Gate.io](https://www.gate.io/ref/6266643)
- [X] [Kraken](https://kraken.com/)
- [X] [OKEX](https://www.okex.com/)
- [ ] [potentially many others through <img alt="ccxt" width="30px" src="assets/ccxt-logo.svg" />](https://github.com/ccxt/ccxt/). _(We cannot guarantee they will work)_
### Community tested
@ -80,4 +81,4 @@ For any questions not covered by the documentation or for further information ab
## Ready to try?
Begin by reading our installation guide [for docker](docker_quickstart.md) (recommended), or for [installation without docker](installation.md).
Begin by reading the installation guide [for docker](docker_quickstart.md) (recommended), or for [installation without docker](installation.md).

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docs/installation.md
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@ -60,7 +60,7 @@ OS Specific steps are listed first, the [Common](#common) section below is neces
sudo apt-get update
# install packages
sudo apt install -y python3-pip python3-venv python3-dev python3-pandas git
sudo apt install -y python3-pip python3-venv python3-dev python3-pandas git curl
```
=== "RaspberryPi/Raspbian"
@ -71,7 +71,7 @@ OS Specific steps are listed first, the [Common](#common) section below is neces
```bash
sudo apt-get install python3-venv libatlas-base-dev cmake
sudo apt-get install python3-venv libatlas-base-dev cmake curl
# Use pywheels.org to speed up installation
sudo echo "[global]\nextra-index-url=https://www.piwheels.org/simple" > tee /etc/pip.conf

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docs/requirements-docs.txt
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@ -1,4 +1,4 @@
mkdocs==1.2.3
mkdocs-material==7.3.4
mkdocs-material==7.3.6
mdx_truly_sane_lists==1.2
pymdown-extensions==9.0
pymdown-extensions==9.1

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docs/rest-api.md
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@ -38,6 +38,11 @@ Sample configuration:
!!! Danger "Security warning"
By default, the configuration listens on localhost only (so it's not reachable from other systems). We strongly recommend to not expose this API to the internet and choose a strong, unique password, since others will potentially be able to control your bot.
??? Note "API/UI Access on a remote servers"
If you're running on a VPS, you should consider using either a ssh tunnel, or setup a VPN (openVPN, wireguard) to connect to your bot.
This will ensure that freqUI is not directly exposed to the internet, which is not recommended for security reasons (freqUI does not support https out of the box).
Setup of these tools is not part of this tutorial, however many good tutorials can be found on the internet.
You can then access the API by going to `http://127.0.0.1:8080/api/v1/ping` in a browser to check if the API is running correctly.
This should return the response:
@ -330,12 +335,15 @@ Since the access token has a short timeout (15 min) - the `token/refresh` reques
### CORS
All web-based front-ends are subject to [CORS](https://developer.mozilla.org/en-US/docs/Web/HTTP/CORS) - Cross-Origin Resource Sharing.
Since most of the requests to the Freqtrade API must be authenticated, a proper CORS policy is key to avoid security problems.
Also, the standard disallows `*` CORS policies for requests with credentials, so this setting must be set appropriately.
This whole section is only necessary in cross-origin cases (where you multiple bot API's running on `localhost:8081`, `localhost:8082`, ...), and want to combine them into one FreqUI instance.
Users can configure this themselves via the `CORS_origins` configuration setting.
It consists of a list of allowed sites that are allowed to consume resources from the bot's API.
??? info "Technical explanation"
All web-based front-ends are subject to [CORS](https://developer.mozilla.org/en-US/docs/Web/HTTP/CORS) - Cross-Origin Resource Sharing.
Since most of the requests to the Freqtrade API must be authenticated, a proper CORS policy is key to avoid security problems.
Also, the standard disallows `*` CORS policies for requests with credentials, so this setting must be set appropriately.
Users can allow access from different origin URL's to the bot API via the `CORS_origins` configuration setting.
It consists of a list of allowed URL's that are allowed to consume resources from the bot's API.
Assuming your application is deployed as `https://frequi.freqtrade.io/home/` - this would mean that the following configuration becomes necessary:
@ -348,5 +356,19 @@ Assuming your application is deployed as `https://frequi.freqtrade.io/home/` - t
}
```
In the following (pretty common) case, FreqUI is accessible on `http://localhost:8080/trade` (this is what you see in your navbar when navigating to freqUI).
![freqUI url](assets/frequi_url.png)
The correct configuration for this case is `http://localhost:8080` - the main part of the URL including the port.
```jsonc
{
//...
"jwt_secret_key": "somethingrandom",
"CORS_origins": ["http://localhost:8080"],
//...
}
```
!!! Note
We strongly recommend to also set `jwt_secret_key` to something random and known only to yourself to avoid unauthorized access to your bot.

2
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@ -182,7 +182,7 @@ For example, simplified math:
* the bot buys an asset at a price of 100$
* the stop loss is defined at -10%
* the stop loss would get triggered once the asset drops below 90$
* stoploss will remain at 90$ unless asset increases to or above our configured offset
* stoploss will remain at 90$ unless asset increases to or above the configured offset
* assuming the asset now increases to 103$ (where we have the offset configured)
* the stop loss will now be -2% of 103$ = 100.94$
* now the asset drops in value to 101\$, the stop loss will still be 100.94$ and would trigger at 100.94$

541
docs/strategy-advanced.md
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@ -77,43 +77,6 @@ class AwesomeStrategy(IStrategy):
***
## Custom sell signal
It is possible to define custom sell signals, indicating that specified position should be sold. This is very useful when we need to customize sell conditions for each individual trade, or if you need the trade profit to take the sell decision.
For example you could implement a 1:2 risk-reward ROI with `custom_sell()`.
Using custom_sell() signals in place of stoploss though *is not recommended*. It is a inferior method to using `custom_stoploss()` in this regard - which also allows you to keep the stoploss on exchange.
!!! Note
Returning a `string` or `True` from this method is equal to setting sell signal on a candle at specified time. This method is not called when sell signal is set already, or if sell signals are disabled (`use_sell_signal=False` or `sell_profit_only=True` while profit is below `sell_profit_offset`). `string` max length is 64 characters. Exceeding this limit will cause the message to be truncated to 64 characters.
An example of how we can use different indicators depending on the current profit and also sell trades that were open longer than one day:
``` python
class AwesomeStrategy(IStrategy):
def custom_sell(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float,
current_profit: float, **kwargs):
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
last_candle = dataframe.iloc[-1].squeeze()
# Above 20% profit, sell when rsi < 80
if current_profit > 0.2:
if last_candle['rsi'] < 80:
return 'rsi_below_80'
# Between 2% and 10%, sell if EMA-long above EMA-short
if 0.02 < current_profit < 0.1:
if last_candle['emalong'] > last_candle['emashort']:
return 'ema_long_below_80'
# Sell any positions at a loss if they are held for more than one day.
if current_profit < 0.0 and (current_time - trade.open_date_utc).days >= 1:
return 'unclog'
```
See [Dataframe access](#dataframe-access) for more information about dataframe use in strategy callbacks.
## Buy Tag
When your strategy has multiple buy signals, you can name the signal that triggered.
@ -143,506 +106,26 @@ def custom_sell(self, pair: str, trade: Trade, current_time: datetime, current_r
!!! Note
`buy_tag` is limited to 100 characters, remaining data will be truncated.
## Exit tag
## Custom stoploss
The stoploss price can only ever move upwards - if the stoploss value returned from `custom_stoploss` would result in a lower stoploss price than was previously set, it will be ignored. The traditional `stoploss` value serves as an absolute lower level and will be instated as the initial stoploss.
The usage of the custom stoploss method must be enabled by setting `use_custom_stoploss=True` on the strategy object.
The method must return a stoploss value (float / number) as a percentage of the current price.
E.g. If the `current_rate` is 200 USD, then returning `0.02` will set the stoploss price 2% lower, at 196 USD.
The absolute value of the return value is used (the sign is ignored), so returning `0.05` or `-0.05` have the same result, a stoploss 5% below the current price.
To simulate a regular trailing stoploss of 4% (trailing 4% behind the maximum reached price) you would use the following very simple method:
Similar to [Buy Tagging](#buy-tag), you can also specify a sell tag.
``` python
# additional imports required
from datetime import datetime
from freqtrade.persistence import Trade
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(dataframe['rsi'] > 70) &
(dataframe['volume'] > 0)
),
['sell', 'exit_tag']] = (1, 'exit_rsi')
class AwesomeStrategy(IStrategy):
# ... populate_* methods
use_custom_stoploss = True
def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
current_rate: float, current_profit: float, **kwargs) -> float:
"""
Custom stoploss logic, returning the new distance relative to current_rate (as ratio).
e.g. returning -0.05 would create a stoploss 5% below current_rate.
The custom stoploss can never be below self.stoploss, which serves as a hard maximum loss.
For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/
When not implemented by a strategy, returns the initial stoploss value
Only called when use_custom_stoploss is set to True.
:param pair: Pair that's currently analyzed
:param trade: trade object.
:param current_time: datetime object, containing the current datetime
:param current_rate: Rate, calculated based on pricing settings in ask_strategy.
:param current_profit: Current profit (as ratio), calculated based on current_rate.
:param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
:return float: New stoploss value, relative to the current rate
"""
return -0.04
return dataframe
```
Stoploss on exchange works similar to `trailing_stop`, and the stoploss on exchange is updated as configured in `stoploss_on_exchange_interval` ([More details about stoploss on exchange](stoploss.md#stop-loss-on-exchange-freqtrade)).
!!! Note "Use of dates"
All time-based calculations should be done based on `current_time` - using `datetime.now()` or `datetime.utcnow()` is discouraged, as this will break backtesting support.
!!! Tip "Trailing stoploss"
It's recommended to disable `trailing_stop` when using custom stoploss values. Both can work in tandem, but you might encounter the trailing stop to move the price higher while your custom function would not want this, causing conflicting behavior.
### Custom stoploss examples
The next section will show some examples on what's possible with the custom stoploss function.
Of course, many more things are possible, and all examples can be combined at will.
#### Time based trailing stop
Use the initial stoploss for the first 60 minutes, after this change to 10% trailing stoploss, and after 2 hours (120 minutes) we use a 5% trailing stoploss.
``` python
from datetime import datetime, timedelta
from freqtrade.persistence import Trade
class AwesomeStrategy(IStrategy):
# ... populate_* methods
use_custom_stoploss = True
def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
current_rate: float, current_profit: float, **kwargs) -> float:
# Make sure you have the longest interval first - these conditions are evaluated from top to bottom.
if current_time - timedelta(minutes=120) > trade.open_date_utc:
return -0.05
elif current_time - timedelta(minutes=60) > trade.open_date_utc:
return -0.10
return 1
```
#### Different stoploss per pair
Use a different stoploss depending on the pair.
In this example, we'll trail the highest price with 10% trailing stoploss for `ETH/BTC` and `XRP/BTC`, with 5% trailing stoploss for `LTC/BTC` and with 15% for all other pairs.
``` python
from datetime import datetime
from freqtrade.persistence import Trade
class AwesomeStrategy(IStrategy):
# ... populate_* methods
use_custom_stoploss = True
def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
current_rate: float, current_profit: float, **kwargs) -> float:
if pair in ('ETH/BTC', 'XRP/BTC'):
return -0.10
elif pair in ('LTC/BTC'):
return -0.05
return -0.15
```
#### Trailing stoploss with positive offset
Use the initial stoploss until the profit is above 4%, then use a trailing stoploss of 50% of the current profit with a minimum of 2.5% and a maximum of 5%.
Please note that the stoploss can only increase, values lower than the current stoploss are ignored.
``` python
from datetime import datetime, timedelta
from freqtrade.persistence import Trade
class AwesomeStrategy(IStrategy):
# ... populate_* methods
use_custom_stoploss = True
def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
current_rate: float, current_profit: float, **kwargs) -> float:
if current_profit < 0.04:
return -1 # return a value bigger than the initial stoploss to keep using the initial stoploss
# After reaching the desired offset, allow the stoploss to trail by half the profit
desired_stoploss = current_profit / 2
# Use a minimum of 2.5% and a maximum of 5%
return max(min(desired_stoploss, 0.05), 0.025)
```
#### Calculating stoploss relative to open price
Stoploss values returned from `custom_stoploss()` always specify a percentage relative to `current_rate`. In order to set a stoploss relative to the *open* price, we need to use `current_profit` to calculate what percentage relative to the `current_rate` will give you the same result as if the percentage was specified from the open price.
The helper function [`stoploss_from_open()`](strategy-customization.md#stoploss_from_open) can be used to convert from an open price relative stop, to a current price relative stop which can be returned from `custom_stoploss()`.
### Calculating stoploss percentage from absolute price
Stoploss values returned from `custom_stoploss()` always specify a percentage relative to `current_rate`. In order to set a stoploss at specified absolute price level, we need to use `stop_rate` to calculate what percentage relative to the `current_rate` will give you the same result as if the percentage was specified from the open price.
The helper function [`stoploss_from_absolute()`](strategy-customization.md#stoploss_from_absolute) can be used to convert from an absolute price, to a current price relative stop which can be returned from `custom_stoploss()`.
#### Stepped stoploss
Instead of continuously trailing behind the current price, this example sets fixed stoploss price levels based on the current profit.
* Use the regular stoploss until 20% profit is reached
* Once profit is > 20% - set stoploss to 7% above open price.
* Once profit is > 25% - set stoploss to 15% above open price.
* Once profit is > 40% - set stoploss to 25% above open price.
``` python
from datetime import datetime
from freqtrade.persistence import Trade
from freqtrade.strategy import stoploss_from_open
class AwesomeStrategy(IStrategy):
# ... populate_* methods
use_custom_stoploss = True
def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
current_rate: float, current_profit: float, **kwargs) -> float:
# evaluate highest to lowest, so that highest possible stop is used
if current_profit > 0.40:
return stoploss_from_open(0.25, current_profit)
elif current_profit > 0.25:
return stoploss_from_open(0.15, current_profit)
elif current_profit > 0.20:
return stoploss_from_open(0.07, current_profit)
# return maximum stoploss value, keeping current stoploss price unchanged
return 1
```
#### Custom stoploss using an indicator from dataframe example
Absolute stoploss value may be derived from indicators stored in dataframe. Example uses parabolic SAR below the price as stoploss.
``` python
class AwesomeStrategy(IStrategy):
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# <...>
dataframe['sar'] = ta.SAR(dataframe)
use_custom_stoploss = True
def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
current_rate: float, current_profit: float, **kwargs) -> float:
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
last_candle = dataframe.iloc[-1].squeeze()
# Use parabolic sar as absolute stoploss price
stoploss_price = last_candle['sar']
# Convert absolute price to percentage relative to current_rate
if stoploss_price < current_rate:
return (stoploss_price / current_rate) - 1
# return maximum stoploss value, keeping current stoploss price unchanged
return 1
```
See [Dataframe access](#dataframe-access) for more information about dataframe use in strategy callbacks.
---
## Custom order price rules
By default, freqtrade use the orderbook to automatically set an order price([Relevant documentation](configuration.md#prices-used-for-orders)), you also have the option to create custom order prices based on your strategy.
You can use this feature by creating a `custom_entry_price()` function in your strategy file to customize entry prices and `custom_exit_price()` for exits.
The provided exit-tag is then used as sell-reason - and shown as such in backtest results.
!!! Note
If your custom pricing function return None or an invalid value, price will fall back to `proposed_rate`, which is based on the regular pricing configuration.
### Custom order entry and exit price example
``` python
from datetime import datetime, timedelta, timezone
from freqtrade.persistence import Trade
class AwesomeStrategy(IStrategy):
# ... populate_* methods
def custom_entry_price(self, pair: str, current_time: datetime,
proposed_rate, **kwargs) -> float:
dataframe, last_updated = self.dp.get_analyzed_dataframe(pair=pair,
timeframe=self.timeframe)
new_entryprice = dataframe['bollinger_10_lowerband'].iat[-1]
return new_entryprice
def custom_exit_price(self, pair: str, trade: Trade,
current_time: datetime, proposed_rate: float,
current_profit: float, **kwargs) -> float:
dataframe, last_updated = self.dp.get_analyzed_dataframe(pair=pair,
timeframe=self.timeframe)
new_exitprice = dataframe['bollinger_10_upperband'].iat[-1]
return new_exitprice
```
!!! Warning
Modifying entry and exit prices will only work for limit orders. Depending on the price chosen, this can result in a lot of unfilled orders. By default the maximum allowed distance between the current price and the custom price is 2%, this value can be changed in config with the `custom_price_max_distance_ratio` parameter.
!!! Example
If the new_entryprice is 97, the proposed_rate is 100 and the `custom_price_max_distance_ratio` is set to 2%, The retained valid custom entry price will be 98.
!!! Warning "No backtesting support"
Custom entry-prices are currently not supported during backtesting.
## Custom order timeout rules
Simple, time-based order-timeouts can be configured either via strategy or in the configuration in the `unfilledtimeout` section.
However, freqtrade also offers a custom callback for both order types, which allows you to decide based on custom criteria if an order did time out or not.
!!! Note
Unfilled order timeouts are not relevant during backtesting or hyperopt, and are only relevant during real (live) trading. Therefore these methods are only called in these circumstances.
### Custom order timeout example
A simple example, which applies different unfilled-timeouts depending on the price of the asset can be seen below.
It applies a tight timeout for higher priced assets, while allowing more time to fill on cheap coins.
The function must return either `True` (cancel order) or `False` (keep order alive).
``` python
from datetime import datetime, timedelta, timezone
from freqtrade.persistence import Trade
class AwesomeStrategy(IStrategy):
# ... populate_* methods
# Set unfilledtimeout to 25 hours, since our maximum timeout from below is 24 hours.
unfilledtimeout = {
'buy': 60 * 25,
'sell': 60 * 25
}
def check_buy_timeout(self, pair: str, trade: 'Trade', order: dict, **kwargs) -> bool:
if trade.open_rate > 100 and trade.open_date_utc < datetime.now(timezone.utc) - timedelta(minutes=5):
return True
elif trade.open_rate > 10 and trade.open_date_utc < datetime.now(timezone.utc) - timedelta(minutes=3):
return True
elif trade.open_rate < 1 and trade.open_date_utc < datetime.now(timezone.utc) - timedelta(hours=24):
return True
return False
def check_sell_timeout(self, pair: str, trade: 'Trade', order: dict, **kwargs) -> bool:
if trade.open_rate > 100 and trade.open_date_utc < datetime.now(timezone.utc) - timedelta(minutes=5):
return True
elif trade.open_rate > 10 and trade.open_date_utc < datetime.now(timezone.utc) - timedelta(minutes=3):
return True
elif trade.open_rate < 1 and trade.open_date_utc < datetime.now(timezone.utc) - timedelta(hours=24):
return True
return False
```
!!! Note
For the above example, `unfilledtimeout` must be set to something bigger than 24h, otherwise that type of timeout will apply first.
### Custom order timeout example (using additional data)
``` python
from datetime import datetime
from freqtrade.persistence import Trade
class AwesomeStrategy(IStrategy):
# ... populate_* methods
# Set unfilledtimeout to 25 hours, since our maximum timeout from below is 24 hours.
unfilledtimeout = {
'buy': 60 * 25,
'sell': 60 * 25
}
def check_buy_timeout(self, pair: str, trade: Trade, order: dict, **kwargs) -> bool:
ob = self.dp.orderbook(pair, 1)
current_price = ob['bids'][0][0]
# Cancel buy order if price is more than 2% above the order.
if current_price > order['price'] * 1.02:
return True
return False
def check_sell_timeout(self, pair: str, trade: Trade, order: dict, **kwargs) -> bool:
ob = self.dp.orderbook(pair, 1)
current_price = ob['asks'][0][0]
# Cancel sell order if price is more than 2% below the order.
if current_price < order['price'] * 0.98:
return True
return False
```
---
## Bot loop start callback
A simple callback which is called once at the start of every bot throttling iteration.
This can be used to perform calculations which are pair independent (apply to all pairs), loading of external data, etc.
``` python
import requests
class AwesomeStrategy(IStrategy):
# ... populate_* methods
def bot_loop_start(self, **kwargs) -> None:
"""
Called at the start of the bot iteration (one loop).
Might be used to perform pair-independent tasks
(e.g. gather some remote resource for comparison)
:param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
"""
if self.config['runmode'].value in ('live', 'dry_run'):
# Assign this to the class by using self.*
# can then be used by populate_* methods
self.remote_data = requests.get('https://some_remote_source.example.com')
```
## Bot order confirmation
### Trade entry (buy order) confirmation
`confirm_trade_entry()` can be used to abort a trade entry at the latest second (maybe because the price is not what we expect).
``` python
class AwesomeStrategy(IStrategy):
# ... populate_* methods
def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float,
time_in_force: str, current_time: datetime, **kwargs) -> bool:
"""
Called right before placing a buy order.
Timing for this function is critical, so avoid doing heavy computations or
network requests in this method.
For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/
When not implemented by a strategy, returns True (always confirming).
:param pair: Pair that's about to be bought.
:param order_type: Order type (as configured in order_types). usually limit or market.
:param amount: Amount in target (quote) currency that's going to be traded.
:param rate: Rate that's going to be used when using limit orders
:param time_in_force: Time in force. Defaults to GTC (Good-til-cancelled).
:param current_time: datetime object, containing the current datetime
:param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
:return bool: When True is returned, then the buy-order is placed on the exchange.
False aborts the process
"""
return True
```
### Trade exit (sell order) confirmation
`confirm_trade_exit()` can be used to abort a trade exit (sell) at the latest second (maybe because the price is not what we expect).
``` python
from freqtrade.persistence import Trade
class AwesomeStrategy(IStrategy):
# ... populate_* methods
def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float,
rate: float, time_in_force: str, sell_reason: str,
current_time: datetime, **kwargs) -> bool:
"""
Called right before placing a regular sell order.
Timing for this function is critical, so avoid doing heavy computations or
network requests in this method.
For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/
When not implemented by a strategy, returns True (always confirming).
:param pair: Pair that's about to be sold.
:param order_type: Order type (as configured in order_types). usually limit or market.
:param amount: Amount in quote currency.
:param rate: Rate that's going to be used when using limit orders
:param time_in_force: Time in force. Defaults to GTC (Good-til-cancelled).
:param sell_reason: Sell reason.
Can be any of ['roi', 'stop_loss', 'stoploss_on_exchange', 'trailing_stop_loss',
'sell_signal', 'force_sell', 'emergency_sell']
:param current_time: datetime object, containing the current datetime
:param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
:return bool: When True is returned, then the sell-order is placed on the exchange.
False aborts the process
"""
if sell_reason == 'force_sell' and trade.calc_profit_ratio(rate) < 0:
# Reject force-sells with negative profit
# This is just a sample, please adjust to your needs
# (this does not necessarily make sense, assuming you know when you're force-selling)
return False
return True
```
### Stake size management
It is possible to manage your risk by reducing or increasing stake amount when placing a new trade.
```python
class AwesomeStrategy(IStrategy):
def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float,
proposed_stake: float, min_stake: float, max_stake: float,
**kwargs) -> float:
dataframe, _ = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe)
current_candle = dataframe.iloc[-1].squeeze()
if current_candle['fastk_rsi_1h'] > current_candle['fastd_rsi_1h']:
if self.config['stake_amount'] == 'unlimited':
# Use entire available wallet during favorable conditions when in compounding mode.
return max_stake
else:
# Compound profits during favorable conditions instead of using a static stake.
return self.wallets.get_total_stake_amount() / self.config['max_open_trades']
# Use default stake amount.
return proposed_stake
```
Freqtrade will fall back to the `proposed_stake` value should your code raise an exception. The exception itself will be logged.
!!! Tip
You do not _have_ to ensure that `min_stake <= returned_value <= max_stake`. Trades will succeed as the returned value will be clamped to supported range and this acton will be logged.
!!! Tip
Returning `0` or `None` will prevent trades from being placed.
---
`sell_reason` is limited to 100 characters, remaining data will be truncated.
## Derived strategies

568
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@ -0,0 +1,568 @@
# Strategy Callbacks
While the main strategy functions (`populate_indicators()`, `populate_buy_trend()`, `populate_sell_trend()`) should be used in a vectorized way, and are only called [once during backtesting](bot-basics.md#backtesting-hyperopt-execution-logic), callbacks are called "whenever needed".
As such, you should avoid doing heavy calculations in callbacks to avoid delays during operations.
Depending on the callback used, they may be called when entering / exiting a trade, or throughout the duration of a trade.
Currently available callbacks:
* [`bot_loop_start()`](#bot-loop-start)
* [`custom_stake_amount()`](#custom-stake-size)
* [`custom_sell()`](#custom-sell-signal)
* [`custom_stoploss()`](#custom-stoploss)
* [`custom_entry_price()` and `custom_exit_price()`](#custom-order-price-rules)
* [`check_buy_timeout()` and `check_sell_timeout()](#custom-order-timeout-rules)
* [`confirm_trade_entry()`](#trade-entry-buy-order-confirmation)
* [`confirm_trade_exit()`](#trade-exit-sell-order-confirmation)
!!! Tip "Callback calling sequence"
You can find the callback calling sequence in [bot-basics](bot-basics.md#bot-execution-logic)
## Bot loop start
A simple callback which is called once at the start of every bot throttling iteration (roughly every 5 seconds, unless configured differently).
This can be used to perform calculations which are pair independent (apply to all pairs), loading of external data, etc.
``` python
import requests
class AwesomeStrategy(IStrategy):
# ... populate_* methods
def bot_loop_start(self, **kwargs) -> None:
"""
Called at the start of the bot iteration (one loop).
Might be used to perform pair-independent tasks
(e.g. gather some remote resource for comparison)
:param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
"""
if self.config['runmode'].value in ('live', 'dry_run'):
# Assign this to the class by using self.*
# can then be used by populate_* methods
self.remote_data = requests.get('https://some_remote_source.example.com')
```
## Custom Stake size
Called before entering a trade, makes it possible to manage your position size when placing a new trade.
```python
class AwesomeStrategy(IStrategy):
def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float,
proposed_stake: float, min_stake: float, max_stake: float,
**kwargs) -> float:
dataframe, _ = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe)
current_candle = dataframe.iloc[-1].squeeze()
if current_candle['fastk_rsi_1h'] > current_candle['fastd_rsi_1h']:
if self.config['stake_amount'] == 'unlimited':
# Use entire available wallet during favorable conditions when in compounding mode.
return max_stake
else:
# Compound profits during favorable conditions instead of using a static stake.
return self.wallets.get_total_stake_amount() / self.config['max_open_trades']
# Use default stake amount.
return proposed_stake
```
Freqtrade will fall back to the `proposed_stake` value should your code raise an exception. The exception itself will be logged.
!!! Tip
You do not _have_ to ensure that `min_stake <= returned_value <= max_stake`. Trades will succeed as the returned value will be clamped to supported range and this acton will be logged.
!!! Tip
Returning `0` or `None` will prevent trades from being placed.
## Custom sell signal
Called for open trade every throttling iteration (roughly every 5 seconds) until a trade is closed.
Allows to define custom sell signals, indicating that specified position should be sold. This is very useful when we need to customize sell conditions for each individual trade, or if you need trade data to make an exit decision.
For example you could implement a 1:2 risk-reward ROI with `custom_sell()`.
Using custom_sell() signals in place of stoploss though *is not recommended*. It is a inferior method to using `custom_stoploss()` in this regard - which also allows you to keep the stoploss on exchange.
!!! Note
Returning a (none-empty) `string` or `True` from this method is equal to setting sell signal on a candle at specified time. This method is not called when sell signal is set already, or if sell signals are disabled (`use_sell_signal=False` or `sell_profit_only=True` while profit is below `sell_profit_offset`). `string` max length is 64 characters. Exceeding this limit will cause the message to be truncated to 64 characters.
An example of how we can use different indicators depending on the current profit and also sell trades that were open longer than one day:
``` python
class AwesomeStrategy(IStrategy):
def custom_sell(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float,
current_profit: float, **kwargs):
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
last_candle = dataframe.iloc[-1].squeeze()
# Above 20% profit, sell when rsi < 80
if current_profit > 0.2:
if last_candle['rsi'] < 80:
return 'rsi_below_80'
# Between 2% and 10%, sell if EMA-long above EMA-short
if 0.02 < current_profit < 0.1:
if last_candle['emalong'] > last_candle['emashort']:
return 'ema_long_below_80'
# Sell any positions at a loss if they are held for more than one day.
if current_profit < 0.0 and (current_time - trade.open_date_utc).days >= 1:
return 'unclog'
```
See [Dataframe access](strategy-advanced.md#dataframe-access) for more information about dataframe use in strategy callbacks.
## Custom stoploss
Called for open trade every throttling iteration (roughly every 5 seconds) until a trade is closed.
The usage of the custom stoploss method must be enabled by setting `use_custom_stoploss=True` on the strategy object.
The stoploss price can only ever move upwards - if the stoploss value returned from `custom_stoploss` would result in a lower stoploss price than was previously set, it will be ignored. The traditional `stoploss` value serves as an absolute lower level and will be instated as the initial stoploss (before this method is called for the first time for a trade).
The method must return a stoploss value (float / number) as a percentage of the current price.
E.g. If the `current_rate` is 200 USD, then returning `0.02` will set the stoploss price 2% lower, at 196 USD.
The absolute value of the return value is used (the sign is ignored), so returning `0.05` or `-0.05` have the same result, a stoploss 5% below the current price.
To simulate a regular trailing stoploss of 4% (trailing 4% behind the maximum reached price) you would use the following very simple method:
``` python
# additional imports required
from datetime import datetime
from freqtrade.persistence import Trade
class AwesomeStrategy(IStrategy):
# ... populate_* methods
use_custom_stoploss = True
def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
current_rate: float, current_profit: float, **kwargs) -> float:
"""
Custom stoploss logic, returning the new distance relative to current_rate (as ratio).
e.g. returning -0.05 would create a stoploss 5% below current_rate.
The custom stoploss can never be below self.stoploss, which serves as a hard maximum loss.
For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/
When not implemented by a strategy, returns the initial stoploss value
Only called when use_custom_stoploss is set to True.
:param pair: Pair that's currently analyzed
:param trade: trade object.
:param current_time: datetime object, containing the current datetime
:param current_rate: Rate, calculated based on pricing settings in ask_strategy.
:param current_profit: Current profit (as ratio), calculated based on current_rate.
:param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
:return float: New stoploss value, relative to the current rate
"""
return -0.04
```
Stoploss on exchange works similar to `trailing_stop`, and the stoploss on exchange is updated as configured in `stoploss_on_exchange_interval` ([More details about stoploss on exchange](stoploss.md#stop-loss-on-exchange-freqtrade)).
!!! Note "Use of dates"
All time-based calculations should be done based on `current_time` - using `datetime.now()` or `datetime.utcnow()` is discouraged, as this will break backtesting support.
!!! Tip "Trailing stoploss"
It's recommended to disable `trailing_stop` when using custom stoploss values. Both can work in tandem, but you might encounter the trailing stop to move the price higher while your custom function would not want this, causing conflicting behavior.
### Custom stoploss examples
The next section will show some examples on what's possible with the custom stoploss function.
Of course, many more things are possible, and all examples can be combined at will.
#### Time based trailing stop
Use the initial stoploss for the first 60 minutes, after this change to 10% trailing stoploss, and after 2 hours (120 minutes) we use a 5% trailing stoploss.
``` python
from datetime import datetime, timedelta
from freqtrade.persistence import Trade
class AwesomeStrategy(IStrategy):
# ... populate_* methods
use_custom_stoploss = True
def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
current_rate: float, current_profit: float, **kwargs) -> float:
# Make sure you have the longest interval first - these conditions are evaluated from top to bottom.
if current_time - timedelta(minutes=120) > trade.open_date_utc:
return -0.05
elif current_time - timedelta(minutes=60) > trade.open_date_utc:
return -0.10
return 1
```
#### Different stoploss per pair
Use a different stoploss depending on the pair.
In this example, we'll trail the highest price with 10% trailing stoploss for `ETH/BTC` and `XRP/BTC`, with 5% trailing stoploss for `LTC/BTC` and with 15% for all other pairs.
``` python
from datetime import datetime
from freqtrade.persistence import Trade
class AwesomeStrategy(IStrategy):
# ... populate_* methods
use_custom_stoploss = True
def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
current_rate: float, current_profit: float, **kwargs) -> float:
if pair in ('ETH/BTC', 'XRP/BTC'):
return -0.10
elif pair in ('LTC/BTC'):
return -0.05
return -0.15
```
#### Trailing stoploss with positive offset
Use the initial stoploss until the profit is above 4%, then use a trailing stoploss of 50% of the current profit with a minimum of 2.5% and a maximum of 5%.
Please note that the stoploss can only increase, values lower than the current stoploss are ignored.
``` python
from datetime import datetime, timedelta
from freqtrade.persistence import Trade
class AwesomeStrategy(IStrategy):
# ... populate_* methods
use_custom_stoploss = True
def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
current_rate: float, current_profit: float, **kwargs) -> float:
if current_profit < 0.04:
return -1 # return a value bigger than the initial stoploss to keep using the initial stoploss
# After reaching the desired offset, allow the stoploss to trail by half the profit
desired_stoploss = current_profit / 2
# Use a minimum of 2.5% and a maximum of 5%
return max(min(desired_stoploss, 0.05), 0.025)
```
#### Stepped stoploss
Instead of continuously trailing behind the current price, this example sets fixed stoploss price levels based on the current profit.
* Use the regular stoploss until 20% profit is reached
* Once profit is > 20% - set stoploss to 7% above open price.
* Once profit is > 25% - set stoploss to 15% above open price.
* Once profit is > 40% - set stoploss to 25% above open price.
``` python
from datetime import datetime
from freqtrade.persistence import Trade
from freqtrade.strategy import stoploss_from_open
class AwesomeStrategy(IStrategy):
# ... populate_* methods
use_custom_stoploss = True
def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
current_rate: float, current_profit: float, **kwargs) -> float:
# evaluate highest to lowest, so that highest possible stop is used
if current_profit > 0.40:
return stoploss_from_open(0.25, current_profit)
elif current_profit > 0.25:
return stoploss_from_open(0.15, current_profit)
elif current_profit > 0.20:
return stoploss_from_open(0.07, current_profit)
# return maximum stoploss value, keeping current stoploss price unchanged
return 1
```
#### Custom stoploss using an indicator from dataframe example
Absolute stoploss value may be derived from indicators stored in dataframe. Example uses parabolic SAR below the price as stoploss.
``` python
class AwesomeStrategy(IStrategy):
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# <...>
dataframe['sar'] = ta.SAR(dataframe)
use_custom_stoploss = True
def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
current_rate: float, current_profit: float, **kwargs) -> float:
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
last_candle = dataframe.iloc[-1].squeeze()
# Use parabolic sar as absolute stoploss price
stoploss_price = last_candle['sar']
# Convert absolute price to percentage relative to current_rate
if stoploss_price < current_rate:
return (stoploss_price / current_rate) - 1
# return maximum stoploss value, keeping current stoploss price unchanged
return 1
```
See [Dataframe access](strategy-advanced.md#dataframe-access) for more information about dataframe use in strategy callbacks.
### Common helpers for stoploss calculations
#### Stoploss relative to open price
Stoploss values returned from `custom_stoploss()` always specify a percentage relative to `current_rate`. In order to set a stoploss relative to the *open* price, we need to use `current_profit` to calculate what percentage relative to the `current_rate` will give you the same result as if the percentage was specified from the open price.
The helper function [`stoploss_from_open()`](strategy-customization.md#stoploss_from_open) can be used to convert from an open price relative stop, to a current price relative stop which can be returned from `custom_stoploss()`.
#### Stoploss percentage from absolute price
Stoploss values returned from `custom_stoploss()` always specify a percentage relative to `current_rate`. In order to set a stoploss at specified absolute price level, we need to use `stop_rate` to calculate what percentage relative to the `current_rate` will give you the same result as if the percentage was specified from the open price.
The helper function [`stoploss_from_absolute()`](strategy-customization.md#stoploss_from_absolute) can be used to convert from an absolute price, to a current price relative stop which can be returned from `custom_stoploss()`.
---
## Custom order price rules
By default, freqtrade use the orderbook to automatically set an order price([Relevant documentation](configuration.md#prices-used-for-orders)), you also have the option to create custom order prices based on your strategy.
You can use this feature by creating a `custom_entry_price()` function in your strategy file to customize entry prices and `custom_exit_price()` for exits.
Each of these methods are called right before placing an order on the exchange.
!!! Note
If your custom pricing function return None or an invalid value, price will fall back to `proposed_rate`, which is based on the regular pricing configuration.
### Custom order entry and exit price example
``` python
from datetime import datetime, timedelta, timezone
from freqtrade.persistence import Trade
class AwesomeStrategy(IStrategy):
# ... populate_* methods
def custom_entry_price(self, pair: str, current_time: datetime,
proposed_rate, **kwargs) -> float:
dataframe, last_updated = self.dp.get_analyzed_dataframe(pair=pair,
timeframe=self.timeframe)
new_entryprice = dataframe['bollinger_10_lowerband'].iat[-1]
return new_entryprice
def custom_exit_price(self, pair: str, trade: Trade,
current_time: datetime, proposed_rate: float,
current_profit: float, **kwargs) -> float:
dataframe, last_updated = self.dp.get_analyzed_dataframe(pair=pair,
timeframe=self.timeframe)
new_exitprice = dataframe['bollinger_10_upperband'].iat[-1]
return new_exitprice
```
!!! Warning
Modifying entry and exit prices will only work for limit orders. Depending on the price chosen, this can result in a lot of unfilled orders. By default the maximum allowed distance between the current price and the custom price is 2%, this value can be changed in config with the `custom_price_max_distance_ratio` parameter.
**Example**:
If the new_entryprice is 97, the proposed_rate is 100 and the `custom_price_max_distance_ratio` is set to 2%, The retained valid custom entry price will be 98, which is 2% below the current (proposed) rate.
!!! Warning "No backtesting support"
Custom entry-prices are currently not supported during backtesting.
## Custom order timeout rules
Simple, time-based order-timeouts can be configured either via strategy or in the configuration in the `unfilledtimeout` section.
However, freqtrade also offers a custom callback for both order types, which allows you to decide based on custom criteria if an order did time out or not.
!!! Note
Unfilled order timeouts are not relevant during backtesting or hyperopt, and are only relevant during real (live) trading. Therefore these methods are only called in these circumstances.
### Custom order timeout example
Called for every open order until that order is either filled or cancelled.
`check_buy_timeout()` is called for trade entries, while `check_sell_timeout()` is called for trade exit orders.
A simple example, which applies different unfilled-timeouts depending on the price of the asset can be seen below.
It applies a tight timeout for higher priced assets, while allowing more time to fill on cheap coins.
The function must return either `True` (cancel order) or `False` (keep order alive).
``` python
from datetime import datetime, timedelta, timezone
from freqtrade.persistence import Trade
class AwesomeStrategy(IStrategy):
# ... populate_* methods
# Set unfilledtimeout to 25 hours, since the maximum timeout from below is 24 hours.
unfilledtimeout = {
'buy': 60 * 25,
'sell': 60 * 25
}
def check_buy_timeout(self, pair: str, trade: 'Trade', order: dict, **kwargs) -> bool:
if trade.open_rate > 100 and trade.open_date_utc < datetime.now(timezone.utc) - timedelta(minutes=5):
return True
elif trade.open_rate > 10 and trade.open_date_utc < datetime.now(timezone.utc) - timedelta(minutes=3):
return True
elif trade.open_rate < 1 and trade.open_date_utc < datetime.now(timezone.utc) - timedelta(hours=24):
return True
return False
def check_sell_timeout(self, pair: str, trade: 'Trade', order: dict, **kwargs) -> bool:
if trade.open_rate > 100 and trade.open_date_utc < datetime.now(timezone.utc) - timedelta(minutes=5):
return True
elif trade.open_rate > 10 and trade.open_date_utc < datetime.now(timezone.utc) - timedelta(minutes=3):
return True
elif trade.open_rate < 1 and trade.open_date_utc < datetime.now(timezone.utc) - timedelta(hours=24):
return True
return False
```
!!! Note
For the above example, `unfilledtimeout` must be set to something bigger than 24h, otherwise that type of timeout will apply first.
### Custom order timeout example (using additional data)
``` python
from datetime import datetime
from freqtrade.persistence import Trade
class AwesomeStrategy(IStrategy):
# ... populate_* methods
# Set unfilledtimeout to 25 hours, since the maximum timeout from below is 24 hours.
unfilledtimeout = {
'buy': 60 * 25,
'sell': 60 * 25
}
def check_buy_timeout(self, pair: str, trade: Trade, order: dict, **kwargs) -> bool:
ob = self.dp.orderbook(pair, 1)
current_price = ob['bids'][0][0]
# Cancel buy order if price is more than 2% above the order.
if current_price > order['price'] * 1.02:
return True
return False
def check_sell_timeout(self, pair: str, trade: Trade, order: dict, **kwargs) -> bool:
ob = self.dp.orderbook(pair, 1)
current_price = ob['asks'][0][0]
# Cancel sell order if price is more than 2% below the order.
if current_price < order['price'] * 0.98:
return True
return False
```
---
## Bot order confirmation
Confirm trade entry / exits.
This are the last methods that will be called before an order is placed.
### Trade entry (buy order) confirmation
`confirm_trade_entry()` can be used to abort a trade entry at the latest second (maybe because the price is not what we expect).
``` python
class AwesomeStrategy(IStrategy):
# ... populate_* methods
def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float,
time_in_force: str, current_time: datetime, **kwargs) -> bool:
"""
Called right before placing a buy order.
Timing for this function is critical, so avoid doing heavy computations or
network requests in this method.
For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/
When not implemented by a strategy, returns True (always confirming).
:param pair: Pair that's about to be bought.
:param order_type: Order type (as configured in order_types). usually limit or market.
:param amount: Amount in target (quote) currency that's going to be traded.
:param rate: Rate that's going to be used when using limit orders
:param time_in_force: Time in force. Defaults to GTC (Good-til-cancelled).
:param current_time: datetime object, containing the current datetime
:param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
:return bool: When True is returned, then the buy-order is placed on the exchange.
False aborts the process
"""
return True
```
### Trade exit (sell order) confirmation
`confirm_trade_exit()` can be used to abort a trade exit (sell) at the latest second (maybe because the price is not what we expect).
``` python
from freqtrade.persistence import Trade
class AwesomeStrategy(IStrategy):
# ... populate_* methods
def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float,
rate: float, time_in_force: str, sell_reason: str,
current_time: datetime, **kwargs) -> bool:
"""
Called right before placing a regular sell order.
Timing for this function is critical, so avoid doing heavy computations or
network requests in this method.
For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/
When not implemented by a strategy, returns True (always confirming).
:param pair: Pair that's about to be sold.
:param order_type: Order type (as configured in order_types). usually limit or market.
:param amount: Amount in quote currency.
:param rate: Rate that's going to be used when using limit orders
:param time_in_force: Time in force. Defaults to GTC (Good-til-cancelled).
:param sell_reason: Sell reason.
Can be any of ['roi', 'stop_loss', 'stoploss_on_exchange', 'trailing_stop_loss',
'sell_signal', 'force_sell', 'emergency_sell']
:param current_time: datetime object, containing the current datetime
:param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
:return bool: When True is returned, then the sell-order is placed on the exchange.
False aborts the process
"""
if sell_reason == 'force_sell' and trade.calc_profit_ratio(rate) < 0:
# Reject force-sells with negative profit
# This is just a sample, please adjust to your needs
# (this does not necessarily make sense, assuming you know when you're force-selling)
return False
return True
```

106
docs/strategy-customization.md
View File

@ -4,33 +4,23 @@ This page explains how to customize your strategies, add new indicators and set
Please familiarize yourself with [Freqtrade basics](bot-basics.md) first, which provides overall info on how the bot operates.
## Install a custom strategy file
This is very simple. Copy paste your strategy file into the directory `user_data/strategies`.
Let assume you have a class called `AwesomeStrategy` in the file `AwesomeStrategy.py`:
1. Move your file into `user_data/strategies` (you should have `user_data/strategies/AwesomeStrategy.py`
2. Start the bot with the param `--strategy AwesomeStrategy` (the parameter is the class name)
```bash
freqtrade trade --strategy AwesomeStrategy
```
## Develop your own strategy
The bot includes a default strategy file.
Also, several other strategies are available in the [strategy repository](https://github.com/freqtrade/freqtrade-strategies).
You will however most likely have your own idea for a strategy.
This document intends to help you develop one for yourself.
This document intends to help you convert your strategy idea into your own strategy.
To get started, use `freqtrade new-strategy --strategy AwesomeStrategy`.
To get started, use `freqtrade new-strategy --strategy AwesomeStrategy` (you can obviously use your own naming for your strategy).
This will create a new strategy file from a template, which will be located under `user_data/strategies/AwesomeStrategy.py`.
!!! Note
This is just a template file, which will most likely not be profitable out of the box.
??? Hint "Different template levels"
`freqtrade new-strategy` has an additional parameter, `--template`, which controls the amount of pre-build information you get in the created strategy. Use `--template minimal` to get an empty strategy without any indicator examples, or `--template advanced` to get a template with most callbacks defined.
### Anatomy of a strategy
A strategy file contains all the information needed to build a good strategy:
@ -67,6 +57,46 @@ file as reference.**
needs to take care to avoid having the strategy utilize data from the future.
Some common patterns for this are listed in the [Common Mistakes](#common-mistakes-when-developing-strategies) section of this document.
### Dataframe
Freqtrade uses [pandas](https://pandas.pydata.org/) to store/provide the candlestick (OHLCV) data.
Pandas is a great library developed for processing large amounts of data.
Each row in a dataframe corresponds to one candle on a chart, with the latest candle always being the last in the dataframe (sorted by date).
``` output
> dataframe.head()
date open high low close volume
0 2021-11-09 23:25:00+00:00 67279.67 67321.84 67255.01 67300.97 44.62253
1 2021-11-09 23:30:00+00:00 67300.97 67301.34 67183.03 67187.01 61.38076
2 2021-11-09 23:35:00+00:00 67187.02 67187.02 67031.93 67123.81 113.42728
3 2021-11-09 23:40:00+00:00 67123.80 67222.40 67080.33 67160.48 78.96008
4 2021-11-09 23:45:00+00:00 67160.48 67160.48 66901.26 66943.37 111.39292
```
Pandas provides fast ways to calculate metrics. To benefit from this speed, it's advised to not use loops, but use vectorized methods instead.
Vectorized operations perform calculations across the whole range of data and are therefore, compared to looping through each row, a lot faster when calculating indicators.
As a dataframe is a table, simple python comparisons like the following will not work
``` python
if dataframe['rsi'] > 30:
dataframe['buy'] = 1
```
The above section will fail with `The truth value of a Series is ambiguous. [...]`.
This must instead be written in a pandas-compatible way, so the operation is performed across the whole dataframe.
``` python
dataframe.loc[
(dataframe['rsi'] > 30)
, 'buy'] = 1
```
With this section, you have a new column in your dataframe, which has `1` assigned whenever RSI is above 30.
### Customize Indicators
Buy and sell strategies need indicators. You can add more indicators by extending the list contained in the method `populate_indicators()` from your strategy file.
@ -134,7 +164,7 @@ Additional technical libraries can be installed as necessary, or custom indicato
### Strategy startup period
Most indicators have an instable startup period, in which they are either not available, or the calculation is incorrect. This can lead to inconsistencies, since Freqtrade does not know how long this instable period should be.
Most indicators have an instable startup period, in which they are either not available (NaN), or the calculation is incorrect. This can lead to inconsistencies, since Freqtrade does not know how long this instable period should be.
To account for this, the strategy can be assigned the `startup_candle_count` attribute.
This should be set to the maximum number of candles that the strategy requires to calculate stable indicators.
@ -146,8 +176,14 @@ In this example strategy, this should be set to 100 (`startup_candle_count = 100
By letting the bot know how much history is needed, backtest trades can start at the specified timerange during backtesting and hyperopt.
!!! Warning "Using x calls to get OHLCV"
If you receive a warning like `WARNING - Using 3 calls to get OHLCV. This can result in slower operations for the bot. Please check if you really need 1500 candles for your strategy` - you should consider if you really need this much historic data for your signals.
Having this will cause Freqtrade to make multiple calls for the same pair, which will obviously be slower than one network request.
As a consequence, Freqtrade will take longer to refresh candles - and should therefore be avoided if possible.
This is capped to 5 total calls to avoid overloading the exchange, or make freqtrade too slow.
!!! Warning
`startup_candle_count` should be below `ohlcv_candle_limit` (which is 500 for most exchanges) - since only this amount of candles will be available during Dry-Run/Live Trade operations.
`startup_candle_count` should be below `ohlcv_candle_limit * 5` (which is 500 * 5 for most exchanges) - since only this amount of candles will be available during Dry-Run/Live Trade operations.
#### Example
@ -281,20 +317,14 @@ class AwesomeStrategy(IStrategy):
Setting a stoploss is highly recommended to protect your capital from strong moves against you.
Sample:
Sample of setting a 10% stoploss:
``` python
stoploss = -0.10
```
This would signify a stoploss of -10%.
For the full documentation on stoploss features, look at the dedicated [stoploss page](stoploss.md).
If your exchange supports it, it's recommended to also set `"stoploss_on_exchange"` in the order_types dictionary, so your stoploss is on the exchange and cannot be missed due to network problems, high load or other reasons.
For more information on order_types please look [here](configuration.md#understand-order_types).
### Timeframe (formerly ticker interval)
This is the set of candles the bot should download and use for the analysis.
@ -310,7 +340,20 @@ The metadata-dict (available for `populate_buy_trend`, `populate_sell_trend`, `p
Currently this is `pair`, which can be accessed using `metadata['pair']` - and will return a pair in the format `XRP/BTC`.
The Metadata-dict should not be modified and does not persist information across multiple calls.
Instead, have a look at the section [Storing information](strategy-advanced.md#Storing-information)
Instead, have a look at the [Storing information](strategy-advanced.md#Storing-information) section.
## Strategy file loading
By default, freqtrade will attempt to load strategies from all `.py` files within `user_data/strategies`.
Assuming your strategy is called `AwesomeStrategy`, stored in the file `user_data/strategies/AwesomeStrategy.py`, then you can start freqtrade with `freqtrade trade --strategy AwesomeStrategy`.
Note that we're using the class-name, not the file name.
You can use `freqtrade list-strategies` to see a list of all strategies Freqtrade is able to load (all strategies in the correct folder).
It will also include a "status" field, highlighting potential problems.
??? Hint "Customize strategy directory"
You can use a different directory by using `--strategy-path user_data/otherPath`. This parameter is available to all commands that require a strategy.
## Informative Pairs
@ -511,9 +554,9 @@ The strategy might look something like this:
*Scan through the top 10 pairs by volume using the `VolumePairList` every 5 minutes and use a 14 day RSI to buy and sell.*
Due to the limited available data, it's very difficult to resample our `5m` candles into daily candles for use in a 14 day RSI. Most exchanges limit us to just 500 candles which effectively gives us around 1.74 daily candles. We need 14 days at least!
Due to the limited available data, it's very difficult to resample `5m` candles into daily candles for use in a 14 day RSI. Most exchanges limit us to just 500 candles which effectively gives us around 1.74 daily candles. We need 14 days at least!
Since we can't resample our data we will have to use an informative pair; and since our whitelist will be dynamic we don't know which pair(s) to use.
Since we can't resample the data we will have to use an informative pair; and since the whitelist will be dynamic we don't know which pair(s) to use.
This is where calling `self.dp.current_whitelist()` comes in handy.
@ -896,7 +939,8 @@ Sometimes it may be desired to lock a pair after certain events happen (e.g. mul
Freqtrade has an easy method to do this from within the strategy, by calling `self.lock_pair(pair, until, [reason])`.
`until` must be a datetime object in the future, after which trading will be re-enabled for that pair, while `reason` is an optional string detailing why the pair was locked.
Locks can also be lifted manually, by calling `self.unlock_pair(pair)`.
Locks can also be lifted manually, by calling `self.unlock_pair(pair)` or `self.unlock_reason(<reason>)` - providing reason the pair was locked with.
`self.unlock_reason(<reason>)` will unlock all pairs currently locked with the provided reason.
To verify if a pair is currently locked, use `self.is_pair_locked(pair)`.
@ -966,9 +1010,13 @@ The following lists some common patterns which should be avoided to prevent frus
- don't use `dataframe['volume'].mean()`. This uses the full DataFrame for backtesting, including data from the future. Use `dataframe['volume'].rolling(<window>).mean()` instead
- don't use `.resample('1h')`. This uses the left border of the interval, so moves data from an hour to the start of the hour. Use `.resample('1h', label='right')` instead.
### Colliding signals
When buy and sell signals collide (both `'buy'` and `'sell'` are 1), freqtrade will do nothing and ignore the entry (buy) signal. This will avoid trades that buy, and sell immediately. Obviously, this can potentially lead to missed entries.
## Further strategy ideas
To get additional Ideas for strategies, head over to our [strategy repository](https://github.com/freqtrade/freqtrade-strategies). Feel free to use them as they are - but results will depend on the current market situation, pairs used etc. - therefore please backtest the strategy for your exchange/desired pairs first, evaluate carefully, use at your own risk.
To get additional Ideas for strategies, head over to the [strategy repository](https://github.com/freqtrade/freqtrade-strategies). Feel free to use them as they are - but results will depend on the current market situation, pairs used etc. - therefore please backtest the strategy for your exchange/desired pairs first, evaluate carefully, use at your own risk.
Feel free to use any of them as inspiration for your own strategies.
We're happy to accept Pull Requests containing new Strategies to that repo.

35
docs/telegram-usage.md
View File

@ -58,6 +58,8 @@ For the Freqtrade configuration, you can then use the the full value (including
```json
"chat_id": "-1001332619709"
```
!!! Warning "Using telegram groups"
When using telegram groups, you're giving every member of the telegram group access to your freqtrade bot and to all commands possible via telegram. Please make sure that you can trust everyone in the telegram group to avoid unpleasent surprises.
## Control telegram noise
@ -175,6 +177,8 @@ official commands. You can ask at any moment for help with `/help`.
| `/performance` | Show performance of each finished trade grouped by pair
| `/balance` | Show account balance per currency
| `/daily <n>` | Shows profit or loss per day, over the last n days (n defaults to 7)
| `/weekly <n>` | Shows profit or loss per week, over the last n weeks (n defaults to 8)
| `/monthly <n>` | Shows profit or loss per month, over the last n months (n defaults to 6)
| `/stats` | Shows Wins / losses by Sell reason as well as Avg. holding durations for buys and sells
| `/whitelist` | Show the current whitelist
| `/blacklist [pair]` | Show the current blacklist, or adds a pair to the blacklist.
@ -307,8 +311,7 @@ Return the balance of all crypto-currency your have on the exchange.
### /daily <n>
Per default `/daily` will return the 7 last days.
The example below if for `/daily 3`:
Per default `/daily` will return the 7 last days. The example below if for `/daily 3`:
> **Daily Profit over the last 3 days:**
```
@ -319,6 +322,34 @@ Day Profit BTC Profit USD
2018-01-01 0.00269130 BTC 34.986 USD
```
### /weekly <n>
Per default `/weekly` will return the 8 last weeks, including the current week. Each week starts
from Monday. The example below if for `/weekly 3`:
> **Weekly Profit over the last 3 weeks (starting from Monday):**
```
Monday Profit BTC Profit USD
---------- -------------- ------------
2018-01-03 0.00224175 BTC 29,142 USD
2017-12-27 0.00033131 BTC 4,307 USD
2017-12-20 0.00269130 BTC 34.986 USD
```
### /monthly <n>
Per default `/monthly` will return the 6 last months, including the current month. The example below
if for `/monthly 3`:
> **Monthly Profit over the last 3 months:**
```
Month Profit BTC Profit USD
---------- -------------- ------------
2018-01 0.00224175 BTC 29,142 USD
2017-12 0.00033131 BTC 4,307 USD
2017-11 0.00269130 BTC 34.986 USD
```
### /whitelist
Shows the current whitelist

40
docs/utils.md
View File

@ -577,6 +577,46 @@ Common arguments:
```
## Show previous Backtest results
Allows you to show previous backtest results.
Adding `--show-pair-list` outputs a sorted pair list you can easily copy/paste into your configuration (omitting bad pairs).
??? Warning "Strategy overfitting"
Only using winning pairs can lead to an overfitted strategy, which will not work well on future data. Make sure to extensively test your strategy in dry-run before risking real money.
```
usage: freqtrade backtesting-show [-h] [-v] [--logfile FILE] [-V] [-c PATH]
[-d PATH] [--userdir PATH]
[--export-filename PATH] [--show-pair-list]
optional arguments:
-h, --help show this help message and exit
--export-filename PATH
Save backtest results to the file with this filename.
Requires `--export` to be set as well. Example:
`--export-filename=user_data/backtest_results/backtest
_today.json`
--show-pair-list Show backtesting pairlist sorted by profit.
Common arguments:
-v, --verbose Verbose mode (-vv for more, -vvv to get all messages).
--logfile FILE Log to the file specified. Special values are:
'syslog', 'journald'. See the documentation for more
details.
-V, --version show program's version number and exit
-c PATH, --config PATH
Specify configuration file (default:
`userdir/config.json` or `config.json` whichever
exists). Multiple --config options may be used. Can be
set to `-` to read config from stdin.
-d PATH, --datadir PATH
Path to directory with historical backtesting data.
--userdir PATH, --user-data-dir PATH
Path to userdata directory.
```
## List Hyperopt results
You can list the hyperoptimization epochs the Hyperopt module evaluated previously with the `hyperopt-list` sub-command.

2
docs/webhook-config.md
View File

@ -48,7 +48,7 @@ Sample configuration (tested using IFTTT).
},
```
The url in `webhook.url` should point to the correct url for your webhook. If you're using [IFTTT](https://ifttt.com) (as shown in the sample above) please insert our event and key to the url.
The url in `webhook.url` should point to the correct url for your webhook. If you're using [IFTTT](https://ifttt.com) (as shown in the sample above) please insert your event and key to the url.
You can set the POST body format to Form-Encoded (default) or JSON-Encoded. Use `"format": "form"` or `"format": "json"` respectively. Example configuration for Mattermost Cloud integration:

2
freqtrade/__init__.py
View File

@ -1,5 +1,5 @@
""" Freqtrade bot """
__version__ = '2021.10'
__version__ = '2021.11'
if __version__ == 'develop':

3
freqtrade/commands/__init__.py
View File

@ -16,7 +16,8 @@ from freqtrade.commands.hyperopt_commands import start_hyperopt_list, start_hype
from freqtrade.commands.list_commands import (start_list_exchanges, start_list_markets,
start_list_strategies, start_list_timeframes,
start_show_trades)
from freqtrade.commands.optimize_commands import start_backtesting, start_edge, start_hyperopt
from freqtrade.commands.optimize_commands import (start_backtesting, start_backtesting_show,
start_edge, start_hyperopt)
from freqtrade.commands.pairlist_commands import start_test_pairlist
from freqtrade.commands.plot_commands import start_plot_dataframe, start_plot_profit
from freqtrade.commands.trade_commands import start_trading

16
freqtrade/commands/arguments.py
View File

@ -41,6 +41,8 @@ ARGS_LIST_STRATEGIES = ["strategy_path", "print_one_column", "print_colorized"]
ARGS_LIST_HYPEROPTS = ["hyperopt_path", "print_one_column", "print_colorized"]
ARGS_BACKTEST_SHOW = ["exportfilename", "backtest_show_pair_list"]
ARGS_LIST_EXCHANGES = ["print_one_column", "list_exchanges_all"]
ARGS_LIST_TIMEFRAMES = ["exchange", "print_one_column"]
@ -94,7 +96,7 @@ ARGS_HYPEROPT_SHOW = ["hyperopt_list_best", "hyperopt_list_profitable", "hyperop
NO_CONF_REQURIED = ["convert-data", "convert-trade-data", "download-data", "list-timeframes",
"list-markets", "list-pairs", "list-strategies", "list-data",
"hyperopt-list", "hyperopt-show",
"hyperopt-list", "hyperopt-show", "backtest-filter",
"plot-dataframe", "plot-profit", "show-trades", "trades-to-ohlcv"]
NO_CONF_ALLOWED = ["create-userdir", "list-exchanges", "new-strategy"]
@ -173,7 +175,8 @@ class Arguments:
self.parser = argparse.ArgumentParser(description='Free, open source crypto trading bot')
self._build_args(optionlist=['version'], parser=self.parser)
from freqtrade.commands import (start_backtesting, start_convert_data, start_convert_trades,
from freqtrade.commands import (start_backtesting, start_backtesting_show,
start_convert_data, start_convert_trades,
start_create_userdir, start_download_data, start_edge,
start_hyperopt, start_hyperopt_list, start_hyperopt_show,
start_install_ui, start_list_data, start_list_exchanges,
@ -264,6 +267,15 @@ class Arguments:
backtesting_cmd.set_defaults(func=start_backtesting)
self._build_args(optionlist=ARGS_BACKTEST, parser=backtesting_cmd)
# Add backtesting-show subcommand
backtesting_show_cmd = subparsers.add_parser(
'backtesting-show',
help='Show past Backtest results',
parents=[_common_parser],
)
backtesting_show_cmd.set_defaults(func=start_backtesting_show)
self._build_args(optionlist=ARGS_BACKTEST_SHOW, parser=backtesting_show_cmd)
# Add edge subcommand
edge_cmd = subparsers.add_parser('edge', help='Edge module.',
parents=[_common_parser, _strategy_parser])

11
freqtrade/commands/build_config_commands.py
View File

@ -83,11 +83,19 @@ def ask_user_config() -> Dict[str, Any]:
if val == UNLIMITED_STAKE_AMOUNT
else val
},
{
"type": "select",
"name": "timeframe_in_config",
"message": "Tim",
"choices": ["Have the strategy define timeframe.", "Override in configuration."]
},
{
"type": "text",
"name": "timeframe",
"message": "Please insert your desired timeframe (e.g. 5m):",
"default": "5m",
"when": lambda x: x["timeframe_in_config"] == 'Override in configuration.'
},
{
"type": "text",
@ -107,6 +115,7 @@ def ask_user_config() -> Dict[str, Any]:
"ftx",
"kucoin",
"gateio",
"okex",
Separator(),
"other",
],
@ -134,7 +143,7 @@ def ask_user_config() -> Dict[str, Any]:
"type": "password",
"name": "exchange_key_password",
"message": "Insert Exchange API Key password",
"when": lambda x: not x['dry_run'] and x['exchange_name'] == 'kucoin'
"when": lambda x: not x['dry_run'] and x['exchange_name'] in ('kucoin', 'okex')
},
{
"type": "confirm",

6
freqtrade/commands/cli_options.py
View File

@ -152,6 +152,12 @@ AVAILABLE_CLI_OPTIONS = {
action='store_false',
default=True,
),
"backtest_show_pair_list": Arg(
'--show-pair-list',
help='Show backtesting pairlist sorted by profit.',
action='store_true',
default=False,
),
"enable_protections": Arg(
'--enable-protections', '--enableprotections',
help='Enable protections for backtesting.'

16
freqtrade/commands/optimize_commands.py
View File

@ -54,6 +54,22 @@ def start_backtesting(args: Dict[str, Any]) -> None:
backtesting.start()
def start_backtesting_show(args: Dict[str, Any]) -> None:
"""
Show previous backtest result
"""
config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
from freqtrade.data.btanalysis import load_backtest_stats
from freqtrade.optimize.optimize_reports import show_backtest_results, show_sorted_pairlist
results = load_backtest_stats(config['exportfilename'])
show_backtest_results(config, results)
show_sorted_pairlist(config, results)
def start_hyperopt(args: Dict[str, Any]) -> None:
"""
Start hyperopt script

4
freqtrade/configuration/configuration.py
View File

@ -245,6 +245,10 @@ class Configuration:
self._args_to_config(config, argname='timeframe_detail',
logstring='Parameter --timeframe-detail detected, '
'using {} for intra-candle backtesting ...')
self._args_to_config(config, argname='backtest_show_pair_list',
logstring='Parameter --show-pair-list detected.')
self._args_to_config(config, argname='stake_amount',
logstring='Parameter --stake-amount detected, '
'overriding stake_amount to: {} ...')

5
freqtrade/configuration/environment_vars.py
View File

@ -32,6 +32,7 @@ def flat_vars_to_nested_dict(env_dict: Dict[str, Any], prefix: str) -> Dict[str,
:param prefix: Prefix to consider (usually FREQTRADE__)
:return: Nested dict based on available and relevant variables.
"""
no_convert = ['CHAT_ID']
relevant_vars: Dict[str, Any] = {}
for env_var, val in sorted(env_dict.items()):
@ -39,9 +40,9 @@ def flat_vars_to_nested_dict(env_dict: Dict[str, Any], prefix: str) -> Dict[str,
logger.info(f"Loading variable '{env_var}'")
key = env_var.replace(prefix, '')
for k in reversed(key.split('__')):
val = {k.lower(): get_var_typed(val) if type(val) != dict else val}
val = {k.lower(): get_var_typed(val)
if type(val) != dict and k not in no_convert else val}
relevant_vars = deep_merge_dicts(val, relevant_vars)
return relevant_vars

19
freqtrade/constants.py
View File

@ -25,6 +25,7 @@ ORDERTIF_POSSIBILITIES = ['gtc', 'fok', 'ioc']
HYPEROPT_LOSS_BUILTIN = ['ShortTradeDurHyperOptLoss', 'OnlyProfitHyperOptLoss',
'SharpeHyperOptLoss', 'SharpeHyperOptLossDaily',
'SortinoHyperOptLoss', 'SortinoHyperOptLossDaily',
'CalmarHyperOptLoss',
'MaxDrawDownHyperOptLoss']
AVAILABLE_PAIRLISTS = ['StaticPairList', 'VolumePairList',
'AgeFilter', 'OffsetFilter', 'PerformanceFilter',
@ -53,7 +54,6 @@ ENV_VAR_PREFIX = 'FREQTRADE__'
NON_OPEN_EXCHANGE_STATES = ('cancelled', 'canceled', 'closed', 'expired')
# Define decimals per coin for outputs
# Only used for outputs.
DECIMAL_PER_COIN_FALLBACK = 3 # Should be low to avoid listing all possible FIAT's
@ -67,7 +67,6 @@ DUST_PER_COIN = {
'ETH': 0.01
}
# Source files with destination directories within user-directory
USER_DATA_FILES = {
'sample_strategy.py': USERPATH_STRATEGIES,
@ -157,6 +156,7 @@ CONF_SCHEMA = {
'properties': {
'buy': {'type': 'number', 'minimum': 1},
'sell': {'type': 'number', 'minimum': 1},
'exit_timeout_count': {'type': 'number', 'minimum': 0, 'default': 0},
'unit': {'type': 'string', 'enum': TIMEOUT_UNITS, 'default': 'minutes'}
}
},
@ -198,7 +198,7 @@ CONF_SCHEMA = {
'required': ['price_side']
},
'custom_price_max_distance_ratio': {
'type': 'number', 'minimum': 0.0
'type': 'number', 'minimum': 0.0
},
'order_types': {
'type': 'object',
@ -207,7 +207,10 @@ CONF_SCHEMA = {
'sell': {'type': 'string', 'enum': ORDERTYPE_POSSIBILITIES},
'forcesell': {'type': 'string', 'enum': ORDERTYPE_POSSIBILITIES},
'forcebuy': {'type': 'string', 'enum': ORDERTYPE_POSSIBILITIES},
'emergencysell': {'type': 'string', 'enum': ORDERTYPE_POSSIBILITIES},
'emergencysell': {
'type': 'string',
'enum': ORDERTYPE_POSSIBILITIES,
'default': 'market'},
'stoploss': {'type': 'string', 'enum': ORDERTYPE_POSSIBILITIES},
'stoploss_on_exchange': {'type': 'boolean'},
'stoploss_on_exchange_interval': {'type': 'number'},
@ -351,13 +354,13 @@ CONF_SCHEMA = {
},
'dataformat_ohlcv': {
'type': 'string',
'enum': AVAILABLE_DATAHANDLERS,
'default': 'json'
'enum': AVAILABLE_DATAHANDLERS,
'default': 'json'
},
'dataformat_trades': {
'type': 'string',
'enum': AVAILABLE_DATAHANDLERS,
'default': 'jsongz'
'enum': AVAILABLE_DATAHANDLERS,
'default': 'jsongz'
}
},
'definitions': {

2
freqtrade/data/converter.py
View File

@ -113,7 +113,7 @@ def ohlcv_fill_up_missing_data(dataframe: DataFrame, timeframe: str, pair: str)
pct_missing = (len_after - len_before) / len_before if len_before > 0 else 0
if len_before != len_after:
message = (f"Missing data fillup for {pair}: before: {len_before} - after: {len_after}"
f" - {round(pct_missing * 100, 2)}%")
f" - {pct_missing:.2%}")
if pct_missing > 0.01:
logger.info(message)
else:

1
freqtrade/enums/signaltype.py
View File

@ -14,3 +14,4 @@ class SignalTagType(Enum):
Enum for signal columns
"""
BUY_TAG = "buy_tag"
EXIT_TAG = "exit_tag"

2
freqtrade/exceptions.py
View File

@ -1,5 +1,3 @@
class FreqtradeException(Exception):
"""
Freqtrade base exception. Handled at the outermost level.

1
freqtrade/exchange/__init__.py
View File

@ -19,3 +19,4 @@ from freqtrade.exchange.gateio import Gateio
from freqtrade.exchange.hitbtc import Hitbtc
from freqtrade.exchange.kraken import Kraken
from freqtrade.exchange.kucoin import Kucoin
from freqtrade.exchange.okex import Okex

10
freqtrade/exchange/binance.py
View File

@ -1,6 +1,6 @@
""" Binance exchange subclass """
import logging
from typing import Dict, List
from typing import Dict, List, Tuple
import arrow
import ccxt
@ -93,8 +93,9 @@ class Binance(Exchange):
raise OperationalException(e) from e
async def _async_get_historic_ohlcv(self, pair: str, timeframe: str,
since_ms: int, is_new_pair: bool
) -> List:
since_ms: int, is_new_pair: bool = False,
raise_: bool = False
) -> Tuple[str, str, List]:
"""
Overwrite to introduce "fast new pair" functionality by detecting the pair's listing date
Does not work for other exchanges, which don't return the earliest data when called with "0"
@ -107,4 +108,5 @@ class Binance(Exchange):
logger.info(f"Candle-data for {pair} available starting with "
f"{arrow.get(since_ms // 1000).isoformat()}.")
return await super()._async_get_historic_ohlcv(
pair=pair, timeframe=timeframe, since_ms=since_ms, is_new_pair=is_new_pair)
pair=pair, timeframe=timeframe, since_ms=since_ms, is_new_pair=is_new_pair,
raise_=raise_)

13
freqtrade/exchange/common.py
View File

@ -81,9 +81,16 @@ def retrier_async(f):
count -= 1
kwargs.update({'count': count})
if isinstance(ex, DDosProtection):
backoff_delay = calculate_backoff(count + 1, API_RETRY_COUNT)
logger.info(f"Applying DDosProtection backoff delay: {backoff_delay}")
await asyncio.sleep(backoff_delay)
if "kucoin" in str(ex) and "429000" in str(ex):
# Temporary fix for 429000 error on kucoin
# see https://github.com/freqtrade/freqtrade/issues/5700 for details.
logger.warning(
f"Kucoin 429 error, avoid triggering DDosProtection backoff delay. "
f"{count} tries left before giving up")
else:
backoff_delay = calculate_backoff(count + 1, API_RETRY_COUNT)
logger.info(f"Applying DDosProtection backoff delay: {backoff_delay}")
await asyncio.sleep(backoff_delay)
return await wrapper(*args, **kwargs)
else:
logger.warning('Giving up retrying: %s()', f.__name__)

70
freqtrade/exchange/exchange.py
View File

@ -7,7 +7,7 @@ import http
import inspect
import logging
from copy import deepcopy
from datetime import datetime, timezone
from datetime import datetime, timedelta, timezone
from math import ceil
from typing import Any, Dict, List, Optional, Tuple
@ -155,8 +155,8 @@ class Exchange:
self.validate_pairs(config['exchange']['pair_whitelist'])
self.validate_ordertypes(config.get('order_types', {}))
self.validate_order_time_in_force(config.get('order_time_in_force', {}))
self.validate_required_startup_candles(config.get('startup_candle_count', 0),
config.get('timeframe', ''))
self.required_candle_call_count = self.validate_required_startup_candles(
config.get('startup_candle_count', 0), config.get('timeframe', ''))
# Converts the interval provided in minutes in config to seconds
self.markets_refresh_interval: int = exchange_config.get(
@ -471,16 +471,29 @@ class Exchange:
raise OperationalException(
f'Time in force policies are not supported for {self.name} yet.')
def validate_required_startup_candles(self, startup_candles: int, timeframe: str) -> None:
def validate_required_startup_candles(self, startup_candles: int, timeframe: str) -> int:
"""
Checks if required startup_candles is more than ohlcv_candle_limit().
Requires a grace-period of 5 candles - so a startup-period up to 494 is allowed by default.
"""
candle_limit = self.ohlcv_candle_limit(timeframe)
if startup_candles + 5 > candle_limit:
# Require one more candle - to account for the still open candle.
candle_count = startup_candles + 1
# Allow 5 calls to the exchange per pair
required_candle_call_count = int(
(candle_count / candle_limit) + (0 if candle_count % candle_limit == 0 else 1))
if required_candle_call_count > 5:
# Only allow 5 calls per pair to somewhat limit the impact
raise OperationalException(
f"This strategy requires {startup_candles} candles to start. "
f"{self.name} only provides {candle_limit - 5} for {timeframe}.")
f"This strategy requires {startup_candles} candles to start, which is more than 5x "
f"the amount of candles {self.name} provides for {timeframe}.")
if required_candle_call_count > 1:
logger.warning(f"Using {required_candle_call_count} calls to get OHLCV. "
f"This can result in slower operations for the bot. Please check "
f"if you really need {startup_candles} candles for your strategy")
return required_candle_call_count
def exchange_has(self, endpoint: str) -> bool:
"""
@ -1205,9 +1218,11 @@ class Exchange:
:param since_ms: Timestamp in milliseconds to get history from
:return: List with candle (OHLCV) data
"""
return asyncio.get_event_loop().run_until_complete(
pair, timeframe, data = asyncio.get_event_loop().run_until_complete(
self._async_get_historic_ohlcv(pair=pair, timeframe=timeframe,
since_ms=since_ms, is_new_pair=is_new_pair))
logger.info(f"Downloaded data for {pair} with length {len(data)}.")
return data
def get_historic_ohlcv_as_df(self, pair: str, timeframe: str,
since_ms: int) -> DataFrame:
@ -1223,8 +1238,9 @@ class Exchange:
drop_incomplete=self._ohlcv_partial_candle)
async def _async_get_historic_ohlcv(self, pair: str, timeframe: str,
since_ms: int, is_new_pair: bool
) -> List:
since_ms: int, is_new_pair: bool = False,
raise_: bool = False
) -> Tuple[str, str, List]:
"""
Download historic ohlcv
:param is_new_pair: used by binance subclass to allow "fast" new pair downloading
@ -1248,15 +1264,17 @@ class Exchange:
for res in results:
if isinstance(res, Exception):
logger.warning("Async code raised an exception: %s", res.__class__.__name__)
if raise_:
raise
continue
# Deconstruct tuple if it's not an exception
p, _, new_data = res
if p == pair:
data.extend(new_data)
else:
# Deconstruct tuple if it's not an exception
p, _, new_data = res
if p == pair:
data.extend(new_data)
# Sort data again after extending the result - above calls return in "async order"
data = sorted(data, key=lambda x: x[0])
logger.info(f"Downloaded data for {pair} with length {len(data)}.")
return data
return pair, timeframe, data
def refresh_latest_ohlcv(self, pair_list: ListPairsWithTimeframes, *,
since_ms: Optional[int] = None, cache: bool = True
@ -1276,10 +1294,22 @@ class Exchange:
cached_pairs = []
# Gather coroutines to run
for pair, timeframe in set(pair_list):
if (((pair, timeframe) not in self._klines)
if ((pair, timeframe) not in self._klines
or self._now_is_time_to_refresh(pair, timeframe)):
input_coroutines.append(self._async_get_candle_history(pair, timeframe,
since_ms=since_ms))
if not since_ms and self.required_candle_call_count > 1:
# Multiple calls for one pair - to get more history
one_call = timeframe_to_msecs(timeframe) * self.ohlcv_candle_limit(timeframe)
move_to = one_call * self.required_candle_call_count
now = timeframe_to_next_date(timeframe)
since_ms = int((now - timedelta(seconds=move_to // 1000)).timestamp() * 1000)
if since_ms:
input_coroutines.append(self._async_get_historic_ohlcv(
pair, timeframe, since_ms=since_ms, raise_=True))
else:
# One call ... "regular" refresh
input_coroutines.append(self._async_get_candle_history(
pair, timeframe, since_ms=since_ms))
else:
logger.debug(
"Using cached candle (OHLCV) data for pair %s, timeframe %s ...",
@ -1534,7 +1564,7 @@ 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', 'kraken']
return exchange_name in ['bittrex', 'binance', 'kraken', 'ftx', 'gateio', 'okex']
def ccxt_exchanges(ccxt_module: CcxtModuleType = None) -> List[str]:

8
freqtrade/exchange/kucoin.py
View File

@ -1,4 +1,4 @@
""" Kucoin exchange subclass """
"""Kucoin exchange subclass."""
import logging
from typing import Dict
@ -9,9 +9,9 @@ logger = logging.getLogger(__name__)
class Kucoin(Exchange):
"""
Kucoin exchange class. Contains adjustments needed for Freqtrade to work
with this exchange.
"""Kucoin exchange class.
Contains adjustments needed for Freqtrade to work with this exchange.
Please note that this exchange is not included in the list of exchanges
officially supported by the Freqtrade development team. So some features

18
freqtrade/exchange/okex.py Normal file
View File

@ -0,0 +1,18 @@
import logging
from typing import Dict
from freqtrade.exchange import Exchange
logger = logging.getLogger(__name__)
class Okex(Exchange):
"""Okex exchange class.
Contains adjustments needed for Freqtrade to work with this exchange.
"""
_ft_has: Dict = {
"ohlcv_candle_limit": 100,
}

75
freqtrade/freqtradebot.py
View File

@ -193,19 +193,20 @@ class FreqtradeBot(LoggingMixin):
def check_for_open_trades(self):
"""
Notify the user when the bot is stopped
Notify the user when the bot is stopped (not reloaded)
and there are still open trades active.
"""
open_trades = Trade.get_trades([Trade.is_open.is_(True)]).all()
if len(open_trades) != 0:
if len(open_trades) != 0 and self.state != State.RELOAD_CONFIG:
msg = {
'type': RPCMessageType.WARNING,
'status': f"{len(open_trades)} open trades active.\n\n"
f"Handle these trades manually on {self.exchange.name}, "
f"or '/start' the bot again and use '/stopbuy' "
f"to handle open trades gracefully. \n"
f"{'Trades are simulated.' if self.config['dry_run'] else ''}",
'status':
f"{len(open_trades)} open trades active.\n\n"
f"Handle these trades manually on {self.exchange.name}, "
f"or '/start' the bot again and use '/stopbuy' "
f"to handle open trades gracefully. \n"
f"{'Note: Trades are simulated (dry run).' if self.config['dry_run'] else ''}",
}
self.rpc.send_msg(msg)
@ -420,7 +421,7 @@ class FreqtradeBot(LoggingMixin):
return False
# running get_signal on historical data fetched
(buy, sell, buy_tag) = self.strategy.get_signal(
(buy, sell, buy_tag, _) = self.strategy.get_signal(
pair,
self.strategy.timeframe,
analyzed_df
@ -500,7 +501,7 @@ class FreqtradeBot(LoggingMixin):
pair=pair, current_time=datetime.now(timezone.utc),
current_rate=enter_limit_requested, proposed_stake=stake_amount,
min_stake=min_stake_amount, max_stake=max_stake_amount)
stake_amount = self.wallets._validate_stake_amount(pair, stake_amount, min_stake_amount)
stake_amount = self.wallets.validate_stake_amount(pair, stake_amount, min_stake_amount)
if not stake_amount:
return False
@ -700,21 +701,22 @@ class FreqtradeBot(LoggingMixin):
logger.debug('Handling %s ...', trade)
(buy, sell) = (False, False)
exit_tag = None
if (self.config.get('use_sell_signal', True) or
self.config.get('ignore_roi_if_buy_signal', False)):
analyzed_df, _ = self.dataprovider.get_analyzed_dataframe(trade.pair,
self.strategy.timeframe)
(buy, sell, _) = self.strategy.get_signal(
(buy, sell, _, exit_tag) = self.strategy.get_signal(
trade.pair,
self.strategy.timeframe,
analyzed_df
)
logger.debug('checking sell')
exit_rate = self.exchange.get_rate(trade.pair, refresh=True, side="sell")
if self._check_and_execute_exit(trade, exit_rate, buy, sell):
sell_rate = self.exchange.get_rate(trade.pair, refresh=True, side="sell")
if self._check_and_execute_exit(trade, sell_rate, buy, sell, exit_tag):
return True
logger.debug('Found no sell signal for %s.', trade)
@ -852,18 +854,21 @@ class FreqtradeBot(LoggingMixin):
f"for pair {trade.pair}.")
def _check_and_execute_exit(self, trade: Trade, exit_rate: float,
buy: bool, sell: bool) -> bool:
buy: bool, sell: bool, exit_tag: Optional[str]) -> bool:
"""
Check and execute exit
"""
should_sell = self.strategy.should_sell(
trade, exit_rate, datetime.now(timezone.utc), buy, sell,
force_stoploss=self.edge.stoploss(trade.pair) if self.edge else 0
)
if should_sell.sell_flag:
logger.info(f'Executing Sell for {trade.pair}. Reason: {should_sell.sell_type}')
self.execute_trade_exit(trade, exit_rate, should_sell)
logger.info(
f'Executing Sell for {trade.pair}. Reason: {should_sell.sell_type}. '
f'Tag: {exit_tag if exit_tag is not None else "None"}')
self.execute_trade_exit(trade, exit_rate, should_sell, exit_tag)
return True
return False
@ -916,6 +921,13 @@ class FreqtradeBot(LoggingMixin):
trade=trade,
order=order))):
self.handle_cancel_exit(trade, order, constants.CANCEL_REASON['TIMEOUT'])
canceled_count = trade.get_exit_order_count()
max_timeouts = self.config.get('unfilledtimeout', {}).get('exit_timeout_count', 0)
if max_timeouts > 0 and canceled_count >= max_timeouts:
logger.warning(f'Emergencyselling trade {trade}, as the sell order '
f'timed out {max_timeouts} times.')
self.execute_trade_exit(trade, order.get('price'), sell_reason=SellCheckTuple(
sell_type=SellType.EMERGENCY_SELL))
def cancel_all_open_orders(self) -> None:
"""
@ -1064,7 +1076,12 @@ class FreqtradeBot(LoggingMixin):
raise DependencyException(
f"Not enough amount to sell. Trade-amount: {amount}, Wallet: {wallet_amount}")
def execute_trade_exit(self, trade: Trade, limit: float, sell_reason: SellCheckTuple) -> bool:
def execute_trade_exit(
self,
trade: Trade,
limit: float,
sell_reason: SellCheckTuple,
exit_tag: Optional[str] = None) -> bool:
"""
Executes a trade exit for the given trade and limit
:param trade: Trade instance
@ -1140,7 +1157,7 @@ class FreqtradeBot(LoggingMixin):
trade.open_order_id = order['id']
trade.sell_order_status = ''
trade.close_rate_requested = limit
trade.sell_reason = sell_reason.sell_reason
trade.sell_reason = exit_tag or sell_reason.sell_reason
# In case of market sell orders the order can be closed immediately
if order.get('status', 'unknown') in ('closed', 'expired'):
self.update_trade_state(trade, trade.open_order_id, order)
@ -1181,6 +1198,7 @@ class FreqtradeBot(LoggingMixin):
'current_rate': current_rate,
'profit_amount': profit_trade,
'profit_ratio': profit_ratio,
'buy_tag': trade.buy_tag,
'sell_reason': trade.sell_reason,
'open_date': trade.open_date,
'close_date': trade.close_date or datetime.utcnow(),
@ -1224,6 +1242,7 @@ class FreqtradeBot(LoggingMixin):
'current_rate': current_rate,
'profit_amount': profit_trade,
'profit_ratio': profit_ratio,
'buy_tag': trade.buy_tag,
'sell_reason': trade.sell_reason,
'open_date': trade.open_date,
'close_date': trade.close_date or datetime.now(timezone.utc),
@ -1270,6 +1289,11 @@ class FreqtradeBot(LoggingMixin):
trade.update_order(order)
if self.exchange.check_order_canceled_empty(order):
# Trade has been cancelled on exchange
# Handling of this will happen in check_handle_timedout.
return True
# Try update amount (binance-fix)
try:
new_amount = self.get_real_amount(trade, order)
@ -1281,10 +1305,6 @@ class FreqtradeBot(LoggingMixin):
except DependencyException as exception:
logger.warning("Could not update trade amount: %s", exception)
if self.exchange.check_order_canceled_empty(order):
# Trade has been cancelled on exchange
# Handling of this will happen in check_handle_timeout.
return True
trade.update(order)
Trade.commit()
@ -1361,14 +1381,17 @@ class FreqtradeBot(LoggingMixin):
return self.apply_fee_conditional(trade, trade_base_currency,
amount=order_amount, fee_abs=fee_cost)
return order_amount
return self.fee_detection_from_trades(trade, order, order_amount)
return self.fee_detection_from_trades(trade, order, order_amount, order.get('trades', []))
def fee_detection_from_trades(self, trade: Trade, order: Dict, order_amount: float) -> float:
def fee_detection_from_trades(self, trade: Trade, order: Dict, order_amount: float,
trades: List) -> float:
"""
fee-detection fallback to Trades. Parses result of fetch_my_trades to get correct fee.
fee-detection fallback to Trades.
Either uses provided trades list or the result of fetch_my_trades to get correct fee.
"""
trades = self.exchange.get_trades_for_order(self.exchange.get_order_id_conditional(order),
trade.pair, trade.open_date)
if not trades:
trades = self.exchange.get_trades_for_order(
self.exchange.get_order_id_conditional(order), trade.pair, trade.open_date)
if len(trades) == 0:
logger.info("Applying fee on amount for %s failed: myTrade-Dict empty found", trade)

31
freqtrade/optimize/backtesting.py
View File

@ -44,6 +44,7 @@ SELL_IDX = 4
LOW_IDX = 5
HIGH_IDX = 6
BUY_TAG_IDX = 7
EXIT_TAG_IDX = 8
class Backtesting:
@ -66,7 +67,7 @@ class Backtesting:
self.all_results: Dict[str, Dict] = {}
self.exchange = ExchangeResolver.load_exchange(self.config['exchange']['name'], self.config)
self.dataprovider = DataProvider(self.config, None)
self.dataprovider = DataProvider(self.config, self.exchange)
if self.config.get('strategy_list', None):
for strat in list(self.config['strategy_list']):
@ -88,7 +89,8 @@ class Backtesting:
self.init_backtest_detail()
self.pairlists = PairListManager(self.exchange, self.config)
if 'VolumePairList' in self.pairlists.name_list:
raise OperationalException("VolumePairList not allowed for backtesting.")
raise OperationalException("VolumePairList not allowed for backtesting. "
"Please use StaticPairlist instead.")
if 'PerformanceFilter' in self.pairlists.name_list:
raise OperationalException("PerformanceFilter not allowed for backtesting.")
@ -247,7 +249,7 @@ class Backtesting:
"""
# Every change to this headers list must evaluate further usages of the resulting tuple
# and eventually change the constants for indexes at the top
headers = ['date', 'buy', 'open', 'close', 'sell', 'low', 'high', 'buy_tag']
headers = ['date', 'buy', 'open', 'close', 'sell', 'low', 'high', 'buy_tag', 'exit_tag']
data: Dict = {}
self.progress.init_step(BacktestState.CONVERT, len(processed))
@ -259,6 +261,7 @@ class Backtesting:
pair_data.loc[:, 'buy'] = 0 # cleanup if buy_signal is exist
pair_data.loc[:, 'sell'] = 0 # cleanup if sell_signal is exist
pair_data.loc[:, 'buy_tag'] = None # cleanup if buy_tag is exist
pair_data.loc[:, 'exit_tag'] = None # cleanup if exit_tag is exist
df_analyzed = self.strategy.advise_sell(
self.strategy.advise_buy(pair_data, {'pair': pair}), {'pair': pair}).copy()
@ -270,6 +273,7 @@ class Backtesting:
df_analyzed.loc[:, 'buy'] = df_analyzed.loc[:, 'buy'].shift(1)
df_analyzed.loc[:, 'sell'] = df_analyzed.loc[:, 'sell'].shift(1)
df_analyzed.loc[:, 'buy_tag'] = df_analyzed.loc[:, 'buy_tag'].shift(1)
df_analyzed.loc[:, 'exit_tag'] = df_analyzed.loc[:, 'exit_tag'].shift(1)
# Update dataprovider cache
self.dataprovider._set_cached_df(pair, self.timeframe, df_analyzed)
@ -312,7 +316,9 @@ class Backtesting:
# Worst case: price ticks tiny bit above open and dives down.
stop_rate = sell_row[OPEN_IDX] * (1 - abs(trade.stop_loss_pct))
assert stop_rate < sell_row[HIGH_IDX]
return stop_rate
# Limit lower-end to candle low to avoid sells below the low.
# This still remains "worst case" - but "worst realistic case".
return max(sell_row[LOW_IDX], stop_rate)
# Set close_rate to stoploss
return trade.stop_loss
@ -357,7 +363,7 @@ class Backtesting:
if sell.sell_flag:
trade.close_date = sell_candle_time
trade.sell_reason = sell.sell_reason
trade_dur = int((trade.close_date_utc - trade.open_date_utc).total_seconds() // 60)
closerate = self._get_close_rate(sell_row, trade, sell, trade_dur)
@ -371,6 +377,17 @@ class Backtesting:
current_time=sell_candle_time):
return None
trade.sell_reason = sell.sell_reason
# Checks and adds an exit tag, after checking that the length of the
# sell_row has the length for an exit tag column
if(
len(sell_row) > EXIT_TAG_IDX
and sell_row[EXIT_TAG_IDX] is not None
and len(sell_row[EXIT_TAG_IDX]) > 0
):
trade.sell_reason = sell_row[EXIT_TAG_IDX]
trade.close(closerate, show_msg=False)
return trade
@ -385,7 +402,7 @@ class Backtesting:
detail_data = detail_data.loc[
(detail_data['date'] >= sell_candle_time) &
(detail_data['date'] < sell_candle_end)
].copy()
].copy()
if len(detail_data) == 0:
# Fall back to "regular" data if no detail data was found for this candle
return self._get_sell_trade_entry_for_candle(trade, sell_row)
@ -415,7 +432,7 @@ class Backtesting:
default_retval=stake_amount)(
pair=pair, current_time=row[DATE_IDX].to_pydatetime(), current_rate=row[OPEN_IDX],
proposed_stake=stake_amount, min_stake=min_stake_amount, max_stake=max_stake_amount)
stake_amount = self.wallets._validate_stake_amount(pair, stake_amount, min_stake_amount)
stake_amount = self.wallets.validate_stake_amount(pair, stake_amount, min_stake_amount)
if not stake_amount:
return None

64
freqtrade/optimize/hyperopt_loss_calmar.py Normal file
View File

@ -0,0 +1,64 @@
"""
CalmarHyperOptLoss
This module defines the alternative HyperOptLoss class which can be used for
Hyperoptimization.
"""
from datetime import datetime
from math import sqrt as msqrt
from typing import Any, Dict
from pandas import DataFrame
from freqtrade.data.btanalysis import calculate_max_drawdown
from freqtrade.optimize.hyperopt import IHyperOptLoss
class CalmarHyperOptLoss(IHyperOptLoss):
"""
Defines the loss function for hyperopt.
This implementation uses the Calmar Ratio calculation.
"""
@staticmethod
def hyperopt_loss_function(
results: DataFrame,
trade_count: int,
min_date: datetime,
max_date: datetime,
config: Dict,
processed: Dict[str, DataFrame],
backtest_stats: Dict[str, Any],
*args,
**kwargs
) -> float:
"""
Objective function, returns smaller number for more optimal results.
Uses Calmar Ratio calculation.
"""
total_profit = backtest_stats["profit_total"]
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 * 100
# calculate max drawdown
try:
_, _, _, high_val, low_val = calculate_max_drawdown(
results, value_col="profit_abs"
)
max_drawdown = (high_val - low_val) / high_val
except ValueError:
max_drawdown = 0
if max_drawdown != 0:
calmar_ratio = expected_returns_mean / max_drawdown * msqrt(365)
else:
# Define high (negative) calmar ratio to be clear that this is NOT optimal.
calmar_ratio = -20.0
# print(expected_returns_mean, max_drawdown, calmar_ratio)
return -calmar_ratio

8
freqtrade/optimize/hyperopt_tools.py
View File

@ -284,10 +284,10 @@ class HyperoptTools():
return (f"{results_metrics['total_trades']:6d} trades. "
f"{results_metrics['wins']}/{results_metrics['draws']}"
f"/{results_metrics['losses']} Wins/Draws/Losses. "
f"Avg profit {results_metrics['profit_mean'] * 100: 6.2f}%. "
f"Median profit {results_metrics['profit_median'] * 100: 6.2f}%. "
f"Total profit {results_metrics['profit_total_abs']: 11.8f} {stake_currency} "
f"({results_metrics['profit_total'] * 100: 7.2f}%). "
f"Avg profit {results_metrics['profit_mean']:7.2%}. "
f"Median profit {results_metrics['profit_median']:7.2%}. "
f"Total profit {results_metrics['profit_total_abs']:11.8f} {stake_currency} "
f"({results_metrics['profit_total']:8.2%}). "
f"Avg duration {results_metrics['holding_avg']} min."
)

109
freqtrade/optimize/optimize_reports.py
View File

@ -46,11 +46,11 @@ def _get_line_floatfmt(stake_currency: str) -> List[str]:
'.2f', 'd', 's', 's']
def _get_line_header(first_column: str, stake_currency: str) -> List[str]:
def _get_line_header(first_column: str, stake_currency: str, direction: str = 'Buys') -> List[str]:
"""
Generate header lines (goes in line with _generate_result_line())
"""
return [first_column, 'Buys', 'Avg Profit %', 'Cum Profit %',
return [first_column, direction, 'Avg Profit %', 'Cum Profit %',
f'Tot Profit {stake_currency}', 'Tot Profit %', 'Avg Duration',
'Win Draw Loss Win%']
@ -127,6 +127,38 @@ def generate_pair_metrics(data: Dict[str, Dict], stake_currency: str, starting_b
return tabular_data
def generate_tag_metrics(tag_type: str,
starting_balance: int,
results: DataFrame,
skip_nan: bool = False) -> List[Dict]:
"""
Generates and returns a list of metrics for the given tag trades and the results dataframe
:param starting_balance: Starting balance
:param results: Dataframe containing the backtest results
:param skip_nan: Print "left open" open trades
:return: List of Dicts containing the metrics per pair
"""
tabular_data = []
if tag_type in results.columns:
for tag, count in results[tag_type].value_counts().iteritems():
result = results[results[tag_type] == tag]
if skip_nan and result['profit_abs'].isnull().all():
continue
tabular_data.append(_generate_result_line(result, starting_balance, tag))
# Sort by total profit %:
tabular_data = sorted(tabular_data, key=lambda k: k['profit_total_abs'], reverse=True)
# Append Total
tabular_data.append(_generate_result_line(results, starting_balance, 'TOTAL'))
return tabular_data
else:
return []
def generate_sell_reason_stats(max_open_trades: int, results: DataFrame) -> List[Dict]:
"""
Generate small table outlining Backtest results
@ -347,6 +379,10 @@ def generate_strategy_stats(btdata: Dict[str, DataFrame],
pair_results = generate_pair_metrics(btdata, stake_currency=stake_currency,
starting_balance=starting_balance,
results=results, skip_nan=False)
buy_tag_results = generate_tag_metrics("buy_tag", starting_balance=starting_balance,
results=results, skip_nan=False)
sell_reason_stats = generate_sell_reason_stats(max_open_trades=max_open_trades,
results=results)
left_open_results = generate_pair_metrics(btdata, stake_currency=stake_currency,
@ -370,6 +406,7 @@ def generate_strategy_stats(btdata: Dict[str, DataFrame],
'best_pair': best_pair,
'worst_pair': worst_pair,
'results_per_pair': pair_results,
'results_per_buy_tag': buy_tag_results,
'sell_reason_summary': sell_reason_stats,
'left_open_trades': left_open_results,
# 'days_breakdown_stats': days_breakdown_stats,
@ -542,6 +579,37 @@ def text_table_sell_reason(sell_reason_stats: List[Dict[str, Any]], stake_curren
return tabulate(output, headers=headers, tablefmt="orgtbl", stralign="right")
def text_table_tags(tag_type: str, tag_results: List[Dict[str, Any]], stake_currency: str) -> str:
"""
Generates and returns a text table for the given backtest data and the results dataframe
:param pair_results: List of Dictionaries - one entry per pair + final TOTAL row
:param stake_currency: stake-currency - used to correctly name headers
:return: pretty printed table with tabulate as string
"""
if(tag_type == "buy_tag"):
headers = _get_line_header("TAG", stake_currency)
else:
headers = _get_line_header("TAG", stake_currency, 'Sells')
floatfmt = _get_line_floatfmt(stake_currency)
output = [
[
t['key'] if t['key'] is not None and len(
t['key']) > 0 else "OTHER",
t['trades'],
t['profit_mean_pct'],
t['profit_sum_pct'],
t['profit_total_abs'],
t['profit_total_pct'],
t['duration_avg'],
_generate_wins_draws_losses(
t['wins'],
t['draws'],
t['losses'])] for t in tag_results]
# Ignore type as floatfmt does allow tuples but mypy does not know that
return tabulate(output, headers=headers,
floatfmt=floatfmt, tablefmt="orgtbl", stralign="right")
def text_table_periodic_breakdown(days_breakdown_stats: List[Dict[str, Any]],
stake_currency: str, period: str) -> str:
"""
@ -615,22 +683,22 @@ def text_table_add_metrics(strat_results: Dict) -> str:
strat_results['stake_currency'])),
('Absolute profit ', round_coin_value(strat_results['profit_total_abs'],
strat_results['stake_currency'])),
('Total profit %', f"{round(strat_results['profit_total'] * 100, 2)}%"),
('Total profit %', f"{strat_results['profit_total']:.2%}"),
('Trades per day', strat_results['trades_per_day']),
('Avg. daily profit %',
f"{round(strat_results['profit_total'] / strat_results['backtest_days'] * 100, 2)}%"),
f"{(strat_results['profit_total'] / strat_results['backtest_days']):.2%}"),
('Avg. stake amount', round_coin_value(strat_results['avg_stake_amount'],
strat_results['stake_currency'])),
('Total trade volume', round_coin_value(strat_results['total_volume'],
strat_results['stake_currency'])),
('', ''), # Empty line to improve readability
('Best Pair', f"{strat_results['best_pair']['key']} "
f"{round(strat_results['best_pair']['profit_sum_pct'], 2)}%"),
f"{strat_results['best_pair']['profit_sum']:.2%}"),
('Worst Pair', f"{strat_results['worst_pair']['key']} "
f"{round(strat_results['worst_pair']['profit_sum_pct'], 2)}%"),
('Best trade', f"{best_trade['pair']} {round(best_trade['profit_ratio'] * 100, 2)}%"),
f"{strat_results['worst_pair']['profit_sum']:.2%}"),
('Best trade', f"{best_trade['pair']} {best_trade['profit_ratio']:.2%}"),
('Worst trade', f"{worst_trade['pair']} "
f"{round(worst_trade['profit_ratio'] * 100, 2)}%"),
f"{worst_trade['profit_ratio']:.2%}"),
('Best day', round_coin_value(strat_results['backtest_best_day_abs'],
strat_results['stake_currency'])),
@ -648,7 +716,7 @@ def text_table_add_metrics(strat_results: Dict) -> str:
('Max balance', round_coin_value(strat_results['csum_max'],
strat_results['stake_currency'])),
('Drawdown', f"{round(strat_results['max_drawdown'] * 100, 2)}%"),
('Drawdown', f"{strat_results['max_drawdown']:.2%}"),
('Drawdown', round_coin_value(strat_results['max_drawdown_abs'],
strat_results['stake_currency'])),
('Drawdown high', round_coin_value(strat_results['max_drawdown_high'],
@ -657,7 +725,7 @@ def text_table_add_metrics(strat_results: Dict) -> str:
strat_results['stake_currency'])),
('Drawdown Start', strat_results['drawdown_start']),
('Drawdown End', strat_results['drawdown_end']),
('Market change', f"{round(strat_results['market_change'] * 100, 2)}%"),
('Market change', f"{strat_results['market_change']:.2%}"),
]
return tabulate(metrics, headers=["Metric", "Value"], tablefmt="orgtbl")
@ -687,6 +755,16 @@ def show_backtest_result(strategy: str, results: Dict[str, Any], stake_currency:
print(' BACKTESTING REPORT '.center(len(table.splitlines()[0]), '='))
print(table)
if results.get('results_per_buy_tag') is not None:
table = text_table_tags(
"buy_tag",
results['results_per_buy_tag'],
stake_currency=stake_currency)
if isinstance(table, str) and len(table) > 0:
print(' BUY TAG STATS '.center(len(table.splitlines()[0]), '='))
print(table)
table = text_table_sell_reason(sell_reason_stats=results['sell_reason_summary'],
stake_currency=stake_currency)
if isinstance(table, str) and len(table) > 0:
@ -714,6 +792,7 @@ def show_backtest_result(strategy: str, results: Dict[str, Any], stake_currency:
if isinstance(table, str) and len(table) > 0:
print('=' * len(table.splitlines()[0]))
print()
@ -735,3 +814,13 @@ def show_backtest_results(config: Dict, backtest_stats: Dict):
print(table)
print('=' * len(table.splitlines()[0]))
print('\nFor more details, please look at the detail tables above')
def show_sorted_pairlist(config: Dict, backtest_stats: Dict):
if config.get('backtest_show_pair_list', False):
for strategy, results in backtest_stats['strategy'].items():
print(f"Pairs for Strategy {strategy}: \n[")
for result in results['results_per_pair']:
if result["key"] != 'TOTAL':
print(f'"{result["key"]}", // {result["profit_mean"]:.2%}')
print("]")

135
freqtrade/persistence/models.py
View File

@ -195,6 +195,8 @@ class Order(_DECL_BASE):
@staticmethod
def get_open_orders() -> List['Order']:
"""
Retrieve open orders from the database
:return: List of open orders
"""
return Order.query.filter(Order.ft_is_open.is_(True)).all()
@ -491,6 +493,13 @@ class LocalTrade():
def update_order(self, order: Dict) -> None:
Order.update_orders(self.orders, order)
def get_exit_order_count(self) -> int:
"""
Get amount of failed exiting orders
assumes full exits.
"""
return len([o for o in self.orders if o.ft_order_side == 'sell'])
def _calc_open_trade_value(self) -> float:
"""
Calculate the open_rate including open_fee.
@ -775,7 +784,7 @@ class Trade(_DECL_BASE, LocalTrade):
return Trade.query
@staticmethod
def get_open_order_trades():
def get_open_order_trades() -> List['Trade']:
"""
Returns all open trades
NOTE: Not supported in Backtesting.
@ -853,13 +862,132 @@ class Trade(_DECL_BASE, LocalTrade):
return [
{
'pair': pair,
'profit': profit,
'profit_ratio': profit,
'profit': round(profit * 100, 2), # Compatibility mode
'profit_pct': round(profit * 100, 2),
'profit_abs': profit_abs,
'count': count
}
for pair, profit, profit_abs, count in pair_rates
]
@staticmethod
def get_buy_tag_performance(pair: Optional[str]) -> List[Dict[str, Any]]:
"""
Returns List of dicts containing all Trades, based on buy tag performance
Can either be average for all pairs or a specific pair provided
NOTE: Not supported in Backtesting.
"""
filters = [Trade.is_open.is_(False)]
if(pair is not None):
filters.append(Trade.pair == pair)
buy_tag_perf = Trade.query.with_entities(
Trade.buy_tag,
func.sum(Trade.close_profit).label('profit_sum'),
func.sum(Trade.close_profit_abs).label('profit_sum_abs'),
func.count(Trade.pair).label('count')
).filter(*filters)\
.group_by(Trade.buy_tag) \
.order_by(desc('profit_sum_abs')) \
.all()
return [
{
'buy_tag': buy_tag if buy_tag is not None else "Other",
'profit_ratio': profit,
'profit_pct': round(profit * 100, 2),
'profit_abs': profit_abs,
'count': count
}
for buy_tag, profit, profit_abs, count in buy_tag_perf
]
@staticmethod
def get_sell_reason_performance(pair: Optional[str]) -> List[Dict[str, Any]]:
"""
Returns List of dicts containing all Trades, based on sell reason performance
Can either be average for all pairs or a specific pair provided
NOTE: Not supported in Backtesting.
"""
filters = [Trade.is_open.is_(False)]
if(pair is not None):
filters.append(Trade.pair == pair)
sell_tag_perf = Trade.query.with_entities(
Trade.sell_reason,
func.sum(Trade.close_profit).label('profit_sum'),
func.sum(Trade.close_profit_abs).label('profit_sum_abs'),
func.count(Trade.pair).label('count')
).filter(*filters)\
.group_by(Trade.sell_reason) \
.order_by(desc('profit_sum_abs')) \
.all()
return [
{
'sell_reason': sell_reason if sell_reason is not None else "Other",
'profit_ratio': profit,
'profit_pct': round(profit * 100, 2),
'profit_abs': profit_abs,
'count': count
}
for sell_reason, profit, profit_abs, count in sell_tag_perf
]
@staticmethod
def get_mix_tag_performance(pair: Optional[str]) -> List[Dict[str, Any]]:
"""
Returns List of dicts containing all Trades, based on buy_tag + sell_reason performance
Can either be average for all pairs or a specific pair provided
NOTE: Not supported in Backtesting.
"""
filters = [Trade.is_open.is_(False)]
if(pair is not None):
filters.append(Trade.pair == pair)
mix_tag_perf = Trade.query.with_entities(
Trade.id,
Trade.buy_tag,
Trade.sell_reason,
func.sum(Trade.close_profit).label('profit_sum'),
func.sum(Trade.close_profit_abs).label('profit_sum_abs'),
func.count(Trade.pair).label('count')
).filter(*filters)\
.group_by(Trade.id) \
.order_by(desc('profit_sum_abs')) \
.all()
return_list: List[Dict] = []
for id, buy_tag, sell_reason, profit, profit_abs, count in mix_tag_perf:
buy_tag = buy_tag if buy_tag is not None else "Other"
sell_reason = sell_reason if sell_reason is not None else "Other"
if(sell_reason is not None and buy_tag is not None):
mix_tag = buy_tag + " " + sell_reason
i = 0
if not any(item["mix_tag"] == mix_tag for item in return_list):
return_list.append({'mix_tag': mix_tag,
'profit': profit,
'profit_pct': round(profit * 100, 2),
'profit_abs': profit_abs,
'count': count})
else:
while i < len(return_list):
if return_list[i]["mix_tag"] == mix_tag:
return_list[i] = {
'mix_tag': mix_tag,
'profit': profit + return_list[i]["profit"],
'profit_pct': round(profit + return_list[i]["profit"] * 100, 2),
'profit_abs': profit_abs + return_list[i]["profit_abs"],
'count': 1 + return_list[i]["count"]}
i += 1
return return_list
@staticmethod
def get_best_pair(start_date: datetime = datetime.fromtimestamp(0)):
"""
@ -896,7 +1024,7 @@ class PairLock(_DECL_BASE):
lock_time = self.lock_time.strftime(DATETIME_PRINT_FORMAT)
lock_end_time = self.lock_end_time.strftime(DATETIME_PRINT_FORMAT)
return (f'PairLock(id={self.id}, pair={self.pair}, lock_time={lock_time}, '
f'lock_end_time={lock_end_time})')
f'lock_end_time={lock_end_time}, reason={self.reason}, active={self.active})')
@staticmethod
def query_pair_locks(pair: Optional[str], now: datetime) -> Query:
@ -905,7 +1033,6 @@ class PairLock(_DECL_BASE):
:param pair: Pair to check for. Returns all current locks if pair is empty
:param now: Datetime object (generated via datetime.now(timezone.utc)).
"""
filters = [PairLock.lock_end_time > now,
# Only active locks
PairLock.active.is_(True), ]

34
freqtrade/persistence/pairlock_middleware.py
View File

@ -103,6 +103,36 @@ class PairLocks():
if PairLocks.use_db:
PairLock.query.session.commit()
@staticmethod
def unlock_reason(reason: str, now: Optional[datetime] = None) -> None:
"""
Release all locks for this reason.
:param reason: Which reason to unlock
:param now: Datetime object (generated via datetime.now(timezone.utc)).
defaults to datetime.now(timezone.utc)
"""
if not now:
now = datetime.now(timezone.utc)
if PairLocks.use_db:
# used in live modes
logger.info(f"Releasing all locks with reason '{reason}':")
filters = [PairLock.lock_end_time > now,
PairLock.active.is_(True),
PairLock.reason == reason
]
locks = PairLock.query.filter(*filters)
for lock in locks:
logger.info(f"Releasing lock for {lock.pair} with reason '{reason}'.")
lock.active = False
PairLock.query.session.commit()
else:
# used in backtesting mode; don't show log messages for speed
locks = PairLocks.get_pair_locks(None)
for lock in locks:
if lock.reason == reason:
lock.active = False
@staticmethod
def is_global_lock(now: Optional[datetime] = None) -> bool:
"""
@ -128,7 +158,9 @@ class PairLocks():
@staticmethod
def get_all_locks() -> List[PairLock]:
"""
Return all locks, also locks with expired end date
"""
if PairLocks.use_db:
return PairLock.query.all()
else:

6
freqtrade/plot/plotting.py
View File

@ -169,8 +169,8 @@ def add_max_drawdown(fig, row, trades: pd.DataFrame, df_comb: pd.DataFrame,
df_comb.loc[timeframe_to_prev_date(timeframe, lowdate), 'cum_profit'],
],
mode='markers',
name=f"Max drawdown {max_drawdown * 100:.2f}%",
text=f"Max drawdown {max_drawdown * 100:.2f}%",
name=f"Max drawdown {max_drawdown:.2%}",
text=f"Max drawdown {max_drawdown:.2%}",
marker=dict(
symbol='square-open',
size=9,
@ -192,7 +192,7 @@ def plot_trades(fig, trades: pd.DataFrame) -> make_subplots:
# Trades can be empty
if trades is not None and len(trades) > 0:
# Create description for sell summarizing the trade
trades['desc'] = trades.apply(lambda row: f"{round(row['profit_ratio'] * 100, 1)}%, "
trades['desc'] = trades.apply(lambda row: f"{row['profit_ratio']:.2%}, "
f"{row['sell_reason']}, "
f"{row['trade_duration']} min",
axis=1)

4
freqtrade/plugins/pairlist/PriceFilter.py
View File

@ -50,7 +50,7 @@ class PriceFilter(IPairList):
"""
active_price_filters = []
if self._low_price_ratio != 0:
active_price_filters.append(f"below {self._low_price_ratio * 100}%")
active_price_filters.append(f"below {self._low_price_ratio:.1%}")
if self._min_price != 0:
active_price_filters.append(f"below {self._min_price:.8f}")
if self._max_price != 0:
@ -82,7 +82,7 @@ class PriceFilter(IPairList):
changeperc = compare / ticker['last']
if changeperc > self._low_price_ratio:
self.log_once(f"Removed {pair} from whitelist, "
f"because 1 unit is {changeperc * 100:.3f}%", logger.info)
f"because 1 unit is {changeperc:.3%}", logger.info)
return False
# Perform low_amount check

4
freqtrade/plugins/pairlist/SpreadFilter.py
View File

@ -34,7 +34,7 @@ class SpreadFilter(IPairList):
Short whitelist method description - used for startup-messages
"""
return (f"{self.name} - Filtering pairs with ask/bid diff above "
f"{self._max_spread_ratio * 100}%.")
f"{self._max_spread_ratio:.2%}.")
def _validate_pair(self, pair: str, ticker: Dict[str, Any]) -> bool:
"""
@ -47,7 +47,7 @@ class SpreadFilter(IPairList):
spread = 1 - ticker['bid'] / ticker['ask']
if spread > self._max_spread_ratio:
self.log_once(f"Removed {pair} from whitelist, because spread "
f"{spread * 100:.3f}% > {self._max_spread_ratio * 100}%",
f"{spread * 100:.3%} > {self._max_spread_ratio:.3%}",
logger.info)
return False
else:

25
freqtrade/rpc/api_server/api_schemas.py
View File

@ -63,6 +63,8 @@ class Count(BaseModel):
class PerformanceEntry(BaseModel):
pair: str
profit: float
profit_ratio: float
profit_pct: float
profit_abs: float
count: int
@ -93,6 +95,7 @@ class Profit(BaseModel):
avg_duration: str
best_pair: str
best_rate: float
best_pair_profit_ratio: float
winning_trades: int
losing_trades: int
@ -121,7 +124,27 @@ class Daily(BaseModel):
stake_currency: str
class UnfilledTimeout(BaseModel):
buy: int
sell: int
unit: str
exit_timeout_count: Optional[int]
class OrderTypes(BaseModel):
buy: str
sell: str
emergencysell: Optional[str]
forcesell: Optional[str]
forcebuy: Optional[str]
stoploss: str
stoploss_on_exchange: bool
stoploss_on_exchange_interval: Optional[int]
class ShowConfig(BaseModel):
version: str
api_version: float
dry_run: bool
stake_currency: str
stake_amount: Union[float, str]
@ -134,6 +157,8 @@ class ShowConfig(BaseModel):
trailing_stop_positive: Optional[float]
trailing_stop_positive_offset: Optional[float]
trailing_only_offset_is_reached: Optional[bool]
unfilledtimeout: UnfilledTimeout
order_types: OrderTypes
use_custom_stoploss: Optional[bool]
timeframe: Optional[str]
timeframe_ms: int

9
freqtrade/rpc/api_server/api_v1.py
View File

@ -26,6 +26,11 @@ from freqtrade.rpc.rpc import RPCException
logger = logging.getLogger(__name__)
# API version
# Pre-1.1, no version was provided
# Version increments should happen in "small" steps (1.1, 1.12, ...) unless big changes happen.
API_VERSION = 1.1
# Public API, requires no auth.
router_public = APIRouter()
# Private API, protected by authentication
@ -117,7 +122,9 @@ def show_config(rpc: Optional[RPC] = Depends(get_rpc_optional), config=Depends(g
state = ''
if rpc:
state = rpc._freqtrade.state
return RPC._rpc_show_config(config, state)
resp = RPC._rpc_show_config(config, state)
resp['api_version'] = API_VERSION
return resp
@router.post('/forcebuy', response_model=ForceBuyResponse, tags=['trading'])

136
freqtrade/rpc/rpc.py
View File

@ -9,9 +9,11 @@ from typing import Any, Dict, List, Optional, Tuple, Union
import arrow
import psutil
from dateutil.relativedelta import relativedelta
from numpy import NAN, inf, int64, mean
from pandas import DataFrame
from freqtrade import __version__
from freqtrade.configuration.timerange import TimeRange
from freqtrade.constants import CANCEL_REASON, DATETIME_PRINT_FORMAT
from freqtrade.data.history import load_data
@ -103,9 +105,10 @@ class RPC:
information via rpc.
"""
val = {
'version': __version__,
'dry_run': config['dry_run'],
'stake_currency': config['stake_currency'],
'stake_currency_decimals': decimals_per_coin(config['stake_currency']),
'stake_currency_decimals': decimals_per_coin(config['stake_currency']),
'stake_amount': config['stake_amount'],
'available_capital': config.get('available_capital'),
'max_open_trades': (config['max_open_trades']
@ -116,7 +119,9 @@ class RPC:
'trailing_stop_positive': config.get('trailing_stop_positive'),
'trailing_stop_positive_offset': config.get('trailing_stop_positive_offset'),
'trailing_only_offset_is_reached': config.get('trailing_only_offset_is_reached'),
'unfilledtimeout': config.get('unfilledtimeout'),
'use_custom_stoploss': config.get('use_custom_stoploss'),
'order_types': config.get('order_types'),
'bot_name': config.get('bot_name', 'freqtrade'),
'timeframe': config.get('timeframe'),
'timeframe_ms': timeframe_to_msecs(config['timeframe']
@ -219,9 +224,8 @@ class RPC:
trade.pair, refresh=False, side="sell")
except (PricingError, ExchangeError):
current_rate = NAN
trade_percent = (100 * trade.calc_profit_ratio(current_rate))
trade_profit = trade.calc_profit(current_rate)
profit_str = f'{trade_percent:.2f}%'
profit_str = f'{trade.calc_profit_ratio(current_rate):.2%}'
if self._fiat_converter:
fiat_profit = self._fiat_converter.convert_amount(
trade_profit,
@ -250,7 +254,7 @@ class RPC:
def _rpc_daily_profit(
self, timescale: int,
stake_currency: str, fiat_display_currency: str) -> Dict[str, Any]:
today = datetime.utcnow().date()
today = datetime.now(timezone.utc).date()
profit_days: Dict[date, Dict] = {}
if not (isinstance(timescale, int) and timescale > 0):
@ -289,6 +293,91 @@ class RPC:
'data': data
}
def _rpc_weekly_profit(
self, timescale: int,
stake_currency: str, fiat_display_currency: str) -> Dict[str, Any]:
today = datetime.now(timezone.utc).date()
first_iso_day_of_week = today - timedelta(days=today.weekday()) # Monday
profit_weeks: Dict[date, Dict] = {}
if not (isinstance(timescale, int) and timescale > 0):
raise RPCException('timescale must be an integer greater than 0')
for week in range(0, timescale):
profitweek = first_iso_day_of_week - timedelta(weeks=week)
trades = Trade.get_trades(trade_filter=[
Trade.is_open.is_(False),
Trade.close_date >= profitweek,
Trade.close_date < (profitweek + timedelta(weeks=1))
]).order_by(Trade.close_date).all()
curweekprofit = sum(
trade.close_profit_abs for trade in trades if trade.close_profit_abs is not None)
profit_weeks[profitweek] = {
'amount': curweekprofit,
'trades': len(trades)
}
data = [
{
'date': key,
'abs_profit': value["amount"],
'fiat_value': self._fiat_converter.convert_amount(
value['amount'],
stake_currency,
fiat_display_currency
) if self._fiat_converter else 0,
'trade_count': value["trades"],
}
for key, value in profit_weeks.items()
]
return {
'stake_currency': stake_currency,
'fiat_display_currency': fiat_display_currency,
'data': data
}
def _rpc_monthly_profit(
self, timescale: int,
stake_currency: str, fiat_display_currency: str) -> Dict[str, Any]:
first_day_of_month = datetime.now(timezone.utc).date().replace(day=1)
profit_months: Dict[date, Dict] = {}
if not (isinstance(timescale, int) and timescale > 0):
raise RPCException('timescale must be an integer greater than 0')
for month in range(0, timescale):
profitmonth = first_day_of_month - relativedelta(months=month)
trades = Trade.get_trades(trade_filter=[
Trade.is_open.is_(False),
Trade.close_date >= profitmonth,
Trade.close_date < (profitmonth + relativedelta(months=1))
]).order_by(Trade.close_date).all()
curmonthprofit = sum(
trade.close_profit_abs for trade in trades if trade.close_profit_abs is not None)
profit_months[profitmonth] = {
'amount': curmonthprofit,
'trades': len(trades)
}
data = [
{
'date': f"{key.year}-{key.month:02d}",
'abs_profit': value["amount"],
'fiat_value': self._fiat_converter.convert_amount(
value['amount'],
stake_currency,
fiat_display_currency
) if self._fiat_converter else 0,
'trade_count': value["trades"],
}
for key, value in profit_months.items()
]
return {
'stake_currency': stake_currency,
'fiat_display_currency': fiat_display_currency,
'data': data
}
def _rpc_trade_history(self, limit: int, offset: int = 0, order_by_id: bool = False) -> Dict:
""" Returns the X last trades """
order_by = Trade.id if order_by_id else Trade.close_date.desc()
@ -444,7 +533,8 @@ class RPC:
'latest_trade_timestamp': int(last_date.timestamp() * 1000) if last_date else 0,
'avg_duration': str(timedelta(seconds=sum(durations) / num)).split('.')[0],
'best_pair': best_pair[0] if best_pair else '',
'best_rate': round(best_pair[1] * 100, 2) if best_pair else 0,
'best_rate': round(best_pair[1] * 100, 2) if best_pair else 0, # Deprecated
'best_pair_profit_ratio': best_pair[1] if best_pair else 0,
'winning_trades': winning_trades,
'losing_trades': losing_trades,
}
@ -682,10 +772,36 @@ class RPC:
Shows a performance statistic from finished trades
"""
pair_rates = Trade.get_overall_performance()
# Round and convert to %
[x.update({'profit': round(x['profit'] * 100, 2)}) for x in pair_rates]
return pair_rates
def _rpc_buy_tag_performance(self, pair: Optional[str]) -> List[Dict[str, Any]]:
"""
Handler for buy tag performance.
Shows a performance statistic from finished trades
"""
buy_tags = Trade.get_buy_tag_performance(pair)
return buy_tags
def _rpc_sell_reason_performance(self, pair: Optional[str]) -> List[Dict[str, Any]]:
"""
Handler for sell reason performance.
Shows a performance statistic from finished trades
"""
sell_reasons = Trade.get_sell_reason_performance(pair)
return sell_reasons
def _rpc_mix_tag_performance(self, pair: Optional[str]) -> List[Dict[str, Any]]:
"""
Handler for mix tag (buy_tag + sell_reason) performance.
Shows a performance statistic from finished trades
"""
mix_tags = Trade.get_mix_tag_performance(pair)
return mix_tags
def _rpc_count(self) -> Dict[str, float]:
""" Returns the number of trades running """
if self._freqtrade.state != State.RUNNING:
@ -793,15 +909,15 @@ class RPC:
if has_content:
dataframe.loc[:, '__date_ts'] = dataframe.loc[:, 'date'].view(int64) // 1000 // 1000
# Move open to separate column when signal for easy plotting
# Move signal close to separate column when signal for easy plotting
if 'buy' in dataframe.columns:
buy_mask = (dataframe['buy'] == 1)
buy_signals = int(buy_mask.sum())
dataframe.loc[buy_mask, '_buy_signal_open'] = dataframe.loc[buy_mask, 'open']
dataframe.loc[buy_mask, '_buy_signal_close'] = dataframe.loc[buy_mask, 'close']
if 'sell' in dataframe.columns:
sell_mask = (dataframe['sell'] == 1)
sell_signals = int(sell_mask.sum())
dataframe.loc[sell_mask, '_sell_signal_open'] = dataframe.loc[sell_mask, 'open']
dataframe.loc[sell_mask, '_sell_signal_close'] = dataframe.loc[sell_mask, 'close']
dataframe = dataframe.replace([inf, -inf], NAN)
dataframe = dataframe.replace({NAN: None})

412
freqtrade/rpc/telegram.py
View File

@ -107,8 +107,8 @@ class Telegram(RPCHandler):
# this needs refactoring of the whole telegram module (same
# problem in _help()).
valid_keys: List[str] = [r'/start$', r'/stop$', r'/status$', r'/status table$',
r'/trades$', r'/performance$', r'/daily$', r'/daily \d+$',
r'/profit$', r'/profit \d+',
r'/trades$', r'/performance$', r'/buys', r'/sells', r'/mix_tags',
r'/daily$', r'/daily \d+$', r'/profit$', r'/profit \d+',
r'/stats$', r'/count$', r'/locks$', r'/balance$',
r'/stopbuy$', r'/reload_config$', r'/show_config$',
r'/logs$', r'/whitelist$', r'/blacklist$', r'/edge$',
@ -154,8 +154,13 @@ class Telegram(RPCHandler):
CommandHandler('trades', self._trades),
CommandHandler('delete', self._delete_trade),
CommandHandler('performance', self._performance),
CommandHandler('buys', self._buy_tag_performance),
CommandHandler('sells', self._sell_reason_performance),
CommandHandler('mix_tags', self._mix_tag_performance),
CommandHandler('stats', self._stats),
CommandHandler('daily', self._daily),
CommandHandler('weekly', self._weekly),
CommandHandler('monthly', self._monthly),
CommandHandler('count', self._count),
CommandHandler('locks', self._locks),
CommandHandler(['unlock', 'delete_locks'], self._delete_locks),
@ -172,9 +177,15 @@ class Telegram(RPCHandler):
callbacks = [
CallbackQueryHandler(self._status_table, pattern='update_status_table'),
CallbackQueryHandler(self._daily, pattern='update_daily'),
CallbackQueryHandler(self._weekly, pattern='update_weekly'),
CallbackQueryHandler(self._monthly, pattern='update_monthly'),
CallbackQueryHandler(self._profit, pattern='update_profit'),
CallbackQueryHandler(self._balance, pattern='update_balance'),
CallbackQueryHandler(self._performance, pattern='update_performance'),
CallbackQueryHandler(self._buy_tag_performance, pattern='update_buy_tag_performance'),
CallbackQueryHandler(self._sell_reason_performance,
pattern='update_sell_reason_performance'),
CallbackQueryHandler(self._mix_tag_performance, pattern='update_mix_tag_performance'),
CallbackQueryHandler(self._count, pattern='update_count'),
CallbackQueryHandler(self._forcebuy_inline),
]
@ -208,26 +219,28 @@ class Telegram(RPCHandler):
msg['stake_amount'], msg['stake_currency'], msg['fiat_currency'])
else:
msg['stake_amount_fiat'] = 0
is_fill = msg['type'] == RPCMessageType.BUY_FILL
emoji = '\N{CHECK MARK}' if is_fill else '\N{LARGE BLUE CIRCLE}'
content = []
content.append(
f"\N{LARGE BLUE CIRCLE} *{msg['exchange']}:* Buying {msg['pair']}"
message = (
f"{emoji} *{msg['exchange']}:* {'Bought' if is_fill else 'Buying'} {msg['pair']}"
f" (#{msg['trade_id']})\n"
)
if msg.get('buy_tag', None):
content.append(f"*Buy Tag:* `{msg['buy_tag']}`\n")
content.append(f"*Amount:* `{msg['amount']:.8f}`\n")
content.append(f"*Open Rate:* `{msg['limit']:.8f}`\n")
content.append(f"*Current Rate:* `{msg['current_rate']:.8f}`\n")
content.append(
f"*Total:* `({round_coin_value(msg['stake_amount'], msg['stake_currency'])}"
)
if msg.get('fiat_currency', None):
content.append(
f", {round_coin_value(msg['stake_amount_fiat'], msg['fiat_currency'])}"
)
message += f"*Buy Tag:* `{msg['buy_tag']}`\n" if msg.get('buy_tag', None) else ""
message += f"*Amount:* `{msg['amount']:.8f}`\n"
if msg['type'] == RPCMessageType.BUY_FILL:
message += f"*Open Rate:* `{msg['open_rate']:.8f}`\n"
elif msg['type'] == RPCMessageType.BUY:
message += f"*Open Rate:* `{msg['limit']:.8f}`\n"\
f"*Current Rate:* `{msg['current_rate']:.8f}`\n"
message += f"*Total:* `({round_coin_value(msg['stake_amount'], msg['stake_currency'])}"
if msg.get('fiat_currency', None):
message += f", {round_coin_value(msg['stake_amount_fiat'], msg['fiat_currency'])}"
message = ''.join(content)
message += ")`"
return message
@ -238,6 +251,7 @@ class Telegram(RPCHandler):
microsecond=0) - msg['open_date'].replace(microsecond=0)
msg['duration_min'] = msg['duration'].total_seconds() / 60
msg['buy_tag'] = msg['buy_tag'] if "buy_tag" in msg.keys() else None
msg['emoji'] = self._get_sell_emoji(msg)
# Check if all sell properties are available.
@ -246,53 +260,57 @@ class Telegram(RPCHandler):
and self._rpc._fiat_converter):
msg['profit_fiat'] = self._rpc._fiat_converter.convert_amount(
msg['profit_amount'], msg['stake_currency'], msg['fiat_currency'])
msg['profit_extra'] = (' ({gain}: {profit_amount:.8f} {stake_currency}'
' / {profit_fiat:.3f} {fiat_currency})').format(**msg)
msg['profit_extra'] = (
f" ({msg['gain']}: {msg['profit_amount']:.8f} {msg['stake_currency']}"
f" / {msg['profit_fiat']:.3f} {msg['fiat_currency']})")
else:
msg['profit_extra'] = ''
is_fill = msg['type'] == RPCMessageType.SELL_FILL
message = (
f"{msg['emoji']} *{msg['exchange']}:* "
f"{'Sold' if is_fill else 'Selling'} {msg['pair']} (#{msg['trade_id']})\n"
f"*{'Profit' if is_fill else 'Unrealized Profit'}:* "
f"`{msg['profit_ratio']:.2%}{msg['profit_extra']}`\n"
f"*Buy Tag:* `{msg['buy_tag']}`\n"
f"*Sell Reason:* `{msg['sell_reason']}`\n"
f"*Duration:* `{msg['duration']} ({msg['duration_min']:.1f} min)`\n"
f"*Amount:* `{msg['amount']:.8f}`\n"
f"*Open Rate:* `{msg['open_rate']:.8f}`\n")
message = ("{emoji} *{exchange}:* Selling {pair} (#{trade_id})\n"
"*Profit:* `{profit_percent:.2f}%{profit_extra}`\n"
"*Sell Reason:* `{sell_reason}`\n"
"*Duration:* `{duration} ({duration_min:.1f} min)`\n"
"*Amount:* `{amount:.8f}`\n"
"*Open Rate:* `{open_rate:.8f}`\n"
"*Current Rate:* `{current_rate:.8f}`\n"
"*Close Rate:* `{limit:.8f}`").format(**msg)
if msg['type'] == RPCMessageType.SELL:
message += (f"*Current Rate:* `{msg['current_rate']:.8f}`\n"
f"*Close Rate:* `{msg['limit']:.8f}`")
elif msg['type'] == RPCMessageType.SELL_FILL:
message += f"*Close Rate:* `{msg['close_rate']:.8f}`"
return message
def compose_message(self, msg: Dict[str, Any], msg_type: RPCMessageType) -> str:
if msg_type == RPCMessageType.BUY:
if msg_type in [RPCMessageType.BUY, RPCMessageType.BUY_FILL]:
message = self._format_buy_msg(msg)
elif msg_type in [RPCMessageType.SELL, RPCMessageType.SELL_FILL]:
message = self._format_sell_msg(msg)
elif msg_type in (RPCMessageType.BUY_CANCEL, RPCMessageType.SELL_CANCEL):
msg['message_side'] = 'buy' if msg_type == RPCMessageType.BUY_CANCEL else 'sell'
message = ("\N{WARNING SIGN} *{exchange}:* "
"Cancelling open {message_side} Order for {pair} (#{trade_id}). "
"Reason: {reason}.".format(**msg))
elif msg_type == RPCMessageType.BUY_FILL:
message = ("\N{LARGE CIRCLE} *{exchange}:* "
"Buy order for {pair} (#{trade_id}) filled "
"for {open_rate}.".format(**msg))
elif msg_type == RPCMessageType.SELL_FILL:
message = ("\N{LARGE CIRCLE} *{exchange}:* "
"Sell order for {pair} (#{trade_id}) filled "
"for {close_rate}.".format(**msg))
elif msg_type == RPCMessageType.SELL:
message = self._format_sell_msg(msg)
elif msg_type == RPCMessageType.PROTECTION_TRIGGER:
message = (
"*Protection* triggered due to {reason}. "
"`{pair}` will be locked until `{lock_end_time}`."
).format(**msg)
elif msg_type == RPCMessageType.PROTECTION_TRIGGER_GLOBAL:
message = (
"*Protection* triggered due to {reason}. "
"*All pairs* will be locked until `{lock_end_time}`."
).format(**msg)
elif msg_type == RPCMessageType.STATUS:
message = '*Status:* `{status}`'.format(**msg)
@ -344,7 +362,7 @@ class Telegram(RPCHandler):
elif float(msg['profit_percent']) >= 0.0:
return "\N{EIGHT SPOKED ASTERISK}"
elif msg['sell_reason'] == "stop_loss":
return"\N{WARNING SIGN}"
return "\N{WARNING SIGN}"
else:
return "\N{CROSS MARK}"
@ -384,19 +402,19 @@ class Telegram(RPCHandler):
"*Close Rate:* `{close_rate}`" if r['close_rate'] else "",
"*Current Rate:* `{current_rate:.8f}`",
("*Current Profit:* " if r['is_open'] else "*Close Profit: *")
+ "`{profit_pct:.2f}%`",
+ "`{profit_ratio:.2%}`",
]
if (r['stop_loss_abs'] != r['initial_stop_loss_abs']
and r['initial_stop_loss_pct'] is not None):
and r['initial_stop_loss_ratio'] is not None):
# Adding initial stoploss only if it is different from stoploss
lines.append("*Initial Stoploss:* `{initial_stop_loss_abs:.8f}` "
"`({initial_stop_loss_pct:.2f}%)`")
"`({initial_stop_loss_ratio:.2%})`")
# Adding stoploss and stoploss percentage only if it is not None
lines.append("*Stoploss:* `{stop_loss_abs:.8f}` " +
("`({stop_loss_pct:.2f}%)`" if r['stop_loss_pct'] else ""))
("`({stop_loss_ratio:.2%})`" if r['stop_loss_ratio'] else ""))
lines.append("*Stoploss distance:* `{stoploss_current_dist:.8f}` "
"`({stoploss_current_dist_pct:.2f}%)`")
"`({stoploss_current_dist_ratio:.2%})`")
if r['open_order']:
if r['sell_order_status']:
lines.append("*Open Order:* `{open_order}` - `{sell_order_status}`")
@ -492,6 +510,86 @@ class Telegram(RPCHandler):
except RPCException as e:
self._send_msg(str(e))
@authorized_only
def _weekly(self, update: Update, context: CallbackContext) -> None:
"""
Handler for /weekly <n>
Returns a weekly profit (in BTC) over the last n weeks.
:param bot: telegram bot
:param update: message update
:return: None
"""
stake_cur = self._config['stake_currency']
fiat_disp_cur = self._config.get('fiat_display_currency', '')
try:
timescale = int(context.args[0]) if context.args else 8
except (TypeError, ValueError, IndexError):
timescale = 8
try:
stats = self._rpc._rpc_weekly_profit(
timescale,
stake_cur,
fiat_disp_cur
)
stats_tab = tabulate(
[[week['date'],
f"{round_coin_value(week['abs_profit'], stats['stake_currency'])}",
f"{week['fiat_value']:.3f} {stats['fiat_display_currency']}",
f"{week['trade_count']} trades"] for week in stats['data']],
headers=[
'Monday',
f'Profit {stake_cur}',
f'Profit {fiat_disp_cur}',
'Trades',
],
tablefmt='simple')
message = f'<b>Weekly Profit over the last {timescale} weeks ' \
f'(starting from Monday)</b>:\n<pre>{stats_tab}</pre> '
self._send_msg(message, parse_mode=ParseMode.HTML, reload_able=True,
callback_path="update_weekly", query=update.callback_query)
except RPCException as e:
self._send_msg(str(e))
@authorized_only
def _monthly(self, update: Update, context: CallbackContext) -> None:
"""
Handler for /monthly <n>
Returns a monthly profit (in BTC) over the last n months.
:param bot: telegram bot
:param update: message update
:return: None
"""
stake_cur = self._config['stake_currency']
fiat_disp_cur = self._config.get('fiat_display_currency', '')
try:
timescale = int(context.args[0]) if context.args else 6
except (TypeError, ValueError, IndexError):
timescale = 6
try:
stats = self._rpc._rpc_monthly_profit(
timescale,
stake_cur,
fiat_disp_cur
)
stats_tab = tabulate(
[[month['date'],
f"{round_coin_value(month['abs_profit'], stats['stake_currency'])}",
f"{month['fiat_value']:.3f} {stats['fiat_display_currency']}",
f"{month['trade_count']} trades"] for month in stats['data']],
headers=[
'Month',
f'Profit {stake_cur}',
f'Profit {fiat_disp_cur}',
'Trades',
],
tablefmt='simple')
message = f'<b>Monthly Profit over the last {timescale} months' \
f'</b>:\n<pre>{stats_tab}</pre> '
self._send_msg(message, parse_mode=ParseMode.HTML, reload_able=True,
callback_path="update_monthly", query=update.callback_query)
except RPCException as e:
self._send_msg(str(e))
@authorized_only
def _profit(self, update: Update, context: CallbackContext) -> None:
"""
@ -519,11 +617,11 @@ class Telegram(RPCHandler):
fiat_disp_cur,
start_date)
profit_closed_coin = stats['profit_closed_coin']
profit_closed_percent_mean = stats['profit_closed_percent_mean']
profit_closed_ratio_mean = stats['profit_closed_ratio_mean']
profit_closed_percent = stats['profit_closed_percent']
profit_closed_fiat = stats['profit_closed_fiat']
profit_all_coin = stats['profit_all_coin']
profit_all_percent_mean = stats['profit_all_percent_mean']
profit_all_ratio_mean = stats['profit_all_ratio_mean']
profit_all_percent = stats['profit_all_percent']
profit_all_fiat = stats['profit_all_fiat']
trade_count = stats['trade_count']
@ -531,7 +629,7 @@ class Telegram(RPCHandler):
latest_trade_date = stats['latest_trade_date']
avg_duration = stats['avg_duration']
best_pair = stats['best_pair']
best_rate = stats['best_rate']
best_pair_profit_ratio = stats['best_pair_profit_ratio']
if stats['trade_count'] == 0:
markdown_msg = 'No trades yet.'
else:
@ -539,7 +637,7 @@ class Telegram(RPCHandler):
if stats['closed_trade_count'] > 0:
markdown_msg = ("*ROI:* Closed trades\n"
f"∙ `{round_coin_value(profit_closed_coin, stake_cur)} "
f"({profit_closed_percent_mean:.2f}%) "
f"({profit_closed_ratio_mean:.2%}) "
f"({profit_closed_percent} \N{GREEK CAPITAL LETTER SIGMA}%)`\n"
f"∙ `{round_coin_value(profit_closed_fiat, fiat_disp_cur)}`\n")
else:
@ -548,7 +646,7 @@ class Telegram(RPCHandler):
markdown_msg += (
f"*ROI:* All trades\n"
f"∙ `{round_coin_value(profit_all_coin, stake_cur)} "
f"({profit_all_percent_mean:.2f}%) "
f"({profit_all_ratio_mean:.2%}) "
f"({profit_all_percent} \N{GREEK CAPITAL LETTER SIGMA}%)`\n"
f"∙ `{round_coin_value(profit_all_fiat, fiat_disp_cur)}`\n"
f"*Total Trade Count:* `{trade_count}`\n"
@ -559,7 +657,7 @@ class Telegram(RPCHandler):
)
if stats['closed_trade_count'] > 0:
markdown_msg += (f"\n*Avg. Duration:* `{avg_duration}`\n"
f"*Best Performing:* `{best_pair}: {best_rate:.2f}%`")
f"*Best Performing:* `{best_pair}: {best_pair_profit_ratio:.2%}`")
self._send_msg(markdown_msg, reload_able=True, callback_path="update_profit",
query=update.callback_query)
@ -588,10 +686,16 @@ class Telegram(RPCHandler):
count['losses']
] for reason, count in stats['sell_reasons'].items()
]
sell_reasons_msg = tabulate(
sell_reasons_tabulate,
headers=['Sell Reason', 'Sells', 'Wins', 'Losses']
)
sell_reasons_msg = 'No trades yet.'
for reason in chunks(sell_reasons_tabulate, 25):
sell_reasons_msg = tabulate(
reason,
headers=['Sell Reason', 'Sells', 'Wins', 'Losses']
)
if len(sell_reasons_tabulate) > 25:
self._send_msg(sell_reasons_msg, ParseMode.MARKDOWN)
sell_reasons_msg = ''
durations = stats['durations']
duration_msg = tabulate(
[
@ -662,10 +766,10 @@ class Telegram(RPCHandler):
output += ("\n*Estimated Value*:\n"
f"\t`{result['stake']}: "
f"{round_coin_value(result['total'], result['stake'], False)}`"
f" `({result['starting_capital_pct']}%)`\n"
f" `({result['starting_capital_ratio']:.2%})`\n"
f"\t`{result['symbol']}: "
f"{round_coin_value(result['value'], result['symbol'], False)}`"
f" `({result['starting_capital_fiat_pct']}%)`\n")
f" `({result['starting_capital_fiat_ratio']:.2%})`\n")
self._send_msg(output, reload_able=True, callback_path="update_balance",
query=update.callback_query)
except RPCException as e:
@ -800,7 +904,7 @@ class Telegram(RPCHandler):
trades_tab = tabulate(
[[arrow.get(trade['close_date']).humanize(),
trade['pair'] + " (#" + str(trade['trade_id']) + ")",
f"{(100 * trade['close_profit']):.2f}% ({trade['close_profit_abs']})"]
f"{(trade['close_profit']):.2%} ({trade['close_profit_abs']})"]
for trade in trades['trades']],
headers=[
'Close Date',
@ -852,7 +956,7 @@ class Telegram(RPCHandler):
stat_line = (
f"{i+1}.\t <code>{trade['pair']}\t"
f"{round_coin_value(trade['profit_abs'], self._config['stake_currency'])} "
f"({trade['profit']:.2f}%) "
f"({trade['profit_ratio']:.2%}) "
f"({trade['count']})</code>\n")
if len(output + stat_line) >= MAX_TELEGRAM_MESSAGE_LENGTH:
@ -867,6 +971,111 @@ class Telegram(RPCHandler):
except RPCException as e:
self._send_msg(str(e))
@authorized_only
def _buy_tag_performance(self, update: Update, context: CallbackContext) -> None:
"""
Handler for /buys PAIR .
Shows a performance statistic from finished trades
:param bot: telegram bot
:param update: message update
:return: None
"""
try:
pair = None
if context.args and isinstance(context.args[0], str):
pair = context.args[0]
trades = self._rpc._rpc_buy_tag_performance(pair)
output = "<b>Buy Tag Performance:</b>\n"
for i, trade in enumerate(trades):
stat_line = (
f"{i+1}.\t <code>{trade['buy_tag']}\t"
f"{round_coin_value(trade['profit_abs'], self._config['stake_currency'])} "
f"({trade['profit_ratio']:.2%}) "
f"({trade['count']})</code>\n")
if len(output + stat_line) >= MAX_TELEGRAM_MESSAGE_LENGTH:
self._send_msg(output, parse_mode=ParseMode.HTML)
output = stat_line
else:
output += stat_line
self._send_msg(output, parse_mode=ParseMode.HTML,
reload_able=True, callback_path="update_buy_tag_performance",
query=update.callback_query)
except RPCException as e:
self._send_msg(str(e))
@authorized_only
def _sell_reason_performance(self, update: Update, context: CallbackContext) -> None:
"""
Handler for /sells.
Shows a performance statistic from finished trades
:param bot: telegram bot
:param update: message update
:return: None
"""
try:
pair = None
if context.args and isinstance(context.args[0], str):
pair = context.args[0]
trades = self._rpc._rpc_sell_reason_performance(pair)
output = "<b>Sell Reason Performance:</b>\n"
for i, trade in enumerate(trades):
stat_line = (
f"{i+1}.\t <code>{trade['sell_reason']}\t"
f"{round_coin_value(trade['profit_abs'], self._config['stake_currency'])} "
f"({trade['profit_ratio']:.2%}) "
f"({trade['count']})</code>\n")
if len(output + stat_line) >= MAX_TELEGRAM_MESSAGE_LENGTH:
self._send_msg(output, parse_mode=ParseMode.HTML)
output = stat_line
else:
output += stat_line
self._send_msg(output, parse_mode=ParseMode.HTML,
reload_able=True, callback_path="update_sell_reason_performance",
query=update.callback_query)
except RPCException as e:
self._send_msg(str(e))
@authorized_only
def _mix_tag_performance(self, update: Update, context: CallbackContext) -> None:
"""
Handler for /mix_tags.
Shows a performance statistic from finished trades
:param bot: telegram bot
:param update: message update
:return: None
"""
try:
pair = None
if context.args and isinstance(context.args[0], str):
pair = context.args[0]
trades = self._rpc._rpc_mix_tag_performance(pair)
output = "<b>Mix Tag Performance:</b>\n"
for i, trade in enumerate(trades):
stat_line = (
f"{i+1}.\t <code>{trade['mix_tag']}\t"
f"{round_coin_value(trade['profit_abs'], self._config['stake_currency'])} "
f"({trade['profit']:.2%}) "
f"({trade['count']})</code>\n")
if len(output + stat_line) >= MAX_TELEGRAM_MESSAGE_LENGTH:
self._send_msg(output, parse_mode=ParseMode.HTML)
output = stat_line
else:
output += stat_line
self._send_msg(output, parse_mode=ParseMode.HTML,
reload_able=True, callback_path="update_mix_tag_performance",
query=update.callback_query)
except RPCException as e:
self._send_msg(str(e))
@authorized_only
def _count(self, update: Update, context: CallbackContext) -> None:
"""
@ -1035,41 +1244,56 @@ class Telegram(RPCHandler):
forcebuy_text = ("*/forcebuy <pair> [<rate>]:* `Instantly buys the given pair. "
"Optionally takes a rate at which to buy "
"(only applies to limit orders).` \n")
message = ("*/start:* `Starts the trader`\n"
"*/stop:* `Stops the trader`\n"
"*/status <trade_id>|[table]:* `Lists all open trades`\n"
" *<trade_id> :* `Lists one or more specific trades.`\n"
" `Separate multiple <trade_id> with a blank space.`\n"
" *table :* `will display trades in a table`\n"
" `pending buy orders are marked with an asterisk (*)`\n"
" `pending sell orders are marked with a double asterisk (**)`\n"
"*/trades [limit]:* `Lists last closed trades (limited to 10 by default)`\n"
"*/profit [<n>]:* `Lists cumulative profit from all finished trades, "
"over the last n days`\n"
"*/forcesell <trade_id>|all:* `Instantly sells the given trade or all trades, "
"regardless of profit`\n"
f"{forcebuy_text if self._config.get('forcebuy_enable', False) else ''}"
"*/delete <trade_id>:* `Instantly delete the given trade in the database`\n"
"*/performance:* `Show performance of each finished trade grouped by pair`\n"
"*/daily <n>:* `Shows profit or loss per day, over the last n days`\n"
"*/stats:* `Shows Wins / losses by Sell reason as well as "
"Avg. holding durationsfor buys and sells.`\n"
"*/count:* `Show number of active trades compared to allowed number of trades`\n"
"*/locks:* `Show currently locked pairs`\n"
"*/unlock <pair|id>:* `Unlock this Pair (or this lock id if it's numeric)`\n"
"*/balance:* `Show account balance per currency`\n"
"*/stopbuy:* `Stops buying, but handles open trades gracefully` \n"
"*/reload_config:* `Reload configuration file` \n"
"*/show_config:* `Show running configuration` \n"
"*/logs [limit]:* `Show latest logs - defaults to 10` \n"
"*/whitelist:* `Show current whitelist` \n"
"*/blacklist [pair]:* `Show current blacklist, or adds one or more pairs "
"to the blacklist.` \n"
"*/edge:* `Shows validated pairs by Edge if it is enabled` \n"
"*/help:* `This help message`\n"
"*/version:* `Show version`")
message = (
"_BotControl_\n"
"------------\n"
"*/start:* `Starts the trader`\n"
"*/stop:* Stops the trader\n"
"*/stopbuy:* `Stops buying, but handles open trades gracefully` \n"
"*/forcesell <trade_id>|all:* `Instantly sells the given trade or all trades, "
"regardless of profit`\n"
f"{forcebuy_text if self._config.get('forcebuy_enable', False) else ''}"
"*/delete <trade_id>:* `Instantly delete the given trade in the database`\n"
"*/whitelist:* `Show current whitelist` \n"
"*/blacklist [pair]:* `Show current blacklist, or adds one or more pairs "
"to the blacklist.` \n"
"*/reload_config:* `Reload configuration file` \n"
"*/unlock <pair|id>:* `Unlock this Pair (or this lock id if it's numeric)`\n"
self._send_msg(message)
"_Current state_\n"
"------------\n"
"*/show_config:* `Show running configuration` \n"
"*/locks:* `Show currently locked pairs`\n"
"*/balance:* `Show account balance per currency`\n"
"*/logs [limit]:* `Show latest logs - defaults to 10` \n"
"*/count:* `Show number of active trades compared to allowed number of trades`\n"
"*/edge:* `Shows validated pairs by Edge if it is enabled` \n"
"_Statistics_\n"
"------------\n"
"*/status <trade_id>|[table]:* `Lists all open trades`\n"
" *<trade_id> :* `Lists one or more specific trades.`\n"
" `Separate multiple <trade_id> with a blank space.`\n"
" *table :* `will display trades in a table`\n"
" `pending buy orders are marked with an asterisk (*)`\n"
" `pending sell orders are marked with a double asterisk (**)`\n"
"*/buys <pair|none>:* `Shows the buy_tag performance`\n"
"*/sells <pair|none>:* `Shows the sell reason performance`\n"
"*/mix_tags <pair|none>:* `Shows combined buy tag + sell reason performance`\n"
"*/trades [limit]:* `Lists last closed trades (limited to 10 by default)`\n"
"*/profit [<n>]:* `Lists cumulative profit from all finished trades, "
"over the last n days`\n"
"*/performance:* `Show performance of each finished trade grouped by pair`\n"
"*/daily <n>:* `Shows profit or loss per day, over the last n days`\n"
"*/weekly <n>:* `Shows statistics per week, over the last n weeks`\n"
"*/monthly <n>:* `Shows statistics per month, over the last n months`\n"
"*/stats:* `Shows Wins / losses by Sell reason as well as "
"Avg. holding durationsfor buys and sells.`\n"
"*/help:* `This help message`\n"
"*/version:* `Show version`"
)
self._send_msg(message, parse_mode=ParseMode.MARKDOWN)
@authorized_only
def _version(self, update: Update, context: CallbackContext) -> None:

2
freqtrade/strategy/hyper.py
View File

@ -292,7 +292,7 @@ class BooleanParameter(CategoricalParameter):
load=load, **kwargs)
class HyperStrategyMixin(object):
class HyperStrategyMixin:
"""
A helper base class which allows HyperOptAuto class to reuse implementations of buy/sell
strategy logic.

11
freqtrade/strategy/informative_decorator.py
View File

@ -80,12 +80,11 @@ def _create_and_merge_informative_pair(strategy, dataframe: DataFrame, metadata:
# Not specifying an asset will define informative dataframe for current pair.
asset = metadata['pair']
if '/' in asset:
base, quote = asset.split('/')
else:
# When futures are supported this may need reevaluation.
# base, quote = asset, ''
raise OperationalException('Not implemented.')
market = strategy.dp.market(asset)
if market is None:
raise OperationalException(f'Market {asset} is not available.')
base = market['base']
quote = market['quote']
# Default format. This optimizes for the common case: informative pairs using same stake
# currency. When quote currency matches stake currency, column name will omit base currency.

25
freqtrade/strategy/interface.py
View File

@ -30,7 +30,7 @@ logger = logging.getLogger(__name__)
CUSTOM_SELL_MAX_LENGTH = 64
class SellCheckTuple(object):
class SellCheckTuple:
"""
NamedTuple for Sell type + reason
"""
@ -443,6 +443,15 @@ class IStrategy(ABC, HyperStrategyMixin):
"""
PairLocks.unlock_pair(pair, datetime.now(timezone.utc))
def unlock_reason(self, reason: str) -> None:
"""
Unlocks all pairs previously locked using lock_pair with specified reason.
Not used by freqtrade itself, but intended to be used if users lock pairs
manually from within the strategy, to allow an easy way to unlock pairs.
:param reason: Unlock pairs to allow trading again
"""
PairLocks.unlock_reason(reason, datetime.now(timezone.utc))
def is_pair_locked(self, pair: str, candle_date: datetime = None) -> bool:
"""
Checks if a pair is currently locked
@ -500,6 +509,7 @@ class IStrategy(ABC, HyperStrategyMixin):
dataframe['buy'] = 0
dataframe['sell'] = 0
dataframe['buy_tag'] = None
dataframe['exit_tag'] = None
# Other Defs in strategy that want to be called every loop here
# twitter_sell = self.watch_twitter_feed(dataframe, metadata)
@ -577,7 +587,7 @@ class IStrategy(ABC, HyperStrategyMixin):
pair: str,
timeframe: str,
dataframe: DataFrame
) -> Tuple[bool, bool, Optional[str]]:
) -> Tuple[bool, bool, Optional[str], Optional[str]]:
"""
Calculates current signal based based on the buy / sell columns of the dataframe.
Used by Bot to get the signal to buy or sell
@ -588,7 +598,7 @@ class IStrategy(ABC, HyperStrategyMixin):
"""
if not isinstance(dataframe, DataFrame) or dataframe.empty:
logger.warning(f'Empty candle (OHLCV) data for pair {pair}')
return False, False, None
return False, False, None, None
latest_date = dataframe['date'].max()
latest = dataframe.loc[dataframe['date'] == latest_date].iloc[-1]
@ -603,7 +613,7 @@ class IStrategy(ABC, HyperStrategyMixin):
'Outdated history for pair %s. Last tick is %s minutes old',
pair, int((arrow.utcnow() - latest_date).total_seconds() // 60)
)
return False, False, None
return False, False, None, None
buy = latest[SignalType.BUY.value] == 1
@ -612,6 +622,7 @@ class IStrategy(ABC, HyperStrategyMixin):
sell = latest[SignalType.SELL.value] == 1
buy_tag = latest.get(SignalTagType.BUY_TAG.value, None)
exit_tag = latest.get(SignalTagType.EXIT_TAG.value, None)
logger.debug('trigger: %s (pair=%s) buy=%s sell=%s',
latest['date'], pair, str(buy), str(sell))
@ -620,8 +631,8 @@ class IStrategy(ABC, HyperStrategyMixin):
current_time=datetime.now(timezone.utc),
timeframe_seconds=timeframe_seconds,
buy=buy):
return False, sell, buy_tag
return buy, sell, buy_tag
return False, sell, buy_tag, exit_tag
return buy, sell, buy_tag, exit_tag
def ignore_expired_candle(self, latest_date: datetime, current_time: datetime,
timeframe_seconds: int, buy: bool):
@ -754,7 +765,7 @@ class IStrategy(ABC, HyperStrategyMixin):
if self.trailing_stop_positive is not None and high_profit > sl_offset:
stop_loss_value = self.trailing_stop_positive
logger.debug(f"{trade.pair} - Using positive stoploss: {stop_loss_value} "
f"offset: {sl_offset:.4g} profit: {current_profit:.4f}%")
f"offset: {sl_offset:.4g} profit: {current_profit:.2%}")
trade.adjust_stop_loss(high or current_rate, stop_loss_value)

4
freqtrade/strategy/strategy_wrapper.py
View File

@ -1,4 +1,5 @@
import logging
from copy import deepcopy
from freqtrade.exceptions import StrategyError
@ -14,6 +15,9 @@ def strategy_safe_wrapper(f, message: str = "", default_retval=None, supress_err
"""
def wrapper(*args, **kwargs):
try:
if 'trade' in kwargs:
# Protect accidental modifications from within the strategy
kwargs['trade'] = deepcopy(kwargs['trade'])
return f(*args, **kwargs)
except ValueError as error:
logger.warning(

3
freqtrade/templates/base_config.json.j2
View File

@ -10,8 +10,7 @@
"stake_currency": "{{ stake_currency }}",
"stake_amount": {{ stake_amount }},
"tradable_balance_ratio": 0.99,
"fiat_display_currency": "{{ fiat_display_currency }}",
"timeframe": "{{ timeframe }}",
"fiat_display_currency": "{{ fiat_display_currency }}",{{ ('\n "timeframe": "' + timeframe + '",') if timeframe else '' }}
"dry_run": {{ dry_run | lower }},
"cancel_open_orders_on_exit": false,
"unfilledtimeout": {

11
freqtrade/templates/base_strategy.py.j2
View File

@ -12,6 +12,7 @@ from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalP
# --------------------------------
# Add your lib to import here
import talib.abstract as ta
import pandas_ta as pta
import freqtrade.vendor.qtpylib.indicators as qtpylib
@ -36,6 +37,9 @@ class {{ strategy }}(IStrategy):
# Check the documentation or the Sample strategy to get the latest version.
INTERFACE_VERSION = 2
# Optimal timeframe for the strategy.
timeframe = '5m'
# Minimal ROI designed for the strategy.
# This attribute will be overridden if the config file contains "minimal_roi".
minimal_roi = {
@ -54,9 +58,6 @@ class {{ strategy }}(IStrategy):
# trailing_stop_positive = 0.01
# trailing_stop_positive_offset = 0.0 # Disabled / not configured
# Optimal timeframe for the strategy.
timeframe = '5m'
# Run "populate_indicators()" only for new candle.
process_only_new_candles = False
@ -68,6 +69,10 @@ class {{ strategy }}(IStrategy):
# Number of candles the strategy requires before producing valid signals
startup_candle_count: int = 30
# Strategy parameters
buy_rsi = IntParameter(10, 40, default=30, space="buy")
sell_rsi = IntParameter(60, 90, default=70, space="sell")
# Optional order type mapping.
order_types = {
'buy': 'limit',

2
freqtrade/templates/subtemplates/buy_trend_full.j2
View File

@ -1,3 +1,3 @@
(qtpylib.crossed_above(dataframe['rsi'], 30)) & # Signal: RSI crosses above 30
(qtpylib.crossed_above(dataframe['rsi'], self.buy_rsi.value)) & # Signal: RSI crosses above buy_rsi
(dataframe['tema'] <= dataframe['bb_middleband']) & # Guard: tema below BB middle
(dataframe['tema'] > dataframe['tema'].shift(1)) & # Guard: tema is raising

2
freqtrade/templates/subtemplates/buy_trend_minimal.j2
View File

@ -1 +1 @@
(qtpylib.crossed_above(dataframe['rsi'], 30)) & # Signal: RSI crosses above 30
(qtpylib.crossed_above(dataframe['rsi'], self.buy_rsi.value)) & # Signal: RSI crosses above buy_rsi

12
freqtrade/templates/subtemplates/exchange_okex.j2 Normal file
View File

@ -0,0 +1,12 @@
"exchange": {
"name": "{{ exchange_name | lower }}",
"key": "{{ exchange_key }}",
"secret": "{{ exchange_secret }}",
"password": "{{ exchange_key_password }}",
"ccxt_config": {},
"ccxt_async_config": {},
"pair_whitelist": [
],
"pair_blacklist": [
]
}

2
freqtrade/templates/subtemplates/sell_trend_full.j2
View File

@ -1,3 +1,3 @@
(qtpylib.crossed_above(dataframe['rsi'], 70)) & # Signal: RSI crosses above 70
(qtpylib.crossed_above(dataframe['rsi'], self.sell_rsi.value)) & # Signal: RSI crosses above sell_rsi
(dataframe['tema'] > dataframe['bb_middleband']) & # Guard: tema above BB middle
(dataframe['tema'] < dataframe['tema'].shift(1)) & # Guard: tema is falling

2
freqtrade/templates/subtemplates/sell_trend_minimal.j2
View File

@ -1 +1 @@
(qtpylib.crossed_above(dataframe['rsi'], 70)) & # Signal: RSI crosses above 70
(qtpylib.crossed_above(dataframe['rsi'], self.sell_rsi.value)) & # Signal: RSI crosses above sell_rsi

18
freqtrade/wallets.py
View File

@ -73,7 +73,7 @@ class Wallets:
tot_profit = Trade.get_total_closed_profit()
else:
tot_profit = LocalTrade.total_profit
tot_in_trades = sum([trade.stake_amount for trade in open_trades])
tot_in_trades = sum(trade.stake_amount for trade in open_trades)
current_stake = self.start_cap + tot_profit - tot_in_trades
_wallets[self._config['stake_currency']] = Wallet(
@ -238,7 +238,7 @@ class Wallets:
return self._check_available_stake_amount(stake_amount, available_amount)
def _validate_stake_amount(self, pair, stake_amount, min_stake_amount):
def validate_stake_amount(self, pair, stake_amount, min_stake_amount):
if not stake_amount:
logger.debug(f"Stake amount is {stake_amount}, ignoring possible trade for {pair}.")
return 0
@ -250,17 +250,27 @@ class Wallets:
logger.warning("Minimum stake amount > available balance.")
return 0
if min_stake_amount is not None and stake_amount < min_stake_amount:
stake_amount = min_stake_amount
if self._log:
logger.info(
f"Stake amount for pair {pair} is too small "
f"({stake_amount} < {min_stake_amount}), adjusting to {min_stake_amount}."
)
if stake_amount * 1.3 < min_stake_amount:
# Top-cap stake-amount adjustments to +30%.
if self._log:
logger.info(
f"Adjusted stake amount for pair {pair} is more than 30% bigger than "
f"the desired stake ({stake_amount} * 1.3 > {max_stake_amount}), "
f"ignoring trade."
)
return 0
stake_amount = min_stake_amount
if stake_amount > max_stake_amount:
stake_amount = max_stake_amount
if self._log:
logger.info(
f"Stake amount for pair {pair} is too big "
f"({stake_amount} > {max_stake_amount}), adjusting to {max_stake_amount}."
)
stake_amount = max_stake_amount
return stake_amount

3
mkdocs.yml
View File

@ -11,8 +11,9 @@ nav:
- Freqtrade Basics: bot-basics.md
- Configuration: configuration.md
- Strategy Customization: strategy-customization.md
- Plugins: plugins.md
- Strategy Callbacks: strategy-callbacks.md
- Stoploss: stoploss.md
- Plugins: plugins.md
- Start the bot: bot-usage.md
- Control the bot:
- Telegram: telegram-usage.md

13
requirements-dev.txt
View File

@ -3,7 +3,7 @@
-r requirements-plot.txt
-r requirements-hyperopt.txt
coveralls==3.2.0
coveralls==3.3.1
flake8==4.0.1
flake8-tidy-imports==4.5.0
mypy==0.910
@ -12,15 +12,18 @@ pytest-asyncio==0.16.0
pytest-cov==3.0.0
pytest-mock==3.6.1
pytest-random-order==1.0.4
isort==5.9.3
isort==5.10.1
# For datetime mocking
time-machine==2.4.0
# Convert jupyter notebooks to markdown documents
nbconvert==6.2.0
nbconvert==6.3.0
# mypy types
types-cachetools==4.2.4
types-cachetools==4.2.5
types-filelock==3.2.1
types-requests==2.25.11
types-requests==2.26.0
types-tabulate==0.8.3
# Extensions to datetime library
types-python-dateutil==2.8.2

6
requirements-hyperopt.txt
View File

@ -2,10 +2,10 @@
-r requirements.txt
# Required for hyperopt
scipy==1.7.1
scikit-learn==1.0
scipy==1.7.2
scikit-learn==1.0.1
scikit-optimize==0.9.0
filelock==3.3.1
filelock==3.4.0
joblib==1.1.0
psutil==5.8.0
progressbar2==3.55.0

2
requirements-plot.txt
View File

@ -1,5 +1,5 @@
# Include all requirements to run the bot.
-r requirements.txt
plotly==5.3.1
plotly==5.4.0

20
requirements.txt
View File

@ -1,23 +1,23 @@
numpy==1.21.3
numpy==1.21.4
pandas==1.3.4
pandas-ta==0.3.14b
ccxt==1.59.2
ccxt==1.61.92
# Pin cryptography for now due to rust build errors with piwheels
cryptography==35.0.0
aiohttp==3.7.4.post0
SQLAlchemy==1.4.26
python-telegram-bot==13.7
cryptography==36.0.0
aiohttp==3.8.1
SQLAlchemy==1.4.27
python-telegram-bot==13.8.1
arrow==1.2.1
cachetools==4.2.2
requests==2.26.0
urllib3==1.26.7
jsonschema==4.1.2
jsonschema==4.2.1
TA-Lib==0.4.21
technical==1.3.0
tabulate==0.8.9
pycoingecko==2.2.0
jinja2==3.0.2
jinja2==3.0.3
tables==3.6.1
blosc==1.10.6
@ -41,4 +41,6 @@ psutil==5.8.0
colorama==0.4.4
# Building config files interactively
questionary==1.10.0
prompt-toolkit==3.0.21
prompt-toolkit==3.0.22
# Extensions to datetime library
python-dateutil==2.8.2

8
scripts/rest_client.py
View File

@ -39,7 +39,7 @@ class FtRestClient():
def _call(self, method, apipath, params: dict = None, data=None, files=None):
if str(method).upper() not in ('GET', 'POST', 'PUT', 'DELETE'):
raise ValueError('invalid method <{0}>'.format(method))
raise ValueError(f'invalid method <{method}>')
basepath = f"{self._serverurl}/api/v1/{apipath}"
hd = {"Accept": "application/json",
@ -124,7 +124,7 @@ class FtRestClient():
:param lock_id: ID for the lock to delete
:return: json object
"""
return self._delete("locks/{}".format(lock_id))
return self._delete(f"locks/{lock_id}")
def daily(self, days=None):
"""Return the profits for each day, and amount of trades.
@ -220,7 +220,7 @@ class FtRestClient():
:param trade_id: Specify which trade to get.
:return: json object
"""
return self._get("trade/{}".format(trade_id))
return self._get(f"trade/{trade_id}")
def delete_trade(self, trade_id):
"""Delete trade from the database.
@ -229,7 +229,7 @@ class FtRestClient():
:param trade_id: Deletes the trade with this ID from the database.
:return: json object
"""
return self._delete("trades/{}".format(trade_id))
return self._delete(f"trades/{trade_id}")
def whitelist(self):
"""Show the current whitelist.

2
setup.py
View File

@ -43,7 +43,7 @@ setup(
],
install_requires=[
# from requirements.txt
'ccxt>=1.50.48',
'ccxt>=1.60.11',
'SQLAlchemy',
'python-telegram-bot>=13.4',
'arrow>=0.17.0',

70
setup.sh
View File

@ -1,12 +1,16 @@
#!/usr/bin/env bash
#encoding=utf8
function echo_block() {
echo "----------------------------"
echo $1
echo "----------------------------"
}
function check_installed_pip() {
${PYTHON} -m pip > /dev/null
if [ $? -ne 0 ]; then
echo "-----------------------------"
echo "Installing Pip for ${PYTHON}"
echo "-----------------------------"
echo_block "Installing Pip for ${PYTHON}"
curl https://bootstrap.pypa.io/get-pip.py -o get-pip.py
${PYTHON} get-pip.py
rm get-pip.py
@ -37,9 +41,7 @@ function check_installed_python() {
}
function updateenv() {
echo "-------------------------"
echo "Updating your virtual env"
echo "-------------------------"
echo_block "Updating your virtual env"
if [ ! -f .env/bin/activate ]; then
echo "Something went wrong, no virtual environment found."
exit 1
@ -110,18 +112,14 @@ function install_mac_newer_python_dependencies() {
if [ ! $(brew --prefix --installed hdf5 2>/dev/null) ]
then
echo "-------------------------"
echo "Installing hdf5"
echo "-------------------------"
echo_block "Installing hdf5"
brew install hdf5
fi
export HDF5_DIR=$(brew --prefix)
if [ ! $(brew --prefix --installed c-blosc 2>/dev/null) ]
then
echo "-------------------------"
echo "Installing c-blosc"
echo "-------------------------"
echo_block "Installing c-blosc"
brew install c-blosc
fi
export CBLOSC_DIR=$(brew --prefix)
@ -131,9 +129,7 @@ function install_mac_newer_python_dependencies() {
function install_macos() {
if [ ! -x "$(command -v brew)" ]
then
echo "-------------------------"
echo "Installing Brew"
echo "-------------------------"
echo_block "Installing Brew"
/usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"
fi
#Gets number after decimal in python version
@ -148,7 +144,14 @@ function install_macos() {
# Install bot Debian_ubuntu
function install_debian() {
sudo apt-get update
sudo apt-get install -y build-essential autoconf libtool pkg-config make wget git $(echo lib${PYTHON}-dev ${PYTHON}-venv)
sudo apt-get install -y gcc build-essential autoconf libtool pkg-config make wget git $(echo lib${PYTHON}-dev ${PYTHON}-venv)
install_talib
}
# Install bot RedHat_CentOS
function install_redhat() {
sudo yum update
sudo yum install -y gcc gcc-c++ make autoconf libtool pkg-config wget git $(echo ${PYTHON}-devel | sed 's/\.//g')
install_talib
}
@ -160,9 +163,7 @@ function update() {
# Reset Develop or Stable branch
function reset() {
echo "----------------------------"
echo "Resetting branch and virtual env"
echo "----------------------------"
echo_block "Resetting branch and virtual env"
if [ "1" == $(git branch -vv |grep -cE "\* develop|\* stable") ]
then
@ -200,48 +201,39 @@ function reset() {
}
function config() {
echo "-------------------------"
echo "Please use 'freqtrade new-config -c config.json' to generate a new configuration file."
echo "-------------------------"
echo_block "Please use 'freqtrade new-config -c config.json' to generate a new configuration file."
}
function install() {
echo "-------------------------"
echo "Installing mandatory dependencies"
echo "-------------------------"
echo_block "Installing mandatory dependencies"
if [ "$(uname -s)" == "Darwin" ]
then
if [ "$(uname -s)" == "Darwin" ]; then
echo "macOS detected. Setup for this system in-progress"
install_macos
elif [ -x "$(command -v apt-get)" ]
then
elif [ -x "$(command -v apt-get)" ]; then
echo "Debian/Ubuntu detected. Setup for this system in-progress"
install_debian
elif [ -x "$(command -v yum)" ]; then
echo "Red Hat/CentOS detected. Setup for this system in-progress"
install_redhat
else
echo "This script does not support your OS."
echo "If you have Python3.6 or Python3.7, pip, virtualenv, ta-lib you can continue."
echo "If you have Python version 3.7 - 3.9, pip, virtualenv, ta-lib you can continue."
echo "Wait 10 seconds to continue the next install steps or use ctrl+c to interrupt this shell."
sleep 10
fi
echo
reset
config
echo "-------------------------"
echo "Run the bot !"
echo "-------------------------"
echo_block "Run the bot !"
echo "You can now use the bot by executing 'source .env/bin/activate; freqtrade <subcommand>'."
echo "You can see the list of available bot sub-commands by executing 'source .env/bin/activate; freqtrade --help'."
echo "You verify that freqtrade is installed successfully by running 'source .env/bin/activate; freqtrade --version'."
}
function plot() {
echo "
-----------------------------------------
Installing dependencies for Plotting scripts
-----------------------------------------
"
echo_block "Installing dependencies for Plotting scripts"
${PYTHON} -m pip install plotly --upgrade
}

28
tests/commands/test_commands.py
View File

@ -8,12 +8,12 @@ from zipfile import ZipFile
import arrow
import pytest
from freqtrade.commands import (start_convert_data, start_convert_trades, start_create_userdir,
start_download_data, start_hyperopt_list, start_hyperopt_show,
start_install_ui, start_list_data, start_list_exchanges,
start_list_markets, start_list_strategies, start_list_timeframes,
start_new_strategy, start_show_trades, start_test_pairlist,
start_trading, start_webserver)
from freqtrade.commands import (start_backtesting_show, start_convert_data, start_convert_trades,
start_create_userdir, start_download_data, start_hyperopt_list,
start_hyperopt_show, start_install_ui, start_list_data,
start_list_exchanges, start_list_markets, start_list_strategies,
start_list_timeframes, start_new_strategy, start_show_trades,
start_test_pairlist, start_trading, start_webserver)
from freqtrade.commands.deploy_commands import (clean_ui_subdir, download_and_install_ui,
get_ui_download_url, read_ui_version)
from freqtrade.configuration import setup_utils_configuration
@ -1389,3 +1389,19 @@ def test_show_trades(mocker, fee, capsys, caplog):
with pytest.raises(OperationalException, match=r"--db-url is required for this command."):
start_show_trades(pargs)
def test_backtesting_show(mocker, testdatadir, capsys):
sbr = mocker.patch('freqtrade.optimize.optimize_reports.show_backtest_results')
args = [
"backtesting-show",
"--export-filename",
f"{testdatadir / 'backtest-result_new.json'}",
"--show-pair-list"
]
pargs = get_args(args)
pargs['config'] = None
start_backtesting_show(pargs)
assert sbr.call_count == 1
out, err = capsys.readouterr()
assert "Pairs for Strategy" in out

49
tests/conftest.py
View File

@ -16,7 +16,7 @@ from telegram import Chat, Message, Update
from freqtrade import constants
from freqtrade.commands import Arguments
from freqtrade.data.converter import ohlcv_to_dataframe
from freqtrade.edge import Edge, PairInfo
from freqtrade.edge import PairInfo
from freqtrade.enums import RunMode
from freqtrade.exchange import Exchange
from freqtrade.freqtradebot import FreqtradeBot
@ -140,11 +140,6 @@ def patch_edge(mocker) -> None:
mocker.patch('freqtrade.edge.Edge.calculate', MagicMock(return_value=True))
def get_patched_edge(mocker, config) -> Edge:
patch_edge(mocker)
edge = Edge(config)
return edge
# Functions for recurrent object patching
@ -186,7 +181,7 @@ def get_patched_worker(mocker, config) -> Worker:
return Worker(args=None, config=config)
def patch_get_signal(freqtrade: FreqtradeBot, value=(True, False, None)) -> None:
def patch_get_signal(freqtrade: FreqtradeBot, value=(True, False, None, None)) -> None:
"""
:param mocker: mocker to patch IStrategy class
:param value: which value IStrategy.get_signal() must return
@ -2221,6 +2216,46 @@ def market_buy_order_usdt():
}
@pytest.fixture
def market_buy_order_usdt_doublefee(market_buy_order_usdt):
order = deepcopy(market_buy_order_usdt)
order['fee'] = None
# Market orders filled with 2 trades can have fees in different currencies
# assuming the account runs out of BNB.
order['fees'] = [
{'cost': 0.00025125, 'currency': 'BNB'},
{'cost': 0.05030681, 'currency': 'USDT'},
]
order['trades'] = [{
'timestamp': None,
'datetime': None,
'symbol': 'ETH/USDT',
'id': None,
'order': '123',
'type': 'market',
'side': 'sell',
'takerOrMaker': None,
'price': 2.01,
'amount': 25.0,
'cost': 50.25,
'fee': {'cost': 0.00025125, 'currency': 'BNB'}
}, {
'timestamp': None,
'datetime': None,
'symbol': 'ETH/USDT',
'id': None,
'order': '123',
'type': 'market',
'side': 'sell',
'takerOrMaker': None,
'price': 2.0,
'amount': 5,
'cost': 10,
'fee': {'cost': 0.0100306, 'currency': 'USDT'}
}]
return order
@pytest.fixture
def market_sell_order_usdt():
return {

3
tests/conftest_trades.py
View File

@ -89,6 +89,7 @@ def mock_trade_2(fee):
open_order_id='dry_run_sell_12345',
strategy='StrategyTestV2',
timeframe=5,
buy_tag='TEST1',
sell_reason='sell_signal',
open_date=datetime.now(tz=timezone.utc) - timedelta(minutes=20),
close_date=datetime.now(tz=timezone.utc) - timedelta(minutes=2),
@ -241,6 +242,7 @@ def mock_trade_5(fee):
open_rate=0.123,
exchange='binance',
strategy='SampleStrategy',
buy_tag='TEST1',
stoploss_order_id='prod_stoploss_3455',
timeframe=5,
)
@ -295,6 +297,7 @@ def mock_trade_6(fee):
open_rate=0.15,
exchange='binance',
strategy='SampleStrategy',
buy_tag='TEST2',
open_order_id="prod_sell_6",
timeframe=5,
)

9
tests/exchange/test_binance.py
View File

@ -126,13 +126,16 @@ async def test__async_get_historic_ohlcv_binance(default_conf, mocker, caplog):
exchange._api_async.fetch_ohlcv = get_mock_coro(ohlcv)
pair = 'ETH/BTC'
res = await exchange._async_get_historic_ohlcv(pair, "5m",
1500000000000, is_new_pair=False)
respair, restf, res = await exchange._async_get_historic_ohlcv(
pair, "5m", 1500000000000, is_new_pair=False)
assert respair == pair
assert restf == '5m'
# Call with very old timestamp - causes tons of requests
assert exchange._api_async.fetch_ohlcv.call_count > 400
# assert res == ohlcv
exchange._api_async.fetch_ohlcv.reset_mock()
res = await exchange._async_get_historic_ohlcv(pair, "5m", 1500000000000, is_new_pair=True)
_, _, res = await exchange._async_get_historic_ohlcv(
pair, "5m", 1500000000000, is_new_pair=True)
# Called twice - one "init" call - and one to get the actual data.
assert exchange._api_async.fetch_ohlcv.call_count == 2

5
tests/exchange/test_ccxt_compat.py
View File

@ -47,6 +47,11 @@ EXCHANGES = {
'hasQuoteVolume': True,
'timeframe': '5m',
},
'okex': {
'pair': 'BTC/USDT',
'hasQuoteVolume': True,
'timeframe': '5m',
},
}

48
tests/exchange/test_exchange.py
View File

@ -238,9 +238,9 @@ def test_validate_order_time_in_force(default_conf, mocker, caplog):
(2.9999, 4, 0.005, 2.995),
])
def test_amount_to_precision(default_conf, mocker, amount, precision_mode, precision, expected):
'''
"""
Test rounds down
'''
"""
markets = PropertyMock(return_value={'ETH/BTC': {'precision': {'amount': precision}}})
@ -279,9 +279,7 @@ def test_amount_to_precision(default_conf, mocker, amount, precision_mode, preci
])
def test_price_to_precision(default_conf, mocker, price, precision_mode, precision, expected):
'''
Test price to precision
'''
"""Test price to precision"""
markets = PropertyMock(return_value={'ETH/BTC': {'precision': {'price': precision}}})
exchange = get_patched_exchange(mocker, default_conf, id="binance")
@ -941,9 +939,22 @@ def test_validate_required_startup_candles(default_conf, mocker, caplog):
default_conf['startup_candle_count'] = 20
ex = Exchange(default_conf)
assert ex
default_conf['startup_candle_count'] = 600
# assumption is that the exchange provides 500 candles per call.s
assert ex.validate_required_startup_candles(200, '5m') == 1
assert ex.validate_required_startup_candles(499, '5m') == 1
assert ex.validate_required_startup_candles(600, '5m') == 2
assert ex.validate_required_startup_candles(501, '5m') == 2
assert ex.validate_required_startup_candles(499, '5m') == 1
assert ex.validate_required_startup_candles(1000, '5m') == 3
assert ex.validate_required_startup_candles(2499, '5m') == 5
assert log_has_re(r'Using 5 calls to get OHLCV. This.*', caplog)
with pytest.raises(OperationalException, match=r'This strategy requires 600.*'):
with pytest.raises(OperationalException, match=r'This strategy requires 2500.*'):
ex.validate_required_startup_candles(2500, '5m')
# Ensure the same also happens on init
default_conf['startup_candle_count'] = 6000
with pytest.raises(OperationalException, match=r'This strategy requires 6000.*'):
Exchange(default_conf)
@ -1506,6 +1517,7 @@ def test_get_historic_ohlcv(default_conf, mocker, caplog, exchange_name):
assert exchange._async_get_candle_history.call_count == 2
# Returns twice the above OHLCV data
assert len(ret) == 2
assert log_has_re(r'Downloaded data for .* with length .*\.', caplog)
caplog.clear()
@ -1587,12 +1599,13 @@ async def test__async_get_historic_ohlcv(default_conf, mocker, caplog, exchange_
exchange._api_async.fetch_ohlcv = get_mock_coro(ohlcv)
pair = 'ETH/USDT'
res = await exchange._async_get_historic_ohlcv(pair, "5m",
1500000000000, is_new_pair=False)
respair, restf, res = await exchange._async_get_historic_ohlcv(
pair, "5m", 1500000000000, is_new_pair=False)
assert respair == pair
assert restf == '5m'
# Call with very old timestamp - causes tons of requests
assert exchange._api_async.fetch_ohlcv.call_count > 200
assert res[0] == ohlcv[0]
assert log_has_re(r'Downloaded data for .* with length .*\.', caplog)
def test_refresh_latest_ohlcv(mocker, default_conf, caplog) -> None:
@ -1630,12 +1643,14 @@ def test_refresh_latest_ohlcv(mocker, default_conf, caplog) -> None:
assert exchange._api_async.fetch_ohlcv.call_count == 2
exchange._api_async.fetch_ohlcv.reset_mock()
exchange.required_candle_call_count = 2
res = exchange.refresh_latest_ohlcv(pairs)
assert len(res) == len(pairs)
assert log_has(f'Refreshing candle (OHLCV) data for {len(pairs)} pairs', caplog)
assert exchange._klines
assert exchange._api_async.fetch_ohlcv.call_count == 2
assert exchange._api_async.fetch_ohlcv.call_count == 4
exchange._api_async.fetch_ohlcv.reset_mock()
for pair in pairs:
assert isinstance(exchange.klines(pair), DataFrame)
assert len(exchange.klines(pair)) > 0
@ -1651,7 +1666,7 @@ def test_refresh_latest_ohlcv(mocker, default_conf, caplog) -> None:
res = exchange.refresh_latest_ohlcv([('IOTA/ETH', '5m'), ('XRP/ETH', '5m')])
assert len(res) == len(pairs)
assert exchange._api_async.fetch_ohlcv.call_count == 2
assert exchange._api_async.fetch_ohlcv.call_count == 0
assert log_has(f"Using cached candle (OHLCV) data for pair {pairs[0][0]}, "
f"timeframe {pairs[0][1]} ...",
caplog)
@ -2002,15 +2017,6 @@ def test_get_sell_rate_exception(default_conf, mocker, caplog):
assert exchange.get_rate(pair, refresh=True, side="sell") == 0.13
def make_fetch_ohlcv_mock(data):
def fetch_ohlcv_mock(pair, timeframe, since):
if since:
assert since > data[-1][0]
return []
return data
return fetch_ohlcv_mock
@pytest.mark.parametrize("exchange_name", EXCHANGES)
@pytest.mark.asyncio
async def test___async_get_candle_history_sort(default_conf, mocker, exchange_name):

8
tests/optimize/__init__.py
View File

@ -54,4 +54,12 @@ def _build_backtest_dataframe(data):
frame[column] = frame[column].astype('float64')
if 'buy_tag' not in columns:
frame['buy_tag'] = None
if 'exit_tag' not in columns:
frame['exit_tag'] = None
# Ensure all candles make kindof sense
assert all(frame['low'] <= frame['close'])
assert all(frame['low'] <= frame['open'])
assert all(frame['high'] >= frame['close'])
assert all(frame['high'] >= frame['open'])
return frame

94
tests/optimize/test_backtest_detail.py
View File

@ -17,10 +17,10 @@ tc0 = BTContainer(data=[
# D O H L C V B S
[0, 5000, 5025, 4975, 4987, 6172, 1, 0],
[1, 5000, 5025, 4975, 4987, 6172, 0, 0], # enter trade (signal on last candle)
[2, 4987, 5012, 4986, 4600, 6172, 0, 0], # exit with stoploss hit
[3, 5010, 5000, 4980, 5010, 6172, 0, 1],
[4, 5010, 4987, 4977, 4995, 6172, 0, 0],
[5, 4995, 4995, 4995, 4950, 6172, 0, 0]],
[2, 4987, 5012, 4986, 4986, 6172, 0, 0], # exit with stoploss hit
[3, 5010, 5010, 4980, 5010, 6172, 0, 1],
[4, 5010, 5011, 4977, 4995, 6172, 0, 0],
[5, 4995, 4995, 4950, 4950, 6172, 0, 0]],
stop_loss=-0.01, roi={"0": 1}, profit_perc=0.002, use_sell_signal=True,
trades=[BTrade(sell_reason=SellType.SELL_SIGNAL, open_tick=1, close_tick=4)]
)
@ -32,9 +32,9 @@ tc1 = BTContainer(data=[
[0, 5000, 5025, 4975, 4987, 6172, 1, 0],
[1, 5000, 5025, 4975, 4987, 6172, 0, 0], # enter trade (signal on last candle)
[2, 4987, 5012, 4600, 4600, 6172, 0, 0], # exit with stoploss hit
[3, 4975, 5000, 4980, 4977, 6172, 0, 0],
[4, 4977, 4987, 4977, 4995, 6172, 0, 0],
[5, 4995, 4995, 4995, 4950, 6172, 0, 0]],
[3, 4975, 5000, 4975, 4977, 6172, 0, 0],
[4, 4977, 4995, 4977, 4995, 6172, 0, 0],
[5, 4995, 4995, 4950, 4950, 6172, 0, 0]],
stop_loss=-0.01, roi={"0": 1}, profit_perc=-0.01,
trades=[BTrade(sell_reason=SellType.STOP_LOSS, open_tick=1, close_tick=2)]
)
@ -69,7 +69,7 @@ tc3 = BTContainer(data=[
[3, 4975, 5000, 4950, 4962, 6172, 1, 0],
[4, 4975, 5000, 4950, 4962, 6172, 0, 0], # enter trade 2 (signal on last candle)
[5, 4962, 4987, 4000, 4000, 6172, 0, 0], # exit with stoploss hit
[6, 4950, 4975, 4975, 4950, 6172, 0, 0]],
[6, 4950, 4975, 4950, 4950, 6172, 0, 0]],
stop_loss=-0.02, roi={"0": 1}, profit_perc=-0.04,
trades=[BTrade(sell_reason=SellType.STOP_LOSS, open_tick=1, close_tick=2),
BTrade(sell_reason=SellType.STOP_LOSS, open_tick=4, close_tick=5)]
@ -99,7 +99,7 @@ tc5 = BTContainer(data=[
[1, 5000, 5025, 4980, 4987, 6172, 0, 0], # enter trade (signal on last candle)
[2, 4987, 5025, 4975, 4987, 6172, 0, 0],
[3, 4975, 6000, 4975, 6000, 6172, 0, 0], # ROI
[4, 4962, 4987, 4972, 4950, 6172, 0, 0],
[4, 4962, 4987, 4962, 4972, 6172, 0, 0],
[5, 4950, 4975, 4925, 4950, 6172, 0, 0]],
stop_loss=-0.01, roi={"0": 0.03}, profit_perc=0.03,
trades=[BTrade(sell_reason=SellType.ROI, open_tick=1, close_tick=3)]
@ -113,7 +113,7 @@ tc6 = BTContainer(data=[
[1, 5000, 5025, 4975, 4987, 6172, 0, 0], # enter trade (signal on last candle)
[2, 4987, 5300, 4850, 5050, 6172, 0, 0], # Exit with stoploss
[3, 4975, 5000, 4950, 4962, 6172, 0, 0],
[4, 4962, 4987, 4972, 4950, 6172, 0, 0],
[4, 4962, 4987, 4950, 4950, 6172, 0, 0],
[5, 4950, 4975, 4925, 4950, 6172, 0, 0]],
stop_loss=-0.02, roi={"0": 0.05}, profit_perc=-0.02,
trades=[BTrade(sell_reason=SellType.STOP_LOSS, open_tick=1, close_tick=2)]
@ -127,7 +127,7 @@ tc7 = BTContainer(data=[
[1, 5000, 5025, 4975, 4987, 6172, 0, 0],
[2, 4987, 5300, 4950, 5050, 6172, 0, 0],
[3, 4975, 5000, 4950, 4962, 6172, 0, 0],
[4, 4962, 4987, 4972, 4950, 6172, 0, 0],
[4, 4962, 4987, 4950, 4950, 6172, 0, 0],
[5, 4950, 4975, 4925, 4950, 6172, 0, 0]],
stop_loss=-0.02, roi={"0": 0.03}, profit_perc=0.03,
trades=[BTrade(sell_reason=SellType.ROI, open_tick=1, close_tick=2)]
@ -167,7 +167,7 @@ tc9 = BTContainer(data=[
tc10 = BTContainer(data=[
# D O H L C V B S
[0, 5000, 5050, 4950, 5000, 6172, 1, 0],
[1, 5000, 5050, 4950, 5100, 6172, 0, 0],
[1, 5000, 5100, 4950, 5100, 6172, 0, 0],
[2, 5100, 5251, 5100, 5100, 6172, 0, 0],
[3, 4850, 5050, 4650, 4750, 6172, 0, 0],
[4, 4750, 4950, 4350, 4750, 6172, 0, 0]],
@ -183,7 +183,7 @@ tc10 = BTContainer(data=[
tc11 = BTContainer(data=[
# D O H L C V B S
[0, 5000, 5050, 4950, 5000, 6172, 1, 0],
[1, 5000, 5050, 4950, 5100, 6172, 0, 0],
[1, 5000, 5100, 4950, 5100, 6172, 0, 0],
[2, 5100, 5251, 5100, 5100, 6172, 0, 0],
[3, 5000, 5150, 4650, 4750, 6172, 0, 0],
[4, 4750, 4950, 4350, 4750, 6172, 0, 0]],
@ -199,7 +199,7 @@ tc11 = BTContainer(data=[
tc12 = BTContainer(data=[
# D O H L C V B S
[0, 5000, 5050, 4950, 5000, 6172, 1, 0],
[1, 5000, 5050, 4950, 5100, 6172, 0, 0],
[1, 5000, 5100, 4950, 5100, 6172, 0, 0],
[2, 5100, 5251, 4650, 5100, 6172, 0, 0],
[3, 4850, 5050, 4650, 4750, 6172, 0, 0],
[4, 4750, 4950, 4350, 4750, 6172, 0, 0]],
@ -216,8 +216,8 @@ tc13 = BTContainer(data=[
[0, 5000, 5050, 4950, 5000, 6172, 1, 0],
[1, 5000, 5100, 4950, 5100, 6172, 0, 0],
[2, 5100, 5251, 4850, 5100, 6172, 0, 0],
[3, 4850, 5050, 4850, 4750, 6172, 0, 0],
[4, 4750, 4950, 4850, 4750, 6172, 0, 0]],
[3, 4850, 5050, 4750, 4750, 6172, 0, 0],
[4, 4750, 4950, 4750, 4750, 6172, 0, 0]],
stop_loss=-0.10, roi={"0": 0.01}, profit_perc=0.01,
trades=[BTrade(sell_reason=SellType.ROI, open_tick=1, close_tick=1)]
)
@ -229,7 +229,7 @@ tc14 = BTContainer(data=[
[0, 5000, 5050, 4950, 5000, 6172, 1, 0],
[1, 5000, 5100, 4600, 5100, 6172, 0, 0],
[2, 5100, 5251, 4850, 5100, 6172, 0, 0],
[3, 4850, 5050, 4850, 4750, 6172, 0, 0],
[3, 4850, 5050, 4750, 4750, 6172, 0, 0],
[4, 4750, 4950, 4350, 4750, 6172, 0, 0]],
stop_loss=-0.05, roi={"0": 0.10}, profit_perc=-0.05,
trades=[BTrade(sell_reason=SellType.STOP_LOSS, open_tick=1, close_tick=1)]
@ -243,7 +243,7 @@ tc15 = BTContainer(data=[
[0, 5000, 5050, 4950, 5000, 6172, 1, 0],
[1, 5000, 5100, 4900, 5100, 6172, 1, 0],
[2, 5100, 5251, 4650, 5100, 6172, 0, 0],
[3, 4850, 5050, 4850, 4750, 6172, 0, 0],
[3, 4850, 5050, 4750, 4750, 6172, 0, 0],
[4, 4750, 4950, 4350, 4750, 6172, 0, 0]],
stop_loss=-0.05, roi={"0": 0.01}, profit_perc=-0.04,
trades=[BTrade(sell_reason=SellType.ROI, open_tick=1, close_tick=1),
@ -259,7 +259,7 @@ tc16 = BTContainer(data=[
[1, 5000, 5025, 4975, 4987, 6172, 0, 0],
[2, 4987, 5300, 4950, 5050, 6172, 0, 0],
[3, 4975, 5000, 4940, 4962, 6172, 0, 0], # ForceSell on ROI (roi=-1)
[4, 4962, 4987, 4972, 4950, 6172, 0, 0],
[4, 4962, 4987, 4950, 4950, 6172, 0, 0],
[5, 4950, 4975, 4925, 4950, 6172, 0, 0]],
stop_loss=-0.10, roi={"0": 0.10, "65": -1}, profit_perc=-0.012,
trades=[BTrade(sell_reason=SellType.ROI, open_tick=1, close_tick=3)]
@ -275,7 +275,7 @@ tc17 = BTContainer(data=[
[1, 5000, 5025, 4975, 4987, 6172, 0, 0],
[2, 4987, 5300, 4950, 5050, 6172, 0, 0],
[3, 4980, 5000, 4940, 4962, 6172, 0, 0], # ForceSell on ROI (roi=-1)
[4, 4962, 4987, 4972, 4950, 6172, 0, 0],
[4, 4962, 4987, 4950, 4950, 6172, 0, 0],
[5, 4950, 4975, 4925, 4950, 6172, 0, 0]],
stop_loss=-0.10, roi={"0": 0.10, "120": -1}, profit_perc=-0.004,
trades=[BTrade(sell_reason=SellType.ROI, open_tick=1, close_tick=3)]
@ -291,7 +291,7 @@ tc18 = BTContainer(data=[
[1, 5000, 5025, 4975, 4987, 6172, 0, 0],
[2, 4987, 5300, 4950, 5200, 6172, 0, 0],
[3, 5200, 5220, 4940, 4962, 6172, 0, 0], # Sell on ROI (sells on open)
[4, 4962, 4987, 4972, 4950, 6172, 0, 0],
[4, 4962, 4987, 4950, 4950, 6172, 0, 0],
[5, 4950, 4975, 4925, 4950, 6172, 0, 0]],
stop_loss=-0.10, roi={"0": 0.10, "120": 0.01}, profit_perc=0.04,
trades=[BTrade(sell_reason=SellType.ROI, open_tick=1, close_tick=3)]
@ -306,8 +306,8 @@ tc19 = BTContainer(data=[
[1, 5000, 5025, 4975, 4987, 6172, 0, 0],
[2, 4987, 5300, 4950, 5200, 6172, 0, 0],
[3, 5000, 5300, 4940, 4962, 6172, 0, 0], # Sell on ROI
[4, 4962, 4987, 4972, 4950, 6172, 0, 0],
[5, 4550, 4975, 4925, 4950, 6172, 0, 0]],
[4, 4962, 4987, 4950, 4950, 6172, 0, 0],
[5, 4550, 4975, 4550, 4950, 6172, 0, 0]],
stop_loss=-0.10, roi={"0": 0.10, "120": 0.01}, profit_perc=0.01,
trades=[BTrade(sell_reason=SellType.ROI, open_tick=1, close_tick=3)]
)
@ -321,8 +321,8 @@ tc20 = BTContainer(data=[
[1, 5000, 5025, 4975, 4987, 6172, 0, 0],
[2, 4987, 5300, 4950, 5200, 6172, 0, 0],
[3, 5200, 5300, 4940, 4962, 6172, 0, 0], # Sell on ROI
[4, 4962, 4987, 4972, 4950, 6172, 0, 0],
[5, 4550, 4975, 4925, 4950, 6172, 0, 0]],
[4, 4962, 4987, 4950, 4950, 6172, 0, 0],
[5, 4925, 4975, 4925, 4950, 6172, 0, 0]],
stop_loss=-0.10, roi={"0": 0.10, "119": 0.01}, profit_perc=0.01,
trades=[BTrade(sell_reason=SellType.ROI, open_tick=1, close_tick=3)]
)
@ -334,7 +334,7 @@ tc20 = BTContainer(data=[
tc21 = BTContainer(data=[
# D O H L C V B S
[0, 5000, 5050, 4950, 5000, 6172, 1, 0],
[1, 5000, 5050, 4950, 5100, 6172, 0, 0],
[1, 5000, 5100, 4950, 5100, 6172, 0, 0],
[2, 5100, 5251, 4650, 5100, 6172, 0, 0],
[3, 4850, 5050, 4650, 4750, 6172, 0, 0],
[4, 4750, 4950, 4350, 4750, 6172, 0, 0]],
@ -350,7 +350,7 @@ tc21 = BTContainer(data=[
tc22 = BTContainer(data=[
# D O H L C V B S
[0, 5000, 5050, 4950, 5000, 6172, 1, 0],
[1, 5000, 5050, 4950, 5100, 6172, 0, 0],
[1, 5000, 5100, 4950, 5100, 6172, 0, 0],
[2, 5100, 5251, 5100, 5100, 6172, 0, 0],
[3, 4850, 5050, 4650, 4750, 6172, 0, 0],
[4, 4750, 4950, 4350, 4750, 6172, 0, 0]],
@ -369,7 +369,7 @@ tc22 = BTContainer(data=[
tc23 = BTContainer(data=[
# D O H L C V B S
[0, 5000, 5050, 4950, 5000, 6172, 1, 0],
[1, 5000, 5050, 4950, 5100, 6172, 0, 0],
[1, 5000, 5100, 4950, 5100, 6172, 0, 0],
[2, 5100, 5251, 5100, 5100, 6172, 0, 0],
[3, 4850, 5251, 4650, 4750, 6172, 0, 0],
[4, 4750, 4950, 4350, 4750, 6172, 0, 0]],
@ -386,10 +386,10 @@ tc24 = BTContainer(data=[
# D O H L C V B S
[0, 5000, 5025, 4975, 4987, 6172, 1, 0],
[1, 5000, 5025, 4975, 4987, 6172, 0, 0], # enter trade (signal on last candle)
[2, 4987, 5012, 4986, 4600, 6172, 0, 0],
[3, 5010, 5000, 4855, 5010, 6172, 0, 1], # Triggers stoploss + sellsignal
[4, 5010, 4987, 4977, 4995, 6172, 0, 0],
[5, 4995, 4995, 4995, 4950, 6172, 0, 0]],
[2, 4987, 5012, 4986, 4986, 6172, 0, 0],
[3, 5010, 5010, 4855, 5010, 6172, 0, 1], # Triggers stoploss + sellsignal
[4, 5010, 5010, 4977, 4995, 6172, 0, 0],
[5, 4995, 4995, 4950, 4950, 6172, 0, 0]],
stop_loss=-0.01, roi={"0": 1}, profit_perc=-0.01, use_sell_signal=True,
trades=[BTrade(sell_reason=SellType.STOP_LOSS, open_tick=1, close_tick=3)]
)
@ -401,10 +401,10 @@ tc25 = BTContainer(data=[
# D O H L C V B S
[0, 5000, 5025, 4975, 4987, 6172, 1, 0],
[1, 5000, 5025, 4975, 4987, 6172, 0, 0], # enter trade (signal on last candle)
[2, 4987, 5012, 4986, 4600, 6172, 0, 0],
[3, 5010, 5000, 4986, 5010, 6172, 0, 1],
[4, 5010, 4987, 4855, 4995, 6172, 0, 0], # Triggers stoploss + sellsignal acted on
[5, 4995, 4995, 4995, 4950, 6172, 0, 0]],
[2, 4987, 5012, 4986, 4986, 6172, 0, 0],
[3, 5010, 5010, 4986, 5010, 6172, 0, 1],
[4, 5010, 5010, 4855, 4995, 6172, 0, 0], # Triggers stoploss + sellsignal acted on
[5, 4995, 4995, 4950, 4950, 6172, 0, 0]],
stop_loss=-0.01, roi={"0": 1}, profit_perc=0.002, use_sell_signal=True,
trades=[BTrade(sell_reason=SellType.SELL_SIGNAL, open_tick=1, close_tick=4)]
)
@ -416,10 +416,10 @@ tc26 = BTContainer(data=[
# D O H L C V B S
[0, 5000, 5025, 4975, 4987, 6172, 1, 0],
[1, 5000, 5025, 4975, 4987, 6172, 0, 0], # enter trade (signal on last candle)
[2, 4987, 5012, 4986, 4600, 6172, 0, 0],
[2, 4987, 5012, 4986, 4986, 6172, 0, 0],
[3, 5010, 5251, 4986, 5010, 6172, 0, 1], # Triggers ROI, sell-signal
[4, 5010, 4987, 4855, 4995, 6172, 0, 0],
[5, 4995, 4995, 4995, 4950, 6172, 0, 0]],
[4, 5010, 5010, 4855, 4995, 6172, 0, 0],
[5, 4995, 4995, 4950, 4950, 6172, 0, 0]],
stop_loss=-0.10, roi={"0": 0.05}, profit_perc=0.05, use_sell_signal=True,
trades=[BTrade(sell_reason=SellType.ROI, open_tick=1, close_tick=3)]
)
@ -432,10 +432,10 @@ tc27 = BTContainer(data=[
# D O H L C V B S
[0, 5000, 5025, 4975, 4987, 6172, 1, 0],
[1, 5000, 5025, 4975, 4987, 6172, 0, 0], # enter trade (signal on last candle)
[2, 4987, 5012, 4986, 4600, 6172, 0, 0],
[2, 4987, 5012, 4986, 4986, 6172, 0, 0],
[3, 5010, 5012, 4986, 5010, 6172, 0, 1], # sell-signal
[4, 5010, 5251, 4855, 4995, 6172, 0, 0], # Triggers ROI, sell-signal acted on
[5, 4995, 4995, 4995, 4950, 6172, 0, 0]],
[5, 4995, 4995, 4950, 4950, 6172, 0, 0]],
stop_loss=-0.10, roi={"0": 0.05}, profit_perc=0.05, use_sell_signal=True,
trades=[BTrade(sell_reason=SellType.ROI, open_tick=1, close_tick=4)]
)
@ -447,7 +447,7 @@ tc27 = BTContainer(data=[
tc28 = BTContainer(data=[
# D O H L C V B S
[0, 5000, 5050, 4950, 5000, 6172, 1, 0],
[1, 5000, 5050, 4950, 5100, 6172, 0, 0],
[1, 5000, 5100, 4950, 5100, 6172, 0, 0],
[2, 5100, 5251, 5100, 5100, 6172, 0, 0],
[3, 4850, 5050, 4650, 4750, 6172, 0, 0],
[4, 4750, 4950, 4350, 4750, 6172, 0, 0]],
@ -463,7 +463,7 @@ tc28 = BTContainer(data=[
tc29 = BTContainer(data=[
# D O H L C V B S
[0, 5000, 5050, 4950, 5000, 6172, 1, 0],
[1, 5000, 5050, 5000, 4900, 6172, 0, 0], # enter trade (signal on last candle)
[1, 5000, 5050, 5000, 5000, 6172, 0, 0], # enter trade (signal on last candle)
[2, 4900, 5250, 4500, 5100, 6172, 0, 0], # Triggers trailing-stoploss
[3, 5100, 5100, 4650, 4750, 6172, 0, 0],
[4, 4750, 4950, 4350, 4750, 6172, 0, 0]],
@ -477,7 +477,7 @@ tc29 = BTContainer(data=[
tc30 = BTContainer(data=[
# D O H L C V B S
[0, 5000, 5050, 4950, 5000, 6172, 1, 0],
[1, 5000, 5500, 5000, 4900, 6172, 0, 0], # enter trade (signal on last candle) and stop
[1, 5000, 5500, 4900, 4900, 6172, 0, 0], # enter trade (signal on last candle) and stop
[2, 4900, 5250, 4500, 5100, 6172, 0, 0],
[3, 5100, 5100, 4650, 4750, 6172, 0, 0],
[4, 4750, 4950, 4350, 4750, 6172, 0, 0]],
@ -491,7 +491,7 @@ tc30 = BTContainer(data=[
tc31 = BTContainer(data=[
# D O H L C V B S
[0, 5000, 5050, 4950, 5000, 6172, 1, 0],
[1, 5000, 5500, 5000, 4900, 6172, 0, 0], # enter trade (signal on last candle) and stop
[1, 5000, 5500, 4900, 4900, 6172, 0, 0], # enter trade (signal on last candle) and stop
[2, 4900, 5250, 4500, 5100, 6172, 0, 0],
[3, 5100, 5100, 4650, 4750, 6172, 0, 0],
[4, 4750, 4950, 4350, 4750, 6172, 0, 0]],
@ -506,7 +506,7 @@ tc31 = BTContainer(data=[
tc32 = BTContainer(data=[
# D O H L C V B S
[0, 5000, 5050, 4950, 5000, 6172, 1, 0],
[1, 5000, 5500, 5000, 4900, 6172, 0, 0], # enter trade (signal on last candle) and stop
[1, 5000, 5500, 4951, 5000, 6172, 0, 0], # enter trade (signal on last candle) and stop
[2, 4900, 5250, 4500, 5100, 6172, 0, 0],
[3, 5100, 5100, 4650, 4750, 6172, 0, 0],
[4, 4750, 4950, 4350, 4750, 6172, 0, 0]],
@ -521,7 +521,7 @@ tc32 = BTContainer(data=[
tc33 = BTContainer(data=[
# D O H L C V B S BT
[0, 5000, 5050, 4950, 5000, 6172, 1, 0, 'buy_signal_01'],
[1, 5000, 5500, 5000, 4900, 6172, 0, 0, None], # enter trade (signal on last candle) and stop
[1, 5000, 5500, 4951, 5000, 6172, 0, 0, None], # enter trade (signal on last candle) and stop
[2, 4900, 5250, 4500, 5100, 6172, 0, 0, None],
[3, 5100, 5100, 4650, 4750, 6172, 0, 0, None],
[4, 4750, 4950, 4350, 4750, 6172, 0, 0, None]],

24
tests/optimize/test_backtesting.py
View File

@ -438,7 +438,8 @@ def test_backtesting_no_pair_left(default_conf, mocker, caplog, testdatadir) ->
Backtesting(default_conf)
default_conf['pairlists'] = [{"method": "VolumePairList", "number_assets": 5}]
with pytest.raises(OperationalException, match='VolumePairList not allowed for backtesting.'):
with pytest.raises(OperationalException,
match=r'VolumePairList not allowed for backtesting\..*StaticPairlist.*'):
Backtesting(default_conf)
default_conf.update({
@ -470,7 +471,8 @@ def test_backtesting_pairlist_list(default_conf, mocker, caplog, testdatadir, ti
default_conf['timerange'] = '20180101-20180102'
default_conf['pairlists'] = [{"method": "VolumePairList", "number_assets": 5}]
with pytest.raises(OperationalException, match='VolumePairList not allowed for backtesting.'):
with pytest.raises(OperationalException,
match=r'VolumePairList not allowed for backtesting\..*StaticPairlist.*'):
Backtesting(default_conf)
default_conf['pairlists'] = [{"method": "StaticPairList"}, {"method": "PerformanceFilter"}]
@ -567,6 +569,7 @@ def test_backtest__get_sell_trade_entry(default_conf, fee, mocker) -> None:
195, # Low
201.5, # High
'', # Buy Signal Name
'', # Exit Signal Name
]
trade = backtesting._enter_trade(pair, row=row)
@ -581,26 +584,27 @@ def test_backtest__get_sell_trade_entry(default_conf, fee, mocker) -> None:
195, # Low
210.5, # High
'', # Buy Signal Name
'', # Exit Signal Name
]
row_detail = pd.DataFrame(
[
[
pd.Timestamp(year=2020, month=1, day=1, hour=5, minute=0, tzinfo=timezone.utc),
1, 200, 199, 0, 197, 200.1, '',
1, 200, 199, 0, 197, 200.1, '', '',
], [
pd.Timestamp(year=2020, month=1, day=1, hour=5, minute=1, tzinfo=timezone.utc),
0, 199, 199.5, 0, 199, 199.7, '',
0, 199, 199.5, 0, 199, 199.7, '', '',
], [
pd.Timestamp(year=2020, month=1, day=1, hour=5, minute=2, tzinfo=timezone.utc),
0, 199.5, 200.5, 0, 199, 200.8, '',
0, 199.5, 200.5, 0, 199, 200.8, '', '',
], [
pd.Timestamp(year=2020, month=1, day=1, hour=5, minute=3, tzinfo=timezone.utc),
0, 200.5, 210.5, 0, 193, 210.5, '', # ROI sell (?)
0, 200.5, 210.5, 0, 193, 210.5, '', '', # ROI sell (?)
], [
pd.Timestamp(year=2020, month=1, day=1, hour=5, minute=4, tzinfo=timezone.utc),
0, 200, 199, 0, 193, 200.1, '',
0, 200, 199, 0, 193, 200.1, '', '',
],
], columns=["date", "buy", "open", "close", "sell", "low", "high", "buy_tag"]
], columns=["date", "buy", "open", "close", "sell", "low", "high", "buy_tag", "exit_tag"]
)
# No data available.
@ -614,7 +618,7 @@ def test_backtest__get_sell_trade_entry(default_conf, fee, mocker) -> None:
assert isinstance(trade, LocalTrade)
# Assign empty ... no result.
backtesting.detail_data[pair] = pd.DataFrame(
[], columns=["date", "buy", "open", "close", "sell", "low", "high", "buy_tag"])
[], columns=["date", "buy", "open", "close", "sell", "low", "high", "buy_tag", "exit_tag"])
res = backtesting._get_sell_trade_entry(trade, row)
assert res is None
@ -678,7 +682,7 @@ def test_backtest_one(default_conf, fee, mocker, testdatadir) -> None:
'min_rate': [0.10370188, 0.10300000000000001],
'max_rate': [0.10501, 0.1038888],
'is_open': [False, False],
'buy_tag': [None, None],
'buy_tag': [None, None]
})
pd.testing.assert_frame_equal(results, expected)
data_pair = processed[pair]

1
tests/optimize/test_hyperopt.py
View File

@ -424,6 +424,7 @@ def test_generate_optimizer(mocker, hyperopt_conf) -> None:
return_value=(Arrow(2017, 12, 10), Arrow(2017, 12, 13)))
patch_exchange(mocker)
mocker.patch.object(Path, 'open')
mocker.patch('freqtrade.configuration.config_validation.validate_config_schema')
mocker.patch('freqtrade.optimize.hyperopt.load', return_value={'XRP/BTC': None})
optimizer_param = {

35
tests/optimize/test_hyperoptloss.py
View File

@ -85,6 +85,8 @@ def test_loss_calculation_has_limited_profit(hyperopt_conf, hyperopt_results) ->
"SharpeHyperOptLoss",
"SharpeHyperOptLossDaily",
"MaxDrawDownHyperOptLoss",
"CalmarHyperOptLoss",
])
def test_loss_functions_better_profits(default_conf, hyperopt_results, lossfunction) -> None:
results_over = hyperopt_results.copy()
@ -96,11 +98,32 @@ def test_loss_functions_better_profits(default_conf, hyperopt_results, lossfunct
default_conf.update({'hyperopt_loss': lossfunction})
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(results_over),
datetime(2019, 1, 1), datetime(2019, 5, 1))
under = hl.hyperopt_loss_function(results_under, len(results_under),
datetime(2019, 1, 1), datetime(2019, 5, 1))
correct = hl.hyperopt_loss_function(
hyperopt_results,
trade_count=len(hyperopt_results),
min_date=datetime(2019, 1, 1),
max_date=datetime(2019, 5, 1),
config=default_conf,
processed=None,
backtest_stats={'profit_total': hyperopt_results['profit_abs'].sum()}
)
over = hl.hyperopt_loss_function(
results_over,
trade_count=len(results_over),
min_date=datetime(2019, 1, 1),
max_date=datetime(2019, 5, 1),
config=default_conf,
processed=None,
backtest_stats={'profit_total': results_over['profit_abs'].sum()}
)
under = hl.hyperopt_loss_function(
results_under,
trade_count=len(results_under),
min_date=datetime(2019, 1, 1),
max_date=datetime(2019, 5, 1),
config=default_conf,
processed=None,
backtest_stats={'profit_total': results_under['profit_abs'].sum()}
)
assert over < correct
assert under > correct

22
tests/optimize/test_optimize_reports.py
View File

@ -10,7 +10,8 @@ from arrow import Arrow
from freqtrade.configuration import TimeRange
from freqtrade.constants import DATETIME_PRINT_FORMAT, LAST_BT_RESULT_FN
from freqtrade.data import history
from freqtrade.data.btanalysis import get_latest_backtest_filename, load_backtest_data
from freqtrade.data.btanalysis import (get_latest_backtest_filename, load_backtest_data,
load_backtest_stats)
from freqtrade.edge import PairInfo
from freqtrade.enums import SellType
from freqtrade.optimize.optimize_reports import (_get_resample_from_period, generate_backtest_stats,
@ -19,9 +20,9 @@ from freqtrade.optimize.optimize_reports import (_get_resample_from_period, gene
generate_periodic_breakdown_stats,
generate_sell_reason_stats,
generate_strategy_comparison,
generate_trading_stats, store_backtest_stats,
text_table_bt_results, text_table_sell_reason,
text_table_strategy)
generate_trading_stats, show_sorted_pairlist,
store_backtest_stats, text_table_bt_results,
text_table_sell_reason, text_table_strategy)
from freqtrade.resolvers.strategy_resolver import StrategyResolver
from tests.data.test_history import _backup_file, _clean_test_file
@ -407,3 +408,16 @@ def test__get_resample_from_period():
assert _get_resample_from_period('month') == '1M'
with pytest.raises(ValueError, match=r"Period noooo is not supported."):
_get_resample_from_period('noooo')
def test_show_sorted_pairlist(testdatadir, default_conf, capsys):
filename = testdatadir / "backtest-result_new.json"
bt_data = load_backtest_stats(filename)
default_conf['backtest_show_pair_list'] = True
show_sorted_pairlist(default_conf, bt_data)
out, err = capsys.readouterr()
assert 'Pairs for Strategy StrategyTestV2: \n[' in out
assert 'TOTAL' not in out
assert '"ETH/BTC", // ' in out

25
tests/plugins/test_pairlocks.py
View File

@ -116,3 +116,28 @@ def test_PairLocks_getlongestlock(use_db):
PairLocks.reset_locks()
PairLocks.use_db = True
@pytest.mark.parametrize('use_db', (False, True))
@pytest.mark.usefixtures("init_persistence")
def test_PairLocks_reason(use_db):
PairLocks.timeframe = '5m'
PairLocks.use_db = use_db
# No lock should be present
if use_db:
assert len(PairLock.query.all()) == 0
assert PairLocks.use_db == use_db
PairLocks.lock_pair('XRP/USDT', arrow.utcnow().shift(minutes=4).datetime, 'TestLock1')
PairLocks.lock_pair('ETH/USDT', arrow.utcnow().shift(minutes=4).datetime, 'TestLock2')
assert PairLocks.is_pair_locked('XRP/USDT')
assert PairLocks.is_pair_locked('ETH/USDT')
PairLocks.unlock_reason('TestLock1')
assert not PairLocks.is_pair_locked('XRP/USDT')
assert PairLocks.is_pair_locked('ETH/USDT')
PairLocks.reset_locks()
PairLocks.use_db = True

220
tests/rpc/test_rpc.py
View File

@ -825,7 +825,225 @@ def test_performance_handle(default_conf, ticker, limit_buy_order, fee,
assert len(res) == 1
assert res[0]['pair'] == 'ETH/BTC'
assert res[0]['count'] == 1
assert prec_satoshi(res[0]['profit'], 6.2)
assert prec_satoshi(res[0]['profit_pct'], 6.2)
def test_buy_tag_performance_handle(default_conf, ticker, limit_buy_order, fee,
limit_sell_order, mocker) -> None:
mocker.patch('freqtrade.rpc.telegram.Telegram', MagicMock())
mocker.patch.multiple(
'freqtrade.exchange.Exchange',
get_balances=MagicMock(return_value=ticker),
fetch_ticker=ticker,
get_fee=fee,
)
freqtradebot = get_patched_freqtradebot(mocker, default_conf)
patch_get_signal(freqtradebot)
rpc = RPC(freqtradebot)
# Create some test data
freqtradebot.enter_positions()
trade = Trade.query.first()
assert trade
# Simulate fulfilled LIMIT_BUY order for trade
trade.update(limit_buy_order)
# Simulate fulfilled LIMIT_SELL order for trade
trade.update(limit_sell_order)
trade.close_date = datetime.utcnow()
trade.is_open = False
res = rpc._rpc_buy_tag_performance(None)
assert len(res) == 1
assert res[0]['buy_tag'] == 'Other'
assert res[0]['count'] == 1
assert prec_satoshi(res[0]['profit_pct'], 6.2)
trade.buy_tag = "TEST_TAG"
res = rpc._rpc_buy_tag_performance(None)
assert len(res) == 1
assert res[0]['buy_tag'] == 'TEST_TAG'
assert res[0]['count'] == 1
assert prec_satoshi(res[0]['profit_pct'], 6.2)
def test_buy_tag_performance_handle_2(mocker, default_conf, markets, fee):
mocker.patch('freqtrade.rpc.telegram.Telegram', MagicMock())
mocker.patch.multiple(
'freqtrade.exchange.Exchange',
markets=PropertyMock(return_value=markets)
)
freqtradebot = get_patched_freqtradebot(mocker, default_conf)
create_mock_trades(fee)
rpc = RPC(freqtradebot)
res = rpc._rpc_buy_tag_performance(None)
assert len(res) == 2
assert res[0]['buy_tag'] == 'TEST1'
assert res[0]['count'] == 1
assert prec_satoshi(res[0]['profit_pct'], 0.5)
assert res[1]['buy_tag'] == 'Other'
assert res[1]['count'] == 1
assert prec_satoshi(res[1]['profit_pct'], 1.0)
# Test for a specific pair
res = rpc._rpc_buy_tag_performance('ETC/BTC')
assert len(res) == 1
assert res[0]['count'] == 1
assert res[0]['buy_tag'] == 'TEST1'
assert prec_satoshi(res[0]['profit_pct'], 0.5)
def test_sell_reason_performance_handle(default_conf, ticker, limit_buy_order, fee,
limit_sell_order, mocker) -> None:
mocker.patch('freqtrade.rpc.telegram.Telegram', MagicMock())
mocker.patch.multiple(
'freqtrade.exchange.Exchange',
get_balances=MagicMock(return_value=ticker),
fetch_ticker=ticker,
get_fee=fee,
)
freqtradebot = get_patched_freqtradebot(mocker, default_conf)
patch_get_signal(freqtradebot)
rpc = RPC(freqtradebot)
# Create some test data
freqtradebot.enter_positions()
trade = Trade.query.first()
assert trade
# Simulate fulfilled LIMIT_BUY order for trade
trade.update(limit_buy_order)
# Simulate fulfilled LIMIT_SELL order for trade
trade.update(limit_sell_order)
trade.close_date = datetime.utcnow()
trade.is_open = False
res = rpc._rpc_sell_reason_performance(None)
assert len(res) == 1
assert res[0]['sell_reason'] == 'Other'
assert res[0]['count'] == 1
assert prec_satoshi(res[0]['profit_pct'], 6.2)
trade.sell_reason = "TEST1"
res = rpc._rpc_sell_reason_performance(None)
assert len(res) == 1
assert res[0]['sell_reason'] == 'TEST1'
assert res[0]['count'] == 1
assert prec_satoshi(res[0]['profit_pct'], 6.2)
def test_sell_reason_performance_handle_2(mocker, default_conf, markets, fee):
mocker.patch('freqtrade.rpc.telegram.Telegram', MagicMock())
mocker.patch.multiple(
'freqtrade.exchange.Exchange',
markets=PropertyMock(return_value=markets)
)
freqtradebot = get_patched_freqtradebot(mocker, default_conf)
create_mock_trades(fee)
rpc = RPC(freqtradebot)
res = rpc._rpc_sell_reason_performance(None)
assert len(res) == 2
assert res[0]['sell_reason'] == 'sell_signal'
assert res[0]['count'] == 1
assert prec_satoshi(res[0]['profit_pct'], 0.5)
assert res[1]['sell_reason'] == 'roi'
assert res[1]['count'] == 1
assert prec_satoshi(res[1]['profit_pct'], 1.0)
# Test for a specific pair
res = rpc._rpc_sell_reason_performance('ETC/BTC')
assert len(res) == 1
assert res[0]['count'] == 1
assert res[0]['sell_reason'] == 'sell_signal'
assert prec_satoshi(res[0]['profit_pct'], 0.5)
def test_mix_tag_performance_handle(default_conf, ticker, limit_buy_order, fee,
limit_sell_order, mocker) -> None:
mocker.patch('freqtrade.rpc.telegram.Telegram', MagicMock())
mocker.patch.multiple(
'freqtrade.exchange.Exchange',
get_balances=MagicMock(return_value=ticker),
fetch_ticker=ticker,
get_fee=fee,
)
freqtradebot = get_patched_freqtradebot(mocker, default_conf)
patch_get_signal(freqtradebot)
rpc = RPC(freqtradebot)
# Create some test data
freqtradebot.enter_positions()
trade = Trade.query.first()
assert trade
# Simulate fulfilled LIMIT_BUY order for trade
trade.update(limit_buy_order)
# Simulate fulfilled LIMIT_SELL order for trade
trade.update(limit_sell_order)
trade.close_date = datetime.utcnow()
trade.is_open = False
res = rpc._rpc_mix_tag_performance(None)
assert len(res) == 1
assert res[0]['mix_tag'] == 'Other Other'
assert res[0]['count'] == 1
assert prec_satoshi(res[0]['profit_pct'], 6.2)
trade.buy_tag = "TESTBUY"
trade.sell_reason = "TESTSELL"
res = rpc._rpc_mix_tag_performance(None)
assert len(res) == 1
assert res[0]['mix_tag'] == 'TESTBUY TESTSELL'
assert res[0]['count'] == 1
assert prec_satoshi(res[0]['profit_pct'], 6.2)
def test_mix_tag_performance_handle_2(mocker, default_conf, markets, fee):
mocker.patch('freqtrade.rpc.telegram.Telegram', MagicMock())
mocker.patch.multiple(
'freqtrade.exchange.Exchange',
markets=PropertyMock(return_value=markets)
)
freqtradebot = get_patched_freqtradebot(mocker, default_conf)
create_mock_trades(fee)
rpc = RPC(freqtradebot)
res = rpc._rpc_mix_tag_performance(None)
assert len(res) == 2
assert res[0]['mix_tag'] == 'TEST1 sell_signal'
assert res[0]['count'] == 1
assert prec_satoshi(res[0]['profit_pct'], 0.5)
assert res[1]['mix_tag'] == 'Other roi'
assert res[1]['count'] == 1
assert prec_satoshi(res[1]['profit_pct'], 1.0)
# Test for a specific pair
res = rpc._rpc_mix_tag_performance('ETC/BTC')
assert len(res) == 1
assert res[0]['count'] == 1
assert res[0]['mix_tag'] == 'TEST1 sell_signal'
assert prec_satoshi(res[0]['profit_pct'], 0.5)
def test_rpc_count(mocker, default_conf, ticker, fee) -> None:

18
tests/rpc/test_rpc_apiserver.py
View File

@ -520,7 +520,7 @@ def test_api_locks(botclient):
assert rc.json()['lock_count'] == 0
def test_api_show_config(botclient, mocker):
def test_api_show_config(botclient):
ftbot, client = botclient
patch_get_signal(ftbot)
@ -536,6 +536,10 @@ def test_api_show_config(botclient, mocker):
assert not rc.json()['trailing_stop']
assert 'bid_strategy' in rc.json()
assert 'ask_strategy' in rc.json()
assert 'unfilledtimeout' in rc.json()
assert 'version' in rc.json()
assert 'api_version' in rc.json()
assert 1.1 <= rc.json()['api_version'] <= 1.2
def test_api_daily(botclient, mocker, ticker, fee, markets):
@ -715,6 +719,7 @@ def test_api_profit(botclient, mocker, ticker, fee, markets):
assert rc.json() == {'avg_duration': ANY,
'best_pair': 'XRP/BTC',
'best_rate': 1.0,
'best_pair_profit_ratio': 0.01,
'first_trade_date': ANY,
'first_trade_timestamp': ANY,
'latest_trade_date': '5 minutes ago',
@ -812,8 +817,10 @@ def test_api_performance(botclient, fee):
rc = client_get(client, f"{BASE_URI}/performance")
assert_response(rc)
assert len(rc.json()) == 2
assert rc.json() == [{'count': 1, 'pair': 'LTC/ETH', 'profit': 7.61, 'profit_abs': 0.01872279},
{'count': 1, 'pair': 'XRP/ETH', 'profit': -5.57, 'profit_abs': -0.1150375}]
assert rc.json() == [{'count': 1, 'pair': 'LTC/ETH', 'profit': 7.61, 'profit_pct': 7.61,
'profit_ratio': 0.07609203, 'profit_abs': 0.01872279},
{'count': 1, 'pair': 'XRP/ETH', 'profit': -5.57, 'profit_pct': -5.57,
'profit_ratio': -0.05570419, 'profit_abs': -0.1150375}]
def test_api_status(botclient, mocker, ticker, fee, markets):
@ -1124,7 +1131,7 @@ def test_api_pair_candles(botclient, ohlcv_history):
assert isinstance(rc.json()['columns'], list)
assert rc.json()['columns'] == ['date', 'open', 'high',
'low', 'close', 'volume', 'sma', 'buy', 'sell',
'__date_ts', '_buy_signal_open', '_sell_signal_open']
'__date_ts', '_buy_signal_close', '_sell_signal_close']
assert 'pair' in rc.json()
assert rc.json()['pair'] == 'XRP/BTC'
@ -1135,7 +1142,8 @@ def test_api_pair_candles(botclient, ohlcv_history):
[['2017-11-26 08:50:00', 8.794e-05, 8.948e-05, 8.794e-05, 8.88e-05, 0.0877869,
None, 0, 0, 1511686200000, None, None],
['2017-11-26 08:55:00', 8.88e-05, 8.942e-05, 8.88e-05,
8.893e-05, 0.05874751, 8.886500000000001e-05, 1, 0, 1511686500000, 8.88e-05, None],
8.893e-05, 0.05874751, 8.886500000000001e-05, 1, 0, 1511686500000, 8.893e-05,
None],
['2017-11-26 09:00:00', 8.891e-05, 8.893e-05, 8.875e-05, 8.877e-05,
0.7039405, 8.885e-05, 0, 0, 1511686800000, None, None]

473
tests/rpc/test_rpc_telegram.py
View File

@ -4,7 +4,7 @@
import logging
import re
from datetime import datetime
from datetime import datetime, timedelta
from functools import reduce
from random import choice, randint
from string import ascii_uppercase
@ -24,6 +24,7 @@ from freqtrade.freqtradebot import FreqtradeBot
from freqtrade.loggers import setup_logging
from freqtrade.persistence import PairLocks, Trade
from freqtrade.rpc import RPC
from freqtrade.rpc.rpc import RPCException
from freqtrade.rpc.telegram import Telegram, authorized_only
from tests.conftest import (create_mock_trades, get_patched_freqtradebot, log_has, log_has_re,
patch_exchange, patch_get_signal, patch_whitelist)
@ -33,6 +34,7 @@ class DummyCls(Telegram):
"""
Dummy class for testing the Telegram @authorized_only decorator
"""
def __init__(self, rpc: RPC, config) -> None:
super().__init__(rpc, config)
self.state = {'called': False}
@ -92,10 +94,12 @@ def test_telegram_init(default_conf, mocker, caplog) -> None:
message_str = ("rpc.telegram is listening for following commands: [['status'], ['profit'], "
"['balance'], ['start'], ['stop'], ['forcesell'], ['forcebuy'], ['trades'], "
"['delete'], ['performance'], ['stats'], ['daily'], ['count'], ['locks'], "
"['unlock', 'delete_locks'], ['reload_config', 'reload_conf'], "
"['show_config', 'show_conf'], ['stopbuy'], "
"['whitelist'], ['blacklist'], ['logs'], ['edge'], ['help'], ['version']"
"['delete'], ['performance'], ['buys'], ['sells'], ['mix_tags'], "
"['stats'], ['daily'], ['weekly'], ['monthly'], "
"['count'], ['locks'], ['unlock', 'delete_locks'], "
"['reload_config', 'reload_conf'], ['show_config', 'show_conf'], "
"['stopbuy'], ['whitelist'], ['blacklist'], "
"['logs'], ['edge'], ['help'], ['version']"
"]")
assert log_has(message_str, caplog)
@ -186,16 +190,16 @@ def test_telegram_status(default_conf, update, mocker) -> None:
'amount': 90.99181074,
'stake_amount': 90.99181074,
'buy_tag': None,
'close_profit_pct': None,
'close_profit_ratio': None,
'profit': -0.0059,
'profit_pct': -0.59,
'profit_ratio': -0.0059,
'initial_stop_loss_abs': 1.098e-05,
'stop_loss_abs': 1.099e-05,
'sell_order_status': None,
'initial_stop_loss_pct': -0.05,
'initial_stop_loss_ratio': -0.0005,
'stoploss_current_dist': 1e-08,
'stoploss_current_dist_pct': -0.02,
'stop_loss_pct': -0.01,
'stoploss_current_dist_ratio': -0.0002,
'stop_loss_ratio': -0.0001,
'open_order': '(limit buy rem=0.00000000)',
'is_open': True
}]),
@ -352,7 +356,8 @@ def test_daily_handle(default_conf, update, ticker, limit_buy_order, fee,
context.args = ["2"]
telegram._daily(update=update, context=context)
assert msg_mock.call_count == 1
assert 'Daily' in msg_mock.call_args_list[0][0][0]
assert "Daily Profit over the last 2 days</b>:" in msg_mock.call_args_list[0][0][0]
assert 'Day ' in msg_mock.call_args_list[0][0][0]
assert str(datetime.utcnow().date()) in msg_mock.call_args_list[0][0][0]
assert str(' 0.00006217 BTC') in msg_mock.call_args_list[0][0][0]
assert str(' 0.933 USD') in msg_mock.call_args_list[0][0][0]
@ -364,7 +369,7 @@ def test_daily_handle(default_conf, update, ticker, limit_buy_order, fee,
context.args = []
telegram._daily(update=update, context=context)
assert msg_mock.call_count == 1
assert 'Daily' in msg_mock.call_args_list[0][0][0]
assert "Daily Profit over the last 7 days</b>:" in msg_mock.call_args_list[0][0][0]
assert str(datetime.utcnow().date()) in msg_mock.call_args_list[0][0][0]
assert str(' 0.00006217 BTC') in msg_mock.call_args_list[0][0][0]
assert str(' 0.933 USD') in msg_mock.call_args_list[0][0][0]
@ -420,7 +425,242 @@ def test_daily_wrong_input(default_conf, update, ticker, mocker) -> None:
context = MagicMock()
context.args = ["today"]
telegram._daily(update=update, context=context)
assert str('Daily Profit over the last 7 days') in msg_mock.call_args_list[0][0][0]
assert str('Daily Profit over the last 7 days</b>:') in msg_mock.call_args_list[0][0][0]
def test_weekly_handle(default_conf, update, ticker, limit_buy_order, fee,
limit_sell_order, mocker) -> None:
default_conf['max_open_trades'] = 1
mocker.patch(
'freqtrade.rpc.rpc.CryptoToFiatConverter._find_price',
return_value=15000.0
)
mocker.patch.multiple(
'freqtrade.exchange.Exchange',
fetch_ticker=ticker,
get_fee=fee,
)
telegram, freqtradebot, msg_mock = get_telegram_testobject(mocker, default_conf)
patch_get_signal(freqtradebot)
# Create some test data
freqtradebot.enter_positions()
trade = Trade.query.first()
assert trade
# Simulate fulfilled LIMIT_BUY order for trade
trade.update(limit_buy_order)
# Simulate fulfilled LIMIT_SELL order for trade
trade.update(limit_sell_order)
trade.close_date = datetime.utcnow()
trade.is_open = False
# Try valid data
# /weekly 2
context = MagicMock()
context.args = ["2"]
telegram._weekly(update=update, context=context)
assert msg_mock.call_count == 1
assert "Weekly Profit over the last 2 weeks (starting from Monday)</b>:" \
in msg_mock.call_args_list[0][0][0]
assert 'Monday ' in msg_mock.call_args_list[0][0][0]
today = datetime.utcnow().date()
first_iso_day_of_current_week = today - timedelta(days=today.weekday())
assert str(first_iso_day_of_current_week) in msg_mock.call_args_list[0][0][0]
assert str(' 0.00006217 BTC') in msg_mock.call_args_list[0][0][0]
assert str(' 0.933 USD') in msg_mock.call_args_list[0][0][0]
assert str(' 1 trade') in msg_mock.call_args_list[0][0][0]
assert str(' 0 trade') in msg_mock.call_args_list[0][0][0]
# Reset msg_mock
msg_mock.reset_mock()
context.args = []
telegram._weekly(update=update, context=context)
assert msg_mock.call_count == 1
assert "Weekly Profit over the last 8 weeks (starting from Monday)</b>:" \
in msg_mock.call_args_list[0][0][0]
assert 'Weekly' in msg_mock.call_args_list[0][0][0]
assert str(' 0.00006217 BTC') in msg_mock.call_args_list[0][0][0]
assert str(' 0.933 USD') in msg_mock.call_args_list[0][0][0]
assert str(' 1 trade') in msg_mock.call_args_list[0][0][0]
assert str(' 0 trade') in msg_mock.call_args_list[0][0][0]
# Reset msg_mock
msg_mock.reset_mock()
freqtradebot.config['max_open_trades'] = 2
# Add two other trades
n = freqtradebot.enter_positions()
assert n == 2
trades = Trade.query.all()
for trade in trades:
trade.update(limit_buy_order)
trade.update(limit_sell_order)
trade.close_date = datetime.utcnow()
trade.is_open = False
# /weekly 1
# By default, the 8 previous weeks are shown
# So the previous modified trade should be excluded from the stats
context = MagicMock()
context.args = ["1"]
telegram._weekly(update=update, context=context)
assert str(' 0.00018651 BTC') in msg_mock.call_args_list[0][0][0]
assert str(' 2.798 USD') in msg_mock.call_args_list[0][0][0]
assert str(' 3 trades') in msg_mock.call_args_list[0][0][0]
def test_weekly_wrong_input(default_conf, update, ticker, mocker) -> None:
mocker.patch.multiple(
'freqtrade.exchange.Exchange',
fetch_ticker=ticker
)
telegram, freqtradebot, msg_mock = get_telegram_testobject(mocker, default_conf)
patch_get_signal(freqtradebot)
# Try invalid data
msg_mock.reset_mock()
freqtradebot.state = State.RUNNING
# /weekly -3
context = MagicMock()
context.args = ["-3"]
telegram._weekly(update=update, context=context)
assert msg_mock.call_count == 1
assert 'must be an integer greater than 0' in msg_mock.call_args_list[0][0][0]
# Try invalid data
msg_mock.reset_mock()
freqtradebot.state = State.RUNNING
# /weekly this week
context = MagicMock()
context.args = ["this week"]
telegram._weekly(update=update, context=context)
assert str('Weekly Profit over the last 8 weeks (starting from Monday)</b>:') \
in msg_mock.call_args_list[0][0][0]
def test_monthly_handle(default_conf, update, ticker, limit_buy_order, fee,
limit_sell_order, mocker) -> None:
default_conf['max_open_trades'] = 1
mocker.patch(
'freqtrade.rpc.rpc.CryptoToFiatConverter._find_price',
return_value=15000.0
)
mocker.patch.multiple(
'freqtrade.exchange.Exchange',
fetch_ticker=ticker,
get_fee=fee,
)
telegram, freqtradebot, msg_mock = get_telegram_testobject(mocker, default_conf)
patch_get_signal(freqtradebot)
# Create some test data
freqtradebot.enter_positions()
trade = Trade.query.first()
assert trade
# Simulate fulfilled LIMIT_BUY order for trade
trade.update(limit_buy_order)
# Simulate fulfilled LIMIT_SELL order for trade
trade.update(limit_sell_order)
trade.close_date = datetime.utcnow()
trade.is_open = False
# Try valid data
# /monthly 2
context = MagicMock()
context.args = ["2"]
telegram._monthly(update=update, context=context)
assert msg_mock.call_count == 1
assert 'Monthly Profit over the last 2 months</b>:' in msg_mock.call_args_list[0][0][0]
assert 'Month ' in msg_mock.call_args_list[0][0][0]
today = datetime.utcnow().date()
current_month = f"{today.year}-{today.month} "
assert current_month in msg_mock.call_args_list[0][0][0]
assert str(' 0.00006217 BTC') in msg_mock.call_args_list[0][0][0]
assert str(' 0.933 USD') in msg_mock.call_args_list[0][0][0]
assert str(' 1 trade') in msg_mock.call_args_list[0][0][0]
assert str(' 0 trade') in msg_mock.call_args_list[0][0][0]
# Reset msg_mock
msg_mock.reset_mock()
context.args = []
telegram._monthly(update=update, context=context)
assert msg_mock.call_count == 1
# Default to 6 months
assert 'Monthly Profit over the last 6 months</b>:' in msg_mock.call_args_list[0][0][0]
assert 'Month ' in msg_mock.call_args_list[0][0][0]
assert current_month in msg_mock.call_args_list[0][0][0]
assert str(' 0.00006217 BTC') in msg_mock.call_args_list[0][0][0]
assert str(' 0.933 USD') in msg_mock.call_args_list[0][0][0]
assert str(' 1 trade') in msg_mock.call_args_list[0][0][0]
assert str(' 0 trade') in msg_mock.call_args_list[0][0][0]
# Reset msg_mock
msg_mock.reset_mock()
freqtradebot.config['max_open_trades'] = 2
# Add two other trades
n = freqtradebot.enter_positions()
assert n == 2
trades = Trade.query.all()
for trade in trades:
trade.update(limit_buy_order)
trade.update(limit_sell_order)
trade.close_date = datetime.utcnow()
trade.is_open = False
# /monthly 12
context = MagicMock()
context.args = ["12"]
telegram._monthly(update=update, context=context)
assert msg_mock.call_count == 1
assert 'Monthly Profit over the last 12 months</b>:' in msg_mock.call_args_list[0][0][0]
assert str(' 0.00018651 BTC') in msg_mock.call_args_list[0][0][0]
assert str(' 2.798 USD') in msg_mock.call_args_list[0][0][0]
assert str(' 3 trades') in msg_mock.call_args_list[0][0][0]
# The one-digit months should contain a zero, Eg: September 2021 = "2021-09"
# Since we loaded the last 12 months, any month should appear
assert str('-09') in msg_mock.call_args_list[0][0][0]
def test_monthly_wrong_input(default_conf, update, ticker, mocker) -> None:
mocker.patch.multiple(
'freqtrade.exchange.Exchange',
fetch_ticker=ticker
)
telegram, freqtradebot, msg_mock = get_telegram_testobject(mocker, default_conf)
patch_get_signal(freqtradebot)
# Try invalid data
msg_mock.reset_mock()
freqtradebot.state = State.RUNNING
# /monthly -3
context = MagicMock()
context.args = ["-3"]
telegram._monthly(update=update, context=context)
assert msg_mock.call_count == 1
assert 'must be an integer greater than 0' in msg_mock.call_args_list[0][0][0]
# Try invalid data
msg_mock.reset_mock()
freqtradebot.state = State.RUNNING
# /monthly february
context = MagicMock()
context.args = ["february"]
telegram._monthly(update=update, context=context)
assert str('Monthly Profit over the last 6 months</b>:') in msg_mock.call_args_list[0][0][0]
def test_profit_handle(default_conf, update, ticker, ticker_sell_up, fee,
@ -492,7 +732,7 @@ def test_telegram_stats(default_conf, update, ticker, ticker_sell_up, fee,
telegram._stats(update=update, context=MagicMock())
assert msg_mock.call_count == 1
# assert 'No trades yet.' in msg_mock.call_args_list[0][0][0]
assert 'No trades yet.' in msg_mock.call_args_list[0][0][0]
msg_mock.reset_mock()
# Create some test data
@ -713,6 +953,7 @@ def test_telegram_forcesell_handle(default_conf, update, ticker, fee,
'profit_ratio': 0.0629778,
'stake_currency': 'BTC',
'fiat_currency': 'USD',
'buy_tag': ANY,
'sell_reason': SellType.FORCE_SELL.value,
'open_date': ANY,
'close_date': ANY,
@ -776,6 +1017,7 @@ def test_telegram_forcesell_down_handle(default_conf, update, ticker, fee,
'profit_ratio': -0.05482878,
'stake_currency': 'BTC',
'fiat_currency': 'USD',
'buy_tag': ANY,
'sell_reason': SellType.FORCE_SELL.value,
'open_date': ANY,
'close_date': ANY,
@ -829,6 +1071,7 @@ def test_forcesell_all_handle(default_conf, update, ticker, fee, mocker) -> None
'profit_ratio': -0.00408133,
'stake_currency': 'BTC',
'fiat_currency': 'USD',
'buy_tag': ANY,
'sell_reason': SellType.FORCE_SELL.value,
'open_date': ANY,
'close_date': ANY,
@ -944,8 +1187,8 @@ def test_forcebuy_no_pair(default_conf, update, mocker) -> None:
assert fbuy_mock.call_count == 1
def test_performance_handle(default_conf, update, ticker, fee,
limit_buy_order, limit_sell_order, mocker) -> None:
def test_telegram_performance_handle(default_conf, update, ticker, fee,
limit_buy_order, limit_sell_order, mocker) -> None:
mocker.patch.multiple(
'freqtrade.exchange.Exchange',
@ -974,6 +1217,139 @@ def test_performance_handle(default_conf, update, ticker, fee,
assert '<code>ETH/BTC\t0.00006217 BTC (6.20%) (1)</code>' in msg_mock.call_args_list[0][0][0]
def test_telegram_buy_tag_performance_handle(default_conf, update, ticker, fee,
limit_buy_order, limit_sell_order, mocker) -> None:
mocker.patch.multiple(
'freqtrade.exchange.Exchange',
fetch_ticker=ticker,
get_fee=fee,
)
telegram, freqtradebot, msg_mock = get_telegram_testobject(mocker, default_conf)
patch_get_signal(freqtradebot)
# Create some test data
freqtradebot.enter_positions()
trade = Trade.query.first()
assert trade
# Simulate fulfilled LIMIT_BUY order for trade
trade.update(limit_buy_order)
trade.buy_tag = "TESTBUY"
# Simulate fulfilled LIMIT_SELL order for trade
trade.update(limit_sell_order)
trade.close_date = datetime.utcnow()
trade.is_open = False
context = MagicMock()
telegram._buy_tag_performance(update=update, context=context)
assert msg_mock.call_count == 1
assert 'Buy Tag Performance' in msg_mock.call_args_list[0][0][0]
assert '<code>TESTBUY\t0.00006217 BTC (6.20%) (1)</code>' in msg_mock.call_args_list[0][0][0]
context.args = [trade.pair]
telegram._buy_tag_performance(update=update, context=context)
assert msg_mock.call_count == 2
msg_mock.reset_mock()
mocker.patch('freqtrade.rpc.rpc.RPC._rpc_buy_tag_performance',
side_effect=RPCException('Error'))
telegram._buy_tag_performance(update=update, context=MagicMock())
assert msg_mock.call_count == 1
assert "Error" in msg_mock.call_args_list[0][0][0]
def test_telegram_sell_reason_performance_handle(default_conf, update, ticker, fee,
limit_buy_order, limit_sell_order, mocker) -> None:
mocker.patch.multiple(
'freqtrade.exchange.Exchange',
fetch_ticker=ticker,
get_fee=fee,
)
telegram, freqtradebot, msg_mock = get_telegram_testobject(mocker, default_conf)
patch_get_signal(freqtradebot)
# Create some test data
freqtradebot.enter_positions()
trade = Trade.query.first()
assert trade
# Simulate fulfilled LIMIT_BUY order for trade
trade.update(limit_buy_order)
trade.sell_reason = 'TESTSELL'
# Simulate fulfilled LIMIT_SELL order for trade
trade.update(limit_sell_order)
trade.close_date = datetime.utcnow()
trade.is_open = False
context = MagicMock()
telegram._sell_reason_performance(update=update, context=context)
assert msg_mock.call_count == 1
assert 'Sell Reason Performance' in msg_mock.call_args_list[0][0][0]
assert '<code>TESTSELL\t0.00006217 BTC (6.20%) (1)</code>' in msg_mock.call_args_list[0][0][0]
context.args = [trade.pair]
telegram._sell_reason_performance(update=update, context=context)
assert msg_mock.call_count == 2
msg_mock.reset_mock()
mocker.patch('freqtrade.rpc.rpc.RPC._rpc_sell_reason_performance',
side_effect=RPCException('Error'))
telegram._sell_reason_performance(update=update, context=MagicMock())
assert msg_mock.call_count == 1
assert "Error" in msg_mock.call_args_list[0][0][0]
def test_telegram_mix_tag_performance_handle(default_conf, update, ticker, fee,
limit_buy_order, limit_sell_order, mocker) -> None:
mocker.patch.multiple(
'freqtrade.exchange.Exchange',
fetch_ticker=ticker,
get_fee=fee,
)
telegram, freqtradebot, msg_mock = get_telegram_testobject(mocker, default_conf)
patch_get_signal(freqtradebot)
# Create some test data
freqtradebot.enter_positions()
trade = Trade.query.first()
assert trade
# Simulate fulfilled LIMIT_BUY order for trade
trade.update(limit_buy_order)
trade.buy_tag = "TESTBUY"
trade.sell_reason = "TESTSELL"
# Simulate fulfilled LIMIT_SELL order for trade
trade.update(limit_sell_order)
trade.close_date = datetime.utcnow()
trade.is_open = False
context = MagicMock()
telegram._mix_tag_performance(update=update, context=context)
assert msg_mock.call_count == 1
assert 'Mix Tag Performance' in msg_mock.call_args_list[0][0][0]
assert ('<code>TESTBUY TESTSELL\t0.00006217 BTC (6.20%) (1)</code>'
in msg_mock.call_args_list[0][0][0])
context.args = [trade.pair]
telegram._mix_tag_performance(update=update, context=context)
assert msg_mock.call_count == 2
msg_mock.reset_mock()
mocker.patch('freqtrade.rpc.rpc.RPC._rpc_mix_tag_performance',
side_effect=RPCException('Error'))
telegram._mix_tag_performance(update=update, context=MagicMock())
assert msg_mock.call_count == 1
assert "Error" in msg_mock.call_args_list[0][0][0]
def test_count_handle(default_conf, update, ticker, fee, mocker) -> None:
mocker.patch.multiple(
'freqtrade.exchange.Exchange',
@ -997,9 +1373,9 @@ def test_count_handle(default_conf, update, ticker, fee, mocker) -> None:
msg = ('<pre> current max total stake\n--------- ----- -------------\n'
' 1 {} {}</pre>').format(
default_conf['max_open_trades'],
default_conf['stake_amount']
)
default_conf['max_open_trades'],
default_conf['stake_amount']
)
assert msg in msg_mock.call_args_list[0][0][0]
@ -1348,17 +1724,25 @@ def test_send_msg_buy_fill_notification(default_conf, mocker) -> None:
telegram.send_msg({
'type': RPCMessageType.BUY_FILL,
'buy_tag': 'buy_signal_01',
'trade_id': 1,
'buy_tag': 'buy_signal_01',
'exchange': 'Binance',
'pair': 'ETH/USDT',
'open_rate': 200,
'stake_amount': 100,
'amount': 0.5,
'open_date': arrow.utcnow().datetime
'pair': 'ETH/BTC',
'stake_amount': 0.001,
# 'stake_amount_fiat': 0.0,
'stake_currency': 'BTC',
'fiat_currency': 'USD',
'open_rate': 1.099e-05,
'amount': 1333.3333333333335,
'open_date': arrow.utcnow().shift(hours=-1)
})
assert (msg_mock.call_args[0][0] == '\N{LARGE CIRCLE} *Binance:* '
'Buy order for ETH/USDT (#1) filled for 200.')
assert msg_mock.call_args[0][0] \
== '\N{CHECK MARK} *Binance:* Bought ETH/BTC (#1)\n' \
'*Buy Tag:* `buy_signal_01`\n' \
'*Amount:* `1333.33333333`\n' \
'*Open Rate:* `0.00001099`\n' \
'*Total:* `(0.00100000 BTC, 12.345 USD)`'
def test_send_msg_sell_notification(default_conf, mocker) -> None:
@ -1382,13 +1766,15 @@ def test_send_msg_sell_notification(default_conf, mocker) -> None:
'profit_ratio': -0.57405275,
'stake_currency': 'ETH',
'fiat_currency': 'USD',
'buy_tag': 'buy_signal1',
'sell_reason': SellType.STOP_LOSS.value,
'open_date': arrow.utcnow().shift(hours=-1),
'close_date': arrow.utcnow(),
})
assert msg_mock.call_args[0][0] \
== ('\N{WARNING SIGN} *Binance:* Selling KEY/ETH (#1)\n'
'*Profit:* `-57.41% (loss: -0.05746268 ETH / -24.812 USD)`\n'
'*Unrealized Profit:* `-57.41% (loss: -0.05746268 ETH / -24.812 USD)`\n'
'*Buy Tag:* `buy_signal1`\n'
'*Sell Reason:* `stop_loss`\n'
'*Duration:* `1:00:00 (60.0 min)`\n'
'*Amount:* `1333.33333333`\n'
@ -1412,13 +1798,15 @@ def test_send_msg_sell_notification(default_conf, mocker) -> None:
'profit_amount': -0.05746268,
'profit_ratio': -0.57405275,
'stake_currency': 'ETH',
'buy_tag': 'buy_signal1',
'sell_reason': SellType.STOP_LOSS.value,
'open_date': arrow.utcnow().shift(days=-1, hours=-2, minutes=-30),
'close_date': arrow.utcnow(),
})
assert msg_mock.call_args[0][0] \
== ('\N{WARNING SIGN} *Binance:* Selling KEY/ETH (#1)\n'
'*Profit:* `-57.41%`\n'
'*Unrealized Profit:* `-57.41%`\n'
'*Buy Tag:* `buy_signal1`\n'
'*Sell Reason:* `stop_loss`\n'
'*Duration:* `1 day, 2:30:00 (1590.0 min)`\n'
'*Amount:* `1333.33333333`\n'
@ -1471,24 +1859,31 @@ def test_send_msg_sell_fill_notification(default_conf, mocker) -> None:
'type': RPCMessageType.SELL_FILL,
'trade_id': 1,
'exchange': 'Binance',
'pair': 'ETH/USDT',
'pair': 'KEY/ETH',
'gain': 'loss',
'limit': 3.201e-05,
'amount': 0.1,
'amount': 1333.3333333333335,
'order_type': 'market',
'open_rate': 500,
'close_rate': 550,
'current_rate': 3.201e-05,
'open_rate': 7.5e-05,
'close_rate': 3.201e-05,
'profit_amount': -0.05746268,
'profit_ratio': -0.57405275,
'stake_currency': 'ETH',
'fiat_currency': 'USD',
'buy_tag': 'buy_signal1',
'sell_reason': SellType.STOP_LOSS.value,
'open_date': arrow.utcnow().shift(hours=-1),
'open_date': arrow.utcnow().shift(days=-1, hours=-2, minutes=-30),
'close_date': arrow.utcnow(),
})
assert msg_mock.call_args[0][0] \
== ('\N{LARGE CIRCLE} *Binance:* Sell order for ETH/USDT (#1) filled for 550.')
== ('\N{WARNING SIGN} *Binance:* Sold KEY/ETH (#1)\n'
'*Profit:* `-57.41%`\n'
'*Buy Tag:* `buy_signal1`\n'
'*Sell Reason:* `stop_loss`\n'
'*Duration:* `1 day, 2:30:00 (1590.0 min)`\n'
'*Amount:* `1333.33333333`\n'
'*Open Rate:* `0.00007500`\n'
'*Close Rate:* `0.00003201`'
)
def test_send_msg_status_notification(default_conf, mocker) -> None:
@ -1574,12 +1969,14 @@ def test_send_msg_sell_notification_no_fiat(default_conf, mocker) -> None:
'profit_ratio': -0.57405275,
'stake_currency': 'ETH',
'fiat_currency': 'USD',
'buy_tag': 'buy_signal1',
'sell_reason': SellType.STOP_LOSS.value,
'open_date': arrow.utcnow().shift(hours=-2, minutes=-35, seconds=-3),
'close_date': arrow.utcnow(),
})
assert msg_mock.call_args[0][0] == ('\N{WARNING SIGN} *Binance:* Selling KEY/ETH (#1)\n'
'*Profit:* `-57.41%`\n'
'*Unrealized Profit:* `-57.41%`\n'
'*Buy Tag:* `buy_signal1`\n'
'*Sell Reason:* `stop_loss`\n'
'*Duration:* `2:35:03 (155.1 min)`\n'
'*Amount:* `1333.33333333`\n'

10
tests/strategy/strats/informative_decorator_strategy.py
View File

@ -20,7 +20,7 @@ class InformativeDecoratorTest(IStrategy):
startup_candle_count: int = 20
def informative_pairs(self):
return [('BTC/USDT', '5m')]
return [('NEO/USDT', '5m')]
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe['buy'] = 0
@ -38,8 +38,8 @@ class InformativeDecoratorTest(IStrategy):
return dataframe
# Simple informative test.
@informative('1h', 'BTC/{stake}')
def populate_indicators_btc_1h(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
@informative('1h', 'NEO/{stake}')
def populate_indicators_neo_1h(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe['rsi'] = 14
return dataframe
@ -50,7 +50,7 @@ class InformativeDecoratorTest(IStrategy):
return dataframe
# Formatting test.
@informative('30m', 'BTC/{stake}', '{column}_{BASE}_{QUOTE}_{base}_{quote}_{asset}_{timeframe}')
@informative('30m', 'NEO/{stake}', '{column}_{BASE}_{QUOTE}_{base}_{quote}_{asset}_{timeframe}')
def populate_indicators_btc_1h_2(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe['rsi'] = 14
return dataframe
@ -68,7 +68,7 @@ class InformativeDecoratorTest(IStrategy):
dataframe['rsi_less'] = dataframe['rsi'] < dataframe['rsi_1h']
# Mixing manual informative pairs with decorators.
informative = self.dp.get_pair_dataframe('BTC/USDT', '5m')
informative = self.dp.get_pair_dataframe('NEO/USDT', '5m')
informative['rsi'] = 14
dataframe = merge_informative_pair(dataframe, informative, self.timeframe, '5m', ffill=True)

55
tests/strategy/test_interface.py
View File

@ -30,7 +30,7 @@ _STRATEGY = StrategyTestV2(config={})
_STRATEGY.dp = DataProvider({}, None, None)
def test_returns_latest_signal(mocker, default_conf, ohlcv_history):
def test_returns_latest_signal(ohlcv_history):
ohlcv_history.loc[1, 'date'] = arrow.utcnow()
# Take a copy to correctly modify the call
mocked_history = ohlcv_history.copy()
@ -38,20 +38,39 @@ def test_returns_latest_signal(mocker, default_conf, ohlcv_history):
mocked_history['buy'] = 0
mocked_history.loc[1, 'sell'] = 1
assert _STRATEGY.get_signal('ETH/BTC', '5m', mocked_history) == (False, True, None)
assert _STRATEGY.get_signal('ETH/BTC', '5m', mocked_history) == (False, True, None, None)
mocked_history.loc[1, 'sell'] = 0
mocked_history.loc[1, 'buy'] = 1
assert _STRATEGY.get_signal('ETH/BTC', '5m', mocked_history) == (True, False, None)
assert _STRATEGY.get_signal('ETH/BTC', '5m', mocked_history) == (True, False, None, None)
mocked_history.loc[1, 'sell'] = 0
mocked_history.loc[1, 'buy'] = 0
assert _STRATEGY.get_signal('ETH/BTC', '5m', mocked_history) == (False, False, None)
assert _STRATEGY.get_signal('ETH/BTC', '5m', mocked_history) == (False, False, None, None)
mocked_history.loc[1, 'sell'] = 0
mocked_history.loc[1, 'buy'] = 1
mocked_history.loc[1, 'buy_tag'] = 'buy_signal_01'
assert _STRATEGY.get_signal('ETH/BTC', '5m', mocked_history) == (True, False, 'buy_signal_01')
assert _STRATEGY.get_signal(
'ETH/BTC',
'5m',
mocked_history) == (
True,
False,
'buy_signal_01',
None)
mocked_history.loc[1, 'buy_tag'] = None
mocked_history.loc[1, 'exit_tag'] = 'sell_signal_01'
assert _STRATEGY.get_signal(
'ETH/BTC',
'5m',
mocked_history) == (
True,
False,
None,
'sell_signal_01')
def test_analyze_pair_empty(default_conf, mocker, caplog, ohlcv_history):
@ -68,17 +87,24 @@ def test_analyze_pair_empty(default_conf, mocker, caplog, ohlcv_history):
def test_get_signal_empty(default_conf, mocker, caplog):
assert (False, False, None) == _STRATEGY.get_signal(
assert (False, False, None, None) == _STRATEGY.get_signal(
'foo', default_conf['timeframe'], DataFrame()
)
assert log_has('Empty candle (OHLCV) data for pair foo', caplog)
caplog.clear()
assert (False, False, None) == _STRATEGY.get_signal('bar', default_conf['timeframe'], None)
assert (
False,
False,
None,
None) == _STRATEGY.get_signal(
'bar',
default_conf['timeframe'],
None)
assert log_has('Empty candle (OHLCV) data for pair bar', caplog)
caplog.clear()
assert (False, False, None) == _STRATEGY.get_signal(
assert (False, False, None, None) == _STRATEGY.get_signal(
'baz',
default_conf['timeframe'],
DataFrame([])
@ -118,7 +144,7 @@ def test_get_signal_old_dataframe(default_conf, mocker, caplog, ohlcv_history):
caplog.set_level(logging.INFO)
mocker.patch.object(_STRATEGY, 'assert_df')
assert (False, False, None) == _STRATEGY.get_signal(
assert (False, False, None, None) == _STRATEGY.get_signal(
'xyz',
default_conf['timeframe'],
mocked_history
@ -140,7 +166,7 @@ def test_get_signal_no_sell_column(default_conf, mocker, caplog, ohlcv_history):
caplog.set_level(logging.INFO)
mocker.patch.object(_STRATEGY, 'assert_df')
assert (True, False, None) == _STRATEGY.get_signal(
assert (True, False, None, None) == _STRATEGY.get_signal(
'xyz',
default_conf['timeframe'],
mocked_history
@ -575,6 +601,13 @@ def test_is_pair_locked(default_conf):
strategy.unlock_pair(pair)
assert not strategy.is_pair_locked(pair)
# Lock with reason
reason = "TestLockR"
strategy.lock_pair(pair, arrow.now(timezone.utc).shift(minutes=4).datetime, reason)
assert strategy.is_pair_locked(pair)
strategy.unlock_reason(reason)
assert not strategy.is_pair_locked(pair)
pair = 'BTC/USDT'
# Lock until 14:30
lock_time = datetime(2020, 5, 1, 14, 30, 0, tzinfo=timezone.utc)
@ -646,7 +679,7 @@ def test_strategy_safe_wrapper(value):
ret = strategy_safe_wrapper(working_method, message='DeadBeef')(value)
assert type(ret) == type(value)
assert isinstance(ret, type(value))
assert ret == value

20
tests/strategy/test_strategy_helpers.py
View File

@ -7,6 +7,7 @@ import pytest
from freqtrade.data.dataprovider import DataProvider
from freqtrade.strategy import (merge_informative_pair, stoploss_from_absolute, stoploss_from_open,
timeframe_to_minutes)
from tests.conftest import get_patched_exchange
def generate_test_data(timeframe: str, size: int, start: str = '2020-07-05'):
@ -155,9 +156,9 @@ def test_informative_decorator(mocker, default_conf):
('LTC/USDT', '5m'): test_data_5m,
('LTC/USDT', '30m'): test_data_30m,
('LTC/USDT', '1h'): test_data_1h,
('BTC/USDT', '30m'): test_data_30m,
('BTC/USDT', '5m'): test_data_5m,
('BTC/USDT', '1h'): test_data_1h,
('NEO/USDT', '30m'): test_data_30m,
('NEO/USDT', '5m'): test_data_5m,
('NEO/USDT', '1h'): test_data_1h,
('ETH/USDT', '1h'): test_data_1h,
('ETH/USDT', '30m'): test_data_30m,
('ETH/BTC', '1h'): test_data_1h,
@ -165,15 +166,16 @@ def test_informative_decorator(mocker, default_conf):
from .strats.informative_decorator_strategy import InformativeDecoratorTest
default_conf['stake_currency'] = 'USDT'
strategy = InformativeDecoratorTest(config=default_conf)
strategy.dp = DataProvider({}, None, None)
exchange = get_patched_exchange(mocker, default_conf)
strategy.dp = DataProvider({}, exchange, None)
mocker.patch.object(strategy.dp, 'current_whitelist', return_value=[
'XRP/USDT', 'LTC/USDT', 'BTC/USDT'
'XRP/USDT', 'LTC/USDT', 'NEO/USDT'
])
assert len(strategy._ft_informative) == 6 # Equal to number of decorators used
informative_pairs = [('XRP/USDT', '1h'), ('LTC/USDT', '1h'), ('XRP/USDT', '30m'),
('LTC/USDT', '30m'), ('BTC/USDT', '1h'), ('BTC/USDT', '30m'),
('BTC/USDT', '5m'), ('ETH/BTC', '1h'), ('ETH/USDT', '30m')]
('LTC/USDT', '30m'), ('NEO/USDT', '1h'), ('NEO/USDT', '30m'),
('NEO/USDT', '5m'), ('ETH/BTC', '1h'), ('ETH/USDT', '30m')]
for inf_pair in informative_pairs:
assert inf_pair in strategy.gather_informative_pairs()
@ -186,8 +188,8 @@ def test_informative_decorator(mocker, default_conf):
{p: data[(p, strategy.timeframe)] for p in ('XRP/USDT', 'LTC/USDT')})
expected_columns = [
'rsi_1h', 'rsi_30m', # Stacked informative decorators
'btc_usdt_rsi_1h', # BTC 1h informative
'rsi_BTC_USDT_btc_usdt_BTC/USDT_30m', # Column formatting
'neo_usdt_rsi_1h', # NEO 1h informative
'rsi_NEO_USDT_neo_usdt_NEO/USDT_30m', # Column formatting
'rsi_from_callable', # Custom column formatter
'eth_btc_rsi_1h', # Quote currency not matching stake currency
'rsi', 'rsi_less', # Non-informative columns

4
tests/test_configuration.py
View File

@ -1359,6 +1359,7 @@ def test_flat_vars_to_nested_dict(caplog):
'FREQTRADE__ASK_STRATEGY__PRICE_SIDE': 'bid',
'FREQTRADE__ASK_STRATEGY__cccc': '500',
'FREQTRADE__STAKE_AMOUNT': '200.05',
'FREQTRADE__TELEGRAM__CHAT_ID': '2151',
'NOT_RELEVANT': '200.0', # Will be ignored
}
expected = {
@ -1373,6 +1374,9 @@ def test_flat_vars_to_nested_dict(caplog):
},
'some_setting': True,
'some_false_setting': False,
},
'telegram': {
'chat_id': '2151'
}
}
res = flat_vars_to_nested_dict(test_args, ENV_VAR_PREFIX)

108
tests/test_freqtradebot.py
View File

@ -170,12 +170,8 @@ def test_edge_called_in_process(mocker, edge_conf) -> None:
patch_RPCManager(mocker)
patch_edge(mocker)
def _refresh_whitelist(list):
return ['ETH/USDT', 'LTC/BTC', 'XRP/BTC', 'NEO/BTC']
patch_exchange(mocker)
freqtrade = FreqtradeBot(edge_conf)
freqtrade.pairlists._validate_whitelist = _refresh_whitelist
patch_get_signal(freqtrade)
freqtrade.process()
assert freqtrade.active_pair_whitelist == ['NEO/BTC', 'LTC/BTC']
@ -236,7 +232,7 @@ def test_edge_overrides_stoploss(limit_buy_order_usdt, fee, caplog, mocker,
# stoploss shoud be hit
assert freqtrade.handle_trade(trade) is not ignore_strat_sl
if not ignore_strat_sl:
assert log_has('Executing Sell for NEO/BTC. Reason: stop_loss', caplog)
assert log_has_re(r'Executing Sell for NEO/BTC. Reason: stop_loss.*', caplog)
assert trade.sell_reason == SellType.STOP_LOSS.value
@ -321,7 +317,7 @@ def test_create_trade_no_stake_amount(default_conf_usdt, ticker_usdt, fee, mocke
@pytest.mark.parametrize('stake_amount,create,amount_enough,max_open_trades', [
(5.0, True, True, 99),
(0.00005, True, False, 99),
(0.04, True, False, 99), # Amount will be adjusted to min - which is 0.051
(0, False, True, 99),
(UNLIMITED_STAKE_AMOUNT, False, True, 0),
])
@ -450,7 +446,7 @@ def test_create_trade_no_signal(default_conf_usdt, fee, mocker) -> None:
)
default_conf_usdt['stake_amount'] = 10
freqtrade = FreqtradeBot(default_conf_usdt)
patch_get_signal(freqtrade, value=(False, False, None))
patch_get_signal(freqtrade, value=(False, False, None, None))
Trade.query = MagicMock()
Trade.query.filter = MagicMock()
@ -664,9 +660,6 @@ def test_process_informative_pairs_added(default_conf_usdt, ticker_usdt, mocker)
patch_RPCManager(mocker)
patch_exchange(mocker)
def _refresh_whitelist(list):
return ['ETH/USDT', 'LTC/BTC', 'XRP/BTC', 'NEO/BTC']
refresh_mock = MagicMock()
mocker.patch.multiple(
'freqtrade.exchange.Exchange',
@ -677,12 +670,11 @@ def test_process_informative_pairs_added(default_conf_usdt, ticker_usdt, mocker)
inf_pairs = MagicMock(return_value=[("BTC/ETH", '1m'), ("ETH/USDT", "1h")])
mocker.patch(
'freqtrade.strategy.interface.IStrategy.get_signal',
return_value=(False, False, '')
return_value=(False, False, '', '')
)
mocker.patch('time.sleep', return_value=None)
freqtrade = FreqtradeBot(default_conf_usdt)
freqtrade.pairlists._validate_whitelist = _refresh_whitelist
freqtrade.strategy.informative_pairs = inf_pairs
# patch_get_signal(freqtrade)
@ -1618,7 +1610,6 @@ def test_exit_positions_exception(mocker, default_conf_usdt, limit_buy_order_usd
trade = MagicMock()
trade.open_order_id = None
trade.open_fee = 0.001
trade.pair = 'ETH/USDT'
trades = [trade]
@ -1675,6 +1666,15 @@ def test_update_trade_state(mocker, default_conf_usdt, limit_buy_order_usdt, cap
freqtrade.update_trade_state(trade, '123')
assert log_has_re('Found open order for.*', caplog)
limit_buy_order_usdt_new = deepcopy(limit_buy_order_usdt)
limit_buy_order_usdt_new['filled'] = 0.0
limit_buy_order_usdt_new['statuss'] = 'canceled'
mocker.patch('freqtrade.freqtradebot.FreqtradeBot.get_real_amount', side_effect=ValueError)
mocker.patch('freqtrade.exchange.Exchange.fetch_order', return_value=limit_buy_order_usdt_new)
res = freqtrade.update_trade_state(trade, '123')
# Cancelled empty
assert res is True
@pytest.mark.parametrize('initial_amount,has_rounding_fee', [
@ -1716,7 +1716,6 @@ def test_update_trade_state_exception(mocker, default_conf_usdt,
trade = MagicMock()
trade.open_order_id = '123'
trade.open_fee = 0.001
# Test raise of OperationalException exception
mocker.patch(
@ -1734,7 +1733,6 @@ def test_update_trade_state_orderexception(mocker, default_conf_usdt, caplog) ->
trade = MagicMock()
trade.open_order_id = '123'
trade.open_fee = 0.001
# Test raise of OperationalException exception
grm_mock = mocker.patch("freqtrade.freqtradebot.FreqtradeBot.get_real_amount", MagicMock())
@ -1808,7 +1806,7 @@ def test_handle_trade(default_conf_usdt, limit_buy_order_usdt, limit_sell_order_
assert trade.is_open is True
freqtrade.wallets.update()
patch_get_signal(freqtrade, value=(False, True, None))
patch_get_signal(freqtrade, value=(False, True, None, 'sell_signal1'))
assert freqtrade.handle_trade(trade) is True
assert trade.open_order_id == limit_sell_order_usdt['id']
@ -1819,6 +1817,7 @@ def test_handle_trade(default_conf_usdt, limit_buy_order_usdt, limit_sell_order_
assert trade.close_profit == 0.09451372
assert trade.calc_profit() == 5.685
assert trade.close_date is not None
assert trade.sell_reason == 'sell_signal1'
def test_handle_overlapping_signals(default_conf_usdt, ticker_usdt, limit_buy_order_usdt_open,
@ -1836,7 +1835,7 @@ def test_handle_overlapping_signals(default_conf_usdt, ticker_usdt, limit_buy_or
)
freqtrade = FreqtradeBot(default_conf_usdt)
patch_get_signal(freqtrade, value=(True, True, None))
patch_get_signal(freqtrade, value=(True, True, None, None))
freqtrade.strategy.min_roi_reached = MagicMock(return_value=False)
freqtrade.enter_positions()
@ -1855,7 +1854,7 @@ def test_handle_overlapping_signals(default_conf_usdt, ticker_usdt, limit_buy_or
assert trades[0].is_open is True
# Buy and Sell are not triggering, so doing nothing ...
patch_get_signal(freqtrade, value=(False, False, None))
patch_get_signal(freqtrade, value=(False, False, None, None))
assert freqtrade.handle_trade(trades[0]) is False
trades = Trade.query.all()
nb_trades = len(trades)
@ -1863,7 +1862,7 @@ def test_handle_overlapping_signals(default_conf_usdt, ticker_usdt, limit_buy_or
assert trades[0].is_open is True
# Buy and Sell are triggering, so doing nothing ...
patch_get_signal(freqtrade, value=(True, True, None))
patch_get_signal(freqtrade, value=(True, True, None, None))
assert freqtrade.handle_trade(trades[0]) is False
trades = Trade.query.all()
nb_trades = len(trades)
@ -1871,7 +1870,7 @@ def test_handle_overlapping_signals(default_conf_usdt, ticker_usdt, limit_buy_or
assert trades[0].is_open is True
# Sell is triggering, guess what : we are Selling!
patch_get_signal(freqtrade, value=(False, True, None))
patch_get_signal(freqtrade, value=(False, True, None, None))
trades = Trade.query.all()
assert freqtrade.handle_trade(trades[0]) is True
@ -1905,7 +1904,7 @@ def test_handle_trade_roi(default_conf_usdt, ticker_usdt, limit_buy_order_usdt_o
# we might just want to check if we are in a sell condition without
# executing
# if ROI is reached we must sell
patch_get_signal(freqtrade, value=(False, True, None))
patch_get_signal(freqtrade, value=(False, True, None, None))
assert freqtrade.handle_trade(trade)
assert log_has("ETH/USDT - Required profit reached. sell_type=SellType.ROI",
caplog)
@ -1934,10 +1933,10 @@ def test_handle_trade_use_sell_signal(default_conf_usdt, ticker_usdt, limit_buy_
trade = Trade.query.first()
trade.is_open = True
patch_get_signal(freqtrade, value=(False, False, None))
patch_get_signal(freqtrade, value=(False, False, None, None))
assert not freqtrade.handle_trade(trade)
patch_get_signal(freqtrade, value=(False, True, None))
patch_get_signal(freqtrade, value=(False, True, None, None))
assert freqtrade.handle_trade(trade)
assert log_has("ETH/USDT - Sell signal received. sell_type=SellType.SELL_SIGNAL",
caplog)
@ -2124,11 +2123,12 @@ def test_check_handle_timedout_buy_exception(default_conf_usdt, ticker_usdt,
def test_check_handle_timedout_sell_usercustom(default_conf_usdt, ticker_usdt, limit_sell_order_old,
mocker, open_trade) -> None:
default_conf_usdt["unfilledtimeout"] = {"buy": 1440, "sell": 1440}
mocker, open_trade, caplog) -> None:
default_conf_usdt["unfilledtimeout"] = {"buy": 1440, "sell": 1440, "exit_timeout_count": 1}
rpc_mock = patch_RPCManager(mocker)
cancel_order_mock = MagicMock()
patch_exchange(mocker)
et_mock = mocker.patch('freqtrade.freqtradebot.FreqtradeBot.execute_trade_exit')
mocker.patch.multiple(
'freqtrade.exchange.Exchange',
fetch_ticker=ticker_usdt,
@ -2171,6 +2171,14 @@ def test_check_handle_timedout_sell_usercustom(default_conf_usdt, ticker_usdt, l
assert open_trade.is_open is True
assert freqtrade.strategy.check_sell_timeout.call_count == 1
# 2nd canceled trade ...
caplog.clear()
open_trade.open_order_id = 'order_id_2'
mocker.patch('freqtrade.persistence.Trade.get_exit_order_count', return_value=1)
freqtrade.check_handle_timedout()
assert log_has_re('Emergencyselling trade.*', caplog)
assert et_mock.call_count == 1
def test_check_handle_timedout_sell(default_conf_usdt, ticker_usdt, limit_sell_order_old, mocker,
open_trade) -> None:
@ -2560,6 +2568,8 @@ def test_execute_trade_exit_up(default_conf_usdt, ticker_usdt, fee, ticker_usdt_
sell_reason=SellCheckTuple(sell_type=SellType.ROI))
assert rpc_mock.call_count == 0
assert freqtrade.strategy.confirm_trade_exit.call_count == 1
assert id(freqtrade.strategy.confirm_trade_exit.call_args_list[0][1]['trade']) != id(trade)
assert freqtrade.strategy.confirm_trade_exit.call_args_list[0][1]['trade'].id == trade.id
# Repatch with true
freqtrade.strategy.confirm_trade_exit = MagicMock(return_value=True)
@ -2579,6 +2589,7 @@ def test_execute_trade_exit_up(default_conf_usdt, ticker_usdt, fee, ticker_usdt_
'limit': 2.2,
'amount': 30.0,
'order_type': 'limit',
'buy_tag': None,
'open_rate': 2.0,
'current_rate': 2.3,
'profit_amount': 5.685,
@ -2632,6 +2643,7 @@ def test_execute_trade_exit_down(default_conf_usdt, ticker_usdt, fee, ticker_usd
'limit': 2.01,
'amount': 30.0,
'order_type': 'limit',
'buy_tag': None,
'open_rate': 2.0,
'current_rate': 2.0,
'profit_amount': -0.00075,
@ -2699,6 +2711,7 @@ def test_execute_trade_exit_custom_exit_price(default_conf_usdt, ticker_usdt, fe
'limit': 2.25,
'amount': 30.0,
'order_type': 'limit',
'buy_tag': None,
'open_rate': 2.0,
'current_rate': 2.3,
'profit_amount': 7.18125,
@ -2758,6 +2771,7 @@ def test_execute_trade_exit_down_stoploss_on_exchange_dry_run(
'limit': 1.98,
'amount': 30.0,
'order_type': 'limit',
'buy_tag': None,
'open_rate': 2.0,
'current_rate': 2.0,
'profit_amount': -0.8985,
@ -2975,6 +2989,7 @@ def test_execute_trade_exit_market_order(default_conf_usdt, ticker_usdt, fee,
'limit': 2.2,
'amount': 30.0,
'order_type': 'market',
'buy_tag': None,
'open_rate': 2.0,
'current_rate': 2.3,
'profit_amount': 5.685,
@ -3068,7 +3083,7 @@ def test_sell_profit_only(
trade = Trade.query.first()
trade.update(limit_buy_order_usdt)
freqtrade.wallets.update()
patch_get_signal(freqtrade, value=(False, True, None))
patch_get_signal(freqtrade, value=(False, True, None, None))
assert freqtrade.handle_trade(trade) is handle_first
if handle_second:
@ -3103,7 +3118,7 @@ def test_sell_not_enough_balance(default_conf_usdt, limit_buy_order_usdt, limit_
trade = Trade.query.first()
amnt = trade.amount
trade.update(limit_buy_order_usdt)
patch_get_signal(freqtrade, value=(False, True, None))
patch_get_signal(freqtrade, value=(False, True, None, None))
mocker.patch('freqtrade.wallets.Wallets.get_free', MagicMock(return_value=trade.amount * 0.985))
assert freqtrade.handle_trade(trade) is True
@ -3212,11 +3227,11 @@ def test_ignore_roi_if_buy_signal(default_conf_usdt, limit_buy_order_usdt,
trade = Trade.query.first()
trade.update(limit_buy_order_usdt)
freqtrade.wallets.update()
patch_get_signal(freqtrade, value=(True, True, None))
patch_get_signal(freqtrade, value=(True, True, None, None))
assert freqtrade.handle_trade(trade) is False
# Test if buy-signal is absent (should sell due to roi = true)
patch_get_signal(freqtrade, value=(False, True, None))
patch_get_signal(freqtrade, value=(False, True, None, None))
assert freqtrade.handle_trade(trade) is True
assert trade.sell_reason == SellType.ROI.value
@ -3329,7 +3344,7 @@ def test_trailing_stop_loss_positive(
)
# stop-loss not reached, adjusted stoploss
assert freqtrade.handle_trade(trade) is False
caplog_text = f"ETH/USDT - Using positive stoploss: 0.01 offset: {offset} profit: 0.0249%"
caplog_text = f"ETH/USDT - Using positive stoploss: 0.01 offset: {offset} profit: 2.49%"
if trail_if_reached:
assert not log_has(caplog_text, caplog)
assert not log_has("ETH/USDT - Adjusting stoploss...", caplog)
@ -3349,7 +3364,7 @@ def test_trailing_stop_loss_positive(
)
assert freqtrade.handle_trade(trade) is False
assert log_has(
f"ETH/USDT - Using positive stoploss: 0.01 offset: {offset} profit: 0.0572%",
f"ETH/USDT - Using positive stoploss: 0.01 offset: {offset} profit: 5.72%",
caplog
)
assert log_has("ETH/USDT - Adjusting stoploss...", caplog)
@ -3402,11 +3417,11 @@ def test_disable_ignore_roi_if_buy_signal(default_conf_usdt, limit_buy_order_usd
trade = Trade.query.first()
trade.update(limit_buy_order_usdt)
# Sell due to min_roi_reached
patch_get_signal(freqtrade, value=(True, True, None))
patch_get_signal(freqtrade, value=(True, True, None, None))
assert freqtrade.handle_trade(trade) is True
# Test if buy-signal is absent
patch_get_signal(freqtrade, value=(False, True, None))
patch_get_signal(freqtrade, value=(False, True, None, None))
assert freqtrade.handle_trade(trade) is True
assert trade.sell_reason == SellType.ROI.value
@ -3611,6 +3626,31 @@ def test_get_real_amount_invalid_order(default_conf_usdt, trades_for_order, buy_
assert freqtrade.get_real_amount(trade, limit_buy_order_usdt) == amount
def test_get_real_amount_fees_order(default_conf_usdt, market_buy_order_usdt_doublefee,
fee, mocker):
tfo_mock = mocker.patch('freqtrade.exchange.Exchange.get_trades_for_order', return_value=[])
mocker.patch('freqtrade.exchange.Exchange.get_valid_pair_combination', return_value='BNB/USDT')
mocker.patch('freqtrade.exchange.Exchange.fetch_ticker', return_value={'last': 200})
trade = Trade(
pair='LTC/USDT',
amount=30.0,
exchange='binance',
fee_open=fee.return_value,
fee_close=fee.return_value,
open_rate=0.245441,
open_order_id="123456"
)
freqtrade = get_patched_freqtradebot(mocker, default_conf_usdt)
# Amount does not change
assert trade.fee_open == 0.0025
assert freqtrade.get_real_amount(trade, market_buy_order_usdt_doublefee) == 30.0
assert tfo_mock.call_count == 0
# Fetch fees from trades dict if available to get "proper" values
assert round(trade.fee_open, 4) == 0.001
def test_get_real_amount_wrong_amount(default_conf_usdt, trades_for_order, buy_order_fee, fee,
mocker):
limit_buy_order_usdt = deepcopy(buy_order_fee)
@ -3848,7 +3888,7 @@ def test_order_book_ask_strategy(
freqtrade.wallets.update()
assert trade.is_open is True
patch_get_signal(freqtrade, value=(False, True, None))
patch_get_signal(freqtrade, value=(False, True, None, None))
assert freqtrade.handle_trade(trade) is True
assert trade.close_rate_requested == order_book_l2.return_value['asks'][0][0]

3
tests/test_misc.py
View File

@ -67,6 +67,9 @@ def test_file_load_json(mocker, testdatadir) -> None:
@pytest.mark.parametrize("pair,expected_result", [
("ETH/BTC", 'ETH_BTC'),
("ETH/USDT", 'ETH_USDT'),
("ETH/USDT:USDT", 'ETH_USDT_USDT'), # swap with USDT as settlement currency
("ETH/USDT:USDT-210625", 'ETH_USDT_USDT_210625'), # expiring futures
("Fabric Token/ETH", 'Fabric_Token_ETH'),
("ETHH20", 'ETHH20'),
(".XBTBON2H", '_XBTBON2H'),

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