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72
.github/workflows/ci.yml vendored
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@ -13,20 +13,24 @@ on:
schedule:
- cron: '0 5 * * 4'
concurrency:
group: ${{ github.workflow }}-${{ github.ref }}
cancel-in-progress: true
jobs:
build_linux:
runs-on: ${{ matrix.os }}
strategy:
matrix:
os: [ ubuntu-18.04, ubuntu-20.04 ]
os: [ ubuntu-18.04, ubuntu-20.04, ubuntu-22.04 ]
python-version: ["3.8", "3.9", "3.10"]
steps:
- uses: actions/checkout@v3
- name: Set up Python
uses: actions/setup-python@v3
uses: actions/setup-python@v4
with:
python-version: ${{ matrix.python-version }}
@ -62,15 +66,15 @@ jobs:
- name: Tests
run: |
pytest --random-order --cov=freqtrade --cov-config=.coveragerc
if: matrix.python-version != '3.9'
if: matrix.python-version != '3.9' || matrix.os != 'ubuntu-22.04'
- name: Tests incl. ccxt compatibility tests
run: |
pytest --random-order --cov=freqtrade --cov-config=.coveragerc --longrun
if: matrix.python-version == '3.9'
if: matrix.python-version == '3.9' && matrix.os == 'ubuntu-22.04'
- name: Coveralls
if: (runner.os == 'Linux' && matrix.python-version == '3.8')
if: (runner.os == 'Linux' && matrix.python-version == '3.9')
env:
# Coveralls token. Not used as secret due to github not providing secrets to forked repositories
COVERALLS_REPO_TOKEN: 6D1m0xupS3FgutfuGao8keFf9Hc0FpIXu
@ -78,11 +82,13 @@ jobs:
# Allow failure for coveralls
coveralls || true
- name: Backtesting
- name: Backtesting (multi)
run: |
cp config_examples/config_bittrex.example.json config.json
freqtrade create-userdir --userdir user_data
freqtrade backtesting --datadir tests/testdata --strategy SampleStrategy
freqtrade new-strategy -s AwesomeStrategy
freqtrade new-strategy -s AwesomeStrategyMin --template minimal
freqtrade backtesting --datadir tests/testdata --strategy-list AwesomeStrategy AwesomeStrategyMin -i 5m
- name: Hyperopt
run: |
@ -121,7 +127,7 @@ jobs:
- uses: actions/checkout@v3
- name: Set up Python
uses: actions/setup-python@v3
uses: actions/setup-python@v4
with:
python-version: ${{ matrix.python-version }}
@ -157,29 +163,15 @@ jobs:
pip install -e .
- name: Tests
if: (runner.os != 'Linux' || matrix.python-version != '3.8')
run: |
pytest --random-order
- name: Tests (with cov)
if: (runner.os == 'Linux' && matrix.python-version == '3.8')
run: |
pytest --random-order --cov=freqtrade --cov-config=.coveragerc
- name: Coveralls
if: (runner.os == 'Linux' && matrix.python-version == '3.8')
env:
# Coveralls token. Not used as secret due to github not providing secrets to forked repositories
COVERALLS_REPO_TOKEN: 6D1m0xupS3FgutfuGao8keFf9Hc0FpIXu
run: |
# Allow failure for coveralls
coveralls -v || true
- name: Backtesting
run: |
cp config_examples/config_bittrex.example.json config.json
freqtrade create-userdir --userdir user_data
freqtrade backtesting --datadir tests/testdata --strategy SampleStrategy
freqtrade new-strategy -s AwesomeStrategyAdv --template advanced
freqtrade backtesting --datadir tests/testdata --strategy AwesomeStrategyAdv
- name: Hyperopt
run: |
@ -219,7 +211,7 @@ jobs:
- uses: actions/checkout@v3
- name: Set up Python
uses: actions/setup-python@v3
uses: actions/setup-python@v4
with:
python-version: ${{ matrix.python-version }}
@ -271,9 +263,9 @@ jobs:
- uses: actions/checkout@v3
- name: Set up Python
uses: actions/setup-python@v3
uses: actions/setup-python@v4
with:
python-version: 3.9
python-version: "3.10"
- name: pre-commit dependencies
run: |
@ -290,9 +282,9 @@ jobs:
./tests/test_docs.sh
- name: Set up Python
uses: actions/setup-python@v3
uses: actions/setup-python@v4
with:
python-version: 3.9
python-version: "3.10"
- name: Documentation build
run: |
@ -308,18 +300,6 @@ jobs:
details: Freqtrade doc test failed!
webhookUrl: ${{ secrets.DISCORD_WEBHOOK }}
cleanup-prior-runs:
permissions:
actions: write # for rokroskar/workflow-run-cleanup-action to obtain workflow name & cancel it
contents: read # for rokroskar/workflow-run-cleanup-action to obtain branch
runs-on: ubuntu-20.04
steps:
- name: Cleanup previous runs on this branch
uses: rokroskar/workflow-run-cleanup-action@v0.3.3
if: "!startsWith(github.ref, 'refs/tags/') && github.ref != 'refs/heads/stable' && github.repository == 'freqtrade/freqtrade'"
env:
GITHUB_TOKEN: "${{ secrets.GITHUB_TOKEN }}"
# Notify only once - when CI completes (and after deploy) in case it's successfull
notify-complete:
needs: [ build_linux, build_macos, build_windows, docs_check, mypy_version_check ]
@ -356,9 +336,9 @@ jobs:
- uses: actions/checkout@v3
- name: Set up Python
uses: actions/setup-python@v3
uses: actions/setup-python@v4
with:
python-version: 3.8
python-version: "3.9"
- name: Extract branch name
shell: bash
@ -371,7 +351,7 @@ jobs:
python setup.py sdist bdist_wheel
- name: Publish to PyPI (Test)
uses: pypa/gh-action-pypi-publish@master
uses: pypa/gh-action-pypi-publish@v1.5.0
if: (github.event_name == 'release')
with:
user: __token__
@ -379,7 +359,7 @@ jobs:
repository_url: https://test.pypi.org/legacy/
- name: Publish to PyPI
uses: pypa/gh-action-pypi-publish@master
uses: pypa/gh-action-pypi-publish@v1.5.0
if: (github.event_name == 'release')
with:
user: __token__
@ -419,7 +399,7 @@ jobs:
- name: Discord notification
uses: rjstone/discord-webhook-notify@v1
if: always() && ( github.event_name != 'pull_request' || github.event.pull_request.head.repo.fork == false)
if: always() && ( github.event_name != 'pull_request' || github.event.pull_request.head.repo.fork == false) && (github.event_name != 'schedule')
with:
severity: info
details: Deploy Succeeded!

10
.pre-commit-config.yaml
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@ -13,11 +13,11 @@ repos:
- id: mypy
exclude: build_helpers
additional_dependencies:
- types-cachetools==5.0.1
- types-filelock==3.2.5
- types-requests==2.27.25
- types-tabulate==0.8.8
- types-python-dateutil==2.8.14
- types-cachetools==5.2.1
- types-filelock==3.2.7
- types-requests==2.28.0
- types-tabulate==0.8.11
- types-python-dateutil==2.8.18
# stages: [push]
- repo: https://github.com/pycqa/isort

2
Dockerfile
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@ -1,4 +1,4 @@
FROM python:3.9.9-slim-bullseye as base
FROM python:3.10.5-slim-bullseye as base
# Setup env
ENV LANG C.UTF-8

6
README.md
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@ -9,10 +9,6 @@ Freqtrade is a free and open source crypto trading bot written in Python. It is
![freqtrade](https://raw.githubusercontent.com/freqtrade/freqtrade/develop/docs/assets/freqtrade-screenshot.png)
## Sponsored promotion
[![tokenbot-promo](https://raw.githubusercontent.com/freqtrade/freqtrade/develop/docs/assets/TokenBot-Freqtrade-banner.png)](https://tokenbot.com/?utm_source=github&utm_medium=freqtrade&utm_campaign=algodevs)
## Disclaimer
This software is for educational purposes only. Do not risk money which
@ -39,7 +35,7 @@ Please read the [exchange specific notes](docs/exchanges.md) to learn about even
- [X] [OKX](https://okx.com/) (Former OKEX)
- [ ] [potentially many others](https://github.com/ccxt/ccxt/). _(We cannot guarantee they will work)_
### Experimentally, freqtrade also supports futures on the following exchanges
### Supported Futures Exchanges (experimental)
- [X] [Binance](https://www.binance.com/)
- [X] [Gate.io](https://www.gate.io/ref/6266643)

3
config_examples/config_full.example.json
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@ -155,7 +155,8 @@
"entry_cancel": "on",
"exit_cancel": "on",
"protection_trigger": "off",
"protection_trigger_global": "on"
"protection_trigger_global": "on",
"show_candle": "off"
},
"reload": true,
"balance_dust_level": 0.01

2
docker/Dockerfile.armhf
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@ -1,4 +1,4 @@
FROM python:3.9.9-slim-bullseye as base
FROM python:3.9.12-slim-bullseye as base
# Setup env
ENV LANG C.UTF-8

1
docker/Dockerfile.custom
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@ -7,4 +7,5 @@ FROM freqtradeorg/freqtrade:develop
# The below dependency - pyti - serves as an example. Please use whatever you need!
RUN pip install --user pyti
# Switch back to user (only if you required root above)
# USER ftuser

57
docs/advanced-backtesting.md
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@ -22,50 +22,79 @@ DataFrame of the candles that resulted in buy signals. Depending on how many buy
makes, this file may get quite large, so periodically check your `user_data/backtest_results`
folder to delete old exports.
To analyze the buy tags, we need to use the `buy_reasons.py` script from
[froggleston's repo](https://github.com/froggleston/freqtrade-buyreasons). Follow the instructions
in their README to copy the script into your `freqtrade/scripts/` folder.
Before running your next backtest, make sure you either delete your old backtest results or run
backtesting with the `--cache none` option to make sure no cached results are used.
If all goes well, you should now see a `backtest-result-{timestamp}_signals.pkl` file in the
`user_data/backtest_results` folder.
Now run the `buy_reasons.py` script, supplying a few options:
To analyze the entry/exit tags, we now need to use the `freqtrade backtesting-analysis` command
with `--analysis-groups` option provided with space-separated arguments (default `0 1 2`):
``` bash
python3 scripts/buy_reasons.py -c <config.json> -s <strategy_name> -t <timerange> -g0,1,2,3,4
freqtrade backtesting-analysis -c <config.json> --analysis-groups 0 1 2 3 4
```
The `-g` option is used to specify the various tabular outputs, ranging from the simplest (0)
to the most detailed per pair, per buy and per sell tag (4). More options are available by
running with the `-h` option.
This command will read from the last backtesting results. The `--analysis-groups` option is
used to specify the various tabular outputs showing the profit fo each group or trade,
ranging from the simplest (0) to the most detailed per pair, per buy and per sell tag (4):
* 1: profit summaries grouped by enter_tag
* 2: profit summaries grouped by enter_tag and exit_tag
* 3: profit summaries grouped by pair and enter_tag
* 4: profit summaries grouped by pair, enter_ and exit_tag (this can get quite large)
More options are available by running with the `-h` option.
### Using export-filename
Normally, `backtesting-analysis` uses the latest backtest results, but if you wanted to go
back to a previous backtest output, you need to supply the `--export-filename` option.
You can supply the same parameter to `backtest-analysis` with the name of the final backtest
output file. This allows you to keep historical versions of backtest results and re-analyse
them at a later date:
``` bash
freqtrade backtesting -c <config.json> --timeframe <tf> --strategy <strategy_name> --timerange=<timerange> --export=signals --export-filename=/tmp/mystrat_backtest.json
```
You should see some output similar to below in the logs with the name of the timestamped
filename that was exported:
```
2022-06-14 16:28:32,698 - freqtrade.misc - INFO - dumping json to "/tmp/mystrat_backtest-2022-06-14_16-28-32.json"
```
You can then use that filename in `backtesting-analysis`:
```
freqtrade backtesting-analysis -c <config.json> --export-filename=/tmp/mystrat_backtest-2022-06-14_16-28-32.json
```
### Tuning the buy tags and sell tags to display
To show only certain buy and sell tags in the displayed output, use the following two options:
```
--enter_reason_list : Comma separated list of enter signals to analyse. Default: "all"
--exit_reason_list : Comma separated list of exit signals to analyse. Default: "stop_loss,trailing_stop_loss"
--enter-reason-list : Space-separated list of enter signals to analyse. Default: "all"
--exit-reason-list : Space-separated list of exit signals to analyse. Default: "all"
```
For example:
```bash
python3 scripts/buy_reasons.py -c <config.json> -s <strategy_name> -t <timerange> -g0,1,2,3,4 --enter_reason_list "enter_tag_a,enter_tag_b" --exit_reason_list "roi,custom_exit_tag_a,stop_loss"
freqtrade backtesting-analysis -c <config.json> --analysis-groups 0 2 --enter-reason-list enter_tag_a enter_tag_b --exit-reason-list roi custom_exit_tag_a stop_loss
```
### Outputting signal candle indicators
The real power of the buy_reasons.py script comes from the ability to print out the indicator
The real power of `freqtrade backtesting-analysis` comes from the ability to print out the indicator
values present on signal candles to allow fine-grained investigation and tuning of buy signal
indicators. To print out a column for a given set of indicators, use the `--indicator-list`
option:
```bash
python3 scripts/buy_reasons.py -c <config.json> -s <strategy_name> -t <timerange> -g0,1,2,3,4 --enter_reason_list "enter_tag_a,enter_tag_b" --exit_reason_list "roi,custom_exit_tag_a,stop_loss" --indicator_list "rsi,rsi_1h,bb_lowerband,ema_9,macd,macdsignal"
freqtrade backtesting-analysis -c <config.json> --analysis-groups 0 2 --enter-reason-list enter_tag_a enter_tag_b --exit-reason-list roi custom_exit_tag_a stop_loss --indicator-list rsi rsi_1h bb_lowerband ema_9 macd macdsignal
```
The indicators have to be present in your strategy's main DataFrame (either for your main

17
docs/advanced-hyperopt.md
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@ -98,6 +98,23 @@ class MyAwesomeStrategy(IStrategy):
!!! Note
All overrides are optional and can be mixed/matched as necessary.
### Dynamic parameters
Parameters can also be defined dynamically, but must be available to the instance once the * [`bot_start()` callback](strategy-callbacks.md#bot-start) has been called.
``` python
class MyAwesomeStrategy(IStrategy):
def bot_start(self, **kwargs) -> None:
self.buy_adx = IntParameter(20, 30, default=30, optimize=True)
# ...
```
!!! Warning
Parameters created this way will not show up in the `list-strategies` parameter count.
### Overriding Base estimator
You can define your own estimator for Hyperopt by implementing `generate_estimator()` in the Hyperopt subclass.

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20
docs/backtesting.md
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@ -300,6 +300,7 @@ A backtesting result will look like that:
| Absolute profit | 0.00762792 BTC |
| Total profit % | 76.2% |
| CAGR % | 460.87% |
| Profit factor | 1.11 |
| Avg. stake amount | 0.001 BTC |
| Total trade volume | 0.429 BTC |
| | |
@ -320,6 +321,9 @@ A backtesting result will look like that:
| Avg. Duration Loser | 6:55:00 |
| Rejected Entry signals | 3089 |
| Entry/Exit Timeouts | 0 / 0 |
| Canceled Trade Entries | 34 |
| Canceled Entry Orders | 123 |
| Replaced Entry Orders | 89 |
| | |
| Min balance | 0.00945123 BTC |
| Max balance | 0.01846651 BTC |
@ -396,6 +400,7 @@ It contains some useful key metrics about performance of your strategy on backte
| Absolute profit | 0.00762792 BTC |
| Total profit % | 76.2% |
| CAGR % | 460.87% |
| Profit factor | 1.11 |
| Avg. stake amount | 0.001 BTC |
| Total trade volume | 0.429 BTC |
| | |
@ -416,6 +421,9 @@ It contains some useful key metrics about performance of your strategy on backte
| Avg. Duration Loser | 6:55:00 |
| Rejected Entry signals | 3089 |
| Entry/Exit Timeouts | 0 / 0 |
| Canceled Trade Entries | 34 |
| Canceled Entry Orders | 123 |
| Replaced Entry Orders | 89 |
| | |
| Min balance | 0.00945123 BTC |
| Max balance | 0.01846651 BTC |
@ -438,6 +446,8 @@ It contains some useful key metrics about performance of your strategy on backte
- `Final balance`: Final balance - starting balance + absolute profit.
- `Absolute profit`: Profit made in stake currency.
- `Total profit %`: Total profit. Aligned to the `TOTAL` row's `Tot Profit %` from the first table. Calculated as `(End capital − Starting capital) / Starting capital`.
- `CAGR %`: Compound annual growth rate.
- `Profit factor`: profit / loss.
- `Avg. stake amount`: Average stake amount, either `stake_amount` or the average when using dynamic stake amount.
- `Total trade volume`: Volume generated on the exchange to reach the above profit.
- `Best Pair` / `Worst Pair`: Best and worst performing pair, and it's corresponding `Cum Profit %`.
@ -447,6 +457,9 @@ It contains some useful key metrics about performance of your strategy on backte
- `Avg. Duration Winners` / `Avg. Duration Loser`: Average durations for winning and losing trades.
- `Rejected Entry signals`: Trade entry signals that could not be acted upon due to `max_open_trades` being reached.
- `Entry/Exit Timeouts`: Entry/exit orders which did not fill (only applicable if custom pricing is used).
- `Canceled Trade Entries`: Number of trades that have been canceled by user request via `adjust_entry_price`.
- `Canceled Entry Orders`: Number of entry orders that have been canceled by user request via `adjust_entry_price`.
- `Replaced Entry Orders`: Number of entry orders that have been replaced by user request via `adjust_entry_price`.
- `Min balance` / `Max balance`: Lowest and Highest Wallet balance during the backtest period.
- `Max % of account underwater`: Maximum percentage your account has decreased from the top since the simulation started.
Calculated as the maximum of `(Max Balance - Current Balance) / (Max Balance)`.
@ -466,7 +479,7 @@ You can get an overview over daily / weekly or monthly results by using the `--b
To visualize daily and weekly breakdowns, you can use the following:
``` bash
freqtrade backtesting --strategy MyAwesomeStrategy --breakdown day month
freqtrade backtesting --strategy MyAwesomeStrategy --breakdown day week
```
``` output
@ -482,7 +495,7 @@ freqtrade backtesting --strategy MyAwesomeStrategy --breakdown day month
```
The output will show a table containing the realized absolute Profit (in stake currency) for the given timeperiod, as well as wins, draws and losses that materialized (closed) on this day.
The output will show a table containing the realized absolute Profit (in stake currency) for the given timeperiod, as well as wins, draws and losses that materialized (closed) on this day. Below that there will be a second table for the summarized values of weeks indicated by the date of the closing Sunday. The same would apply to a monthly breakdown indicated by the last day of the month.
### Backtest result caching
@ -521,8 +534,9 @@ Since backtesting lacks some detailed information about what happens within a ca
- Exit-reason does not explain if a trade was positive or negative, just what triggered the exit (this can look odd if negative ROI values are used)
- Evaluation sequence (if multiple signals happen on the same candle)
- Exit-signal
- ROI (if not stoploss)
- Stoploss
- ROI
- Trailing stoploss
Taking these assumptions, backtesting tries to mirror real trading as closely as possible. However, backtesting will **never** replace running a strategy in dry-run mode.
Also, keep in mind that past results don't guarantee future success.

7
docs/bot-basics.md
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@ -20,7 +20,9 @@ All profit calculations of Freqtrade include fees. For Backtesting / Hyperopt /
## Bot execution logic
Starting freqtrade in dry-run or live mode (using `freqtrade trade`) will start the bot and start the bot iteration loop.
By default, loop runs every few seconds (`internals.process_throttle_secs`) and does roughly the following in the following sequence:
This will also run the `bot_start()` callback.
By default, the bot loop runs every few seconds (`internals.process_throttle_secs`) and performs the following actions:
* Fetch open trades from persistence.
* Calculate current list of tradable pairs.
@ -34,6 +36,7 @@ By default, loop runs every few seconds (`internals.process_throttle_secs`) and
* Check timeouts for open orders.
* Calls `check_entry_timeout()` strategy callback for open entry orders.
* Calls `check_exit_timeout()` strategy callback for open exit orders.
* Calls `adjust_entry_price()` strategy callback for open entry orders.
* Verifies existing positions and eventually places exit orders.
* Considers stoploss, ROI and exit-signal, `custom_exit()` and `custom_stoploss()`.
* Determine exit-price based on `exit_pricing` configuration setting or by using the `custom_exit_price()` callback.
@ -53,11 +56,13 @@ This loop will be repeated again and again until the bot is stopped.
[backtesting](backtesting.md) or [hyperopt](hyperopt.md) do only part of the above logic, since most of the trading operations are fully simulated.
* Load historic data for configured pairlist.
* Calls `bot_start()` once.
* Calls `bot_loop_start()` once.
* Calculate indicators (calls `populate_indicators()` once per pair).
* Calculate entry / exit signals (calls `populate_entry_trend()` and `populate_exit_trend()` once per pair).
* Loops per candle simulating entry and exit points.
* Check for Order timeouts, either via the `unfilledtimeout` configuration, or via `check_entry_timeout()` / `check_exit_timeout()` strategy callbacks.
* Calls `adjust_entry_price()` strategy callback for open entry orders.
* Check for trade entry signals (`enter_long` / `enter_short` columns).
* Confirm trade entry / exits (calls `confirm_trade_entry()` and `confirm_trade_exit()` if implemented in the strategy).
* Call `custom_entry_price()` (if implemented in the strategy) to determine entry price (Prices are moved to be within the opening candle).

5
docs/configuration.md
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@ -140,7 +140,7 @@ Mandatory parameters are marked as **Required**, which means that they are requi
| `dry_run` | **Required.** Define if the bot must be in Dry Run or production mode. <br>*Defaults to `true`.* <br> **Datatype:** Boolean
| `dry_run_wallet` | Define the starting amount in stake currency for the simulated wallet used by the bot running in Dry Run mode.<br>*Defaults to `1000`.* <br> **Datatype:** Float
| `cancel_open_orders_on_exit` | Cancel open orders when the `/stop` RPC command is issued, `Ctrl+C` is pressed or the bot dies unexpectedly. When set to `true`, this allows you to use `/stop` to cancel unfilled and partially filled orders in the event of a market crash. It does not impact open positions. <br>*Defaults to `false`.* <br> **Datatype:** Boolean
| `process_only_new_candles` | Enable processing of indicators only when new candles arrive. If false each loop populates the indicators, this will mean the same candle is processed many times creating system load but can be useful of your strategy depends on tick data not only candle. [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `false`.* <br> **Datatype:** Boolean
| `process_only_new_candles` | Enable processing of indicators only when new candles arrive. If false each loop populates the indicators, this will mean the same candle is processed many times creating system load but can be useful of your strategy depends on tick data not only candle. [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `true`.* <br> **Datatype:** Boolean
| `minimal_roi` | **Required.** Set the threshold as ratio the bot will use to exit a trade. [More information below](#understand-minimal_roi). [Strategy Override](#parameters-in-the-strategy). <br> **Datatype:** Dict
| `stoploss` | **Required.** Value as ratio of the stoploss used by the bot. More details in the [stoploss documentation](stoploss.md). [Strategy Override](#parameters-in-the-strategy). <br> **Datatype:** Float (as ratio)
| `trailing_stop` | Enables trailing stoploss (based on `stoploss` in either configuration or strategy file). More details in the [stoploss documentation](stoploss.md#trailing-stop-loss). [Strategy Override](#parameters-in-the-strategy). <br> **Datatype:** Boolean
@ -230,6 +230,7 @@ Mandatory parameters are marked as **Required**, which means that they are requi
| `dataformat_trades` | Data format to use to store historical trades data. <br> *Defaults to `jsongz`*. <br> **Datatype:** String
| `position_adjustment_enable` | Enables the strategy to use position adjustments (additional buys or sells). [More information here](strategy-callbacks.md#adjust-trade-position). <br> [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `false`.*<br> **Datatype:** Boolean
| `max_entry_position_adjustment` | Maximum additional order(s) for each open trade on top of the first entry Order. Set it to `-1` for unlimited additional orders. [More information here](strategy-callbacks.md#adjust-trade-position). <br> [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `-1`.*<br> **Datatype:** Positive Integer or -1
| `futures_funding_rate` | User-specified funding rate to be used when historical funding rates are not available from the exchange. This does not overwrite real historical rates. It is recommended that this be set to 0 unless you are testing a specific coin and you understand how the funding rate will affect freqtrade's profit calculations. [More information here](leverage.md#unavailable-funding-rates) <br>*Defaults to None.*<br> **Datatype:** Float
### Parameters in the strategy
@ -583,7 +584,7 @@ Once you will be happy with your bot performance running in the Dry-run mode, yo
* Market orders fill based on orderbook volume the moment the order is placed.
* Limit orders fill once the price reaches the defined level - or time out based on `unfilledtimeout` settings.
* In combination with `stoploss_on_exchange`, the stop_loss price is assumed to be filled.
* Open orders (not trades, which are stored in the database) are reset on bot restart.
* Open orders (not trades, which are stored in the database) are kept open after bot restarts, with the assumption that they were not filled while being offline.
## Switch to production mode

26
docs/developer.md
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@ -314,6 +314,32 @@ The output will show the last entry from the Exchange as well as the current UTC
If the day shows the same day, then the last candle can be assumed as incomplete and should be dropped (leave the setting `"ohlcv_partial_candle"` from the exchange-class untouched / True). Otherwise, set `"ohlcv_partial_candle"` to `False` to not drop Candles (shown in the example above).
Another way is to run this command multiple times in a row and observe if the volume is changing (while the date remains the same).
### Update binance cached leverage tiers
Updating leveraged tiers should be done regularly - and requires an authenticated account with futures enabled.
``` python
import ccxt
import json
from pathlib import Path
exchange = ccxt.binance({
'apiKey': '<apikey>',
'secret': '<secret>'
'options': {'defaultType': 'future'}
})
_ = exchange.load_markets()
lev_tiers = exchange.fetch_leverage_tiers()
# Assumes this is running in the root of the repository.
file = Path('freqtrade/exchange/binance_leverage_tiers.json')
json.dump(dict(sorted(lev_tiers.items())), file.open('w'), indent=2)
```
This file should then be contributed upstream, so others can benefit from this, too.
## Updating example notebooks
To keep the jupyter notebooks aligned with the documentation, the following should be ran after updating a example notebook.

5
docs/exchanges.md
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@ -230,6 +230,11 @@ OKX requires a passphrase for each api key, you will therefore need to add this
!!! Warning
OKX only provides 100 candles per api call. Therefore, the strategy will only have a pretty low amount of data available in backtesting mode.
!!! Warning "Futures"
OKX Futures has the concept of "position mode" - which can be Net or long/short (hedge mode).
Freqtrade supports both modes - but changing the mode mid-trading is not supported and will lead to exceptions and failures to place trades.
OKX also only provides MARK candles for the past ~3 months. Backtesting futures prior to that date will therefore lead to slight deviations, as funding-fees cannot be calculated correctly without this data.
## Gate.io
!!! Tip "Stoploss on Exchange"

25
docs/hyperopt.md
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@ -272,6 +272,7 @@ The last one we call `trigger` and use it to decide which buy trigger we want to
!!! Note "Parameter space assignment"
Parameters must either be assigned to a variable named `buy_*` or `sell_*` - or contain `space='buy'` | `space='sell'` to be assigned to a space correctly.
If no parameter is available for a space, you'll receive the error that no space was found when running hyperopt.
Parameters with unclear space (e.g. `adx_period = IntParameter(4, 24, default=14)` - no explicit nor implicit space) will not be detected and will therefore be ignored.
So let's write the buy strategy using these values:
@ -334,6 +335,7 @@ There are four parameter types each suited for different purposes.
## Optimizing an indicator parameter
Assuming you have a simple strategy in mind - a EMA cross strategy (2 Moving averages crossing) - and you'd like to find the ideal parameters for this strategy.
By default, we assume a stoploss of 5% - and a take-profit (`minimal_roi`) of 10% - which means freqtrade will sell the trade once 10% profit has been reached.
``` python
from pandas import DataFrame
@ -348,6 +350,9 @@ import freqtrade.vendor.qtpylib.indicators as qtpylib
class MyAwesomeStrategy(IStrategy):
stoploss = -0.05
timeframe = '15m'
minimal_roi = {
"0": 0.10
},
# Define the parameter spaces
buy_ema_short = IntParameter(3, 50, default=5)
buy_ema_long = IntParameter(15, 200, default=50)
@ -403,7 +408,7 @@ Using `self.buy_ema_short.range` will return a range object containing all entri
In this case (`IntParameter(3, 50, default=5)`), the loop would run for all numbers between 3 and 50 (`[3, 4, 5, ... 49, 50]`).
By using this in a loop, hyperopt will generate 48 new columns (`['buy_ema_3', 'buy_ema_4', ... , 'buy_ema_50']`).
Hyperopt itself will then use the selected value to create the buy and sell signals
Hyperopt itself will then use the selected value to create the buy and sell signals.
While this strategy is most likely too simple to provide consistent profit, it should serve as an example how optimize indicator parameters.
@ -680,7 +685,7 @@ class MyAwesomeStrategy(IStrategy):
!!! Note
Values in the configuration file will overwrite Parameter-file level parameters - and both will overwrite parameters within the strategy.
The prevalence is therefore: config > parameter file > strategy
The prevalence is therefore: config > parameter file > strategy `*_params` > parameter default
### Understand Hyperopt ROI results
@ -867,6 +872,22 @@ To combat these, you have multiple options:
* reduce the number of parallel processes (`-j <n>`)
* Increase the memory of your machine
## The objective has been evaluated at this point before.
If you see `The objective has been evaluated at this point before.` - then this is a sign that your space has been exhausted, or is close to that.
Basically all points in your space have been hit (or a local minima has been hit) - and hyperopt does no longer find points in the multi-dimensional space it did not try yet.
Freqtrade tries to counter the "local minima" problem by using new, randomized points in this case.
Example:
``` python
buy_ema_short = IntParameter(5, 20, default=10, space="buy", optimize=True)
# This is the only parameter in the buy space
```
The `buy_ema_short` space has 15 possible values (`5, 6, ... 19, 20`). If you now run hyperopt for the buy space, hyperopt will only have 15 values to try before running out of options.
Your epochs should therefore be aligned to the possible values - or you should be ready to interrupt a run if you norice a lot of `The objective has been evaluated at this point before.` warnings.
## Show details of Hyperopt results
After you run Hyperopt for the desired amount of epochs, you can later list all results for analysis, select only best or profitable once, and show the details for any of the epochs previously evaluated. This can be done with the `hyperopt-list` and `hyperopt-show` sub-commands. The usage of these sub-commands is described in the [Utils](utils.md#list-hyperopt-results) chapter.

12
docs/includes/pairlists.md
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@ -44,7 +44,7 @@ It uses configuration from `exchange.pair_whitelist` and `exchange.pair_blacklis
```json
"pairlists": [
{"method": "StaticPairList"}
],
],
```
By default, only currently enabled pairs are allowed.
@ -160,17 +160,17 @@ This filter allows freqtrade to ignore pairs until they have been listed for at
Offsets an incoming pairlist by a given `offset` value.
As an example it can be used in conjunction with `VolumeFilter` to remove the top X volume pairs. Or to split
a larger pairlist on two bot instances.
As an example it can be used in conjunction with `VolumeFilter` to remove the top X volume pairs. Or to split a larger pairlist on two bot instances.
Example to remove the first 10 pairs from the pairlist:
Example to remove the first 10 pairs from the pairlist, and takes the next 20 (taking items 10-30 of the initial list):
```json
"pairlists": [
// ...
{
"method": "OffsetFilter",
"offset": 10
"offset": 10,
"number_assets": 20
}
],
```
@ -181,7 +181,7 @@ Example to remove the first 10 pairs from the pairlist:
`VolumeFilter`.
!!! Note
An offset larger then the total length of the incoming pairlist will result in an empty pairlist.
An offset larger than the total length of the incoming pairlist will result in an empty pairlist.
#### PerformanceFilter

5
docs/includes/protections.md
View File

@ -96,6 +96,8 @@ def protections(self):
`LowProfitPairs` uses all trades for a pair within `lookback_period` in minutes (or in candles when using `lookback_period_candles`) to determine the overall profit ratio.
If that ratio is below `required_profit`, that pair will be locked for `stop_duration` in minutes (or in candles when using `stop_duration_candles`).
For futures bots, setting `only_per_side` will make the bot only consider one side, and will then only lock this one side, allowing for example shorts to continue after a series of long losses.
The below example will stop trading a pair for 60 minutes if the pair does not have a required profit of 2% (and a minimum of 2 trades) within the last 6 candles.
``` python
@ -107,7 +109,8 @@ def protections(self):
"lookback_period_candles": 6,
"trade_limit": 2,
"stop_duration": 60,
"required_profit": 0.02
"required_profit": 0.02,
"only_per_pair": False,
}
]
```

6
docs/index.md
View File

@ -22,10 +22,6 @@ Freqtrade is a free and open source crypto trading bot written in Python. It is
![freqtrade screenshot](assets/freqtrade-screenshot.png)
## Sponsored promotion
[![tokenbot-promo](assets/TokenBot-Freqtrade-banner.png)](https://tokenbot.com/?utm_source=github&utm_medium=freqtrade&utm_campaign=algodevs)
## Features
- Develop your Strategy: Write your strategy in python, using [pandas](https://pandas.pydata.org/). Example strategies to inspire you are available in the [strategy repository](https://github.com/freqtrade/freqtrade-strategies).
@ -51,7 +47,7 @@ Please read the [exchange specific notes](exchanges.md) to learn about eventual,
- [X] [OKX](https://okx.com/) (Former OKEX)
- [ ] [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)_
### Experimentally, freqtrade also supports futures on the following exchanges:
### Supported Futures Exchanges (experimental)
- [X] [Binance](https://www.binance.com/)
- [X] [Gate.io](https://www.gate.io/ref/6266643)

22
docs/leverage.md
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@ -64,7 +64,10 @@ You will also have to pick a "margin mode" (explanation below) - with freqtrade
### Margin mode
The possible values are: `isolated`, or `cross`(*currently unavailable*)
On top of `trading_mode` - you will also have to configure your `margin_mode`.
While freqtrade currently only supports one margin mode, this will change, and by configuring it now you're all set for future updates.
The possible values are: `isolated`, or `cross`(*currently unavailable*).
#### Isolated margin mode
@ -82,6 +85,16 @@ One account is used to share collateral between markets (trading pairs). Margin
"margin_mode": "cross"
```
## Set leverage to use
Different strategies and risk profiles will require different levels of leverage.
While you could configure one static leverage value - freqtrade offers you the flexibility to adjust this via [strategy leverage callback](strategy-callbacks.md#leverage-callback) - which allows you to use different leverages by pair, or based on some other factor benefitting your strategy result.
If not implemented, leverage defaults to 1x (no leverage).
!!! Warning
Higher leverage also equals higher risk - be sure you fully understand the implications of using leverage!
## Understand `liquidation_buffer`
*Defaults to `0.05`*
@ -101,6 +114,13 @@ Possible values are any floats between 0.0 and 0.99
!!! Danger "A `liquidation_buffer` of 0.0, or a low `liquidation_buffer` is likely to result in liquidations, and liquidation fees"
Currently Freqtrade is able to calculate liquidation prices, but does not calculate liquidation fees. Setting your `liquidation_buffer` to 0.0, or using a low `liquidation_buffer` could result in your positions being liquidated. Freqtrade does not track liquidation fees, so liquidations will result in inaccurate profit/loss results for your bot. If you use a low `liquidation_buffer`, it is recommended to use `stoploss_on_exchange` if your exchange supports this.
## Unavailable funding rates
For futures data, exchanges commonly provide the futures candles, the marks, and the funding rates. However, it is common that whilst candles and marks might be available, the funding rates are not. This can affect backtesting timeranges, i.e. you may only be able to test recent timeranges and not earlier, experiencing the `No data found. Terminating.` error. To get around this, add the `futures_funding_rate` config option as listed in [configuration.md](configuration.md), and it is recommended that you set this to `0`, unless you know a given specific funding rate for your pair, exchange and timerange. Setting this to anything other than `0` can have drastic effects on your profit calculations within strategy, e.g. within the `custom_exit`, `custom_stoploss`, etc functions.
!!! Warning "This will mean your backtests are inaccurate."
This will not overwrite funding rates that are available from the exchange, but bear in mind that setting a false funding rate will mean backtesting results will be inaccurate for historical timeranges where funding rates are not available.
### Developer
#### Margin mode

5
docs/requirements-docs.txt
View File

@ -1,5 +1,6 @@
markdown==3.3.7
mkdocs==1.3.0
mkdocs-material==8.2.12
mkdocs-material==8.3.9
mdx_truly_sane_lists==1.2
pymdown-extensions==9.4
pymdown-extensions==9.5
jinja2==3.1.2

20
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@ -89,11 +89,12 @@ WHERE id=31;
If you'd still like to remove a trade from the database directly, you can use the below query.
```sql
DELETE FROM trades WHERE id = <tradeid>;
```
!!! Danger
Some systems (Ubuntu) disable foreign keys in their sqlite3 packaging. When using sqlite - please ensure that foreign keys are on by running `PRAGMA foreign_keys = ON` before the above query.
```sql
DELETE FROM trades WHERE id = <tradeid>;
DELETE FROM trades WHERE id = 31;
```
@ -102,13 +103,20 @@ DELETE FROM trades WHERE id = 31;
## Use a different database system
Freqtrade is using SQLAlchemy, which supports multiple different database systems. As such, a multitude of database systems should be supported.
Freqtrade does not depend or install any additional database driver. Please refer to the [SQLAlchemy docs](https://docs.sqlalchemy.org/en/14/core/engines.html#database-urls) on installation instructions for the respective database systems.
The following systems have been tested and are known to work with freqtrade:
* sqlite (default)
* PostgreSQL)
* MariaDB
!!! Warning
By using one of the below database systems, you acknowledge that you know how to manage such a system. Freqtrade will not provide any support with setup or maintenance (or backups) of the below database systems.
By using one of the below database systems, you acknowledge that you know how to manage such a system. The freqtrade team will not provide any support with setup or maintenance (or backups) of the below database systems.
### PostgreSQL
Freqtrade supports PostgreSQL by using SQLAlchemy, which supports multiple different database systems.
Installation:
`pip install psycopg2-binary`

40
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@ -130,7 +130,7 @@ In summary: The stoploss will be adjusted to be always be -10% of the highest ob
### Trailing stop loss, custom positive loss
It is also possible to have a default stop loss, when you are in the red with your buy (buy - fee), but once you hit positive result the system will utilize a new stop loss, which can have a different value.
You could also have a default stop loss when you are in the red with your buy (buy - fee), but once you hit a positive result (or an offset you define) the system will utilize a new stop loss, which can have a different value.
For example, your default stop loss is -10%, but once you have more than 0% profit (example 0.1%) a different trailing stoploss will be used.
!!! Note
@ -142,6 +142,8 @@ Both values require `trailing_stop` to be set to true and `trailing_stop_positiv
stoploss = -0.10
trailing_stop = True
trailing_stop_positive = 0.02
trailing_stop_positive_offset = 0.0
trailing_only_offset_is_reached = False # Default - not necessary for this example
```
For example, simplified math:
@ -156,11 +158,31 @@ For example, simplified math:
The 0.02 would translate to a -2% stop loss.
Before this, `stoploss` is used for the trailing stoploss.
!!! Tip "Use an offset to change your stoploss"
Use `trailing_stop_positive_offset` to ensure that your new trailing stoploss will be in profit by setting `trailing_stop_positive_offset` higher than `trailing_stop_positive`. Your first new stoploss value will then already have locked in profits.
Example with simplified math:
``` python
stoploss = -0.10
trailing_stop = True
trailing_stop_positive = 0.02
trailing_stop_positive_offset = 0.03
```
* the bot buys an asset at a price of 100$
* the stop loss is defined at -10%, so the stop loss would get triggered once the asset drops below 90$
* assuming the asset now increases to 102$
* the stoploss will now be at 91.8$ - 10% below the highest observed rate
* assuming the asset now increases to 103.5$ (above the offset configured)
* the stop loss will now be -2% of 103$ = 101.42$
* now the asset drops in value to 102\$, the stop loss will still be 101.42$ and would trigger once price breaks below 101.42$
### Trailing stop loss only once the trade has reached a certain offset
It is also possible to use a static stoploss until the offset is reached, and then trail the trade to take profits once the market turns.
You can also keep a static stoploss until the offset is reached, and then trail the trade to take profits once the market turns.
If `"trailing_only_offset_is_reached": true` then the trailing stoploss is only activated once the offset is reached. Until then, the stoploss remains at the configured `stoploss`.
If `trailing_only_offset_is_reached = True` then the trailing stoploss is only activated once the offset is reached. Until then, the stoploss remains at the configured `stoploss`.
This option can be used with or without `trailing_stop_positive`, but uses `trailing_stop_positive_offset` as offset.
``` python
@ -191,6 +213,18 @@ For example, simplified math:
!!! Tip
Make sure to have this value (`trailing_stop_positive_offset`) lower than minimal ROI, otherwise minimal ROI will apply first and sell the trade.
## Stoploss and Leverage
Stoploss should be thought of as "risk on this trade" - so a stoploss of 10% on a 100$ trade means you are willing to lose 10$ (10%) on this trade - which would trigger if the price moves 10% to the downside.
When using leverage, the same principle is applied - with stoploss defining the risk on the trade (the amount you are willing to lose).
Therefore, a stoploss of 10% on a 10x trade would trigger on a 1% price move.
If your stake amount (own capital) was 100$ - this trade would be 1000$ at 10x (after leverage).
If price moves 1% - you've lost 10$ of your own capital - therfore stoploss will trigger in this case.
Make sure to be aware of this, and avoid using too tight stoploss (at 10x leverage, 10% risk may be too little to allow the trade to "breath" a little).
## Changing stoploss on open trades
A stoploss on an open trade can be changed by changing the value in the configuration or strategy and use the `/reload_config` command (alternatively, completely stopping and restarting the bot also works).

2
docs/strategy-advanced.md
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@ -224,3 +224,5 @@ for val in self.buy_ema_short.range:
# Append columns to existing dataframe
merged_frame = pd.concat(frames, axis=1)
```
Freqtrade does however also counter this by running `dataframe.copy()` on the dataframe right after the `populate_indicators()` method - so performance implications of this should be low to non-existant.

116
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@ -17,6 +17,7 @@ Currently available callbacks:
* [`confirm_trade_entry()`](#trade-entry-buy-order-confirmation)
* [`confirm_trade_exit()`](#trade-exit-sell-order-confirmation)
* [`adjust_trade_position()`](#adjust-trade-position)
* [`adjust_entry_price()`](#adjust-entry-price)
* [`leverage()`](#leverage-callback)
!!! Tip "Callback calling sequence"
@ -45,6 +46,9 @@ class AwesomeStrategy(IStrategy):
self.cust_remote_data = requests.get('https://some_remote_source.example.com')
```
During hyperopt, this runs only once at startup.
## 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).
@ -78,8 +82,9 @@ Called before entering a trade, makes it possible to manage your position size w
```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,
entry_tag: Optional[str], side: str, **kwargs) -> float:
proposed_stake: float, min_stake: Optional[float], max_stake: float,
leverage: float, entry_tag: Optional[str], side: str,
**kwargs) -> float:
dataframe, _ = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe)
current_candle = dataframe.iloc[-1].squeeze()
@ -545,10 +550,12 @@ class AwesomeStrategy(IStrategy):
:param pair: Pair that's about to be bought/shorted.
: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 amount: Amount in target (base) currency that's going to be traded.
:param rate: Rate that's going to be used when using limit orders
or current rate for market orders.
:param time_in_force: Time in force. Defaults to GTC (Good-til-cancelled).
:param current_time: datetime object, containing the current datetime
:param entry_tag: Optional entry_tag (buy_tag) if provided with the buy signal.
:param side: 'long' or 'short' - indicating the direction of the proposed trade
: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.
@ -562,6 +569,14 @@ class AwesomeStrategy(IStrategy):
`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).
`confirm_trade_exit()` may be called multiple times within one iteration for the same trade if different exit-reasons apply.
The exit-reasons (if applicable) will be in the following sequence:
* `exit_signal` / `custom_exit`
* `stop_loss`
* `roi`
* `trailing_stop_loss`
``` python
from freqtrade.persistence import Trade
@ -574,7 +589,7 @@ class AwesomeStrategy(IStrategy):
rate: float, time_in_force: str, exit_reason: str,
current_time: datetime, **kwargs) -> bool:
"""
Called right before placing a regular sell order.
Called right before placing a regular exit order.
Timing for this function is critical, so avoid doing heavy computations or
network requests in this method.
@ -582,17 +597,19 @@ class AwesomeStrategy(IStrategy):
When not implemented by a strategy, returns True (always confirming).
:param pair: Pair that's about to be sold.
:param pair: Pair for trade that's about to be exited.
:param trade: trade object.
:param order_type: Order type (as configured in order_types). usually limit or market.
:param amount: Amount in quote currency.
:param amount: Amount in base currency.
:param rate: Rate that's going to be used when using limit orders
or current rate for market orders.
:param time_in_force: Time in force. Defaults to GTC (Good-til-cancelled).
:param exit_reason: Exit reason.
Can be any of ['roi', 'stop_loss', 'stoploss_on_exchange', 'trailing_stop_loss',
'exit_signal', 'force_exit', 'emergency_exit']
: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 exit-order is placed on the exchange.
:return bool: When True, then the exit-order is placed on the exchange.
False aborts the process
"""
if exit_reason == 'force_exit' and trade.calc_profit_ratio(rate) < 0:
@ -604,6 +621,9 @@ class AwesomeStrategy(IStrategy):
```
!!! Warning
`confirm_trade_exit()` can prevent stoploss exits, causing significant losses as this would ignore stoploss exits.
## Adjust trade position
The `position_adjustment_enable` strategy property enables the usage of `adjust_trade_position()` callback in the strategy.
@ -654,16 +674,17 @@ class DigDeeperStrategy(IStrategy):
max_dca_multiplier = 5.5
# This is called when placing the initial order (opening trade)
def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float,
proposed_stake: float, min_stake: float, max_stake: float,
entry_tag: Optional[str], side: str, **kwargs) -> float:
def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float,
proposed_stake: float, min_stake: Optional[float], max_stake: float,
leverage: float, entry_tag: Optional[str], side: str,
**kwargs) -> float:
# We need to leave most of the funds for possible further DCA orders
# This also applies to fixed stakes
return proposed_stake / self.max_dca_multiplier
def adjust_trade_position(self, trade: Trade, current_time: datetime,
current_rate: float, current_profit: float, min_stake: float,
current_rate: float, current_profit: float, min_stake: Optional[float],
max_stake: float, **kwargs):
"""
Custom trade adjustment logic, returning the stake amount that a trade should be increased.
@ -713,6 +734,69 @@ class DigDeeperStrategy(IStrategy):
```
## Adjust Entry Price
The `adjust_entry_price()` callback may be used by strategy developer to refresh/replace limit orders upon arrival of new candles.
Be aware that `custom_entry_price()` is still the one dictating initial entry limit order price target at the time of entry trigger.
Orders can be cancelled out of this callback by returning `None`.
Returning `current_order_rate` will keep the order on the exchange "as is".
Returning any other price will cancel the existing order, and replace it with a new order.
The trade open-date (`trade.open_date_utc`) will remain at the time of the very first order placed.
Please make sure to be aware of this - and eventually adjust your logic in other callbacks to account for this, and use the date of the first filled order instead.
!!! Warning "Regular timeout"
Entry `unfilledtimeout` mechanism (as well as `check_entry_timeout()`) takes precedence over this.
Entry Orders that are cancelled via the above methods will not have this callback called. Be sure to update timeout values to match your expectations.
```python
from freqtrade.persistence import Trade
from datetime import timedelta
class AwesomeStrategy(IStrategy):
# ... populate_* methods
def adjust_entry_price(self, trade: Trade, order: Optional[Order], pair: str,
current_time: datetime, proposed_rate: float, current_order_rate: float,
entry_tag: Optional[str], side: str, **kwargs) -> float:
"""
Entry price re-adjustment logic, returning the user desired limit price.
This only executes when a order was already placed, still open (unfilled fully or partially)
and not timed out on subsequent candles after entry trigger.
When not implemented by a strategy, returns current_order_rate as default.
If current_order_rate is returned then the existing order is maintained.
If None is returned then order gets canceled but not replaced by a new one.
:param pair: Pair that's currently analyzed
:param trade: Trade object.
:param order: Order object
:param current_time: datetime object, containing the current datetime
:param proposed_rate: Rate, calculated based on pricing settings in entry_pricing.
:param current_order_rate: Rate of the existing order in place.
:param entry_tag: Optional entry_tag (buy_tag) if provided with the buy signal.
:param side: 'long' or 'short' - indicating the direction of the proposed trade
:param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
:return float: New entry price value if provided
"""
# Limit orders to use and follow SMA200 as price target for the first 10 minutes since entry trigger for BTC/USDT pair.
if pair == 'BTC/USDT' and entry_tag == 'long_sma200' and side == 'long' and (current_time - timedelta(minutes=10) > trade.open_date_utc:
# just cancel the order if it has been filled more than half of the amount
if order.filled > order.remaining:
return None
else:
dataframe, _ = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe)
current_candle = dataframe.iloc[-1].squeeze()
# desired price
return current_candle['sma_200']
# default: maintain existing order
return current_order_rate
```
## Leverage Callback
When trading in markets that allow leverage, this method must return the desired Leverage (Defaults to 1 -> No leverage).
@ -724,19 +808,23 @@ For markets / exchanges that don't support leverage, this method is ignored.
``` python
class AwesomeStrategy(IStrategy):
def leverage(self, pair: str, current_time: 'datetime', current_rate: float,
proposed_leverage: float, max_leverage: float, side: str,
def leverage(self, pair: str, current_time: datetime, current_rate: float,
proposed_leverage: float, max_leverage: float, entry_tag: Optional[str], side: str,
**kwargs) -> float:
"""
Customize leverage for each new trade.
Customize leverage for each new trade. This method is only called in futures mode.
:param pair: Pair that's currently analyzed
:param current_time: datetime object, containing the current datetime
:param current_rate: Rate, calculated based on pricing settings in exit_pricing.
:param proposed_leverage: A leverage proposed by the bot.
:param max_leverage: Max leverage allowed on this pair
:param entry_tag: Optional entry_tag (buy_tag) if provided with the buy signal.
:param side: 'long' or 'short' - indicating the direction of the proposed trade
:return: A leverage amount, which is between 1.0 and max_leverage.
"""
return 1.0
```
All profit calculations include leverage. Stoploss / ROI also include leverage in their calculation.
Defining a stoploss of 10% at 10x leverage would trigger the stoploss with a 1% move to the downside.

2
docs/strategy_analysis_example.md
View File

@ -31,11 +31,13 @@ pair = "BTC/USDT"
```python
# Load data using values set above
from freqtrade.data.history import load_pair_history
from freqtrade.enums import CandleType
candles = load_pair_history(datadir=data_location,
timeframe=config["timeframe"],
pair=pair,
data_format = "hdf5",
candle_type=CandleType.SPOT,
)
# Confirm success

4
docs/strategy_migration.md
View File

@ -199,7 +199,7 @@ New string argument `side` - which can be either `"long"` or `"short"`.
``` python hl_lines="4"
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,
proposed_stake: float, min_stake: Optional[float], max_stake: float,
entry_tag: Optional[str], **kwargs) -> float:
# ...
return proposed_stake
@ -208,7 +208,7 @@ class AwesomeStrategy(IStrategy):
``` python hl_lines="4"
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,
proposed_stake: float, min_stake: Optional[float], max_stake: float,
entry_tag: Optional[str], side: str, **kwargs) -> float:
# ...
return proposed_stake

45
docs/telegram-usage.md
View File

@ -97,7 +97,8 @@ Example configuration showing the different settings:
"entry_fill": "off",
"exit_fill": "off",
"protection_trigger": "off",
"protection_trigger_global": "on"
"protection_trigger_global": "on",
"show_candle": "off"
},
"reload": true,
"balance_dust_level": 0.01
@ -108,7 +109,7 @@ Example configuration showing the different settings:
`exit` notifications are sent when the order is placed, while `exit_fill` notifications are sent when the order is filled on the exchange.
`*_fill` notifications are off by default and must be explicitly enabled.
`protection_trigger` notifications are sent when a protection triggers and `protection_trigger_global` notifications trigger when global protections are triggered.
`show_candle` - show candle values as part of entry/exit messages. Only possible value is "ohlc".
`balance_dust_level` will define what the `/balance` command takes as "dust" - Currencies with a balance below this will be shown.
`reload` allows you to disable reload-buttons on selected messages.
@ -171,8 +172,8 @@ official commands. You can ask at any moment for help with `/help`.
| `/locks` | Show currently locked pairs.
| `/unlock <pair or lock_id>` | Remove the lock for this pair (or for this lock id).
| `/profit [<n>]` | Display a summary of your profit/loss from close trades and some stats about your performance, over the last n days (all trades by default)
| `/forceexit <trade_id>` | Instantly exits the given trade (Ignoring `minimum_roi`).
| `/forceexit all` | Instantly exits all open trades (Ignoring `minimum_roi`).
| `/forceexit <trade_id> | /fx <tradeid>` | Instantly exits the given trade (Ignoring `minimum_roi`).
| `/forceexit all | /fx all` | Instantly exits all open trades (Ignoring `minimum_roi`).
| `/fx` | alias for `/forceexit`
| `/forcelong <pair> [rate]` | Instantly buys the given pair. Rate is optional and only applies to limit orders. (`force_entry_enable` must be set to True)
| `/forceshort <pair> [rate]` | Instantly shorts the given pair. Rate is optional and only applies to limit orders. This will only work on non-spot markets. (`force_entry_enable` must be set to True)
@ -270,10 +271,15 @@ Return a summary of your profit/loss and performance.
> **Latest Trade opened:** `2 minutes ago`
> **Avg. Duration:** `2:33:45`
> **Best Performing:** `PAY/BTC: 50.23%`
> **Trading volume:** `0.5 BTC`
> **Profit factor:** `1.04`
> **Max Drawdown:** `9.23% (0.01255 BTC)`
The relative profit of `1.2%` is the average profit per trade.
The relative profit of `15.2 Σ%` is be based on the starting capital - so in this case, the starting capital was `0.00485701 * 1.152 = 0.00738 BTC`.
Starting capital is either taken from the `available_capital` setting, or calculated by using current wallet size - profits.
Profit Factor is calculated as gross profits / gross losses - and should serve as an overall metric for the strategy.
Max drawdown corresponds to the backtesting metric `Absolute Drawdown (Account)` - calculated as `(Absolute Drawdown) / (DrawdownHigh + startingBalance)`.
### /forceexit <trade_id>
@ -281,6 +287,7 @@ Starting capital is either taken from the `available_capital` setting, or calcul
!!! Tip
You can get a list of all open trades by calling `/forceexit` without parameter, which will show a list of buttons to simply exit a trade.
This command has an alias in `/fx` - which has the same capabilities, but is faster to type in "emergency" situations.
### /forcelong <pair> [rate] | /forceshort <pair> [rate]
@ -328,11 +335,11 @@ Per default `/daily` will return the 7 last days. The example below if for `/dai
> **Daily Profit over the last 3 days:**
```
Day Profit BTC Profit USD
---------- -------------- ------------
2018-01-03 0.00224175 BTC 29,142 USD
2018-01-02 0.00033131 BTC 4,307 USD
2018-01-01 0.00269130 BTC 34.986 USD
Day (count) USDT USD Profit %
-------------- ------------ ---------- ----------
2022-06-11 (1) -0.746 USDT -0.75 USD -0.08%
2022-06-10 (0) 0 USDT 0.00 USD 0.00%
2022-06-09 (5) 20 USDT 20.10 USD 5.00%
```
### /weekly <n>
@ -342,11 +349,11 @@ 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
Monday (count) Profit BTC Profit USD Profit %
------------- -------------- ------------ ----------
2018-01-03 (5) 0.00224175 BTC 29,142 USD 4.98%
2017-12-27 (1) 0.00033131 BTC 4,307 USD 0.00%
2017-12-20 (4) 0.00269130 BTC 34.986 USD 5.12%
```
### /monthly <n>
@ -356,11 +363,11 @@ 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
Month (count) Profit BTC Profit USD Profit %
------------- -------------- ------------ ----------
2018-01 (20) 0.00224175 BTC 29,142 USD 4.98%
2017-12 (5) 0.00033131 BTC 4,307 USD 0.00%
2017-11 (10) 0.00269130 BTC 34.986 USD 5.10%
```
### /whitelist

4
docs/updating.md
View File

@ -32,4 +32,8 @@ Please ensure that you're also updating dependencies - otherwise things might br
``` bash
git pull
pip install -U -r requirements.txt
pip install -e .
# Ensure freqUI is at the latest version
freqtrade install-ui
```

87
docs/utils.md
View File

@ -119,6 +119,7 @@ This subcommand is useful for finding problems in your environment with loading
usage: freqtrade list-strategies [-h] [-v] [--logfile FILE] [-V] [-c PATH]
[-d PATH] [--userdir PATH]
[--strategy-path PATH] [-1] [--no-color]
[--recursive-strategy-search]
optional arguments:
-h, --help show this help message and exit
@ -126,6 +127,9 @@ optional arguments:
-1, --one-column Print output in one column.
--no-color Disable colorization of hyperopt results. May be
useful if you are redirecting output to a file.
--recursive-strategy-search
Recursively search for a strategy in the strategies
folder.
Common arguments:
-v, --verbose Verbose mode (-vv for more, -vvv to get all messages).
@ -134,9 +138,10 @@ Common arguments:
details.
-V, --version show program's version number and exit
-c PATH, --config PATH
Specify configuration file (default: `config.json`).
Multiple --config options may be used. Can be set to
`-` to read config from stdin.
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
@ -549,6 +554,27 @@ Show whitelist when using a [dynamic pairlist](plugins.md#pairlists).
freqtrade test-pairlist --config config.json --quote USDT BTC
```
## Convert database
`freqtrade convert-db` can be used to convert your database from one system to another (sqlite -> postgres, postgres -> other postgres), migrating all trades, orders and Pairlocks.
Please refer to the [SQL cheatsheet](sql_cheatsheet.md#use-a-different-database-system) to learn about requirements for different database systems.
```
usage: freqtrade convert-db [-h] [--db-url PATH] [--db-url-from PATH]
optional arguments:
-h, --help show this help message and exit
--db-url PATH Override trades database URL, this is useful in custom
deployments (default: `sqlite:///tradesv3.sqlite` for
Live Run mode, `sqlite:///tradesv3.dryrun.sqlite` for
Dry Run).
--db-url-from PATH Source db url to use when migrating a database.
```
!!! Warning
Please ensure to only use this on an empty target database. Freqtrade will perform a regular migration, but may fail if entries already existed.
## Webserver mode
!!! Warning "Experimental"
@ -625,6 +651,61 @@ Common arguments:
```
## Detailed backtest analysis
Advanced backtest result analysis.
More details in the [Backtesting analysis](advanced-backtesting.md#analyze-the-buyentry-and-sellexit-tags) Section.
```
usage: freqtrade backtesting-analysis [-h] [-v] [--logfile FILE] [-V]
[-c PATH] [-d PATH] [--userdir PATH]
[--export-filename PATH]
[--analysis-groups {0,1,2,3,4} [{0,1,2,3,4} ...]]
[--enter-reason-list ENTER_REASON_LIST [ENTER_REASON_LIST ...]]
[--exit-reason-list EXIT_REASON_LIST [EXIT_REASON_LIST ...]]
[--indicator-list INDICATOR_LIST [INDICATOR_LIST ...]]
optional arguments:
-h, --help show this help message and exit
--export-filename PATH, --backtest-filename PATH
Use this filename for backtest results.Requires
`--export` to be set as well. Example: `--export-filen
ame=user_data/backtest_results/backtest_today.json`
--analysis-groups {0,1,2,3,4} [{0,1,2,3,4} ...]
grouping output - 0: simple wins/losses by enter tag,
1: by enter_tag, 2: by enter_tag and exit_tag, 3: by
pair and enter_tag, 4: by pair, enter_ and exit_tag
(this can get quite large)
--enter-reason-list ENTER_REASON_LIST [ENTER_REASON_LIST ...]
Comma separated list of entry signals to analyse.
Default: all. e.g. 'entry_tag_a,entry_tag_b'
--exit-reason-list EXIT_REASON_LIST [EXIT_REASON_LIST ...]
Comma separated list of exit signals to analyse.
Default: all. e.g.
'exit_tag_a,roi,stop_loss,trailing_stop_loss'
--indicator-list INDICATOR_LIST [INDICATOR_LIST ...]
Comma separated list of indicators to analyse. e.g.
'close,rsi,bb_lowerband,profit_abs'
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.

49
docs/webhook-config.md
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@ -239,3 +239,52 @@ Possible parameters are:
The fields in `webhook.webhookstatus` are used for regular status messages (Started / Stopped / ...). Parameters are filled using string.format.
The only possible value here is `{status}`.
## Discord
A special form of webhooks is available for discord.
You can configure this as follows:
```json
"discord": {
"enabled": true,
"webhook_url": "https://discord.com/api/webhooks/<Your webhook URL ...>",
"exit_fill": [
{"Trade ID": "{trade_id}"},
{"Exchange": "{exchange}"},
{"Pair": "{pair}"},
{"Direction": "{direction}"},
{"Open rate": "{open_rate}"},
{"Close rate": "{close_rate}"},
{"Amount": "{amount}"},
{"Open date": "{open_date:%Y-%m-%d %H:%M:%S}"},
{"Close date": "{close_date:%Y-%m-%d %H:%M:%S}"},
{"Profit": "{profit_amount} {stake_currency}"},
{"Profitability": "{profit_ratio:.2%}"},
{"Enter tag": "{enter_tag}"},
{"Exit Reason": "{exit_reason}"},
{"Strategy": "{strategy}"},
{"Timeframe": "{timeframe}"},
],
"entry_fill": [
{"Trade ID": "{trade_id}"},
{"Exchange": "{exchange}"},
{"Pair": "{pair}"},
{"Direction": "{direction}"},
{"Open rate": "{open_rate}"},
{"Amount": "{amount}"},
{"Open date": "{open_date:%Y-%m-%d %H:%M:%S}"},
{"Enter tag": "{enter_tag}"},
{"Strategy": "{strategy} {timeframe}"},
]
}
```
The above represents the default (`exit_fill` and `entry_fill` are optional and will default to the above configuration) - modifications are obviously possible.
Available fields correspond to the fields for webhooks and are documented in the corresponding webhook sections.
The notifications will look as follows by default.
![discord-notification](assets/discord_notification.png)

2
freqtrade/commands/__init__.py
View File

@ -6,10 +6,12 @@ Contains all start-commands, subcommands and CLI Interface creation.
Note: Be careful with file-scoped imports in these subfiles.
as they are parsed on startup, nothing containing optional modules should be loaded.
"""
from freqtrade.commands.analyze_commands import start_analysis_entries_exits
from freqtrade.commands.arguments import Arguments
from freqtrade.commands.build_config_commands import start_new_config
from freqtrade.commands.data_commands import (start_convert_data, start_convert_trades,
start_download_data, start_list_data)
from freqtrade.commands.db_commands import start_convert_db
from freqtrade.commands.deploy_commands import (start_create_userdir, start_install_ui,
start_new_strategy)
from freqtrade.commands.hyperopt_commands import start_hyperopt_list, start_hyperopt_show

69
freqtrade/commands/analyze_commands.py Executable file
View File

@ -0,0 +1,69 @@
import logging
from pathlib import Path
from typing import Any, Dict
from freqtrade.configuration import setup_utils_configuration
from freqtrade.enums import RunMode
from freqtrade.exceptions import OperationalException
logger = logging.getLogger(__name__)
def setup_analyze_configuration(args: Dict[str, Any], method: RunMode) -> Dict[str, Any]:
"""
Prepare the configuration for the entry/exit reason analysis module
:param args: Cli args from Arguments()
:param method: Bot running mode
:return: Configuration
"""
config = setup_utils_configuration(args, method)
no_unlimited_runmodes = {
RunMode.BACKTEST: 'backtesting',
}
if method in no_unlimited_runmodes.keys():
from freqtrade.data.btanalysis import get_latest_backtest_filename
if 'exportfilename' in config:
if config['exportfilename'].is_dir():
btfile = Path(get_latest_backtest_filename(config['exportfilename']))
signals_file = f"{config['exportfilename']}/{btfile.stem}_signals.pkl"
else:
if config['exportfilename'].exists():
btfile = Path(config['exportfilename'])
signals_file = f"{btfile.parent}/{btfile.stem}_signals.pkl"
else:
raise OperationalException(f"{config['exportfilename']} does not exist.")
else:
raise OperationalException('exportfilename not in config.')
if (not Path(signals_file).exists()):
raise OperationalException(
(f"Cannot find latest backtest signals file: {signals_file}."
"Run backtesting with `--export signals`.")
)
return config
def start_analysis_entries_exits(args: Dict[str, Any]) -> None:
"""
Start analysis script
:param args: Cli args from Arguments()
:return: None
"""
from freqtrade.data.entryexitanalysis import process_entry_exit_reasons
# Initialize configuration
config = setup_analyze_configuration(args, RunMode.BACKTEST)
logger.info('Starting freqtrade in analysis mode')
process_entry_exit_reasons(config['exportfilename'],
config['exchange']['pair_whitelist'],
config['analysis_groups'],
config['enter_reason_list'],
config['exit_reason_list'],
config['indicator_list']
)

27
freqtrade/commands/arguments.py
View File

@ -82,7 +82,9 @@ ARGS_PLOT_DATAFRAME = ["pairs", "indicators1", "indicators2", "plot_limit",
ARGS_PLOT_PROFIT = ["pairs", "timerange", "export", "exportfilename", "db_url",
"trade_source", "timeframe", "plot_auto_open", ]
ARGS_INSTALL_UI = ["erase_ui_only", 'ui_version']
ARGS_CONVERT_DB = ["db_url", "db_url_from"]
ARGS_INSTALL_UI = ["erase_ui_only", "ui_version"]
ARGS_SHOW_TRADES = ["db_url", "trade_ids", "print_json"]
@ -99,6 +101,9 @@ ARGS_HYPEROPT_SHOW = ["hyperopt_list_best", "hyperopt_list_profitable", "hyperop
"print_json", "hyperoptexportfilename", "hyperopt_show_no_header",
"disableparamexport", "backtest_breakdown"]
ARGS_ANALYZE_ENTRIES_EXITS = ["exportfilename", "analysis_groups", "enter_reason_list",
"exit_reason_list", "indicator_list"]
NO_CONF_REQURIED = ["convert-data", "convert-trade-data", "download-data", "list-timeframes",
"list-markets", "list-pairs", "list-strategies", "list-data",
"hyperopt-list", "hyperopt-show", "backtest-filter",
@ -180,8 +185,9 @@ 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_backtesting_show,
start_convert_data, start_convert_trades,
from freqtrade.commands import (start_analysis_entries_exits, start_backtesting,
start_backtesting_show, start_convert_data,
start_convert_db, 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,
@ -281,6 +287,13 @@ class Arguments:
backtesting_show_cmd.set_defaults(func=start_backtesting_show)
self._build_args(optionlist=ARGS_BACKTEST_SHOW, parser=backtesting_show_cmd)
# Add backtesting analysis subcommand
analysis_cmd = subparsers.add_parser('backtesting-analysis',
help='Backtest Analysis module.',
parents=[_common_parser])
analysis_cmd.set_defaults(func=start_analysis_entries_exits)
self._build_args(optionlist=ARGS_ANALYZE_ENTRIES_EXITS, parser=analysis_cmd)
# Add edge subcommand
edge_cmd = subparsers.add_parser('edge', help='Edge module.',
parents=[_common_parser, _strategy_parser])
@ -374,6 +387,14 @@ class Arguments:
test_pairlist_cmd.set_defaults(func=start_test_pairlist)
self._build_args(optionlist=ARGS_TEST_PAIRLIST, parser=test_pairlist_cmd)
# Add db-convert subcommand
convert_db = subparsers.add_parser(
"convert-db",
help="Migrate database to different system",
)
convert_db.set_defaults(func=start_convert_db)
self._build_args(optionlist=ARGS_CONVERT_DB, parser=convert_db)
# Add install-ui subcommand
install_ui_cmd = subparsers.add_parser(
'install-ui',

38
freqtrade/commands/cli_options.py
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@ -106,6 +106,11 @@ AVAILABLE_CLI_OPTIONS = {
f'`{constants.DEFAULT_DB_DRYRUN_URL}` for Dry Run).',
metavar='PATH',
),
"db_url_from": Arg(
'--db-url-from',
help='Source db url to use when migrating a database.',
metavar='PATH',
),
"sd_notify": Arg(
'--sd-notify',
help='Notify systemd service manager.',
@ -609,4 +614,37 @@ AVAILABLE_CLI_OPTIONS = {
"that do not contain any parameters."),
action="store_true",
),
"analysis_groups": Arg(
"--analysis-groups",
help=("grouping output - "
"0: simple wins/losses by enter tag, "
"1: by enter_tag, "
"2: by enter_tag and exit_tag, "
"3: by pair and enter_tag, "
"4: by pair, enter_ and exit_tag (this can get quite large)"),
nargs='+',
default=['0', '1', '2'],
choices=['0', '1', '2', '3', '4'],
),
"enter_reason_list": Arg(
"--enter-reason-list",
help=("Comma separated list of entry signals to analyse. Default: all. "
"e.g. 'entry_tag_a,entry_tag_b'"),
nargs='+',
default=['all'],
),
"exit_reason_list": Arg(
"--exit-reason-list",
help=("Comma separated list of exit signals to analyse. Default: all. "
"e.g. 'exit_tag_a,roi,stop_loss,trailing_stop_loss'"),
nargs='+',
default=['all'],
),
"indicator_list": Arg(
"--indicator-list",
help=("Comma separated list of indicators to analyse. "
"e.g. 'close,rsi,bb_lowerband,profit_abs'"),
nargs='+',
default=[],
),
}

6
freqtrade/commands/data_commands.py
View File

@ -79,6 +79,12 @@ def start_download_data(args: Dict[str, Any]) -> None:
data_format_trades=config['dataformat_trades'],
)
else:
if not exchange._ft_has.get('ohlcv_has_history', True):
raise OperationalException(
f"Historic klines not available for {exchange.name}. "
"Please use `--dl-trades` instead for this exchange "
"(will unfortunately take a long time)."
)
pairs_not_available = refresh_backtest_ohlcv_data(
exchange, pairs=expanded_pairs, timeframes=config['timeframes'],
datadir=config['datadir'], timerange=timerange,

55
freqtrade/commands/db_commands.py Normal file
View File

@ -0,0 +1,55 @@
import logging
from typing import Any, Dict
from sqlalchemy import func
from freqtrade.configuration.config_setup import setup_utils_configuration
from freqtrade.enums.runmode import RunMode
logger = logging.getLogger(__name__)
def start_convert_db(args: Dict[str, Any]) -> None:
from sqlalchemy.orm import make_transient
from freqtrade.persistence import Order, Trade, init_db
from freqtrade.persistence.migrations import set_sequence_ids
from freqtrade.persistence.pairlock import PairLock
config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
init_db(config['db_url'])
session_target = Trade._session
init_db(config['db_url_from'])
logger.info("Starting db migration.")
trade_count = 0
pairlock_count = 0
for trade in Trade.get_trades():
trade_count += 1
make_transient(trade)
for o in trade.orders:
make_transient(o)
session_target.add(trade)
session_target.commit()
for pairlock in PairLock.query:
pairlock_count += 1
make_transient(pairlock)
session_target.add(pairlock)
session_target.commit()
# Update sequences
max_trade_id = session_target.query(func.max(Trade.id)).scalar()
max_order_id = session_target.query(func.max(Order.id)).scalar()
max_pairlock_id = session_target.query(func.max(PairLock.id)).scalar()
set_sequence_ids(session_target.get_bind(),
trade_id=max_trade_id,
order_id=max_order_id,
pairlock_id=max_pairlock_id)
logger.info(f"Migrated {trade_count} Trades, and {pairlock_count} Pairlocks.")

2
freqtrade/commands/hyperopt_commands.py
View File

@ -24,7 +24,7 @@ def start_hyperopt_list(args: Dict[str, Any]) -> None:
print_colorized = config.get('print_colorized', False)
print_json = config.get('print_json', False)
export_csv = config.get('export_csv', None)
export_csv = config.get('export_csv')
no_details = config.get('hyperopt_list_no_details', False)
no_header = False

2
freqtrade/commands/list_commands.py
View File

@ -212,7 +212,7 @@ def start_show_trades(args: Dict[str, Any]) -> None:
raise OperationalException("--db-url is required for this command.")
logger.info(f'Using DB: "{parse_db_uri_for_logging(config["db_url"])}"')
init_db(config['db_url'], clean_open_orders=False)
init_db(config['db_url'])
tfilter = []
if config.get('trade_ids'):

2
freqtrade/configuration/check_exchange.py
View File

@ -27,7 +27,7 @@ def check_exchange(config: Dict[str, Any], check_for_bad: bool = True) -> bool:
return True
logger.info("Checking exchange...")
exchange = config.get('exchange', {}).get('name').lower()
exchange = config.get('exchange', {}).get('name', '').lower()
if not exchange:
raise OperationalException(
f'This command requires a configured exchange. You should either use '

27
freqtrade/configuration/configuration.py
View File

@ -95,6 +95,8 @@ class Configuration:
self._process_data_options(config)
self._process_analyze_options(config)
# Check if the exchange set by the user is supported
check_exchange(config, config.get('experimental', {}).get('block_bad_exchanges', True))
@ -127,7 +129,7 @@ class Configuration:
# Default to in-memory db for dry_run if not specified
config['db_url'] = constants.DEFAULT_DB_DRYRUN_URL
else:
if not config.get('db_url', None):
if not config.get('db_url'):
config['db_url'] = constants.DEFAULT_DB_PROD_URL
logger.info('Dry run is disabled')
@ -147,6 +149,9 @@ class Configuration:
config.update({'db_url': self.args['db_url']})
logger.info('Parameter --db-url detected ...')
self._args_to_config(config, argname='db_url_from',
logstring='Parameter --db-url-from detected ...')
if config.get('force_entry_enable', False):
logger.warning('`force_entry_enable` RPC message enabled.')
@ -177,7 +182,7 @@ class Configuration:
config['user_data_dir'] = create_userdata_dir(config['user_data_dir'], create_dir=False)
logger.info('Using user-data directory: %s ...', config['user_data_dir'])
config.update({'datadir': create_datadir(config, self.args.get('datadir', None))})
config.update({'datadir': create_datadir(config, self.args.get('datadir'))})
logger.info('Using data directory: %s ...', config.get('datadir'))
if self.args.get('exportfilename'):
@ -216,7 +221,7 @@ class Configuration:
if config.get('max_open_trades') == -1:
config['max_open_trades'] = float('inf')
if self.args.get('stake_amount', None):
if self.args.get('stake_amount'):
# Convert explicitly to float to support CLI argument for both unlimited and value
try:
self.args['stake_amount'] = float(self.args['stake_amount'])
@ -430,6 +435,19 @@ class Configuration:
self._args_to_config(config, argname='candle_types',
logstring='Detected --candle-types: {}')
def _process_analyze_options(self, config: Dict[str, Any]) -> None:
self._args_to_config(config, argname='analysis_groups',
logstring='Analysis reason groups: {}')
self._args_to_config(config, argname='enter_reason_list',
logstring='Analysis enter tag list: {}')
self._args_to_config(config, argname='exit_reason_list',
logstring='Analysis exit tag list: {}')
self._args_to_config(config, argname='indicator_list',
logstring='Analysis indicator list: {}')
def _process_runmode(self, config: Dict[str, Any]) -> None:
self._args_to_config(config, argname='dry_run',
@ -487,6 +505,7 @@ class Configuration:
if not pairs_file.exists():
raise OperationalException(f'No pairs file found with path "{pairs_file}".')
config['pairs'] = load_file(pairs_file)
if isinstance(config['pairs'], list):
config['pairs'].sort()
return
@ -498,5 +517,5 @@ class Configuration:
pairs_file = config['datadir'] / 'pairs.json'
if pairs_file.exists():
config['pairs'] = load_file(pairs_file)
if 'pairs' in config:
if 'pairs' in config and isinstance(config['pairs'], list):
config['pairs'].sort()

2
freqtrade/configuration/deprecated_settings.py
View File

@ -113,7 +113,7 @@ def process_temporary_deprecated_settings(config: Dict[str, Any]) -> None:
process_removed_setting(config, 'experimental', 'ignore_roi_if_buy_signal',
None, 'ignore_roi_if_entry_signal')
process_removed_setting(config, 'ask_strategy', 'use_sell_signal', None, 'exit_sell_signal')
process_removed_setting(config, 'ask_strategy', 'use_sell_signal', None, 'use_exit_signal')
process_removed_setting(config, 'ask_strategy', 'sell_profit_only', None, 'exit_profit_only')
process_removed_setting(config, 'ask_strategy', 'sell_profit_offset',
None, 'exit_profit_offset')

2
freqtrade/configuration/directory_operations.py
View File

@ -15,7 +15,7 @@ def create_datadir(config: Dict[str, Any], datadir: Optional[str] = None) -> Pat
folder = Path(datadir) if datadir else Path(f"{config['user_data_dir']}/data")
if not datadir:
# set datadir
exchange_name = config.get('exchange', {}).get('name').lower()
exchange_name = config.get('exchange', {}).get('name', '').lower()
folder = folder.joinpath(exchange_name)
if not folder.is_dir():

52
freqtrade/constants.py
View File

@ -302,17 +302,21 @@ CONF_SCHEMA = {
'exit_fill': {
'type': 'string',
'enum': TELEGRAM_SETTING_OPTIONS,
'default': 'off'
'default': 'on'
},
'protection_trigger': {
'type': 'string',
'enum': TELEGRAM_SETTING_OPTIONS,
'default': 'off'
'default': 'on'
},
'protection_trigger_global': {
'type': 'string',
'enum': TELEGRAM_SETTING_OPTIONS,
},
'show_candle': {
'type': 'string',
'enum': ['off', 'ohlc'],
},
}
},
'reload': {'type': 'boolean'},
@ -336,6 +340,47 @@ CONF_SCHEMA = {
'webhookstatus': {'type': 'object'},
},
},
'discord': {
'type': 'object',
'properties': {
'enabled': {'type': 'boolean'},
'webhook_url': {'type': 'string'},
"exit_fill": {
'type': 'array', 'items': {'type': 'object'},
'default': [
{"Trade ID": "{trade_id}"},
{"Exchange": "{exchange}"},
{"Pair": "{pair}"},
{"Direction": "{direction}"},
{"Open rate": "{open_rate}"},
{"Close rate": "{close_rate}"},
{"Amount": "{amount}"},
{"Open date": "{open_date:%Y-%m-%d %H:%M:%S}"},
{"Close date": "{close_date:%Y-%m-%d %H:%M:%S}"},
{"Profit": "{profit_amount} {stake_currency}"},
{"Profitability": "{profit_ratio:.2%}"},
{"Enter tag": "{enter_tag}"},
{"Exit Reason": "{exit_reason}"},
{"Strategy": "{strategy}"},
{"Timeframe": "{timeframe}"},
]
},
"entry_fill": {
'type': 'array', 'items': {'type': 'object'},
'default': [
{"Trade ID": "{trade_id}"},
{"Exchange": "{exchange}"},
{"Pair": "{pair}"},
{"Direction": "{direction}"},
{"Open rate": "{open_rate}"},
{"Amount": "{amount}"},
{"Open date": "{open_date:%Y-%m-%d %H:%M:%S}"},
{"Enter tag": "{enter_tag}"},
{"Strategy": "{strategy} {timeframe}"},
]
},
}
},
'api_server': {
'type': 'object',
'properties': {
@ -483,6 +528,8 @@ CANCEL_REASON = {
"ALL_CANCELLED": "cancelled (all unfilled and partially filled open orders cancelled)",
"CANCELLED_ON_EXCHANGE": "cancelled on exchange",
"FORCE_EXIT": "forcesold",
"REPLACE": "cancelled to be replaced by new limit order",
"USER_CANCEL": "user requested order cancel"
}
# List of pairs with their timeframes
@ -494,3 +541,4 @@ TradeList = List[List]
LongShort = Literal['long', 'short']
EntryExit = Literal['entry', 'exit']
BuySell = Literal['buy', 'sell']

8
freqtrade/data/btanalysis.py
View File

@ -26,7 +26,7 @@ BT_DATA_COLUMNS = ['pair', 'stake_amount', 'amount', 'open_date', 'close_date',
'profit_ratio', 'profit_abs', 'exit_reason',
'initial_stop_loss_abs', 'initial_stop_loss_ratio', 'stop_loss_abs',
'stop_loss_ratio', 'min_rate', 'max_rate', 'is_open', 'enter_tag',
'is_short'
'is_short', 'open_timestamp', 'close_timestamp', 'orders'
]
@ -283,6 +283,8 @@ def load_backtest_data(filename: Union[Path, str], strategy: Optional[str] = Non
if 'enter_tag' not in df.columns:
df['enter_tag'] = df['buy_tag']
df = df.drop(['buy_tag'], axis=1)
if 'orders' not in df.columns:
df.loc[:, 'orders'] = None
else:
# old format - only with lists.
@ -337,7 +339,7 @@ def trade_list_to_dataframe(trades: List[LocalTrade]) -> pd.DataFrame:
:param trades: List of trade objects
:return: Dataframe with BT_DATA_COLUMNS
"""
df = pd.DataFrame.from_records([t.to_json() for t in trades], columns=BT_DATA_COLUMNS)
df = pd.DataFrame.from_records([t.to_json(True) for t in trades], columns=BT_DATA_COLUMNS)
if len(df) > 0:
df.loc[:, 'close_date'] = pd.to_datetime(df['close_date'], utc=True)
df.loc[:, 'open_date'] = pd.to_datetime(df['open_date'], utc=True)
@ -353,7 +355,7 @@ def load_trades_from_db(db_url: str, strategy: Optional[str] = None) -> pd.DataF
Can also serve as protection to load the correct result.
:return: Dataframe containing Trades
"""
init_db(db_url, clean_open_orders=False)
init_db(db_url)
filters = []
if strategy:

227
freqtrade/data/entryexitanalysis.py Executable file
View File

@ -0,0 +1,227 @@
import logging
from pathlib import Path
from typing import List, Optional
import joblib
import pandas as pd
from tabulate import tabulate
from freqtrade.data.btanalysis import (get_latest_backtest_filename, load_backtest_data,
load_backtest_stats)
from freqtrade.exceptions import OperationalException
logger = logging.getLogger(__name__)
def _load_signal_candles(backtest_dir: Path):
if backtest_dir.is_dir():
scpf = Path(backtest_dir,
Path(get_latest_backtest_filename(backtest_dir)).stem + "_signals.pkl"
)
else:
scpf = Path(backtest_dir.parent / f"{backtest_dir.stem}_signals.pkl")
try:
scp = open(scpf, "rb")
signal_candles = joblib.load(scp)
logger.info(f"Loaded signal candles: {str(scpf)}")
except Exception as e:
logger.error("Cannot load signal candles from pickled results: ", e)
return signal_candles
def _process_candles_and_indicators(pairlist, strategy_name, trades, signal_candles):
analysed_trades_dict = {}
analysed_trades_dict[strategy_name] = {}
try:
logger.info(f"Processing {strategy_name} : {len(pairlist)} pairs")
for pair in pairlist:
if pair in signal_candles[strategy_name]:
analysed_trades_dict[strategy_name][pair] = _analyze_candles_and_indicators(
pair,
trades,
signal_candles[strategy_name][pair])
except Exception as e:
print(f"Cannot process entry/exit reasons for {strategy_name}: ", e)
return analysed_trades_dict
def _analyze_candles_and_indicators(pair, trades, signal_candles):
buyf = signal_candles
if len(buyf) > 0:
buyf = buyf.set_index('date', drop=False)
trades_red = trades.loc[trades['pair'] == pair].copy()
trades_inds = pd.DataFrame()
if trades_red.shape[0] > 0 and buyf.shape[0] > 0:
for t, v in trades_red.open_date.items():
allinds = buyf.loc[(buyf['date'] < v)]
if allinds.shape[0] > 0:
tmp_inds = allinds.iloc[[-1]]
trades_red.loc[t, 'signal_date'] = tmp_inds['date'].values[0]
trades_red.loc[t, 'enter_reason'] = trades_red.loc[t, 'enter_tag']
tmp_inds.index.rename('signal_date', inplace=True)
trades_inds = pd.concat([trades_inds, tmp_inds])
if 'signal_date' in trades_red:
trades_red['signal_date'] = pd.to_datetime(trades_red['signal_date'], utc=True)
trades_red.set_index('signal_date', inplace=True)
try:
trades_red = pd.merge(trades_red, trades_inds, on='signal_date', how='outer')
except Exception as e:
raise e
return trades_red
else:
return pd.DataFrame()
def _do_group_table_output(bigdf, glist):
for g in glist:
# 0: summary wins/losses grouped by enter tag
if g == "0":
group_mask = ['enter_reason']
wins = bigdf.loc[bigdf['profit_abs'] >= 0] \
.groupby(group_mask) \
.agg({'profit_abs': ['sum']})
wins.columns = ['profit_abs_wins']
loss = bigdf.loc[bigdf['profit_abs'] < 0] \
.groupby(group_mask) \
.agg({'profit_abs': ['sum']})
loss.columns = ['profit_abs_loss']
new = bigdf.groupby(group_mask).agg({'profit_abs': [
'count',
lambda x: sum(x > 0),
lambda x: sum(x <= 0)]})
new = pd.concat([new, wins, loss], axis=1).fillna(0)
new['profit_tot'] = new['profit_abs_wins'] - abs(new['profit_abs_loss'])
new['wl_ratio_pct'] = (new.iloc[:, 1] / new.iloc[:, 0] * 100).fillna(0)
new['avg_win'] = (new['profit_abs_wins'] / new.iloc[:, 1]).fillna(0)
new['avg_loss'] = (new['profit_abs_loss'] / new.iloc[:, 2]).fillna(0)
new.columns = ['total_num_buys', 'wins', 'losses', 'profit_abs_wins', 'profit_abs_loss',
'profit_tot', 'wl_ratio_pct', 'avg_win', 'avg_loss']
sortcols = ['total_num_buys']
_print_table(new, sortcols, show_index=True)
else:
agg_mask = {'profit_abs': ['count', 'sum', 'median', 'mean'],
'profit_ratio': ['sum', 'median', 'mean']}
agg_cols = ['num_buys', 'profit_abs_sum', 'profit_abs_median',
'profit_abs_mean', 'median_profit_pct', 'mean_profit_pct',
'total_profit_pct']
sortcols = ['profit_abs_sum', 'enter_reason']
# 1: profit summaries grouped by enter_tag
if g == "1":
group_mask = ['enter_reason']
# 2: profit summaries grouped by enter_tag and exit_tag
if g == "2":
group_mask = ['enter_reason', 'exit_reason']
# 3: profit summaries grouped by pair and enter_tag
if g == "3":
group_mask = ['pair', 'enter_reason']
# 4: profit summaries grouped by pair, enter_ and exit_tag (this can get quite large)
if g == "4":
group_mask = ['pair', 'enter_reason', 'exit_reason']
if group_mask:
new = bigdf.groupby(group_mask).agg(agg_mask).reset_index()
new.columns = group_mask + agg_cols
new['median_profit_pct'] = new['median_profit_pct'] * 100
new['mean_profit_pct'] = new['mean_profit_pct'] * 100
new['total_profit_pct'] = new['total_profit_pct'] * 100
_print_table(new, sortcols)
else:
logger.warning("Invalid group mask specified.")
def _print_results(analysed_trades, stratname, analysis_groups,
enter_reason_list, exit_reason_list,
indicator_list, columns=None):
if columns is None:
columns = ['pair', 'open_date', 'close_date', 'profit_abs', 'enter_reason', 'exit_reason']
bigdf = pd.DataFrame()
for pair, trades in analysed_trades[stratname].items():
bigdf = pd.concat([bigdf, trades], ignore_index=True)
if bigdf.shape[0] > 0 and ('enter_reason' in bigdf.columns):
if analysis_groups:
_do_group_table_output(bigdf, analysis_groups)
if enter_reason_list and "all" not in enter_reason_list:
bigdf = bigdf.loc[(bigdf['enter_reason'].isin(enter_reason_list))]
if exit_reason_list and "all" not in exit_reason_list:
bigdf = bigdf.loc[(bigdf['exit_reason'].isin(exit_reason_list))]
if "all" in indicator_list:
print(bigdf)
elif indicator_list is not None:
available_inds = []
for ind in indicator_list:
if ind in bigdf:
available_inds.append(ind)
ilist = ["pair", "enter_reason", "exit_reason"] + available_inds
_print_table(bigdf[ilist], sortcols=['exit_reason'], show_index=False)
else:
print("\\_ No trades to show")
def _print_table(df, sortcols=None, show_index=False):
if (sortcols is not None):
data = df.sort_values(sortcols)
else:
data = df
print(
tabulate(
data,
headers='keys',
tablefmt='psql',
showindex=show_index
)
)
def process_entry_exit_reasons(backtest_dir: Path,
pairlist: List[str],
analysis_groups: Optional[List[str]] = ["0", "1", "2"],
enter_reason_list: Optional[List[str]] = ["all"],
exit_reason_list: Optional[List[str]] = ["all"],
indicator_list: Optional[List[str]] = []):
try:
backtest_stats = load_backtest_stats(backtest_dir)
for strategy_name, results in backtest_stats['strategy'].items():
trades = load_backtest_data(backtest_dir, strategy_name)
if not trades.empty:
signal_candles = _load_signal_candles(backtest_dir)
analysed_trades_dict = _process_candles_and_indicators(pairlist, strategy_name,
trades, signal_candles)
_print_results(analysed_trades_dict,
strategy_name,
analysis_groups,
enter_reason_list,
exit_reason_list,
indicator_list)
except ValueError as e:
raise OperationalException(e) from e

6
freqtrade/data/history/hdf5datahandler.py
View File

@ -40,7 +40,7 @@ class HDF5DataHandler(IDataHandler):
return [
(
cls.rebuild_pair_from_filename(match[1]),
match[2],
cls.rebuild_timeframe_from_filename(match[2]),
CandleType.from_string(match[3])
) for match in _tmp if match and len(match.groups()) > 1]
@ -108,6 +108,10 @@ class HDF5DataHandler(IDataHandler):
candle_type=candle_type
)
if not filename.exists():
# Fallback mode for 1M files
filename = self._pair_data_filename(
self._datadir, pair, timeframe, candle_type=candle_type, no_timeframe_modify=True)
if not filename.exists():
return pd.DataFrame(columns=self._columns)
where = []

10
freqtrade/data/history/history_utils.py
View File

@ -68,7 +68,8 @@ def load_data(datadir: Path,
startup_candles: int = 0,
fail_without_data: bool = False,
data_format: str = 'json',
candle_type: CandleType = CandleType.SPOT
candle_type: CandleType = CandleType.SPOT,
user_futures_funding_rate: int = None,
) -> Dict[str, DataFrame]:
"""
Load ohlcv history data for a list of pairs.
@ -100,6 +101,10 @@ def load_data(datadir: Path,
)
if not hist.empty:
result[pair] = hist
else:
if candle_type is CandleType.FUNDING_RATE and user_futures_funding_rate is not None:
logger.warn(f"{pair} using user specified [{user_futures_funding_rate}]")
result[pair] = DataFrame(columns=["open", "close", "high", "low", "volume"])
if fail_without_data and not result:
raise OperationalException("No data found. Terminating.")
@ -216,7 +221,7 @@ def _download_pair_history(pair: str, *,
prepend=prepend)
logger.info(f'({process}) - Download history data for "{pair}", {timeframe}, '
f'{candle_type} and store in {datadir}.'
f'{candle_type} and store in {datadir}. '
f'From {format_ms_time(since_ms) if since_ms else "start"} to '
f'{format_ms_time(until_ms) if until_ms else "now"}'
)
@ -277,6 +282,7 @@ def refresh_backtest_ohlcv_data(exchange: Exchange, pairs: List[str], timeframes
pairs_not_available = []
data_handler = get_datahandler(datadir, data_format)
candle_type = CandleType.get_default(trading_mode)
process = ''
for idx, pair in enumerate(pairs, start=1):
if pair not in exchange.markets:
pairs_not_available.append(pair)

20
freqtrade/data/history/idatahandler.py
View File

@ -26,7 +26,7 @@ logger = logging.getLogger(__name__)
class IDataHandler(ABC):
_OHLCV_REGEX = r'^([a-zA-Z_-]+)\-(\d+\S)\-?([a-zA-Z_]*)?(?=\.)'
_OHLCV_REGEX = r'^([a-zA-Z_-]+)\-(\d+[a-zA-Z]{1,2})\-?([a-zA-Z_]*)?(?=\.)'
def __init__(self, datadir: Path) -> None:
self._datadir = datadir
@ -193,10 +193,14 @@ class IDataHandler(ABC):
datadir: Path,
pair: str,
timeframe: str,
candle_type: CandleType
candle_type: CandleType,
no_timeframe_modify: bool = False
) -> Path:
pair_s = misc.pair_to_filename(pair)
candle = ""
if not no_timeframe_modify:
timeframe = cls.timeframe_to_file(timeframe)
if candle_type != CandleType.SPOT:
datadir = datadir.joinpath('futures')
candle = f"-{candle_type}"
@ -210,6 +214,18 @@ class IDataHandler(ABC):
filename = datadir.joinpath(f'{pair_s}-trades.{cls._get_file_extension()}')
return filename
@staticmethod
def timeframe_to_file(timeframe: str):
return timeframe.replace('M', 'Mo')
@staticmethod
def rebuild_timeframe_from_filename(timeframe: str) -> str:
"""
converts timeframe from disk to file
Replaces mo with M (to avoid problems on case-insensitive filesystems)
"""
return re.sub('1mo', '1M', timeframe, flags=re.IGNORECASE)
@staticmethod
def rebuild_pair_from_filename(pair: str) -> str:
"""

9
freqtrade/data/history/jsondatahandler.py
View File

@ -41,7 +41,7 @@ class JsonDataHandler(IDataHandler):
return [
(
cls.rebuild_pair_from_filename(match[1]),
match[2],
cls.rebuild_timeframe_from_filename(match[2]),
CandleType.from_string(match[3])
) for match in _tmp if match and len(match.groups()) > 1]
@ -103,7 +103,12 @@ class JsonDataHandler(IDataHandler):
:param candle_type: Any of the enum CandleType (must match trading mode!)
:return: DataFrame with ohlcv data, or empty DataFrame
"""
filename = self._pair_data_filename(self._datadir, pair, timeframe, candle_type=candle_type)
filename = self._pair_data_filename(
self._datadir, pair, timeframe, candle_type=candle_type)
if not filename.exists():
# Fallback mode for 1M files
filename = self._pair_data_filename(
self._datadir, pair, timeframe, candle_type=candle_type, no_timeframe_modify=True)
if not filename.exists():
return DataFrame(columns=self._columns)
try:

6
freqtrade/enums/exitchecktuple.py
View File

@ -15,3 +15,9 @@ class ExitCheckTuple:
@property
def exit_flag(self):
return self.exit_type != ExitType.NONE
def __eq__(self, other):
return self.exit_type == other.exit_type and self.exit_reason == other.exit_reason
def __repr__(self):
return f"ExitCheckTuple({self.exit_type}, {self.exit_reason})"

15
freqtrade/exchange/binance.py
View File

@ -52,12 +52,17 @@ class Binance(Exchange):
ordertype = 'stop' if self.trading_mode == TradingMode.FUTURES else 'stop_loss_limit'
return order['type'] == ordertype and (
(side == "sell" and stop_loss > float(order['info']['stopPrice'])) or
(side == "buy" and stop_loss < float(order['info']['stopPrice']))
return (
order.get('stopPrice', None) is None
or (
order['type'] == ordertype
and (
(side == "sell" and stop_loss > float(order['stopPrice'])) or
(side == "buy" and stop_loss < float(order['stopPrice']))
)
))
def get_tickers(self, symbols: List[str] = None, cached: bool = False) -> Dict:
def get_tickers(self, symbols: Optional[List[str]] = None, cached: bool = False) -> Dict:
tickers = super().get_tickers(symbols=symbols, cached=cached)
if self.trading_mode == TradingMode.FUTURES:
# Binance's future result has no bid/ask values.
@ -95,7 +100,7 @@ class Binance(Exchange):
async def _async_get_historic_ohlcv(self, pair: str, timeframe: str,
since_ms: int, candle_type: CandleType,
is_new_pair: bool = False, raise_: bool = False,
until_ms: int = None
until_ms: Optional[int] = None
) -> Tuple[str, str, str, List]:
"""
Overwrite to introduce "fast new pair" functionality by detecting the pair's listing date

30717
freqtrade/exchange/binance_leverage_tiers.json
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File diff suppressed because it is too large Load Diff

14
freqtrade/exchange/bybit.py
View File

@ -29,3 +29,17 @@ class Bybit(Exchange):
# (TradingMode.FUTURES, MarginMode.CROSS),
# (TradingMode.FUTURES, MarginMode.ISOLATED)
]
@property
def _ccxt_config(self) -> Dict:
# Parameters to add directly to ccxt sync/async initialization.
# ccxt defaults to swap mode.
config = {}
if self.trading_mode == TradingMode.SPOT:
config.update({
"options": {
"defaultType": "spot"
}
})
config.update(super()._ccxt_config)
return config

33
freqtrade/exchange/common.py
View File

@ -2,6 +2,7 @@ import asyncio
import logging
import time
from functools import wraps
from typing import Any, Callable, Optional, TypeVar, cast, overload
from freqtrade.exceptions import DDosProtection, RetryableOrderError, TemporaryError
from freqtrade.mixins import LoggingMixin
@ -11,6 +12,14 @@ logger = logging.getLogger(__name__)
__logging_mixin = None
def _reset_logging_mixin():
"""
Reset global logging mixin - used in tests only.
"""
global __logging_mixin
__logging_mixin = LoggingMixin(logger)
def _get_logging_mixin():
# Logging-mixin to cache kucoin responses
# Only to be used in retrier
@ -37,6 +46,7 @@ MAP_EXCHANGE_CHILDCLASS = {
'binanceje': 'binance',
'binanceusdm': 'binance',
'okex': 'okx',
'gate': 'gateio',
}
SUPPORTED_EXCHANGES = [
@ -54,17 +64,16 @@ EXCHANGE_HAS_REQUIRED = [
'fetchOrder',
'cancelOrder',
'createOrder',
# 'createLimitOrder', 'createMarketOrder',
'fetchBalance',
# Public endpoints
'loadMarkets',
'fetchOHLCV',
]
EXCHANGE_HAS_OPTIONAL = [
# Private
'fetchMyTrades', # Trades for order - fee detection
'createLimitOrder', 'createMarketOrder', # Either OR for orders
# 'setLeverage', # Margin/Futures trading
# 'setMarginMode', # Margin/Futures trading
# 'fetchFundingHistory', # Futures trading
@ -133,8 +142,22 @@ def retrier_async(f):
return wrapper
def retrier(_func=None, retries=API_RETRY_COUNT):
def decorator(f):
F = TypeVar('F', bound=Callable[..., Any])
# Type shenanigans
@overload
def retrier(_func: F) -> F:
...
@overload
def retrier(*, retries=API_RETRY_COUNT) -> Callable[[F], F]:
...
def retrier(_func: Optional[F] = None, *, retries=API_RETRY_COUNT):
def decorator(f: F) -> F:
@wraps(f)
def wrapper(*args, **kwargs):
count = kwargs.pop('count', retries)
@ -155,7 +178,7 @@ def retrier(_func=None, retries=API_RETRY_COUNT):
else:
logger.warning(msg + 'Giving up.')
raise ex
return wrapper
return cast(F, wrapper)
# Support both @retrier and @retrier(retries=2) syntax
if _func is None:
return decorator

288
freqtrade/exchange/exchange.py
View File

@ -16,11 +16,10 @@ import arrow
import ccxt
import ccxt.async_support as ccxt_async
from cachetools import TTLCache
from ccxt.base.decimal_to_precision import (ROUND_DOWN, ROUND_UP, TICK_SIZE, TRUNCATE,
decimal_to_precision)
from ccxt import ROUND_DOWN, ROUND_UP, TICK_SIZE, TRUNCATE, Precise, decimal_to_precision
from pandas import DataFrame
from freqtrade.constants import (DEFAULT_AMOUNT_RESERVE_PERCENT, NON_OPEN_EXCHANGE_STATES,
from freqtrade.constants import (DEFAULT_AMOUNT_RESERVE_PERCENT, NON_OPEN_EXCHANGE_STATES, BuySell,
EntryExit, ListPairsWithTimeframes, PairWithTimeframe)
from freqtrade.data.converter import ohlcv_to_dataframe, trades_dict_to_list
from freqtrade.enums import OPTIMIZE_MODES, CandleType, MarginMode, TradingMode
@ -64,6 +63,7 @@ class Exchange:
"time_in_force_parameter": "timeInForce",
"ohlcv_params": {},
"ohlcv_candle_limit": 500,
"ohlcv_has_history": True, # Some exchanges (Kraken) don't provide history via ohlcv
"ohlcv_partial_candle": True,
"ohlcv_require_since": False,
# Check https://github.com/ccxt/ccxt/issues/10767 for removal of ohlcv_volume_currency
@ -77,7 +77,9 @@ class Exchange:
"mark_ohlcv_price": "mark",
"mark_ohlcv_timeframe": "8h",
"ccxt_futures_name": "swap",
"fee_cost_in_contracts": False, # Fee cost needs contract conversion
"needs_trading_fees": False, # use fetch_trading_fees to cache fees
"order_props_in_contracts": ['amount', 'cost', 'filled', 'remaining'],
}
_ft_has: Dict = {}
_ft_has_futures: Dict = {}
@ -92,7 +94,7 @@ class Exchange:
it does basic validation whether the specified exchange and pairs are valid.
:return: None
"""
self._api: ccxt.Exchange = None
self._api: ccxt.Exchange
self._api_async: ccxt_async.Exchange = None
self._markets: Dict = {}
self._trading_fees: Dict[str, Any] = {}
@ -174,23 +176,11 @@ class Exchange:
logger.info(f'Using Exchange "{self.name}"')
if validate:
# Check if timeframe is available
self.validate_timeframes(config.get('timeframe'))
# Initial markets load
self._load_markets()
# Check if all pairs are available
self.validate_stakecurrency(config['stake_currency'])
if not exchange_config.get('skip_pair_validation'):
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_config(config)
self.required_candle_call_count = self.validate_required_startup_candles(
config.get('startup_candle_count', 0), config.get('timeframe', ''))
self.validate_trading_mode_and_margin_mode(self.trading_mode, self.margin_mode)
self.validate_pricing(config['exit_pricing'])
self.validate_pricing(config['entry_pricing'])
# Converts the interval provided in minutes in config to seconds
self.markets_refresh_interval: int = exchange_config.get(
@ -198,6 +188,7 @@ class Exchange:
if self.trading_mode != TradingMode.SPOT:
self.fill_leverage_tiers()
self.additional_exchange_init()
def __del__(self):
"""
@ -212,6 +203,20 @@ class Exchange:
logger.info("Closing async ccxt session.")
self.loop.run_until_complete(self._api_async.close())
def validate_config(self, config):
# Check if timeframe is available
self.validate_timeframes(config.get('timeframe'))
# Check if all pairs are available
self.validate_stakecurrency(config['stake_currency'])
if not config['exchange'].get('skip_pair_validation'):
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_trading_mode_and_margin_mode(self.trading_mode, self.margin_mode)
self.validate_pricing(config['exit_pricing'])
self.validate_pricing(config['entry_pricing'])
def _init_ccxt(self, exchange_config: Dict[str, Any], ccxt_module: CcxtModuleType = ccxt,
ccxt_kwargs: Dict = {}) -> ccxt.Exchange:
"""
@ -290,27 +295,38 @@ class Exchange:
return self._markets
@property
def precisionMode(self) -> str:
def precisionMode(self) -> int:
"""exchange ccxt precisionMode"""
return self._api.precisionMode
def additional_exchange_init(self) -> None:
"""
Additional exchange initialization logic.
.api will be available at this point.
Must be overridden in child methods if required.
"""
pass
def _log_exchange_response(self, endpoint, response) -> None:
""" Log exchange responses """
if self.log_responses:
logger.info(f"API {endpoint}: {response}")
def ohlcv_candle_limit(self, timeframe: str) -> int:
def ohlcv_candle_limit(
self, timeframe: str, candle_type: CandleType, since_ms: Optional[int] = None) -> int:
"""
Exchange ohlcv candle limit
Uses ohlcv_candle_limit_per_timeframe if the exchange has different limits
per timeframe (e.g. bittrex), otherwise falls back to ohlcv_candle_limit
:param timeframe: Timeframe to check
:param candle_type: Candle-type
:param since_ms: Starting timestamp
:return: Candle limit as integer
"""
return int(self._ft_has.get('ohlcv_candle_limit_per_timeframe', {}).get(
timeframe, self._ft_has.get('ohlcv_candle_limit')))
def get_markets(self, base_currencies: List[str] = None, quote_currencies: List[str] = None,
def get_markets(self, base_currencies: List[str] = [], quote_currencies: List[str] = [],
spot_only: bool = False, margin_only: bool = False, futures_only: bool = False,
tradable_only: bool = True,
active_only: bool = False) -> Dict[str, Any]:
@ -375,7 +391,7 @@ class Exchange:
and market.get('base', None) is not None
and (self.precisionMode != TICK_SIZE
# Too low precision will falsify calculations
or market.get('precision', {}).get('price', None) > 1e-11)
or market.get('precision', {}).get('price') > 1e-11)
and ((self.trading_mode == TradingMode.SPOT and self.market_is_spot(market))
or (self.trading_mode == TradingMode.MARGIN and self.market_is_margin(market))
or (self.trading_mode == TradingMode.FUTURES and self.market_is_future(market)))
@ -410,7 +426,7 @@ class Exchange:
if 'symbol' in order and order['symbol'] is not None:
contract_size = self._get_contract_size(order['symbol'])
if contract_size != 1:
for prop in ['amount', 'cost', 'filled', 'remaining']:
for prop in self._ft_has.get('order_props_in_contracts', []):
if prop in order and order[prop] is not None:
order[prop] = order[prop] * contract_size
return order
@ -525,7 +541,7 @@ class Exchange:
# The internal info array is different for each particular market,
# its contents depend on the exchange.
# It can also be a string or similar ... so we need to verify that first.
elif (isinstance(self.markets[pair].get('info', None), dict)
elif (isinstance(self.markets[pair].get('info'), dict)
and self.markets[pair].get('info', {}).get('prohibitedIn', False)):
# Warn users about restricted pairs in whitelist.
# We cannot determine reliably if Users are affected.
@ -606,19 +622,28 @@ class Exchange:
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)
candle_limit = self.ohlcv_candle_limit(
timeframe, self._config['candle_type_def'],
int(date_minus_candles(timeframe, startup_candles).timestamp() * 1000)
if timeframe else None)
# 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 self._ft_has['ohlcv_has_history']:
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, which is more than 5x "
f"This strategy requires {startup_candles} candles to start, "
"which is more than 5x "
f"the amount of candles {self.name} provides for {timeframe}.")
elif required_candle_call_count > 1:
raise OperationalException(
f"This strategy requires {startup_candles} candles to start, which is more than "
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 "
@ -682,10 +707,11 @@ class Exchange:
# counting_mode=self.precisionMode,
# ))
if self.precisionMode == TICK_SIZE:
precision = self.markets[pair]['precision']['price']
missing = price % precision
if missing != 0:
price = round(price - missing + precision, 10)
precision = Precise(str(self.markets[pair]['precision']['price']))
price_str = Precise(str(price))
missing = price_str % precision
if not missing == Precise("0"):
price = round(float(str(price_str - missing + precision)), 14)
else:
symbol_prec = self.markets[pair]['precision']['price']
big_price = price * pow(10, symbol_prec)
@ -818,7 +844,7 @@ class Exchange:
'price': rate,
'average': rate,
'amount': _amount,
'cost': _amount * rate / leverage,
'cost': _amount * rate,
'type': ordertype,
'side': side,
'filled': 0,
@ -965,19 +991,26 @@ class Exchange:
order = self.check_dry_limit_order_filled(order)
return order
except KeyError as e:
from freqtrade.persistence import Order
order = Order.order_by_id(order_id)
if order:
ccxt_order = order.to_ccxt_object()
self._dry_run_open_orders[order_id] = ccxt_order
return ccxt_order
# Gracefully handle errors with dry-run orders.
raise InvalidOrderException(
f'Tried to get an invalid dry-run-order (id: {order_id}). Message: {e}') from e
# Order handling
def _lev_prep(self, pair: str, leverage: float, side: str):
def _lev_prep(self, pair: str, leverage: float, side: BuySell):
if self.trading_mode != TradingMode.SPOT:
self.set_margin_mode(pair, self.margin_mode)
self._set_leverage(leverage, pair)
def _get_params(
self,
side: BuySell,
ordertype: str,
leverage: float,
reduceOnly: bool,
@ -996,7 +1029,7 @@ class Exchange:
*,
pair: str,
ordertype: str,
side: str,
side: BuySell,
amount: float,
rate: float,
leverage: float,
@ -1007,7 +1040,7 @@ class Exchange:
dry_order = self.create_dry_run_order(pair, ordertype, side, amount, rate, leverage)
return dry_order
params = self._get_params(ordertype, leverage, reduceOnly, time_in_force)
params = self._get_params(side, ordertype, leverage, reduceOnly, time_in_force)
try:
# Set the precision for amount and price(rate) as accepted by the exchange
@ -1092,7 +1125,7 @@ class Exchange:
@retrier(retries=0)
def stoploss(self, pair: str, amount: float, stop_price: float, order_types: Dict,
side: str, leverage: float) -> Dict:
side: BuySell, leverage: float) -> Dict:
"""
creates a stoploss order.
requires `_ft_has['stoploss_order_types']` to be set as a dict mapping limit and market
@ -1169,7 +1202,7 @@ class Exchange:
raise OperationalException(e) from e
@retrier(retries=API_FETCH_ORDER_RETRY_COUNT)
def fetch_order(self, order_id: str, pair: str, params={}) -> Dict:
def fetch_order(self, order_id: str, pair: str, params: Dict = {}) -> Dict:
if self._config['dry_run']:
return self.fetch_dry_run_order(order_id)
try:
@ -1191,8 +1224,8 @@ class Exchange:
except ccxt.BaseError as e:
raise OperationalException(e) from e
# Assign method to fetch_stoploss_order to allow easy overriding in other classes
fetch_stoploss_order = fetch_order
def fetch_stoploss_order(self, order_id: str, pair: str, params: Dict = {}) -> Dict:
return self.fetch_order(order_id, pair, params)
def fetch_order_or_stoploss_order(self, order_id: str, pair: str,
stoploss_order: bool = False) -> Dict:
@ -1217,7 +1250,7 @@ class Exchange:
and order.get('filled') == 0.0)
@retrier
def cancel_order(self, order_id: str, pair: str, params={}) -> Dict:
def cancel_order(self, order_id: str, pair: str, params: Dict = {}) -> Dict:
if self._config['dry_run']:
try:
order = self.fetch_dry_run_order(order_id)
@ -1243,8 +1276,8 @@ class Exchange:
except ccxt.BaseError as e:
raise OperationalException(e) from e
# Assign method to cancel_stoploss_order to allow easy overriding in other classes
cancel_stoploss_order = cancel_order
def cancel_stoploss_order(self, order_id: str, pair: str, params: Dict = {}) -> Dict:
return self.cancel_order(order_id, pair, params)
def is_cancel_order_result_suitable(self, corder) -> bool:
if not isinstance(corder, dict):
@ -1356,7 +1389,7 @@ class Exchange:
raise OperationalException(e) from e
@retrier
def fetch_bids_asks(self, symbols: List[str] = None, cached: bool = False) -> Dict:
def fetch_bids_asks(self, symbols: Optional[List[str]] = None, cached: bool = False) -> Dict:
"""
:param cached: Allow cached result
:return: fetch_tickers result
@ -1384,7 +1417,7 @@ class Exchange:
raise OperationalException(e) from e
@retrier
def get_tickers(self, symbols: List[str] = None, cached: bool = False) -> Dict:
def get_tickers(self, symbols: Optional[List[str]] = None, cached: bool = False) -> Dict:
"""
:param cached: Allow cached result
:return: fetch_tickers result
@ -1468,6 +1501,23 @@ class Exchange:
except ccxt.BaseError as e:
raise OperationalException(e) from e
def _get_price_side(self, side: str, is_short: bool, conf_strategy: Dict) -> str:
price_side = conf_strategy['price_side']
if price_side in ('same', 'other'):
price_map = {
('entry', 'long', 'same'): 'bid',
('entry', 'long', 'other'): 'ask',
('entry', 'short', 'same'): 'ask',
('entry', 'short', 'other'): 'bid',
('exit', 'long', 'same'): 'ask',
('exit', 'long', 'other'): 'bid',
('exit', 'short', 'same'): 'bid',
('exit', 'short', 'other'): 'ask',
}
price_side = price_map[(side, 'short' if is_short else 'long', price_side)]
return price_side
def get_rate(self, pair: str, refresh: bool,
side: EntryExit, is_short: bool) -> float:
"""
@ -1494,20 +1544,7 @@ class Exchange:
conf_strategy = self._config.get(strat_name, {})
price_side = conf_strategy['price_side']
if price_side in ('same', 'other'):
price_map = {
('entry', 'long', 'same'): 'bid',
('entry', 'long', 'other'): 'ask',
('entry', 'short', 'same'): 'ask',
('entry', 'short', 'other'): 'bid',
('exit', 'long', 'same'): 'ask',
('exit', 'long', 'other'): 'bid',
('exit', 'short', 'same'): 'bid',
('exit', 'short', 'other'): 'ask',
}
price_side = price_map[(side, 'short' if is_short else 'long', price_side)]
price_side = self._get_price_side(side, is_short, conf_strategy)
price_side_word = price_side.capitalize()
@ -1632,27 +1669,35 @@ class Exchange:
and order['fee']['cost'] is not None
)
def calculate_fee_rate(self, order: Dict) -> Optional[float]:
def calculate_fee_rate(
self, fee: Dict, symbol: str, cost: float, amount: float) -> Optional[float]:
"""
Calculate fee rate if it's not given by the exchange.
:param order: Order or trade (one trade) dict
:param fee: ccxt Fee dict - must contain cost / currency / rate
:param symbol: Symbol of the order
:param cost: Total cost of the order
:param amount: Amount of the order
"""
if order['fee'].get('rate') is not None:
return order['fee'].get('rate')
fee_curr = order['fee']['currency']
if fee.get('rate') is not None:
return fee.get('rate')
fee_curr = fee.get('currency')
if fee_curr is None:
return None
fee_cost = float(fee['cost'])
if self._ft_has['fee_cost_in_contracts']:
# Convert cost via "contracts" conversion
fee_cost = self._contracts_to_amount(symbol, fee['cost'])
# Calculate fee based on order details
if fee_curr in self.get_pair_base_currency(order['symbol']):
if fee_curr == self.get_pair_base_currency(symbol):
# Base currency - divide by amount
return round(
order['fee']['cost'] / safe_value_fallback2(order, order, 'filled', 'amount'), 8)
elif fee_curr in self.get_pair_quote_currency(order['symbol']):
return round(fee_cost / amount, 8)
elif fee_curr == self.get_pair_quote_currency(symbol):
# Quote currency - divide by cost
return round(self._contracts_to_amount(
order['symbol'], order['fee']['cost']) / order['cost'],
8) if order['cost'] else None
return round(fee_cost / cost, 8) if cost else None
else:
# If Fee currency is a different currency
if not order['cost']:
if not cost:
# If cost is None or 0.0 -> falsy, return None
return None
try:
@ -1664,19 +1709,28 @@ class Exchange:
fee_to_quote_rate = self._config['exchange'].get('unknown_fee_rate', None)
if not fee_to_quote_rate:
return None
return round((self._contracts_to_amount(
order['symbol'], order['fee']['cost']) * fee_to_quote_rate) / order['cost'], 8)
return round((fee_cost * fee_to_quote_rate) / cost, 8)
def extract_cost_curr_rate(self, order: Dict) -> Tuple[float, str, Optional[float]]:
def extract_cost_curr_rate(self, fee: Dict, symbol: str, cost: float,
amount: float) -> Tuple[float, str, Optional[float]]:
"""
Extract tuple of cost, currency, rate.
Requires order_has_fee to run first!
:param order: Order or trade (one trade) dict
:param fee: ccxt Fee dict - must contain cost / currency / rate
:param symbol: Symbol of the order
:param cost: Total cost of the order
:param amount: Amount of the order
:return: Tuple with cost, currency, rate of the given fee dict
"""
return (order['fee']['cost'],
order['fee']['currency'],
self.calculate_fee_rate(order))
return (float(fee['cost']),
fee['currency'],
self.calculate_fee_rate(
fee,
symbol,
cost,
amount
)
)
# Historic data
@ -1719,7 +1773,7 @@ class Exchange:
async def _async_get_historic_ohlcv(self, pair: str, timeframe: str,
since_ms: int, candle_type: CandleType,
is_new_pair: bool = False, raise_: bool = False,
until_ms: int = None
until_ms: Optional[int] = None
) -> Tuple[str, str, str, List]:
"""
Download historic ohlcv
@ -1727,7 +1781,8 @@ class Exchange:
:param candle_type: Any of the enum CandleType (must match trading mode!)
"""
one_call = timeframe_to_msecs(timeframe) * self.ohlcv_candle_limit(timeframe)
one_call = timeframe_to_msecs(timeframe) * self.ohlcv_candle_limit(
timeframe, candle_type, since_ms)
logger.debug(
"one_call: %s msecs (%s)",
one_call,
@ -1763,7 +1818,8 @@ class Exchange:
if (not since_ms
and (self._ft_has["ohlcv_require_since"] or 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)
one_call = timeframe_to_msecs(timeframe) * self.ohlcv_candle_limit(
timeframe, candle_type, since_ms)
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)
@ -1778,7 +1834,7 @@ class Exchange:
def refresh_latest_ohlcv(self, pair_list: ListPairsWithTimeframes, *,
since_ms: Optional[int] = None, cache: bool = True,
drop_incomplete: bool = None
drop_incomplete: Optional[bool] = None
) -> Dict[PairWithTimeframe, DataFrame]:
"""
Refresh in-memory OHLCV asynchronously and set `_klines` with the result
@ -1881,7 +1937,9 @@ class Exchange:
pair, timeframe, since_ms, s
)
params = deepcopy(self._ft_has.get('ohlcv_params', {}))
candle_limit = self.ohlcv_candle_limit(timeframe)
candle_limit = self.ohlcv_candle_limit(
timeframe, candle_type=candle_type, since_ms=since_ms)
if candle_type != CandleType.SPOT:
params.update({'price': candle_type})
if candle_type != CandleType.FUNDING_RATE:
@ -2128,10 +2186,11 @@ class Exchange:
except ccxt.BaseError as e:
raise OperationalException(e) from e
@retrier
def get_market_leverage_tiers(self, symbol) -> List[Dict]:
@retrier_async
async def get_market_leverage_tiers(self, symbol: str) -> Tuple[str, List[Dict]]:
try:
return self._api.fetch_market_leverage_tiers(symbol)
tier = await self._api_async.fetch_market_leverage_tiers(symbol)
return symbol, tier
except ccxt.DDoSProtection as e:
raise DDosProtection(e) from e
except (ccxt.NetworkError, ccxt.ExchangeError) as e:
@ -2165,8 +2224,14 @@ class Exchange:
f"Initializing leverage_tiers for {len(symbols)} markets. "
"This will take about a minute.")
for symbol in sorted(symbols):
tiers[symbol] = self.get_market_leverage_tiers(symbol)
coros = [self.get_market_leverage_tiers(symbol) for symbol in sorted(symbols)]
for input_coro in chunks(coros, 100):
results = self.loop.run_until_complete(
asyncio.gather(*input_coro, return_exceptions=True))
for symbol, res in results:
tiers[symbol] = res
logger.info(f"Done initializing {len(symbols)} markets.")
@ -2416,14 +2481,35 @@ class Exchange:
)
@staticmethod
def combine_funding_and_mark(funding_rates: DataFrame, mark_rates: DataFrame) -> DataFrame:
def combine_funding_and_mark(funding_rates: DataFrame, mark_rates: DataFrame,
futures_funding_rate: Optional[int] = None) -> DataFrame:
"""
Combine funding-rates and mark-rates dataframes
:param funding_rates: Dataframe containing Funding rates (Type FUNDING_RATE)
:param mark_rates: Dataframe containing Mark rates (Type mark_ohlcv_price)
:param futures_funding_rate: Fake funding rate to use if funding_rates are not available
"""
if futures_funding_rate is None:
return mark_rates.merge(
funding_rates, on='date', how="inner", suffixes=["_mark", "_fund"])
else:
if len(funding_rates) == 0:
# No funding rate candles - full fillup with fallback variable
mark_rates['open_fund'] = futures_funding_rate
return mark_rates.rename(
columns={'open': 'open_mark',
'close': 'close_mark',
'high': 'high_mark',
'low': 'low_mark',
'volume': 'volume_mark'})
return funding_rates.merge(mark_rates, on='date', how="inner", suffixes=["_fund", "_mark"])
else:
# Fill up missing funding_rate candles with fallback value
combined = mark_rates.merge(
funding_rates, on='date', how="outer", suffixes=["_mark", "_fund"]
)
combined['open_fund'] = combined['open_fund'].fillna(futures_funding_rate)
return combined
def calculate_funding_fees(
self,
@ -2698,9 +2784,10 @@ def timeframe_to_msecs(timeframe: str) -> int:
def timeframe_to_prev_date(timeframe: str, date: datetime = None) -> datetime:
"""
Use Timeframe and determine last possible candle.
Use Timeframe and determine the candle start date for this date.
Does not round when given a candle start date.
:param timeframe: timeframe in string format (e.g. "5m")
:param date: date to use. Defaults to utcnow()
:param date: date to use. Defaults to now(utc)
:returns: date of previous candle (with utc timezone)
"""
if not date:
@ -2715,7 +2802,7 @@ def timeframe_to_next_date(timeframe: str, date: datetime = None) -> datetime:
"""
Use Timeframe and determine next candle.
:param timeframe: timeframe in string format (e.g. "5m")
:param date: date to use. Defaults to utcnow()
:param date: date to use. Defaults to now(utc)
:returns: date of next candle (with utc timezone)
"""
if not date:
@ -2725,6 +2812,23 @@ def timeframe_to_next_date(timeframe: str, date: datetime = None) -> datetime:
return datetime.fromtimestamp(new_timestamp, tz=timezone.utc)
def date_minus_candles(
timeframe: str, candle_count: int, date: Optional[datetime] = None) -> datetime:
"""
subtract X candles from a date.
:param timeframe: timeframe in string format (e.g. "5m")
:param candle_count: Amount of candles to subtract.
:param date: date to use. Defaults to now(utc)
"""
if not date:
date = datetime.now(timezone.utc)
tf_min = timeframe_to_minutes(timeframe)
new_date = timeframe_to_prev_date(timeframe, date) - timedelta(minutes=tf_min * candle_count)
return new_date
def market_is_active(market: Dict) -> bool:
"""
Return True if the market is active.

7
freqtrade/exchange/ftx.py
View File

@ -4,6 +4,7 @@ from typing import Any, Dict, List, Tuple
import ccxt
from freqtrade.constants import BuySell
from freqtrade.enums import MarginMode, TradingMode
from freqtrade.exceptions import (DDosProtection, InsufficientFundsError, InvalidOrderException,
OperationalException, TemporaryError)
@ -44,7 +45,7 @@ class Ftx(Exchange):
@retrier(retries=0)
def stoploss(self, pair: str, amount: float, stop_price: float,
order_types: Dict, side: str, leverage: float) -> Dict:
order_types: Dict, side: BuySell, leverage: float) -> Dict:
"""
Creates a stoploss order.
depending on order_types.stoploss configuration, uses 'market' or limit order.
@ -103,7 +104,7 @@ class Ftx(Exchange):
raise OperationalException(e) from e
@retrier(retries=API_FETCH_ORDER_RETRY_COUNT)
def fetch_stoploss_order(self, order_id: str, pair: str) -> Dict:
def fetch_stoploss_order(self, order_id: str, pair: str, params: Dict = {}) -> Dict:
if self._config['dry_run']:
return self.fetch_dry_run_order(order_id)
@ -144,7 +145,7 @@ class Ftx(Exchange):
raise OperationalException(e) from e
@retrier
def cancel_stoploss_order(self, order_id: str, pair: str) -> Dict:
def cancel_stoploss_order(self, order_id: str, pair: str, params: Dict = {}) -> Dict:
if self._config['dry_run']:
return {}
try:

64
freqtrade/exchange/gateio.py
View File

@ -1,11 +1,13 @@
""" Gate.io exchange subclass """
import logging
from datetime import datetime
from typing import Dict, List, Optional, Tuple
from typing import Any, Dict, List, Optional, Tuple
from freqtrade.constants import BuySell
from freqtrade.enums import MarginMode, TradingMode
from freqtrade.exceptions import OperationalException
from freqtrade.exchange import Exchange
from freqtrade.misc import safe_value_fallback2
logger = logging.getLogger(__name__)
@ -24,12 +26,16 @@ class Gateio(Exchange):
_ft_has: Dict = {
"ohlcv_candle_limit": 1000,
"ohlcv_volume_currency": "quote",
"time_in_force_parameter": "timeInForce",
"order_time_in_force": ['gtc', 'ioc'],
"stoploss_order_types": {"limit": "limit"},
"stoploss_on_exchange": True,
}
_ft_has_futures: Dict = {
"needs_trading_fees": True
"needs_trading_fees": True,
"fee_cost_in_contracts": False, # Set explicitly to false for clarity
"order_props_in_contracts": ['amount', 'filled', 'remaining'],
}
_supported_trading_mode_margin_pairs: List[Tuple[TradingMode, MarginMode]] = [
@ -40,13 +46,33 @@ class Gateio(Exchange):
]
def validate_ordertypes(self, order_types: Dict) -> None:
super().validate_ordertypes(order_types)
if self.trading_mode != TradingMode.FUTURES:
if any(v == 'market' for k, v in order_types.items()):
raise OperationalException(
f'Exchange {self.name} does not support market orders.')
def _get_params(
self,
side: BuySell,
ordertype: str,
leverage: float,
reduceOnly: bool,
time_in_force: str = 'gtc',
) -> Dict:
params = super()._get_params(
side=side,
ordertype=ordertype,
leverage=leverage,
reduceOnly=reduceOnly,
time_in_force=time_in_force,
)
if ordertype == 'market' and self.trading_mode == TradingMode.FUTURES:
params['type'] = 'market'
param = self._ft_has.get('time_in_force_parameter', '')
params.update({param: 'ioc'})
return params
def get_trades_for_order(self, order_id: str, pair: str, since: datetime,
params: Optional[Dict] = None) -> List:
trades = super().get_trades_for_order(order_id, pair, since, params)
@ -61,7 +87,8 @@ class Gateio(Exchange):
pair_fees = self._trading_fees.get(pair, {})
if pair_fees:
for idx, trade in enumerate(trades):
if trade.get('fee', {}).get('cost') is None:
fee = trade.get('fee', {})
if fee and fee.get('cost') is None:
takerOrMaker = trade.get('takerOrMaker', 'taker')
if pair_fees.get(takerOrMaker) is not None:
trades[idx]['fee'] = {
@ -71,14 +98,31 @@ class Gateio(Exchange):
}
return trades
def fetch_stoploss_order(self, order_id: str, pair: str, params={}) -> Dict:
return self.fetch_order(
def get_order_id_conditional(self, order: Dict[str, Any]) -> str:
if self.trading_mode == TradingMode.FUTURES:
return safe_value_fallback2(order, order, 'id_stop', 'id')
return order['id']
def fetch_stoploss_order(self, order_id: str, pair: str, params: Dict = {}) -> Dict:
order = self.fetch_order(
order_id=order_id,
pair=pair,
params={'stop': True}
)
if self.trading_mode == TradingMode.FUTURES:
if order['status'] == 'closed':
# Places a real order - which we need to fetch explicitly.
new_orderid = order.get('info', {}).get('trade_id')
if new_orderid:
order1 = self.fetch_order(order_id=new_orderid, pair=pair, params=params)
order1['id_stop'] = order1['id']
order1['id'] = order_id
order1['stopPrice'] = order.get('stopPrice')
def cancel_stoploss_order(self, order_id: str, pair: str, params={}) -> Dict:
return order1
return order
def cancel_stoploss_order(self, order_id: str, pair: str, params: Dict = {}) -> Dict:
return self.cancel_order(
order_id=order_id,
pair=pair,
@ -90,5 +134,7 @@ class Gateio(Exchange):
Verify stop_loss against stoploss-order value (limit or price)
Returns True if adjustment is necessary.
"""
return ((side == "sell" and stop_loss > float(order['stopPrice'])) or
(side == "buy" and stop_loss < float(order['stopPrice'])))
return (order.get('stopPrice', None) is None or (
side == "sell" and stop_loss > float(order['stopPrice'])) or
(side == "buy" and stop_loss < float(order['stopPrice']))
)

8
freqtrade/exchange/huobi.py
View File

@ -27,7 +27,13 @@ class Huobi(Exchange):
Verify stop_loss against stoploss-order value (limit or price)
Returns True if adjustment is necessary.
"""
return order['type'] == 'stop' and stop_loss > float(order['stopPrice'])
return (
order.get('stopPrice', None) is None
or (
order['type'] == 'stop'
and stop_loss > float(order['stopPrice'])
)
)
def _get_stop_params(self, ordertype: str, stop_price: float) -> Dict:

8
freqtrade/exchange/kraken.py
View File

@ -6,6 +6,7 @@ from typing import Any, Dict, List, Optional, Tuple
import ccxt
from pandas import DataFrame
from freqtrade.constants import BuySell
from freqtrade.enums import MarginMode, TradingMode
from freqtrade.exceptions import (DDosProtection, InsufficientFundsError, InvalidOrderException,
OperationalException, TemporaryError)
@ -22,6 +23,7 @@ class Kraken(Exchange):
_ft_has: Dict = {
"stoploss_on_exchange": True,
"ohlcv_candle_limit": 720,
"ohlcv_has_history": False,
"trades_pagination": "id",
"trades_pagination_arg": "since",
"mark_ohlcv_timeframe": "4h",
@ -43,7 +45,7 @@ class Kraken(Exchange):
return (parent_check and
market.get('darkpool', False) is False)
def get_tickers(self, symbols: List[str] = None, cached: bool = False) -> Dict:
def get_tickers(self, symbols: Optional[List[str]] = None, cached: bool = False) -> Dict:
# Only fetch tickers for current stake currency
# Otherwise the request for kraken becomes too large.
symbols = list(self.get_markets(quote_currencies=[self._config['stake_currency']]))
@ -95,7 +97,7 @@ class Kraken(Exchange):
@retrier(retries=0)
def stoploss(self, pair: str, amount: float, stop_price: float,
order_types: Dict, side: str, leverage: float) -> Dict:
order_types: Dict, side: BuySell, leverage: float) -> Dict:
"""
Creates a stoploss market order.
Stoploss market orders is the only stoploss type supported by kraken.
@ -165,12 +167,14 @@ class Kraken(Exchange):
def _get_params(
self,
side: BuySell,
ordertype: str,
leverage: float,
reduceOnly: bool,
time_in_force: str = 'gtc'
) -> Dict:
params = super()._get_params(
side=side,
ordertype=ordertype,
leverage=leverage,
reduceOnly=reduceOnly,

5
freqtrade/exchange/kucoin.py
View File

@ -33,7 +33,10 @@ class Kucoin(Exchange):
Verify stop_loss against stoploss-order value (limit or price)
Returns True if adjustment is necessary.
"""
return order['info'].get('stop') is not None and stop_loss > float(order['stopPrice'])
return (
order.get('stopPrice', None) is None
or stop_loss > float(order['stopPrice'])
)
def _get_stop_params(self, ordertype: str, stop_price: float) -> Dict:

68
freqtrade/exchange/okx.py
View File

@ -1,12 +1,15 @@
import logging
from typing import Dict, List, Tuple
from typing import Dict, List, Optional, Tuple
import ccxt
from freqtrade.constants import BuySell
from freqtrade.enums import MarginMode, TradingMode
from freqtrade.enums.candletype import CandleType
from freqtrade.exceptions import DDosProtection, OperationalException, TemporaryError
from freqtrade.exchange import Exchange
from freqtrade.exchange.common import retrier
from freqtrade.exchange.exchange import date_minus_candles
logger = logging.getLogger(__name__)
@ -19,12 +22,13 @@ class Okx(Exchange):
"""
_ft_has: Dict = {
"ohlcv_candle_limit": 300,
"ohlcv_candle_limit": 100, # Warning, special case with data prior to X months
"mark_ohlcv_timeframe": "4h",
"funding_fee_timeframe": "8h",
}
_ft_has_futures: Dict = {
"tickers_have_quoteVolume": False,
"fee_cost_in_contracts": True,
}
_supported_trading_mode_margin_pairs: List[Tuple[TradingMode, MarginMode]] = [
@ -34,14 +38,69 @@ class Okx(Exchange):
(TradingMode.FUTURES, MarginMode.ISOLATED),
]
net_only = True
def ohlcv_candle_limit(
self, timeframe: str, candle_type: CandleType, since_ms: Optional[int] = None) -> int:
"""
Exchange ohlcv candle limit
OKX has the following behaviour:
* 300 candles for uptodate data
* 100 candles for historic data
* 100 candles for additional candles (not futures or spot).
:param timeframe: Timeframe to check
:param candle_type: Candle-type
:param since_ms: Starting timestamp
:return: Candle limit as integer
"""
if (
candle_type in (CandleType.FUTURES, CandleType.SPOT) and
(not since_ms or since_ms > (date_minus_candles(timeframe, 300).timestamp() * 1000))
):
return 300
return super().ohlcv_candle_limit(timeframe, candle_type, since_ms)
@retrier
def additional_exchange_init(self) -> None:
"""
Additional exchange initialization logic.
.api will be available at this point.
Must be overridden in child methods if required.
"""
try:
if self.trading_mode == TradingMode.FUTURES and not self._config['dry_run']:
accounts = self._api.fetch_accounts()
if len(accounts) > 0:
self.net_only = accounts[0].get('info', {}).get('posMode') == 'net_mode'
except ccxt.DDoSProtection as e:
raise DDosProtection(e) from e
except (ccxt.NetworkError, ccxt.ExchangeError) as e:
raise TemporaryError(
f'Could not set leverage due to {e.__class__.__name__}. Message: {e}') from e
except ccxt.BaseError as e:
raise OperationalException(e) from e
def _get_posSide(self, side: BuySell, reduceOnly: bool):
if self.net_only:
return 'net'
if not reduceOnly:
# Enter
return 'long' if side == 'buy' else 'short'
else:
# Exit
return 'long' if side == 'sell' else 'short'
def _get_params(
self,
side: BuySell,
ordertype: str,
leverage: float,
reduceOnly: bool,
time_in_force: str = 'gtc',
) -> Dict:
params = super()._get_params(
side=side,
ordertype=ordertype,
leverage=leverage,
reduceOnly=reduceOnly,
@ -49,10 +108,11 @@ class Okx(Exchange):
)
if self.trading_mode == TradingMode.FUTURES and self.margin_mode:
params['tdMode'] = self.margin_mode.value
params['posSide'] = self._get_posSide(side, reduceOnly)
return params
@retrier
def _lev_prep(self, pair: str, leverage: float, side: str):
def _lev_prep(self, pair: str, leverage: float, side: BuySell):
if self.trading_mode != TradingMode.SPOT and self.margin_mode is not None:
try:
# TODO-lev: Test me properly (check mgnMode passed)
@ -61,7 +121,7 @@ class Okx(Exchange):
symbol=pair,
params={
"mgnMode": self.margin_mode.value,
# "posSide": "net"",
"posSide": self._get_posSide(side, False),
})
except ccxt.DDoSProtection as e:
raise DDosProtection(e) from e

242
freqtrade/freqtradebot.py
View File

@ -4,7 +4,7 @@ Freqtrade is the main module of this bot. It contains the class Freqtrade()
import copy
import logging
import traceback
from datetime import datetime, time, timezone
from datetime import datetime, time, timedelta, timezone
from math import isclose
from threading import Lock
from typing import Any, Dict, List, Optional, Tuple
@ -13,7 +13,7 @@ from schedule import Scheduler
from freqtrade import __version__, constants
from freqtrade.configuration import validate_config_consistency
from freqtrade.constants import LongShort
from freqtrade.constants import BuySell, LongShort
from freqtrade.data.converter import order_book_to_dataframe
from freqtrade.data.dataprovider import DataProvider
from freqtrade.edge import Edge
@ -22,6 +22,7 @@ from freqtrade.enums import (ExitCheckTuple, ExitType, RPCMessageType, RunMode,
from freqtrade.exceptions import (DependencyException, ExchangeError, InsufficientFundsError,
InvalidOrderException, PricingError)
from freqtrade.exchange import timeframe_to_minutes, timeframe_to_seconds
from freqtrade.exchange.exchange import timeframe_to_next_date
from freqtrade.misc import safe_value_fallback, safe_value_fallback2
from freqtrade.mixins import LoggingMixin
from freqtrade.persistence import Order, PairLocks, Trade, cleanup_db, init_db
@ -66,14 +67,12 @@ class FreqtradeBot(LoggingMixin):
self.exchange = ExchangeResolver.load_exchange(self.config['exchange']['name'], self.config)
init_db(self.config.get('db_url', None), clean_open_orders=self.config['dry_run'])
init_db(self.config['db_url'])
self.wallets = Wallets(self.config, self.exchange)
PairLocks.timeframe = self.config['timeframe']
self.protections = ProtectionManager(self.config, self.strategy.protections)
# RPC runs in separate threads, can start handling external commands just after
# initialization, even before Freqtradebot has a chance to start its throttling,
# so anything in the Freqtradebot instance should be ready (initialized), including
@ -122,7 +121,9 @@ class FreqtradeBot(LoggingMixin):
self._schedule.every().day.at(t).do(update)
self.last_process = datetime(1970, 1, 1, tzinfo=timezone.utc)
self.strategy.bot_start()
self.strategy.ft_bot_start()
# Initialize protections AFTER bot start - otherwise parameters are not loaded.
self.protections = ProtectionManager(self.config, self.strategy.protections)
def notify_status(self, msg: str) -> None:
"""
@ -190,8 +191,8 @@ class FreqtradeBot(LoggingMixin):
self.strategy.analyze(self.active_pair_whitelist)
with self._exit_lock:
# Check and handle any timed out open orders
self.check_handle_timedout()
# Check for exchange cancelations, timeouts and user requested replace
self.manage_open_orders()
# Protect from collisions with force_exit.
# Without this, freqtrade my try to recreate stoploss_on_exchange orders
@ -226,7 +227,7 @@ class FreqtradeBot(LoggingMixin):
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()
open_trades = Trade.get_open_trades()
if len(open_trades) != 0 and self.state != State.RELOAD_CONFIG:
msg = {
@ -298,7 +299,17 @@ class FreqtradeBot(LoggingMixin):
fo = self.exchange.fetch_order_or_stoploss_order(order.order_id, order.ft_pair,
order.ft_order_side == 'stoploss')
self.update_trade_state(order.trade, order.order_id, fo)
self.update_trade_state(order.trade, order.order_id, fo,
stoploss_order=(order.ft_order_side == 'stoploss'))
except InvalidOrderException as e:
logger.warning(f"Error updating Order {order.order_id} due to {e}.")
if order.order_date_utc - timedelta(days=5) < datetime.now(timezone.utc):
logger.warning(
"Order is older than 5 days. Assuming order was fully cancelled.")
fo = order.to_ccxt_object()
fo['status'] = 'canceled'
self.handle_timedout_order(fo, order.trade)
except ExchangeError as e:
@ -321,6 +332,8 @@ class FreqtradeBot(LoggingMixin):
if not trade.is_open and not trade.fee_updated(trade.exit_side):
# Get sell fee
order = trade.select_order(trade.exit_side, False)
if not order:
order = trade.select_order('stoploss', False)
if order:
logger.info(
f"Updating {trade.exit_side}-fee on trade {trade}"
@ -535,7 +548,8 @@ class FreqtradeBot(LoggingMixin):
if stake_amount is not None and stake_amount > 0.0:
# We should increase our position
self.execute_entry(trade.pair, stake_amount, trade=trade, is_short=trade.is_short)
self.execute_entry(trade.pair, stake_amount, price=current_rate,
trade=trade, is_short=trade.is_short)
if stake_amount is not None and stake_amount < 0.0:
# We should decrease our position
@ -585,6 +599,7 @@ class FreqtradeBot(LoggingMixin):
ordertype: Optional[str] = None,
enter_tag: Optional[str] = None,
trade: Optional[Trade] = None,
order_adjust: bool = False
) -> bool:
"""
Executes a limit buy for the given pair
@ -594,12 +609,13 @@ class FreqtradeBot(LoggingMixin):
"""
time_in_force = self.strategy.order_time_in_force['entry']
[side, name] = ['sell', 'Short'] if is_short else ['buy', 'Long']
side: BuySell = 'sell' if is_short else 'buy'
name = 'Short' if is_short else 'Long'
trade_side: LongShort = 'short' if is_short else 'long'
pos_adjust = trade is not None
enter_limit_requested, stake_amount, leverage = self.get_valid_enter_price_and_stake(
pair, price, stake_amount, trade_side, enter_tag, trade)
pair, price, stake_amount, trade_side, enter_tag, trade, order_adjust)
if not stake_amount:
return False
@ -620,7 +636,7 @@ class FreqtradeBot(LoggingMixin):
pair=pair, order_type=order_type, amount=amount, rate=enter_limit_requested,
time_in_force=time_in_force, current_time=datetime.now(timezone.utc),
entry_tag=enter_tag, side=trade_side):
logger.info(f"User requested abortion of buying {pair}")
logger.info(f"User denied entry for {pair}.")
return False
order = self.exchange.create_order(
pair=pair,
@ -634,7 +650,7 @@ class FreqtradeBot(LoggingMixin):
)
order_obj = Order.parse_from_ccxt_object(order, pair, side)
order_id = order['id']
order_status = order.get('status', None)
order_status = order.get('status')
logger.info(f"Order #{order_id} was created for {pair} and status is {order_status}.")
# we assume the order is executed at the price requested
@ -744,23 +760,26 @@ class FreqtradeBot(LoggingMixin):
self, pair: str, price: Optional[float], stake_amount: float,
trade_side: LongShort,
entry_tag: Optional[str],
trade: Optional[Trade]
trade: Optional[Trade],
order_adjust: bool,
) -> Tuple[float, float, float]:
if price:
enter_limit_requested = price
else:
# Calculate price
proposed_enter_rate = self.exchange.get_rate(
enter_limit_requested = self.exchange.get_rate(
pair, side='entry', is_short=(trade_side == 'short'), refresh=True)
if not order_adjust:
# Don't call custom_entry_price in order-adjust scenario
custom_entry_price = strategy_safe_wrapper(self.strategy.custom_entry_price,
default_retval=proposed_enter_rate)(
default_retval=enter_limit_requested)(
pair=pair, current_time=datetime.now(timezone.utc),
proposed_rate=proposed_enter_rate, entry_tag=entry_tag,
proposed_rate=enter_limit_requested, entry_tag=entry_tag,
side=trade_side,
)
enter_limit_requested = self.get_valid_price(custom_entry_price, proposed_enter_rate)
enter_limit_requested = self.get_valid_price(custom_entry_price, enter_limit_requested)
if not enter_limit_requested:
raise PricingError('Could not determine entry price.')
@ -773,7 +792,7 @@ class FreqtradeBot(LoggingMixin):
current_rate=enter_limit_requested,
proposed_leverage=1.0,
max_leverage=max_leverage,
side=trade_side,
side=trade_side, entry_tag=entry_tag,
) if self.trading_mode != TradingMode.SPOT else 1.0
# Cap leverage between 1.0 and max_leverage.
leverage = min(max(leverage, 1.0), max_leverage)
@ -797,7 +816,7 @@ 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=min(max_stake_amount, stake_available),
entry_tag=entry_tag, side=trade_side
leverage=leverage, entry_tag=entry_tag, side=trade_side
)
stake_amount = self.wallets.validate_stake_amount(
@ -829,7 +848,7 @@ class FreqtradeBot(LoggingMixin):
'type': msg_type,
'buy_tag': trade.enter_tag,
'enter_tag': trade.enter_tag,
'exchange': self.exchange.name.capitalize(),
'exchange': trade.exchange.capitalize(),
'pair': trade.pair,
'leverage': trade.leverage if trade.leverage else None,
'direction': 'Short' if trade.is_short else 'Long',
@ -859,7 +878,7 @@ class FreqtradeBot(LoggingMixin):
'type': RPCMessageType.ENTRY_CANCEL,
'buy_tag': trade.enter_tag,
'enter_tag': trade.enter_tag,
'exchange': self.exchange.name.capitalize(),
'exchange': trade.exchange.capitalize(),
'pair': trade.pair,
'leverage': trade.leverage,
'direction': 'Short' if trade.is_short else 'Long',
@ -942,6 +961,29 @@ class FreqtradeBot(LoggingMixin):
logger.debug(f'Found no {exit_signal_type} signal for %s.', trade)
return False
def _check_and_execute_exit(self, trade: Trade, exit_rate: float,
enter: bool, exit_: bool, exit_tag: Optional[str]) -> bool:
"""
Check and execute trade exit
"""
exits: List[ExitCheckTuple] = self.strategy.should_exit(
trade,
exit_rate,
datetime.now(timezone.utc),
enter=enter,
exit_=exit_,
force_stoploss=self.edge.stoploss(trade.pair) if self.edge else 0
)
for should_exit in exits:
if should_exit.exit_flag:
exit_tag1 = exit_tag if should_exit.exit_type == ExitType.EXIT_SIGNAL else None
logger.info(f'Exit for {trade.pair} detected. Reason: {should_exit.exit_type}'
f'{f" Tag: {exit_tag1}" if exit_tag1 is not None else ""}')
exited = self.execute_trade_exit(trade, exit_rate, should_exit, exit_tag=exit_tag1)
if exited:
return True
return False
def create_stoploss_order(self, trade: Trade, stop_price: float) -> bool:
"""
Abstracts creating stoploss orders from the logic.
@ -1011,7 +1053,7 @@ class FreqtradeBot(LoggingMixin):
# Lock pair for one candle to prevent immediate rebuys
self.strategy.lock_pair(trade.pair, datetime.now(timezone.utc),
reason='Auto lock')
self._notify_exit(trade, "stoploss")
self._notify_exit(trade, "stoploss", True)
return True
if trade.open_order_id or not trade.is_open:
@ -1093,34 +1135,13 @@ class FreqtradeBot(LoggingMixin):
logger.warning(f"Could not create trailing stoploss order "
f"for pair {trade.pair}.")
def _check_and_execute_exit(self, trade: Trade, exit_rate: float,
enter: bool, exit_: bool, exit_tag: Optional[str]) -> bool:
def manage_open_orders(self) -> None:
"""
Check and execute trade exit
"""
should_exit: ExitCheckTuple = self.strategy.should_exit(
trade,
exit_rate,
datetime.now(timezone.utc),
enter=enter,
exit_=exit_,
force_stoploss=self.edge.stoploss(trade.pair) if self.edge else 0
)
if should_exit.exit_flag:
logger.info(f'Exit for {trade.pair} detected. Reason: {should_exit.exit_type}'
f'Tag: {exit_tag if exit_tag is not None else "None"}')
self.execute_trade_exit(trade, exit_rate, should_exit, exit_tag=exit_tag)
return True
return False
def check_handle_timedout(self) -> None:
"""
Check if any orders are timed out and cancel if necessary
:param timeoutvalue: Number of minutes until order is considered timed out
Management of open orders on exchange. Unfilled orders might be cancelled if timeout
was met or replaced if there's a new candle and user has requested it.
Timeout setting takes priority over limit order adjustment request.
:return: None
"""
for trade in Trade.get_open_order_trades():
try:
if not trade.open_order_id:
@ -1131,34 +1152,89 @@ class FreqtradeBot(LoggingMixin):
continue
fully_cancelled = self.update_trade_state(trade, trade.open_order_id, order)
is_entering = order['side'] == trade.entry_side
not_closed = order['status'] == 'open' or fully_cancelled
max_timeouts = self.config.get('unfilledtimeout', {}).get('exit_timeout_count', 0)
order_obj = trade.select_order_by_order_id(trade.open_order_id)
if not_closed and (fully_cancelled or (order_obj and self.strategy.ft_check_timed_out(
trade, order_obj, datetime.now(timezone.utc)))
):
if is_entering:
if not_closed:
if fully_cancelled or (order_obj and self.strategy.ft_check_timed_out(
trade, order_obj, datetime.now(timezone.utc))):
self.handle_timedout_order(order, trade)
else:
self.replace_order(order, order_obj, trade)
def handle_timedout_order(self, order: Dict, trade: Trade) -> None:
"""
Check if current analyzed order timed out and cancel if necessary.
:param order: Order dict grabbed with exchange.fetch_order()
:param trade: Trade object.
:return: None
"""
if order['side'] == trade.entry_side:
self.handle_cancel_enter(trade, order, constants.CANCEL_REASON['TIMEOUT'])
else:
canceled = 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)
max_timeouts = self.config.get('unfilledtimeout', {}).get('exit_timeout_count', 0)
if canceled and max_timeouts > 0 and canceled_count >= max_timeouts:
logger.warning(f'Emergency exiting trade {trade}, as the exit order '
f'timed out {max_timeouts} times.')
try:
self.execute_trade_exit(
trade, order.get('price'),
trade, order['price'],
exit_check=ExitCheckTuple(exit_type=ExitType.EMERGENCY_EXIT))
except DependencyException as exception:
logger.warning(
f'Unable to emergency sell trade {trade.pair}: {exception}')
def replace_order(self, order: Dict, order_obj: Optional[Order], trade: Trade) -> None:
"""
Check if current analyzed entry order should be replaced or simply cancelled.
To simply cancel the existing order(no replacement) adjust_entry_price() should return None
To maintain existing order adjust_entry_price() should return order_obj.price
To replace existing order adjust_entry_price() should return desired price for limit order
:param order: Order dict grabbed with exchange.fetch_order()
:param order_obj: Order object.
:param trade: Trade object.
:return: None
"""
analyzed_df, _ = self.dataprovider.get_analyzed_dataframe(trade.pair,
self.strategy.timeframe)
latest_candle_open_date = analyzed_df.iloc[-1]['date'] if len(analyzed_df) > 0 else None
latest_candle_close_date = timeframe_to_next_date(self.strategy.timeframe,
latest_candle_open_date)
# Check if new candle
if order_obj and latest_candle_close_date > order_obj.order_date_utc:
# New candle
proposed_rate = self.exchange.get_rate(
trade.pair, side='entry', is_short=trade.is_short, refresh=True)
adjusted_entry_price = strategy_safe_wrapper(self.strategy.adjust_entry_price,
default_retval=order_obj.price)(
trade=trade, order=order_obj, pair=trade.pair,
current_time=datetime.now(timezone.utc), proposed_rate=proposed_rate,
current_order_rate=order_obj.price, entry_tag=trade.enter_tag,
side=trade.entry_side)
replacing = True
cancel_reason = constants.CANCEL_REASON['REPLACE']
if not adjusted_entry_price:
replacing = False
cancel_reason = constants.CANCEL_REASON['USER_CANCEL']
if order_obj.price != adjusted_entry_price:
# cancel existing order if new price is supplied or None
self.handle_cancel_enter(trade, order, cancel_reason,
replacing=replacing)
if adjusted_entry_price:
# place new order only if new price is supplied
self.execute_entry(
pair=trade.pair,
stake_amount=(order_obj.remaining * order_obj.price),
price=adjusted_entry_price,
trade=trade,
is_short=trade.is_short,
order_adjust=True,
)
def cancel_all_open_orders(self) -> None:
"""
Cancel all orders that are currently open
@ -1179,9 +1255,13 @@ class FreqtradeBot(LoggingMixin):
self.handle_cancel_exit(trade, order, constants.CANCEL_REASON['ALL_CANCELLED'])
Trade.commit()
def handle_cancel_enter(self, trade: Trade, order: Dict, reason: str) -> bool:
def handle_cancel_enter(
self, trade: Trade, order: Dict, reason: str,
replacing: Optional[bool] = False
) -> bool:
"""
Buy cancel - cancel order
:param replacing: Replacing order - prevent trade deletion.
:return: True if order was fully cancelled
"""
was_trade_fully_canceled = False
@ -1217,9 +1297,10 @@ class FreqtradeBot(LoggingMixin):
# Using filled to determine the filled amount
filled_amount = safe_value_fallback2(corder, order, 'filled', 'filled')
if isclose(filled_amount, 0.0, abs_tol=constants.MATH_CLOSE_PREC):
logger.info(f'{side} order fully cancelled. Removing {trade} from database.')
# if trade is not partially completed and it's the only order, just delete the trade
if len(trade.orders) <= 1:
open_order_count = len([order for order in trade.orders if order.status == 'open'])
if open_order_count <= 1 and trade.nr_of_successful_entries == 0 and not replacing:
logger.info(f'{side} order fully cancelled. Removing {trade} from database.')
trade.delete()
was_trade_fully_canceled = True
reason += f", {constants.CANCEL_REASON['FULLY_CANCELLED']}"
@ -1227,7 +1308,7 @@ class FreqtradeBot(LoggingMixin):
# FIXME TODO: This could possibly reworked to not duplicate the code 15 lines below.
self.update_trade_state(trade, trade.open_order_id, corder)
trade.open_order_id = None
logger.info(f'Partial {side} order timeout for {trade}.')
logger.info(f'{side} Order timeout for {trade}.')
else:
# if trade is partially complete, edit the stake details for the trade
# and close the order
@ -1339,7 +1420,7 @@ class FreqtradeBot(LoggingMixin):
:param trade: Trade instance
:param limit: limit rate for the sell order
:param exit_check: CheckTuple with signal and reason
:return: True if it succeeds (supported) False (not supported)
:return: True if it succeeds False
"""
trade.funding_fees = self.exchange.get_funding_fees(
pair=trade.pair,
@ -1348,6 +1429,7 @@ class FreqtradeBot(LoggingMixin):
open_date=trade.open_date_utc,
)
exit_type = 'exit'
exit_reason = exit_tag or exit_check.exit_reason
if exit_check.exit_type in (ExitType.STOP_LOSS, ExitType.TRAILING_STOP_LOSS):
exit_type = 'stoploss'
@ -1365,7 +1447,7 @@ class FreqtradeBot(LoggingMixin):
pair=trade.pair, trade=trade,
current_time=datetime.now(timezone.utc),
proposed_rate=proposed_limit_rate, current_profit=current_profit,
exit_tag=exit_check.exit_reason)
exit_tag=exit_reason)
limit = self.get_valid_price(custom_exit_price, proposed_limit_rate)
@ -1382,10 +1464,10 @@ class FreqtradeBot(LoggingMixin):
if not strategy_safe_wrapper(self.strategy.confirm_trade_exit, default_retval=True)(
pair=trade.pair, trade=trade, order_type=order_type, amount=amount, rate=limit,
time_in_force=time_in_force, exit_reason=exit_check.exit_reason,
sell_reason=exit_check.exit_reason, # sellreason -> compatibility
time_in_force=time_in_force, exit_reason=exit_reason,
sell_reason=exit_reason, # sellreason -> compatibility
current_time=datetime.now(timezone.utc)):
logger.info(f"User requested abortion of exiting {trade.pair}")
logger.info(f"User denied exit for {trade.pair}.")
return False
try:
@ -1412,7 +1494,7 @@ class FreqtradeBot(LoggingMixin):
trade.open_order_id = order['id']
trade.exit_order_status = ''
trade.close_rate_requested = limit
trade.exit_reason = exit_tag or exit_check.exit_reason
trade.exit_reason = exit_reason
# Lock pair for one candle to prevent immediate re-trading
self.strategy.lock_pair(trade.pair, datetime.now(timezone.utc),
@ -1462,7 +1544,7 @@ class FreqtradeBot(LoggingMixin):
'open_date': trade.open_date,
'close_date': trade.close_date or datetime.utcnow(),
'stake_currency': self.config['stake_currency'],
'fiat_currency': self.config.get('fiat_display_currency', None),
'fiat_currency': self.config.get('fiat_display_currency'),
}
if 'fiat_display_currency' in self.config:
@ -1573,12 +1655,11 @@ class FreqtradeBot(LoggingMixin):
if order['status'] in constants.NON_OPEN_EXCHANGE_STATES:
# If a entry order was closed, force update on stoploss on exchange
if order.get('side', None) == trade.entry_side:
if order.get('side') == trade.entry_side:
trade = self.cancel_stoploss_on_exchange(trade)
# TODO: Margin will need to use interest_rate as well.
# interest_rate = self.exchange.get_interest_rate()
trade.set_isolated_liq(self.exchange.get_liquidation_price(
leverage=trade.leverage,
pair=trade.pair,
amount=trade.amount,
@ -1597,7 +1678,7 @@ class FreqtradeBot(LoggingMixin):
if send_msg and not stoploss_order and not trade.open_order_id:
self._notify_exit(trade, '', True)
self.handle_protections(trade.pair, trade.trade_direction)
elif send_msg and not trade.open_order_id:
elif send_msg and not trade.open_order_id and not stoploss_order:
# Enter fill
self._notify_enter(trade, order, fill=True)
@ -1663,7 +1744,8 @@ class FreqtradeBot(LoggingMixin):
trade_base_currency = self.exchange.get_pair_base_currency(trade.pair)
# use fee from order-dict if possible
if self.exchange.order_has_fee(order):
fee_cost, fee_currency, fee_rate = self.exchange.extract_cost_curr_rate(order)
fee_cost, fee_currency, fee_rate = self.exchange.extract_cost_curr_rate(
order['fee'], order['symbol'], order['cost'], order_obj.safe_filled)
logger.info(f"Fee for Trade {trade} [{order_obj.ft_order_side}]: "
f"{fee_cost:.8g} {fee_currency} - rate: {fee_rate}")
if fee_rate is None or fee_rate < 0.02:
@ -1701,7 +1783,15 @@ class FreqtradeBot(LoggingMixin):
for exectrade in trades:
amount += exectrade['amount']
if self.exchange.order_has_fee(exectrade):
fee_cost_, fee_currency, fee_rate_ = self.exchange.extract_cost_curr_rate(exectrade)
# Prefer singular fee
fees = [exectrade['fee']]
else:
fees = exectrade.get('fees', [])
for fee in fees:
fee_cost_, fee_currency, fee_rate_ = self.exchange.extract_cost_curr_rate(
fee, exectrade['symbol'], exectrade['cost'], exectrade['amount']
)
fee_cost += fee_cost_
if fee_rate_ is not None:
fee_rate_array.append(fee_rate_)

226
freqtrade/optimize/backtesting.py
View File

@ -87,7 +87,7 @@ class Backtesting:
self.exchange = ExchangeResolver.load_exchange(self._exchange_name, self.config)
self.dataprovider = DataProvider(self.config, self.exchange)
if self.config.get('strategy_list', None):
if self.config.get('strategy_list'):
for strat in list(self.config['strategy_list']):
stratconf = deepcopy(self.config)
stratconf['strategy'] = strat
@ -187,7 +187,9 @@ class Backtesting:
# since a "perfect" stoploss-exit is assumed anyway
# And the regular "stoploss" function would not apply to that case
self.strategy.order_types['stoploss_on_exchange'] = False
self.strategy.bot_start()
self.strategy.ft_bot_start()
strategy_safe_wrapper(self.strategy.bot_loop_start, supress_error=True)()
def _load_protections(self, strategy: IStrategy):
if self.config.get('enable_protections', False):
@ -275,8 +277,12 @@ class Backtesting:
if pair not in self.exchange._leverage_tiers:
unavailable_pairs.append(pair)
continue
self.futures_data[pair] = funding_rates_dict[pair].merge(
mark_rates_dict[pair], on='date', how="inner", suffixes=["_fund", "_mark"])
self.futures_data[pair] = self.exchange.combine_funding_and_mark(
funding_rates=funding_rates_dict[pair],
mark_rates=mark_rates_dict[pair],
futures_funding_rate=self.config.get('futures_funding_rate', None),
)
if unavailable_pairs:
raise OperationalException(
@ -297,6 +303,9 @@ class Backtesting:
self.rejected_trades = 0
self.timedout_entry_orders = 0
self.timedout_exit_orders = 0
self.canceled_trade_entries = 0
self.canceled_entry_orders = 0
self.replaced_entry_orders = 0
self.dataprovider.clear_cache()
if enable_protections:
self._load_protections(self.strategy)
@ -493,7 +502,8 @@ class Backtesting:
stake_available = self.wallets.get_available_stake_amount()
stake_amount = strategy_safe_wrapper(self.strategy.adjust_trade_position,
default_retval=None)(
trade=trade, current_time=row[DATE_IDX].to_pydatetime(), current_rate=row[OPEN_IDX],
trade=trade, # type: ignore[arg-type]
current_time=row[DATE_IDX].to_pydatetime(), current_rate=row[OPEN_IDX],
current_profit=current_profit, min_stake=min_stake,
max_stake=min(max_stake, stake_available))
@ -524,55 +534,36 @@ class Backtesting:
if check_adjust_entry:
trade = self._get_adjust_trade_entry_for_candle(trade, row)
exit_candle_time: datetime = row[DATE_IDX].to_pydatetime()
enter = row[SHORT_IDX] if trade.is_short else row[LONG_IDX]
exit_sig = row[ESHORT_IDX] if trade.is_short else row[ELONG_IDX]
exit_ = self.strategy.should_exit(
trade, row[OPEN_IDX], exit_candle_time, # type: ignore
exits = self.strategy.should_exit(
trade, row[OPEN_IDX], row[DATE_IDX].to_pydatetime(), # type: ignore
enter=enter, exit_=exit_sig,
low=row[LOW_IDX], high=row[HIGH_IDX]
)
for exit_ in exits:
t = self._get_exit_for_signal(trade, row, exit_)
if t:
return t
return None
def _get_exit_for_signal(self, trade: LocalTrade, row: Tuple,
exit_: ExitCheckTuple) -> Optional[LocalTrade]:
exit_candle_time: datetime = row[DATE_IDX].to_pydatetime()
if exit_.exit_flag:
trade.close_date = exit_candle_time
exit_reason = exit_.exit_reason
trade_dur = int((trade.close_date_utc - trade.open_date_utc).total_seconds() // 60)
try:
closerate = self._get_close_rate(row, trade, exit_, trade_dur)
close_rate = self._get_close_rate(row, trade, exit_, trade_dur)
except ValueError:
return None
# call the custom exit price,with default value as previous closerate
current_profit = trade.calc_profit_ratio(closerate)
# call the custom exit price,with default value as previous close_rate
current_profit = trade.calc_profit_ratio(close_rate)
order_type = self.strategy.order_types['exit']
if exit_.exit_type in (ExitType.EXIT_SIGNAL, ExitType.CUSTOM_EXIT):
# Custom exit pricing only for exit-signals
if order_type == 'limit':
closerate = strategy_safe_wrapper(self.strategy.custom_exit_price,
default_retval=closerate)(
pair=trade.pair, trade=trade,
current_time=exit_candle_time,
proposed_rate=closerate, current_profit=current_profit,
exit_tag=exit_.exit_reason)
# We can't place orders lower than current low.
# freqtrade does not support this in live, and the order would fill immediately
if trade.is_short:
closerate = min(closerate, row[HIGH_IDX])
else:
closerate = max(closerate, row[LOW_IDX])
# Confirm trade exit:
time_in_force = self.strategy.order_time_in_force['exit']
if not strategy_safe_wrapper(self.strategy.confirm_trade_exit, default_retval=True)(
pair=trade.pair, trade=trade, order_type='limit', amount=trade.amount,
rate=closerate,
time_in_force=time_in_force,
sell_reason=exit_.exit_reason, # deprecated
exit_reason=exit_.exit_reason,
current_time=exit_candle_time):
return None
trade.exit_reason = exit_.exit_reason
# Checks and adds an exit tag, after checking that the length of the
# row has the length for an exit tag column
if(
@ -581,7 +572,38 @@ class Backtesting:
and len(row[EXIT_TAG_IDX]) > 0
and exit_.exit_type in (ExitType.EXIT_SIGNAL,)
):
trade.exit_reason = row[EXIT_TAG_IDX]
exit_reason = row[EXIT_TAG_IDX]
# Custom exit pricing only for exit-signals
if order_type == 'limit':
close_rate = strategy_safe_wrapper(self.strategy.custom_exit_price,
default_retval=close_rate)(
pair=trade.pair,
trade=trade, # type: ignore[arg-type]
current_time=exit_candle_time,
proposed_rate=close_rate, current_profit=current_profit,
exit_tag=exit_reason)
# We can't place orders lower than current low.
# freqtrade does not support this in live, and the order would fill immediately
if trade.is_short:
close_rate = min(close_rate, row[HIGH_IDX])
else:
close_rate = max(close_rate, row[LOW_IDX])
# Confirm trade exit:
time_in_force = self.strategy.order_time_in_force['exit']
if not strategy_safe_wrapper(self.strategy.confirm_trade_exit, default_retval=True)(
pair=trade.pair,
trade=trade, # type: ignore[arg-type]
order_type='limit',
amount=trade.amount,
rate=close_rate,
time_in_force=time_in_force,
sell_reason=exit_reason, # deprecated
exit_reason=exit_reason,
current_time=exit_candle_time):
return None
trade.exit_reason = exit_reason
self.order_id_counter += 1
order = Order(
@ -597,12 +619,12 @@ class Backtesting:
side=trade.exit_side,
order_type=order_type,
status="open",
price=closerate,
average=closerate,
price=close_rate,
average=close_rate,
amount=trade.amount,
filled=0,
remaining=trade.amount,
cost=trade.amount * closerate,
cost=trade.amount * close_rate,
)
trade.orders.append(order)
return trade
@ -649,7 +671,7 @@ class Backtesting:
return self._get_exit_trade_entry_for_candle(trade, row)
def get_valid_price_and_stake(
self, pair: str, row: Tuple, propose_rate: float, stake_amount: Optional[float],
self, pair: str, row: Tuple, propose_rate: float, stake_amount: float,
direction: LongShort, current_time: datetime, entry_tag: Optional[str],
trade: Optional[LocalTrade], order_type: str
) -> Tuple[float, float, float, float]:
@ -683,7 +705,7 @@ class Backtesting:
current_rate=row[OPEN_IDX],
proposed_leverage=1.0,
max_leverage=max_leverage,
side=direction,
side=direction, entry_tag=entry_tag,
) if self._can_short else 1.0
# Cap leverage between 1.0 and max_leverage.
leverage = min(max(leverage, 1.0), max_leverage)
@ -700,7 +722,7 @@ class Backtesting:
pair=pair, current_time=current_time, current_rate=propose_rate,
proposed_stake=stake_amount, min_stake=min_stake_amount,
max_stake=min(stake_available, max_stake_amount),
entry_tag=entry_tag, side=direction)
leverage=leverage, entry_tag=entry_tag, side=direction)
stake_amount_val = self.wallets.validate_stake_amount(
pair=pair,
@ -713,19 +735,26 @@ class Backtesting:
def _enter_trade(self, pair: str, row: Tuple, direction: LongShort,
stake_amount: Optional[float] = None,
trade: Optional[LocalTrade] = None) -> Optional[LocalTrade]:
trade: Optional[LocalTrade] = None,
requested_rate: Optional[float] = None,
requested_stake: Optional[float] = None) -> Optional[LocalTrade]:
current_time = row[DATE_IDX].to_pydatetime()
entry_tag = row[ENTER_TAG_IDX] if len(row) >= ENTER_TAG_IDX + 1 else None
# let's call the custom entry price, using the open price as default price
order_type = self.strategy.order_types['entry']
pos_adjust = trade is not None
pos_adjust = trade is not None and requested_rate is None
stake_amount_ = stake_amount or (trade.stake_amount if trade else 0.0)
propose_rate, stake_amount, leverage, min_stake_amount = self.get_valid_price_and_stake(
pair, row, row[OPEN_IDX], stake_amount, direction, current_time, entry_tag, trade,
pair, row, row[OPEN_IDX], stake_amount_, direction, current_time, entry_tag, trade,
order_type
)
# replace proposed rate if another rate was requested
propose_rate = requested_rate if requested_rate else propose_rate
stake_amount = requested_stake if requested_stake else stake_amount
if not stake_amount:
# In case of pos adjust, still return the original trade
# If not pos adjust, trade is None
@ -806,11 +835,11 @@ class Backtesting:
remaining=amount,
cost=stake_amount + trade.fee_open,
)
trade.orders.append(order)
if pos_adjust and self._get_order_filled(order.price, row):
order.close_bt_order(current_time)
order.close_bt_order(current_time, trade)
else:
trade.open_order_id = str(self.order_id_counter)
trade.orders.append(order)
trade.recalc_trade_from_orders()
return trade
@ -867,14 +896,34 @@ class Backtesting:
self.protections.stop_per_pair(pair, current_time, side)
self.protections.global_stop(current_time, side)
def check_order_cancel(self, trade: LocalTrade, current_time) -> bool:
def manage_open_orders(self, trade: LocalTrade, current_time: datetime, row: Tuple) -> bool:
"""
Check if an order has been canceled.
Returns True if the trade should be Deleted (initial order was canceled).
Check if any open order needs to be cancelled or replaced.
Returns True if the trade should be deleted.
"""
for order in [o for o in trade.orders if o.ft_is_open]:
oc = self.check_order_cancel(trade, order, current_time)
if oc:
# delete trade due to order timeout
return True
elif oc is None and self.check_order_replace(trade, order, current_time, row):
# delete trade due to user request
self.canceled_trade_entries += 1
return True
# default maintain trade
return False
timedout = self.strategy.ft_check_timed_out(trade, order, current_time)
def check_order_cancel(
self, trade: LocalTrade, order: Order, current_time: datetime) -> Optional[bool]:
"""
Check if current analyzed order has to be canceled.
Returns True if the trade should be Deleted (initial order was canceled),
False if it's Canceled
None if the order is still active.
"""
timedout = self.strategy.ft_check_timed_out(
trade, # type: ignore[arg-type]
order, current_time)
if timedout:
if order.side == trade.entry_side:
self.timedout_entry_orders += 1
@ -884,11 +933,53 @@ class Backtesting:
else:
# Close additional entry order
del trade.orders[trade.orders.index(order)]
trade.open_order_id = None
return False
if order.side == trade.exit_side:
self.timedout_exit_orders += 1
# Close exit order and retry exiting on next signal.
del trade.orders[trade.orders.index(order)]
trade.open_order_id = None
return False
return None
def check_order_replace(self, trade: LocalTrade, order: Order, current_time,
row: Tuple) -> bool:
"""
Check if current analyzed entry order has to be replaced and do so.
If user requested cancellation and there are no filled orders in the trade will
instruct caller to delete the trade.
Returns True if the trade should be deleted.
"""
# only check on new candles for open entry orders
if order.side == trade.entry_side and current_time > order.order_date_utc:
requested_rate = strategy_safe_wrapper(self.strategy.adjust_entry_price,
default_retval=order.price)(
trade=trade, # type: ignore[arg-type]
order=order, pair=trade.pair, current_time=current_time,
proposed_rate=row[OPEN_IDX], current_order_rate=order.price,
entry_tag=trade.enter_tag, side=trade.trade_direction
) # default value is current order price
# cancel existing order whenever a new rate is requested (or None)
if requested_rate == order.price:
# assumption: there can't be multiple open entry orders at any given time
return False
else:
del trade.orders[trade.orders.index(order)]
trade.open_order_id = None
self.canceled_entry_orders += 1
# place new order if result was not None
if requested_rate:
self._enter_trade(pair=trade.pair, row=row, trade=trade,
requested_rate=requested_rate,
requested_stake=(order.remaining * order.price),
direction='short' if trade.is_short else 'long')
self.replaced_entry_orders += 1
else:
# assumption: there can't be multiple open entry orders at any given time
return (trade.nr_of_successful_entries == 0)
return False
def validate_row(
@ -960,11 +1051,12 @@ class Backtesting:
self.dataprovider._set_dataframe_max_index(row_index)
for t in list(open_trades[pair]):
# 1. Cancel expired entry/exit orders.
if self.check_order_cancel(t, current_time):
# Close trade due to entry timeout expiration.
# 1. Manage currently open orders of active trades
if self.manage_open_orders(t, current_time, row):
# Close trade
open_trade_count -= 1
open_trades[pair].remove(t)
LocalTrade.trades_open.remove(t)
self.wallets.update()
# 2. Process entries.
@ -988,14 +1080,15 @@ class Backtesting:
open_trade_count += 1
# logger.debug(f"{pair} - Emulate creation of new trade: {trade}.")
open_trades[pair].append(trade)
LocalTrade.add_bt_trade(trade)
self.wallets.update()
for trade in list(open_trades[pair]):
# 3. Process entry orders.
order = trade.select_order(trade.entry_side, is_open=True)
if order and self._get_order_filled(order.price, row):
order.close_bt_order(current_time)
order.close_bt_order(current_time, trade)
trade.open_order_id = None
LocalTrade.add_bt_trade(trade)
self.wallets.update()
# 4. Create exit orders (if any)
@ -1005,6 +1098,7 @@ class Backtesting:
# 5. Process exit orders.
order = trade.select_order(trade.exit_side, is_open=True)
if order and self._get_order_filled(order.price, row):
order.close_bt_order(current_time, trade)
trade.open_order_id = None
trade.close_date = current_time
trade.close(order.price, show_msg=False)
@ -1033,6 +1127,9 @@ class Backtesting:
'rejected_signals': self.rejected_trades,
'timedout_entry_orders': self.timedout_entry_orders,
'timedout_exit_orders': self.timedout_exit_orders,
'canceled_trade_entries': self.canceled_trade_entries,
'canceled_entry_orders': self.canceled_entry_orders,
'replaced_entry_orders': self.replaced_entry_orders,
'final_balance': self.wallets.get_total(self.strategy.config['stake_currency']),
}
@ -1044,8 +1141,6 @@ class Backtesting:
backtest_start_time = datetime.now(timezone.utc)
self._set_strategy(strat)
strategy_safe_wrapper(self.strategy.bot_loop_start, supress_error=True)()
# Use max_open_trades in backtesting, except --disable-max-market-positions is set
if self.config.get('use_max_market_positions', True):
# Must come from strategy config, as the strategy may modify this setting.
@ -1170,13 +1265,14 @@ class Backtesting:
self.results['strategy_comparison'].extend(results['strategy_comparison'])
else:
self.results = results
dt_appendix = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
if self.config.get('export', 'none') in ('trades', 'signals'):
store_backtest_stats(self.config['exportfilename'], self.results)
store_backtest_stats(self.config['exportfilename'], self.results, dt_appendix)
if (self.config.get('export', 'none') == 'signals' and
self.dataprovider.runmode == RunMode.BACKTEST):
store_backtest_signal_candles(self.config['exportfilename'], self.processed_dfs)
store_backtest_signal_candles(
self.config['exportfilename'], self.processed_dfs, dt_appendix)
# Results may be mixed up now. Sort them so they follow --strategy-list order.
if 'strategy_list' in self.config and len(self.results) > 0:

2
freqtrade/optimize/edge_cli.py
View File

@ -44,7 +44,7 @@ class EdgeCli:
self.edge._timerange = TimeRange.parse_timerange(None if self.config.get(
'timerange') is None else str(self.config.get('timerange')))
self.strategy.bot_start()
self.strategy.ft_bot_start()
def start(self) -> None:
result = self.edge.calculate(self.config['exchange']['pair_whitelist'])

30
freqtrade/optimize/hyperopt.py
View File

@ -6,6 +6,7 @@ This module contains the hyperopt logic
import logging
import random
import sys
import warnings
from datetime import datetime, timezone
from math import ceil
@ -17,6 +18,7 @@ import rapidjson
from colorama import Fore, Style
from colorama import init as colorama_init
from joblib import Parallel, cpu_count, delayed, dump, load, wrap_non_picklable_objects
from joblib.externals import cloudpickle
from pandas import DataFrame
from freqtrade.constants import DATETIME_PRINT_FORMAT, FTHYPT_FILEVERSION, LAST_BT_RESULT_FN
@ -27,8 +29,7 @@ from freqtrade.misc import deep_merge_dicts, file_dump_json, plural
from freqtrade.optimize.backtesting import Backtesting
# Import IHyperOpt and IHyperOptLoss to allow unpickling classes from these modules
from freqtrade.optimize.hyperopt_auto import HyperOptAuto
from freqtrade.optimize.hyperopt_interface import IHyperOpt # noqa: F401
from freqtrade.optimize.hyperopt_loss_interface import IHyperOptLoss # noqa: F401
from freqtrade.optimize.hyperopt_loss_interface import IHyperOptLoss
from freqtrade.optimize.hyperopt_tools import HyperoptTools, hyperopt_serializer
from freqtrade.optimize.optimize_reports import generate_strategy_stats
from freqtrade.resolvers.hyperopt_resolver import HyperOptLossResolver
@ -62,7 +63,6 @@ class Hyperopt:
hyperopt = Hyperopt(config)
hyperopt.start()
"""
custom_hyperopt: IHyperOpt
def __init__(self, config: Dict[str, Any]) -> None:
self.buy_space: List[Dimension] = []
@ -77,6 +77,7 @@ class Hyperopt:
self.backtesting = Backtesting(self.config)
self.pairlist = self.backtesting.pairlists.whitelist
self.custom_hyperopt: HyperOptAuto
if not self.config.get('hyperopt'):
self.custom_hyperopt = HyperOptAuto(self.config)
@ -88,7 +89,9 @@ class Hyperopt:
self.backtesting._set_strategy(self.backtesting.strategylist[0])
self.custom_hyperopt.strategy = self.backtesting.strategy
self.custom_hyperoptloss = HyperOptLossResolver.load_hyperoptloss(self.config)
self.hyperopt_pickle_magic(self.backtesting.strategy.__class__.__bases__)
self.custom_hyperoptloss: IHyperOptLoss = HyperOptLossResolver.load_hyperoptloss(
self.config)
self.calculate_loss = self.custom_hyperoptloss.hyperopt_loss_function
time_now = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
strategy = str(self.config['strategy'])
@ -137,6 +140,17 @@ class Hyperopt:
logger.info(f"Removing `{p}`.")
p.unlink()
def hyperopt_pickle_magic(self, bases) -> None:
"""
Hyperopt magic to allow strategy inheritance across files.
For this to properly work, we need to register the module of the imported class
to pickle as value.
"""
for modules in bases:
if modules.__name__ != 'IStrategy':
cloudpickle.register_pickle_by_value(sys.modules[modules.__module__])
self.hyperopt_pickle_magic(modules.__bases__)
def _get_params_dict(self, dimensions: List[Dimension], raw_params: List[Any]) -> Dict:
# Ensure the number of dimensions match
@ -429,7 +443,7 @@ class Hyperopt:
return new_list
i = 0
asked_non_tried: List[List[Any]] = []
is_random: List[bool] = []
is_random_non_tried: List[bool] = []
while i < 5 and len(asked_non_tried) < n_points:
if i < 3:
self.opt.cache_ = {}
@ -438,7 +452,7 @@ class Hyperopt:
else:
asked = unique_list(self.opt.space.rvs(n_samples=n_points * 5))
is_random = [True for _ in range(len(asked))]
is_random += [rand for x, rand in zip(asked, is_random)
is_random_non_tried += [rand for x, rand in zip(asked, is_random)
if x not in self.opt.Xi
and x not in asked_non_tried]
asked_non_tried += [x for x in asked
@ -449,13 +463,13 @@ class Hyperopt:
if asked_non_tried:
return (
asked_non_tried[:min(len(asked_non_tried), n_points)],
is_random[:min(len(asked_non_tried), n_points)]
is_random_non_tried[:min(len(asked_non_tried), n_points)]
)
else:
return self.opt.ask(n_points=n_points), [False for _ in range(n_points)]
def start(self) -> None:
self.random_state = self._set_random_state(self.config.get('hyperopt_random_state', None))
self.random_state = self._set_random_state(self.config.get('hyperopt_random_state'))
logger.info(f"Using optimizer random state: {self.random_state}")
self.hyperopt_table_header = -1
# Initialize spaces ...

16
freqtrade/optimize/hyperopt_tools.py
View File

@ -127,14 +127,14 @@ class HyperoptTools():
'only_profitable': config.get('hyperopt_list_profitable', False),
'filter_min_trades': config.get('hyperopt_list_min_trades', 0),
'filter_max_trades': config.get('hyperopt_list_max_trades', 0),
'filter_min_avg_time': config.get('hyperopt_list_min_avg_time', None),
'filter_max_avg_time': config.get('hyperopt_list_max_avg_time', None),
'filter_min_avg_profit': config.get('hyperopt_list_min_avg_profit', None),
'filter_max_avg_profit': config.get('hyperopt_list_max_avg_profit', None),
'filter_min_total_profit': config.get('hyperopt_list_min_total_profit', None),
'filter_max_total_profit': config.get('hyperopt_list_max_total_profit', None),
'filter_min_objective': config.get('hyperopt_list_min_objective', None),
'filter_max_objective': config.get('hyperopt_list_max_objective', None),
'filter_min_avg_time': config.get('hyperopt_list_min_avg_time'),
'filter_max_avg_time': config.get('hyperopt_list_max_avg_time'),
'filter_min_avg_profit': config.get('hyperopt_list_min_avg_profit'),
'filter_max_avg_profit': config.get('hyperopt_list_max_avg_profit'),
'filter_min_total_profit': config.get('hyperopt_list_min_total_profit'),
'filter_max_total_profit': config.get('hyperopt_list_max_total_profit'),
'filter_min_objective': config.get('hyperopt_list_min_objective'),
'filter_max_objective': config.get('hyperopt_list_max_objective'),
}
if not HyperoptTools._test_hyperopt_results_exist(results_file):
# No file found.

42
freqtrade/optimize/optimize_reports.py
View File

@ -4,7 +4,6 @@ from datetime import datetime, timedelta, timezone
from pathlib import Path
from typing import Any, Dict, List, Union
from numpy import int64
from pandas import DataFrame, to_datetime
from tabulate import tabulate
@ -18,21 +17,21 @@ from freqtrade.optimize.backtest_caching import get_backtest_metadata_filename
logger = logging.getLogger(__name__)
def store_backtest_stats(recordfilename: Path, stats: Dict[str, DataFrame]) -> None:
def store_backtest_stats(
recordfilename: Path, stats: Dict[str, DataFrame], dtappendix: str) -> None:
"""
Stores backtest results
:param recordfilename: Path object, which can either be a filename or a directory.
Filenames will be appended with a timestamp right before the suffix
while for directories, <directory>/backtest-result-<datetime>.json will be used as filename
:param stats: Dataframe containing the backtesting statistics
:param dtappendix: Datetime to use for the filename
"""
if recordfilename.is_dir():
filename = (recordfilename /
f'backtest-result-{datetime.now().strftime("%Y-%m-%d_%H-%M-%S")}.json')
filename = (recordfilename / f'backtest-result-{dtappendix}.json')
else:
filename = Path.joinpath(
recordfilename.parent,
f'{recordfilename.stem}-{datetime.now().strftime("%Y-%m-%d_%H-%M-%S")}'
recordfilename.parent, f'{recordfilename.stem}-{dtappendix}'
).with_suffix(recordfilename.suffix)
# Store metadata separately.
@ -45,7 +44,8 @@ def store_backtest_stats(recordfilename: Path, stats: Dict[str, DataFrame]) -> N
file_dump_json(latest_filename, {'latest_backtest': str(filename.name)})
def store_backtest_signal_candles(recordfilename: Path, candles: Dict[str, Dict]) -> Path:
def store_backtest_signal_candles(
recordfilename: Path, candles: Dict[str, Dict], dtappendix: str) -> Path:
"""
Stores backtest trade signal candles
:param recordfilename: Path object, which can either be a filename or a directory.
@ -53,14 +53,13 @@ def store_backtest_signal_candles(recordfilename: Path, candles: Dict[str, Dict]
while for directories, <directory>/backtest-result-<datetime>_signals.pkl will be used
as filename
:param stats: Dict containing the backtesting signal candles
:param dtappendix: Datetime to use for the filename
"""
if recordfilename.is_dir():
filename = (recordfilename /
f'backtest-result-{datetime.now().strftime("%Y-%m-%d_%H-%M-%S")}_signals.pkl')
filename = (recordfilename / f'backtest-result-{dtappendix}_signals.pkl')
else:
filename = Path.joinpath(
recordfilename.parent,
f'{recordfilename.stem}-{datetime.now().strftime("%Y-%m-%d_%H-%M-%S")}_signals.pkl'
recordfilename.parent, f'{recordfilename.stem}-{dtappendix}_signals.pkl'
)
file_dump_joblib(filename, candles)
@ -417,9 +416,9 @@ def generate_strategy_stats(pairlist: List[str],
key=lambda x: x['profit_sum']) if len(pair_results) > 1 else None
worst_pair = min([pair for pair in pair_results if pair['key'] != 'TOTAL'],
key=lambda x: x['profit_sum']) if len(pair_results) > 1 else None
if not results.empty:
results['open_timestamp'] = results['open_date'].view(int64) // 1e6
results['close_timestamp'] = results['close_date'].view(int64) // 1e6
winning_profit = results.loc[results['profit_abs'] > 0, 'profit_abs'].sum()
losing_profit = results.loc[results['profit_abs'] < 0, 'profit_abs'].sum()
profit_factor = winning_profit / abs(losing_profit) if losing_profit else 0.0
backtest_days = (max_date - min_date).days or 1
strat_stats = {
@ -447,6 +446,7 @@ def generate_strategy_stats(pairlist: List[str],
'profit_total_long_abs': results.loc[~results['is_short'], 'profit_abs'].sum(),
'profit_total_short_abs': results.loc[results['is_short'], 'profit_abs'].sum(),
'cagr': calculate_cagr(backtest_days, start_balance, content['final_balance']),
'profit_factor': profit_factor,
'backtest_start': min_date.strftime(DATETIME_PRINT_FORMAT),
'backtest_start_ts': int(min_date.timestamp() * 1000),
'backtest_end': max_date.strftime(DATETIME_PRINT_FORMAT),
@ -468,6 +468,9 @@ def generate_strategy_stats(pairlist: List[str],
'rejected_signals': content['rejected_signals'],
'timedout_entry_orders': content['timedout_entry_orders'],
'timedout_exit_orders': content['timedout_exit_orders'],
'canceled_trade_entries': content['canceled_trade_entries'],
'canceled_entry_orders': content['canceled_entry_orders'],
'replaced_entry_orders': content['replaced_entry_orders'],
'max_open_trades': max_open_trades,
'max_open_trades_setting': (config['max_open_trades']
if config['max_open_trades'] != float('inf') else -1),
@ -498,8 +501,10 @@ def generate_strategy_stats(pairlist: List[str],
(drawdown_abs, drawdown_start, drawdown_end, high_val, low_val,
max_drawdown) = calculate_max_drawdown(
results, value_col='profit_abs', starting_balance=start_balance)
# max_relative_drawdown = Underwater
(_, _, _, _, _, max_relative_drawdown) = calculate_max_drawdown(
results, value_col='profit_abs', starting_balance=start_balance, relative=True)
strat_stats.update({
'max_drawdown': max_drawdown_legacy, # Deprecated - do not use
'max_drawdown_account': max_drawdown,
@ -753,6 +758,12 @@ def text_table_add_metrics(strat_results: Dict) -> str:
('Drawdown End', strat_results['drawdown_end']),
])
entry_adjustment_metrics = [
('Canceled Trade Entries', strat_results.get('canceled_trade_entries', 'N/A')),
('Canceled Entry Orders', strat_results.get('canceled_entry_orders', 'N/A')),
('Replaced Entry Orders', strat_results.get('replaced_entry_orders', 'N/A')),
] if strat_results.get('canceled_entry_orders', 0) > 0 else []
# Newly added fields should be ignored if they are missing in strat_results. hyperopt-show
# command stores these results and newer version of freqtrade must be able to handle old
# results with missing new fields.
@ -772,6 +783,8 @@ def text_table_add_metrics(strat_results: Dict) -> str:
strat_results['stake_currency'])),
('Total profit %', f"{strat_results['profit_total']:.2%}"),
('CAGR %', f"{strat_results['cagr']:.2%}" if 'cagr' in strat_results else 'N/A'),
('Profit factor', f'{strat_results["profit_factor"]:.2f}' if 'profit_factor'
in strat_results else 'N/A'),
('Trades per day', strat_results['trades_per_day']),
('Avg. daily profit %',
f"{(strat_results['profit_total'] / strat_results['backtest_days']):.2%}"),
@ -801,6 +814,7 @@ def text_table_add_metrics(strat_results: Dict) -> str:
('Entry/Exit Timeouts',
f"{strat_results.get('timedout_entry_orders', 'N/A')} / "
f"{strat_results.get('timedout_exit_orders', 'N/A')}"),
*entry_adjustment_metrics,
('', ''), # Empty line to improve readability
('Min balance', round_coin_value(strat_results['csum_min'],

4
freqtrade/persistence/__init__.py
View File

@ -1,5 +1,5 @@
# flake8: noqa: F401
from freqtrade.persistence.models import (LocalTrade, Order, Trade, clean_dry_run_db, cleanup_db,
init_db)
from freqtrade.persistence.models import cleanup_db, init_db
from freqtrade.persistence.pairlock_middleware import PairLocks
from freqtrade.persistence.trade_model import LocalTrade, Order, Trade

7
freqtrade/persistence/base.py Normal file
View File

@ -0,0 +1,7 @@
from typing import Any
from sqlalchemy.orm import declarative_base
_DECL_BASE: Any = declarative_base()

52
freqtrade/persistence/migrations.py
View File

@ -1,9 +1,10 @@
import logging
from typing import List
from sqlalchemy import inspect, text
from sqlalchemy import inspect, select, text, tuple_, update
from freqtrade.exceptions import OperationalException
from freqtrade.persistence.trade_model import Order, Trade
logger = logging.getLogger(__name__)
@ -46,7 +47,7 @@ def get_last_sequence_ids(engine, trade_back_name, order_back_name):
return order_id, trade_id
def set_sequence_ids(engine, order_id, trade_id):
def set_sequence_ids(engine, order_id, trade_id, pairlock_id=None):
if engine.name == 'postgresql':
with engine.begin() as connection:
@ -54,6 +55,9 @@ def set_sequence_ids(engine, order_id, trade_id):
connection.execute(text(f"ALTER SEQUENCE orders_id_seq RESTART WITH {order_id}"))
if trade_id:
connection.execute(text(f"ALTER SEQUENCE trades_id_seq RESTART WITH {trade_id}"))
if pairlock_id:
connection.execute(
text(f"ALTER SEQUENCE pairlocks_id_seq RESTART WITH {pairlock_id}"))
def drop_index_on_table(engine, inspector, table_bak_name):
@ -99,6 +103,9 @@ def migrate_trades_and_orders_table(
liquidation_price = get_column_def(cols, 'liquidation_price',
get_column_def(cols, 'isolated_liq', 'null'))
# sqlite does not support literals for booleans
if engine.name == 'postgresql':
is_short = get_column_def(cols, 'is_short', 'false')
else:
is_short = get_column_def(cols, 'is_short', '0')
# Margin Properties
@ -195,16 +202,18 @@ def migrate_orders_table(engine, table_back_name: str, cols_order: List):
ft_fee_base = get_column_def(cols_order, 'ft_fee_base', 'null')
average = get_column_def(cols_order, 'average', 'null')
stop_price = get_column_def(cols_order, 'stop_price', 'null')
# sqlite does not support literals for booleans
with engine.begin() as connection:
connection.execute(text(f"""
insert into orders (id, ft_trade_id, ft_order_side, ft_pair, ft_is_open, order_id,
status, symbol, order_type, side, price, amount, filled, average, remaining, cost,
order_date, order_filled_date, order_update_date, ft_fee_base)
stop_price, order_date, order_filled_date, order_update_date, ft_fee_base)
select id, ft_trade_id, ft_order_side, ft_pair, ft_is_open, order_id,
status, symbol, order_type, side, price, amount, filled, {average} average, remaining,
cost, order_date, order_filled_date, order_update_date, {ft_fee_base} ft_fee_base
cost, {stop_price} stop_price, order_date, order_filled_date,
order_update_date, {ft_fee_base} ft_fee_base
from {table_back_name}
"""))
@ -241,6 +250,35 @@ def set_sqlite_to_wal(engine):
connection.execute(text("PRAGMA journal_mode=wal"))
def fix_old_dry_orders(engine):
with engine.begin() as connection:
stmt = update(Order).where(
Order.ft_is_open.is_(True),
tuple_(Order.ft_trade_id, Order.order_id).not_in(
select(
Trade.id, Trade.stoploss_order_id
).where(Trade.stoploss_order_id.is_not(None))
),
Order.ft_order_side == 'stoploss',
Order.order_id.like('dry%'),
).values(ft_is_open=False)
connection.execute(stmt)
stmt = update(Order).where(
Order.ft_is_open.is_(True),
tuple_(Order.ft_trade_id, Order.order_id).not_in(
select(
Trade.id, Trade.open_order_id
).where(Trade.open_order_id.is_not(None))
),
Order.ft_order_side != 'stoploss',
Order.order_id.like('dry%')
).values(ft_is_open=False)
connection.execute(stmt)
def check_migrate(engine, decl_base, previous_tables) -> None:
"""
Checks if migration is necessary and migrates if necessary
@ -259,9 +297,8 @@ def check_migrate(engine, decl_base, previous_tables) -> None:
# Check if migration necessary
# Migrates both trades and orders table!
# if ('orders' not in previous_tables
# or not has_column(cols_orders, 'leverage')):
if not has_column(cols_trades, 'base_currency'):
if not has_column(cols_orders, 'stop_price'):
# if not has_column(cols_trades, 'base_currency'):
logger.info(f"Running database migration for trades - "
f"backup: {table_back_name}, {order_table_bak_name}")
migrate_trades_and_orders_table(
@ -282,3 +319,4 @@ def check_migrate(engine, decl_base, previous_tables) -> None:
"start with a fresh database.")
set_sqlite_to_wal(engine)
fix_old_dry_orders(engine)

1422
freqtrade/persistence/models.py
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File diff suppressed because it is too large Load Diff

70
freqtrade/persistence/pairlock.py Normal file
View File

@ -0,0 +1,70 @@
from datetime import datetime, timezone
from typing import Any, Dict, Optional
from sqlalchemy import Boolean, Column, DateTime, Integer, String, or_
from sqlalchemy.orm import Query
from freqtrade.constants import DATETIME_PRINT_FORMAT
from freqtrade.persistence.base import _DECL_BASE
class PairLock(_DECL_BASE):
"""
Pair Locks database model.
"""
__tablename__ = 'pairlocks'
id = Column(Integer, primary_key=True)
pair = Column(String(25), nullable=False, index=True)
# lock direction - long, short or * (for both)
side = Column(String(25), nullable=False, default="*")
reason = Column(String(255), nullable=True)
# Time the pair was locked (start time)
lock_time = Column(DateTime, nullable=False)
# Time until the pair is locked (end time)
lock_end_time = Column(DateTime, nullable=False, index=True)
active = Column(Boolean, nullable=False, default=True, index=True)
def __repr__(self):
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}, side={self.side}, lock_time={lock_time}, '
f'lock_end_time={lock_end_time}, reason={self.reason}, active={self.active})')
@staticmethod
def query_pair_locks(pair: Optional[str], now: datetime, side: str = '*') -> Query:
"""
Get all currently active locks for this pair
: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), ]
if pair:
filters.append(PairLock.pair == pair)
if side != '*':
filters.append(or_(PairLock.side == side, PairLock.side == '*'))
else:
filters.append(PairLock.side == '*')
return PairLock.query.filter(
*filters
)
def to_json(self) -> Dict[str, Any]:
return {
'id': self.id,
'pair': self.pair,
'lock_time': self.lock_time.strftime(DATETIME_PRINT_FORMAT),
'lock_timestamp': int(self.lock_time.replace(tzinfo=timezone.utc).timestamp() * 1000),
'lock_end_time': self.lock_end_time.strftime(DATETIME_PRINT_FORMAT),
'lock_end_timestamp': int(self.lock_end_time.replace(tzinfo=timezone.utc
).timestamp() * 1000),
'reason': self.reason,
'side': self.side,
'active': self.active,
}

1377
freqtrade/persistence/trade_model.py Normal file
View File

File diff suppressed because it is too large Load Diff

3
freqtrade/plot/plotting.py
View File

@ -633,7 +633,8 @@ def load_and_plot_trades(config: Dict[str, Any]):
exchange = ExchangeResolver.load_exchange(config['exchange']['name'], config)
IStrategy.dp = DataProvider(config, exchange)
strategy.bot_start()
strategy.ft_bot_start()
strategy.bot_loop_start()
plot_elements = init_plotscript(config, list(exchange.markets), strategy.startup_candle_count)
timerange = plot_elements['timerange']
trades = plot_elements['trades']

11
freqtrade/plugins/pairlist/AgeFilter.py
View File

@ -30,20 +30,21 @@ class AgeFilter(IPairList):
self._symbolsCheckFailed = PeriodicCache(maxsize=1000, ttl=86_400)
self._min_days_listed = pairlistconfig.get('min_days_listed', 10)
self._max_days_listed = pairlistconfig.get('max_days_listed', None)
self._max_days_listed = pairlistconfig.get('max_days_listed')
candle_limit = exchange.ohlcv_candle_limit('1d', self._config['candle_type_def'])
if self._min_days_listed < 1:
raise OperationalException("AgeFilter requires min_days_listed to be >= 1")
if self._min_days_listed > exchange.ohlcv_candle_limit('1d'):
if self._min_days_listed > candle_limit:
raise OperationalException("AgeFilter requires min_days_listed to not exceed "
"exchange max request size "
f"({exchange.ohlcv_candle_limit('1d')})")
f"({candle_limit})")
if self._max_days_listed and self._max_days_listed <= self._min_days_listed:
raise OperationalException("AgeFilter max_days_listed <= min_days_listed not permitted")
if self._max_days_listed and self._max_days_listed > exchange.ohlcv_candle_limit('1d'):
if self._max_days_listed and self._max_days_listed > candle_limit:
raise OperationalException("AgeFilter requires max_days_listed to not exceed "
"exchange max request size "
f"({exchange.ohlcv_candle_limit('1d')})")
f"({candle_limit})")
@property
def needstickers(self) -> bool:

9
freqtrade/plugins/pairlist/OffsetFilter.py
View File

@ -19,6 +19,7 @@ class OffsetFilter(IPairList):
super().__init__(exchange, pairlistmanager, config, pairlistconfig, pairlist_pos)
self._offset = pairlistconfig.get('offset', 0)
self._number_pairs = pairlistconfig.get('number_assets', 0)
if self._offset < 0:
raise OperationalException("OffsetFilter requires offset to be >= 0")
@ -36,7 +37,9 @@ class OffsetFilter(IPairList):
"""
Short whitelist method description - used for startup-messages
"""
return f"{self.name} - Offseting pairs by {self._offset}."
if self._number_pairs:
return f"{self.name} - Taking {self._number_pairs} Pairs, starting from {self._offset}."
return f"{self.name} - Offsetting pairs by {self._offset}."
def filter_pairlist(self, pairlist: List[str], tickers: Dict) -> List[str]:
"""
@ -50,5 +53,9 @@ class OffsetFilter(IPairList):
self.log_once(f"Offset of {self._offset} is larger than " +
f"pair count of {len(pairlist)}", logger.warning)
pairs = pairlist[self._offset:]
if self._number_pairs:
pairs = pairs[:self._number_pairs]
self.log_once(f"Searching {len(pairs)} pairs: {pairs}", logger.info)
return pairs

2
freqtrade/plugins/pairlist/PerformanceFilter.py
View File

@ -21,7 +21,7 @@ class PerformanceFilter(IPairList):
super().__init__(exchange, pairlistmanager, config, pairlistconfig, pairlist_pos)
self._minutes = pairlistconfig.get('minutes', 0)
self._min_profit = pairlistconfig.get('min_profit', None)
self._min_profit = pairlistconfig.get('min_profit')
@property
def needstickers(self) -> bool:

2
freqtrade/plugins/pairlist/SpreadFilter.py
View File

@ -50,7 +50,7 @@ class SpreadFilter(IPairList):
:param ticker: ticker dict as returned from ccxt.fetch_tickers()
:return: True if the pair can stay, false if it should be removed
"""
if 'bid' in ticker and 'ask' in ticker and ticker['ask']:
if 'bid' in ticker and 'ask' in ticker and ticker['ask'] and ticker['bid']:
spread = 1 - ticker['bid'] / ticker['ask']
if spread > self._max_spread_ratio:
self.log_once(f"Removed {pair} from whitelist, because spread "

6
freqtrade/plugins/pairlist/VolatilityFilter.py
View File

@ -38,12 +38,12 @@ class VolatilityFilter(IPairList):
self._pair_cache: TTLCache = TTLCache(maxsize=1000, ttl=self._refresh_period)
candle_limit = exchange.ohlcv_candle_limit('1d', self._config['candle_type_def'])
if self._days < 1:
raise OperationalException("VolatilityFilter requires lookback_days to be >= 1")
if self._days > exchange.ohlcv_candle_limit('1d'):
if self._days > candle_limit:
raise OperationalException("VolatilityFilter requires lookback_days to not "
"exceed exchange max request size "
f"({exchange.ohlcv_candle_limit('1d')})")
f"exceed exchange max request size ({candle_limit})")
@property
def needstickers(self) -> bool:

7
freqtrade/plugins/pairlist/VolumePairList.py
View File

@ -84,12 +84,13 @@ class VolumePairList(IPairList):
raise OperationalException(
f'key {self._sort_key} not in {SORT_VALUES}')
candle_limit = exchange.ohlcv_candle_limit(
self._lookback_timeframe, self._config['candle_type_def'])
if self._lookback_period < 0:
raise OperationalException("VolumeFilter requires lookback_period to be >= 0")
if self._lookback_period > exchange.ohlcv_candle_limit(self._lookback_timeframe):
if self._lookback_period > candle_limit:
raise OperationalException("VolumeFilter requires lookback_period to not "
"exceed exchange max request size "
f"({exchange.ohlcv_candle_limit(self._lookback_timeframe)})")
f"exceed exchange max request size ({candle_limit})")
@property
def needstickers(self) -> bool:

8
freqtrade/plugins/pairlist/rangestabilityfilter.py
View File

@ -27,18 +27,18 @@ class RangeStabilityFilter(IPairList):
self._days = pairlistconfig.get('lookback_days', 10)
self._min_rate_of_change = pairlistconfig.get('min_rate_of_change', 0.01)
self._max_rate_of_change = pairlistconfig.get('max_rate_of_change', None)
self._max_rate_of_change = pairlistconfig.get('max_rate_of_change')
self._refresh_period = pairlistconfig.get('refresh_period', 1440)
self._def_candletype = self._config['candle_type_def']
self._pair_cache: TTLCache = TTLCache(maxsize=1000, ttl=self._refresh_period)
candle_limit = exchange.ohlcv_candle_limit('1d', self._config['candle_type_def'])
if self._days < 1:
raise OperationalException("RangeStabilityFilter requires lookback_days to be >= 1")
if self._days > exchange.ohlcv_candle_limit('1d'):
if self._days > candle_limit:
raise OperationalException("RangeStabilityFilter requires lookback_days to not "
"exceed exchange max request size "
f"({exchange.ohlcv_candle_limit('1d')})")
f"exceed exchange max request size ({candle_limit})")
@property
def needstickers(self) -> bool:

2
freqtrade/plugins/pairlistmanager.py
View File

@ -28,7 +28,7 @@ class PairListManager(LoggingMixin):
self._blacklist = self._config['exchange'].get('pair_blacklist', [])
self._pairlist_handlers: List[IPairList] = []
self._tickers_needed = False
for pairlist_handler_config in self._config.get('pairlists', None):
for pairlist_handler_config in self._config.get('pairlists', []):
pairlist_handler = PairListResolver.load_pairlist(
pairlist_handler_config['method'],
exchange=exchange,

12
freqtrade/plugins/protections/low_profit_pairs.py
View File

@ -21,6 +21,7 @@ class LowProfitPairs(IProtection):
self._trade_limit = protection_config.get('trade_limit', 1)
self._required_profit = protection_config.get('required_profit', 0.0)
self._only_per_side = protection_config.get('only_per_side', False)
def short_desc(self) -> str:
"""
@ -36,7 +37,8 @@ class LowProfitPairs(IProtection):
return (f'{profit} < {self._required_profit} in {self.lookback_period_str}, '
f'locking for {self.stop_duration_str}.')
def _low_profit(self, date_now: datetime, pair: str) -> Optional[ProtectionReturn]:
def _low_profit(
self, date_now: datetime, pair: str, side: LongShort) -> Optional[ProtectionReturn]:
"""
Evaluate recent trades for pair
"""
@ -54,7 +56,10 @@ class LowProfitPairs(IProtection):
# Not enough trades in the relevant period
return None
profit = sum(trade.close_profit for trade in trades if trade.close_profit)
profit = sum(
trade.close_profit for trade in trades if trade.close_profit
and (not self._only_per_side or trade.trade_direction == side)
)
if profit < self._required_profit:
self.log_once(
f"Trading for {pair} stopped due to {profit:.2f} < {self._required_profit} "
@ -65,6 +70,7 @@ class LowProfitPairs(IProtection):
lock=True,
until=until,
reason=self._reason(profit),
lock_side=(side if self._only_per_side else '*')
)
return None
@ -86,4 +92,4 @@ class LowProfitPairs(IProtection):
:return: Tuple of [bool, until, reason].
If true, this pair will be locked with <reason> until <until>
"""
return self._low_profit(date_now, pair=pair)
return self._low_profit(date_now, pair=pair, side=side)

4
freqtrade/plugins/protections/stoploss_guard.py
View File

@ -38,8 +38,8 @@ class StoplossGuard(IProtection):
return (f'{self._trade_limit} stoplosses in {self._lookback_period} min, '
f'locking for {self._stop_duration} min.')
def _stoploss_guard(
self, date_now: datetime, pair: Optional[str], side: str) -> Optional[ProtectionReturn]:
def _stoploss_guard(self, date_now: datetime, pair: Optional[str],
side: LongShort) -> Optional[ProtectionReturn]:
"""
Evaluate recent trades
"""

21
freqtrade/resolvers/strategy_resolver.py
View File

@ -47,26 +47,7 @@ class StrategyResolver(IResolver):
strategy: IStrategy = StrategyResolver._load_strategy(
strategy_name, config=config,
extra_dir=config.get('strategy_path'))
if strategy._ft_params_from_file:
# Set parameters from Hyperopt results file
params = strategy._ft_params_from_file
strategy.minimal_roi = params.get('roi', getattr(strategy, 'minimal_roi', {}))
strategy.stoploss = params.get('stoploss', {}).get(
'stoploss', getattr(strategy, 'stoploss', -0.1))
trailing = params.get('trailing', {})
strategy.trailing_stop = trailing.get(
'trailing_stop', getattr(strategy, 'trailing_stop', False))
strategy.trailing_stop_positive = trailing.get(
'trailing_stop_positive', getattr(strategy, 'trailing_stop_positive', None))
strategy.trailing_stop_positive_offset = trailing.get(
'trailing_stop_positive_offset',
getattr(strategy, 'trailing_stop_positive_offset', 0))
strategy.trailing_only_offset_is_reached = trailing.get(
'trailing_only_offset_is_reached',
getattr(strategy, 'trailing_only_offset_is_reached', 0.0))
strategy.ft_load_params_from_file()
# Set attributes
# Check if we need to override configuration
# (Attribute name, default, subkey)

7
freqtrade/rpc/api_server/api_backtest.py
View File

@ -1,6 +1,7 @@
import asyncio
import logging
from copy import deepcopy
from datetime import datetime
from typing import Any, Dict, List
from fastapi import APIRouter, BackgroundTasks, Depends
@ -102,7 +103,10 @@ async def api_start_backtest(bt_settings: BacktestRequest, background_tasks: Bac
min_date=min_date, max_date=max_date)
if btconfig.get('export', 'none') == 'trades':
store_backtest_stats(btconfig['exportfilename'], ApiServer._bt.results)
store_backtest_stats(
btconfig['exportfilename'], ApiServer._bt.results,
datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
)
logger.info("Backtest finished.")
@ -172,6 +176,7 @@ def api_delete_backtest(ws_mode=Depends(is_webserver_mode)):
"status_msg": "Backtest running",
}
if ApiServer._bt:
ApiServer._bt.cleanup()
del ApiServer._bt
ApiServer._bt = None
del ApiServer._bt_data

10
freqtrade/rpc/api_server/api_schemas.py
View File

@ -104,6 +104,10 @@ class Profit(BaseModel):
best_pair_profit_ratio: float
winning_trades: int
losing_trades: int
profit_factor: float
max_drawdown: float
max_drawdown_abs: float
trading_volume: Optional[float]
class SellReason(BaseModel):
@ -120,6 +124,8 @@ class Stats(BaseModel):
class DailyRecord(BaseModel):
date: date
abs_profit: float
rel_profit: float
starting_balance: float
fiat_value: float
trade_count: int
@ -166,7 +172,7 @@ class ShowConfig(BaseModel):
trailing_stop_positive: Optional[float]
trailing_stop_positive_offset: Optional[float]
trailing_only_offset_is_reached: Optional[bool]
unfilledtimeout: UnfilledTimeout
unfilledtimeout: Optional[UnfilledTimeout] # Empty in webserver mode
order_types: Optional[OrderTypes]
use_custom_stoploss: Optional[bool]
timeframe: Optional[str]
@ -256,6 +262,7 @@ class TradeSchema(BaseModel):
leverage: Optional[float]
interest_rate: Optional[float]
liquidation_price: Optional[float]
funding_fees: Optional[float]
trading_mode: Optional[TradingMode]
@ -276,6 +283,7 @@ class OpenTradeSchema(TradeSchema):
class TradeResponse(BaseModel):
trades: List[TradeSchema]
trades_count: int
offset: int
total_trades: int

7
freqtrade/rpc/api_server/api_v1.py
View File

@ -36,7 +36,8 @@ logger = logging.getLogger(__name__)
# versions 2.xx -> futures/short branch
# 2.14: Add entry/exit orders to trade response
# 2.15: Add backtest history endpoints
API_VERSION = 2.15
# 2.16: Additional daily metrics
API_VERSION = 2.16
# Public API, requires no auth.
router_public = APIRouter()
@ -86,7 +87,7 @@ def stats(rpc: RPC = Depends(get_rpc)):
@router.get('/daily', response_model=Daily, tags=['info'])
def daily(timescale: int = 7, rpc: RPC = Depends(get_rpc), config=Depends(get_config)):
return rpc._rpc_daily_profit(timescale, config['stake_currency'],
return rpc._rpc_timeunit_profit(timescale, config['stake_currency'],
config.get('fiat_display_currency', ''))
@ -281,7 +282,7 @@ def get_strategy(strategy: str, config=Depends(get_config)):
def list_available_pairs(timeframe: Optional[str] = None, stake_currency: Optional[str] = None,
candletype: Optional[CandleType] = None, config=Depends(get_config)):
dh = get_datahandler(config['datadir'], config.get('dataformat_ohlcv', None))
dh = get_datahandler(config['datadir'], config.get('dataformat_ohlcv'))
trading_mode: TradingMode = config.get('trading_mode', TradingMode.SPOT)
pair_interval = dh.ohlcv_get_available_data(config['datadir'], trading_mode)

59
freqtrade/rpc/discord.py Normal file
View File

@ -0,0 +1,59 @@
import logging
from typing import Any, Dict
from freqtrade.enums.rpcmessagetype import RPCMessageType
from freqtrade.rpc import RPC
from freqtrade.rpc.webhook import Webhook
logger = logging.getLogger(__name__)
class Discord(Webhook):
def __init__(self, rpc: 'RPC', config: Dict[str, Any]):
# super().__init__(rpc, config)
self.rpc = rpc
self.config = config
self.strategy = config.get('strategy', '')
self.timeframe = config.get('timeframe', '')
self._url = self.config['discord']['webhook_url']
self._format = 'json'
self._retries = 1
self._retry_delay = 0.1
def cleanup(self) -> None:
"""
Cleanup pending module resources.
This will do nothing for webhooks, they will simply not be called anymore
"""
pass
def send_msg(self, msg) -> None:
logger.info(f"Sending discord message: {msg}")
if msg['type'].value in self.config['discord']:
msg['strategy'] = self.strategy
msg['timeframe'] = self.timeframe
fields = self.config['discord'].get(msg['type'].value)
color = 0x0000FF
if msg['type'] in (RPCMessageType.EXIT, RPCMessageType.EXIT_FILL):
profit_ratio = msg.get('profit_ratio')
color = (0x00FF00 if profit_ratio > 0 else 0xFF0000)
embeds = [{
'title': f"Trade: {msg['pair']} {msg['type'].value}",
'color': color,
'fields': [],
}]
for f in fields:
for k, v in f.items():
v = v.format(**msg)
embeds[0]['fields'].append( # type: ignore
{'name': k, 'value': v, 'inline': True})
# Send the message to discord channel
payload = {'embeds': embeds}
self._send_msg(payload)

189
freqtrade/rpc/rpc.py
View File

@ -18,6 +18,7 @@ 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
from freqtrade.data.metrics import calculate_max_drawdown
from freqtrade.enums import (CandleType, ExitCheckTuple, ExitType, SignalDirection, State,
TradingMode)
from freqtrade.exceptions import ExchangeError, PricingError
@ -96,7 +97,7 @@ class RPC:
"""
self._freqtrade = freqtrade
self._config: Dict[str, Any] = freqtrade.config
if self._config.get('fiat_display_currency', None):
if self._config.get('fiat_display_currency'):
self._fiat_converter = CryptoToFiatConverter()
@staticmethod
@ -177,6 +178,7 @@ class RPC:
current_rate = NAN
else:
current_rate = trade.close_rate
if len(trade.select_filled_orders(trade.entry_side)) > 0:
current_profit = trade.calc_profit_ratio(current_rate)
current_profit_abs = trade.calc_profit(current_rate)
current_profit_fiat: Optional[float] = None
@ -187,6 +189,8 @@ class RPC:
self._freqtrade.config['stake_currency'],
self._freqtrade.config['fiat_display_currency']
)
else:
current_profit = current_profit_abs = current_profit_fiat = 0.0
# Calculate guaranteed profit (in case of trailing stop)
stoploss_entry_dist = trade.calc_profit(trade.stop_loss)
@ -235,8 +239,12 @@ class RPC:
trade.pair, side='exit', is_short=trade.is_short, refresh=False)
except (PricingError, ExchangeError):
current_rate = NAN
if len(trade.select_filled_orders(trade.entry_side)) > 0:
trade_profit = trade.calc_profit(current_rate)
profit_str = f'{trade.calc_profit_ratio(current_rate):.2%}'
else:
trade_profit = 0.0
profit_str = f'{0.0:.2f}'
direction_str = ('S' if trade.is_short else 'L') if nonspot else ''
if self._fiat_converter:
fiat_profit = self._fiat_converter.convert_amount(
@ -244,7 +252,7 @@ class RPC:
stake_currency,
fiat_display_currency
)
if fiat_profit and not isnan(fiat_profit):
if not isnan(fiat_profit):
profit_str += f" ({fiat_profit:.2f})"
fiat_profit_sum = fiat_profit if isnan(fiat_profit_sum) \
else fiat_profit_sum + fiat_profit
@ -276,33 +284,57 @@ class RPC:
columns.append('# Entries')
return trades_list, columns, fiat_profit_sum
def _rpc_daily_profit(
def _rpc_timeunit_profit(
self, timescale: int,
stake_currency: str, fiat_display_currency: str) -> Dict[str, Any]:
today = datetime.now(timezone.utc).date()
profit_days: Dict[date, Dict] = {}
stake_currency: str, fiat_display_currency: str,
timeunit: str = 'days') -> Dict[str, Any]:
"""
:param timeunit: Valid entries are 'days', 'weeks', 'months'
"""
start_date = datetime.now(timezone.utc).date()
if timeunit == 'weeks':
# weekly
start_date = start_date - timedelta(days=start_date.weekday()) # Monday
if timeunit == 'months':
start_date = start_date.replace(day=1)
def time_offset(step: int):
if timeunit == 'months':
return relativedelta(months=step)
return timedelta(**{timeunit: step})
if not (isinstance(timescale, int) and timescale > 0):
raise RPCException('timescale must be an integer greater than 0')
profit_units: Dict[date, Dict] = {}
daily_stake = self._freqtrade.wallets.get_total_stake_amount()
for day in range(0, timescale):
profitday = today - timedelta(days=day)
trades = Trade.get_trades(trade_filter=[
profitday = start_date - time_offset(day)
# Only query for necessary columns for performance reasons.
trades = Trade.query.session.query(Trade.close_profit_abs).filter(
Trade.is_open.is_(False),
Trade.close_date >= profitday,
Trade.close_date < (profitday + timedelta(days=1))
]).order_by(Trade.close_date).all()
Trade.close_date < (profitday + time_offset(1))
).order_by(Trade.close_date).all()
curdayprofit = sum(
trade.close_profit_abs for trade in trades if trade.close_profit_abs is not None)
profit_days[profitday] = {
# Calculate this periods starting balance
daily_stake = daily_stake - curdayprofit
profit_units[profitday] = {
'amount': curdayprofit,
'trades': len(trades)
'daily_stake': daily_stake,
'rel_profit': round(curdayprofit / daily_stake, 8) if daily_stake > 0 else 0,
'trades': len(trades),
}
data = [
{
'date': key,
'date': f"{key.year}-{key.month:02d}" if timeunit == 'months' else key,
'abs_profit': value["amount"],
'starting_balance': value["daily_stake"],
'rel_profit': value["rel_profit"],
'fiat_value': self._fiat_converter.convert_amount(
value['amount'],
stake_currency,
@ -310,92 +342,7 @@ class RPC:
) if self._fiat_converter else 0,
'trade_count': value["trades"],
}
for key, value in profit_days.items()
]
return {
'stake_currency': stake_currency,
'fiat_display_currency': fiat_display_currency,
'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()
for key, value in profit_units.items()
]
return {
'stake_currency': stake_currency,
@ -418,6 +365,7 @@ class RPC:
return {
"trades": output,
"trades_count": len(output),
"offset": offset,
"total_trades": Trade.get_trades([Trade.is_open.is_(False)]).count(),
}
@ -432,7 +380,7 @@ class RPC:
return 'losses'
else:
return 'draws'
trades: List[Trade] = Trade.get_trades([Trade.is_open.is_(False)])
trades: List[Trade] = Trade.get_trades([Trade.is_open.is_(False)], include_orders=False)
# Sell reason
exit_reasons = {}
for trade in trades:
@ -460,7 +408,8 @@ class RPC:
""" Returns cumulative profit statistics """
trade_filter = ((Trade.is_open.is_(False) & (Trade.close_date >= start_date)) |
Trade.is_open.is_(True))
trades: List[Trade] = Trade.get_trades(trade_filter).order_by(Trade.id).all()
trades: List[Trade] = Trade.get_trades(
trade_filter, include_orders=False).order_by(Trade.id).all()
profit_all_coin = []
profit_all_ratio = []
@ -469,6 +418,8 @@ class RPC:
durations = []
winning_trades = 0
losing_trades = 0
winning_profit = 0.0
losing_profit = 0.0
for trade in trades:
current_rate: float = 0.0
@ -484,8 +435,10 @@ class RPC:
profit_closed_ratio.append(profit_ratio)
if trade.close_profit >= 0:
winning_trades += 1
winning_profit += trade.close_profit_abs
else:
losing_trades += 1
losing_profit += trade.close_profit_abs
else:
# Get current rate
try:
@ -501,6 +454,7 @@ class RPC:
profit_all_ratio.append(profit_ratio)
best_pair = Trade.get_best_pair(start_date)
trading_volume = Trade.get_trading_volume(start_date)
# Prepare data to display
profit_closed_coin_sum = round(sum(profit_closed_coin), 8)
@ -524,6 +478,21 @@ class RPC:
profit_closed_ratio_fromstart = profit_closed_coin_sum / starting_balance
profit_all_ratio_fromstart = profit_all_coin_sum / starting_balance
profit_factor = winning_profit / abs(losing_profit) if losing_profit else float('inf')
trades_df = DataFrame([{'close_date': trade.close_date.strftime(DATETIME_PRINT_FORMAT),
'profit_abs': trade.close_profit_abs}
for trade in trades if not trade.is_open])
max_drawdown_abs = 0.0
max_drawdown = 0.0
if len(trades_df) > 0:
try:
(max_drawdown_abs, _, _, _, _, max_drawdown) = calculate_max_drawdown(
trades_df, value_col='profit_abs', starting_balance=starting_balance)
except ValueError:
# ValueError if no losing trade.
pass
profit_all_fiat = self._fiat_converter.convert_amount(
profit_all_coin_sum,
stake_currency,
@ -562,11 +531,15 @@ class RPC:
'best_pair_profit_ratio': best_pair[1] if best_pair else 0,
'winning_trades': winning_trades,
'losing_trades': losing_trades,
'profit_factor': profit_factor,
'max_drawdown': max_drawdown,
'max_drawdown_abs': max_drawdown_abs,
'trading_volume': trading_volume,
}
def _rpc_balance(self, stake_currency: str, fiat_display_currency: str) -> Dict:
""" Returns current account balance per crypto """
currencies = []
currencies: List[Dict] = []
total = 0.0
try:
tickers = self._freqtrade.exchange.get_tickers(cached=True)
@ -593,7 +566,7 @@ class RPC:
else:
try:
pair = self._freqtrade.exchange.get_valid_pair_combination(coin, stake_currency)
rate = tickers.get(pair, {}).get('last', None)
rate = tickers.get(pair, {}).get('last')
if rate:
if pair.startswith(stake_currency) and not pair.endswith(stake_currency):
rate = 1.0 / rate
@ -601,13 +574,12 @@ class RPC:
except (ExchangeError):
logger.warning(f" Could not get rate for pair {coin}.")
continue
total = total + (est_stake or 0)
total = total + est_stake
currencies.append({
'currency': coin,
# TODO: The below can be simplified if we don't assign None to values.
'free': balance.free if balance.free is not None else 0,
'balance': balance.total if balance.total is not None else 0,
'used': balance.used if balance.used is not None else 0,
'free': balance.free,
'balance': balance.total,
'used': balance.used,
'est_stake': est_stake or 0,
'stake': stake_currency,
'side': 'long',
@ -637,7 +609,6 @@ class RPC:
total, stake_currency, fiat_display_currency) if self._fiat_converter else 0
trade_count = len(Trade.get_trades_proxy())
starting_capital_ratio = 0.0
starting_capital_ratio = (total / starting_capital) - 1 if starting_capital else 0.0
starting_cap_fiat_ratio = (value / starting_cap_fiat) - 1 if starting_cap_fiat else 0.0

6
freqtrade/rpc/rpc_manager.py
View File

@ -27,6 +27,12 @@ class RPCManager:
from freqtrade.rpc.telegram import Telegram
self.registered_modules.append(Telegram(self._rpc, config))
# Enable discord
if config.get('discord', {}).get('enabled', False):
logger.info('Enabling rpc.discord ...')
from freqtrade.rpc.discord import Discord
self.registered_modules.append(Discord(self._rpc, config))
# Enable Webhook
if config.get('webhook', {}).get('enabled', False):
logger.info('Enabling rpc.webhook ...')

250
freqtrade/rpc/telegram.py
View File

@ -6,6 +6,7 @@ This module manage Telegram communication
import json
import logging
import re
from dataclasses import dataclass
from datetime import date, datetime, timedelta
from functools import partial
from html import escape
@ -37,6 +38,15 @@ logger.debug('Included module rpc.telegram ...')
MAX_TELEGRAM_MESSAGE_LENGTH = 4096
@dataclass
class TimeunitMappings:
header: str
message: str
message2: str
callback: str
default: int
def authorized_only(command_handler: Callable[..., None]) -> Callable[..., Any]:
"""
Decorator to check if the message comes from the correct chat_id
@ -225,6 +235,30 @@ class Telegram(RPCHandler):
# This can take up to `timeout` from the call to `start_polling`.
self._updater.stop()
def _exchange_from_msg(self, msg: Dict[str, Any]) -> str:
"""
Extracts the exchange name from the given message.
:param msg: The message to extract the exchange name from.
:return: The exchange name.
"""
return f"{msg['exchange']}{' (dry)' if self._config['dry_run'] else ''}"
def _add_analyzed_candle(self, pair: str) -> str:
candle_val = self._config['telegram'].get(
'notification_settings', {}).get('show_candle', 'off')
if candle_val != 'off':
if candle_val == 'ohlc':
analyzed_df, _ = self._rpc._freqtrade.dataprovider.get_analyzed_dataframe(
pair, self._config['timeframe'])
candle = analyzed_df.iloc[-1].squeeze() if len(analyzed_df) > 0 else None
if candle is not None:
return (
f"*Candle OHLC*: `{candle['open']}, {candle['high']}, "
f"{candle['low']}, {candle['close']}`\n"
)
return ''
def _format_entry_msg(self, msg: Dict[str, Any]) -> str:
if self._rpc._fiat_converter:
msg['stake_amount_fiat'] = self._rpc._fiat_converter.convert_amount(
@ -237,11 +271,12 @@ class Telegram(RPCHandler):
entry_side = ({'enter': 'Long', 'entered': 'Longed'} if msg['direction'] == 'Long'
else {'enter': 'Short', 'entered': 'Shorted'})
message = (
f"{emoji} *{msg['exchange']}:*"
f"{emoji} *{self._exchange_from_msg(msg)}:*"
f" {entry_side['entered'] if is_fill else entry_side['enter']} {msg['pair']}"
f" (#{msg['trade_id']})\n"
)
message += f"*Enter Tag:* `{msg['enter_tag']}`\n" if msg.get('enter_tag', None) else ""
message += self._add_analyzed_candle(msg['pair'])
message += f"*Enter Tag:* `{msg['enter_tag']}`\n" if msg.get('enter_tag') else ""
message += f"*Amount:* `{msg['amount']:.8f}`\n"
if msg.get('leverage') and msg.get('leverage', 1.0) != 1.0:
message += f"*Leverage:* `{msg['leverage']}`\n"
@ -254,7 +289,7 @@ class Telegram(RPCHandler):
message += f"*Total:* `({round_coin_value(msg['stake_amount'], msg['stake_currency'])}"
if msg.get('fiat_currency', None):
if msg.get('fiat_currency'):
message += f", {round_coin_value(msg['stake_amount_fiat'], msg['fiat_currency'])}"
message += ")`"
@ -270,7 +305,7 @@ class Telegram(RPCHandler):
msg['enter_tag'] = msg['enter_tag'] if "enter_tag" in msg.keys() else None
msg['emoji'] = self._get_sell_emoji(msg)
msg['leverage_text'] = (f"*Leverage:* `{msg['leverage']:.1f}`\n"
if msg.get('leverage', None) and msg.get('leverage', 1.0) != 1.0
if msg.get('leverage') and msg.get('leverage', 1.0) != 1.0
else "")
# Check if all sell properties are available.
@ -286,8 +321,9 @@ class Telegram(RPCHandler):
msg['profit_extra'] = ''
is_fill = msg['type'] == RPCMessageType.EXIT_FILL
message = (
f"{msg['emoji']} *{msg['exchange']}:* "
f"{msg['emoji']} *{self._exchange_from_msg(msg)}:* "
f"{'Exited' if is_fill else 'Exiting'} {msg['pair']} (#{msg['trade_id']})\n"
f"{self._add_analyzed_candle(msg['pair'])}"
f"*{'Profit' if is_fill else 'Unrealized Profit'}:* "
f"`{msg['profit_ratio']:.2%}{msg['profit_extra']}`\n"
f"*Enter Tag:* `{msg['enter_tag']}`\n"
@ -316,33 +352,33 @@ class Telegram(RPCHandler):
elif msg_type in (RPCMessageType.ENTRY_CANCEL, RPCMessageType.EXIT_CANCEL):
msg['message_side'] = 'enter' if msg_type in [RPCMessageType.ENTRY_CANCEL] else 'exit'
message = ("\N{WARNING SIGN} *{exchange}:* "
"Cancelling {message_side} Order for {pair} (#{trade_id}). "
"Reason: {reason}.".format(**msg))
message = (f"\N{WARNING SIGN} *{self._exchange_from_msg(msg)}:* "
f"Cancelling {msg['message_side']} Order for {msg['pair']} "
f"(#{msg['trade_id']}). Reason: {msg['reason']}.")
elif msg_type == RPCMessageType.PROTECTION_TRIGGER:
message = (
"*Protection* triggered due to {reason}. "
"`{pair}` will be locked until `{lock_end_time}`."
).format(**msg)
f"*Protection* triggered due to {msg['reason']}. "
f"`{msg['pair']}` will be locked until `{msg['lock_end_time']}`."
)
elif msg_type == RPCMessageType.PROTECTION_TRIGGER_GLOBAL:
message = (
"*Protection* triggered due to {reason}. "
"*All pairs* will be locked until `{lock_end_time}`."
).format(**msg)
f"*Protection* triggered due to {msg['reason']}. "
f"*All pairs* will be locked until `{msg['lock_end_time']}`."
)
elif msg_type == RPCMessageType.STATUS:
message = '*Status:* `{status}`'.format(**msg)
message = f"*Status:* `{msg['status']}`"
elif msg_type == RPCMessageType.WARNING:
message = '\N{WARNING SIGN} *Warning:* `{status}`'.format(**msg)
message = f"\N{WARNING SIGN} *Warning:* `{msg['status']}`"
elif msg_type == RPCMessageType.STARTUP:
message = '{status}'.format(**msg)
message = f"{msg['status']}"
else:
raise NotImplementedError('Unknown message type: {}'.format(msg_type))
raise NotImplementedError(f"Unknown message type: {msg_type}")
return message
def send_msg(self, msg: Dict[str, Any]) -> None:
@ -396,7 +432,7 @@ class Telegram(RPCHandler):
first_avg = filled_orders[0]["safe_price"]
for x, order in enumerate(filled_orders):
if not order['ft_is_entry']:
if not order['ft_is_entry'] or order['is_open'] is True:
continue
cur_entry_datetime = arrow.get(order["order_filled_date"])
cur_entry_amount = order["amount"]
@ -563,6 +599,60 @@ class Telegram(RPCHandler):
except RPCException as e:
self._send_msg(str(e))
@authorized_only
def _timeunit_stats(self, update: Update, context: CallbackContext, unit: str) -> None:
"""
Handler for /daily <n>
Returns a daily profit (in BTC) over the last n days.
:param bot: telegram bot
:param update: message update
:return: None
"""
vals = {
'days': TimeunitMappings('Day', 'Daily', 'days', 'update_daily', 7),
'weeks': TimeunitMappings('Monday', 'Weekly', 'weeks (starting from Monday)',
'update_weekly', 8),
'months': TimeunitMappings('Month', 'Monthly', 'months', 'update_monthly', 6),
}
val = vals[unit]
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 val.default
except (TypeError, ValueError, IndexError):
timescale = val.default
try:
stats = self._rpc._rpc_timeunit_profit(
timescale,
stake_cur,
fiat_disp_cur,
unit
)
stats_tab = tabulate(
[[f"{period['date']} ({period['trade_count']})",
f"{round_coin_value(period['abs_profit'], stats['stake_currency'])}",
f"{period['fiat_value']:.2f} {stats['fiat_display_currency']}",
f"{period['rel_profit']:.2%}",
] for period in stats['data']],
headers=[
f"{val.header} (count)",
f'{stake_cur}',
f'{fiat_disp_cur}',
'Profit %',
'Trades',
],
tablefmt='simple')
message = (
f'<b>{val.message} Profit over the last {timescale} {val.message2}</b>:\n'
f'<pre>{stats_tab}</pre>'
)
self._send_msg(message, parse_mode=ParseMode.HTML, reload_able=True,
callback_path=val.callback, query=update.callback_query)
except RPCException as e:
self._send_msg(str(e))
@authorized_only
def _daily(self, update: Update, context: CallbackContext) -> None:
"""
@ -572,35 +662,7 @@ class Telegram(RPCHandler):
: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 7
except (TypeError, ValueError, IndexError):
timescale = 7
try:
stats = self._rpc._rpc_daily_profit(
timescale,
stake_cur,
fiat_disp_cur
)
stats_tab = tabulate(
[[day['date'],
f"{round_coin_value(day['abs_profit'], stats['stake_currency'])}",
f"{day['fiat_value']:.3f} {stats['fiat_display_currency']}",
f"{day['trade_count']} trades"] for day in stats['data']],
headers=[
'Day',
f'Profit {stake_cur}',
f'Profit {fiat_disp_cur}',
'Trades',
],
tablefmt='simple')
message = f'<b>Daily Profit over the last {timescale} days</b>:\n<pre>{stats_tab}</pre>'
self._send_msg(message, parse_mode=ParseMode.HTML, reload_able=True,
callback_path="update_daily", query=update.callback_query)
except RPCException as e:
self._send_msg(str(e))
self._timeunit_stats(update, context, 'days')
@authorized_only
def _weekly(self, update: Update, context: CallbackContext) -> None:
@ -611,36 +673,7 @@ class Telegram(RPCHandler):
: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))
self._timeunit_stats(update, context, 'weeks')
@authorized_only
def _monthly(self, update: Update, context: CallbackContext) -> None:
@ -651,36 +684,7 @@ class Telegram(RPCHandler):
: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))
self._timeunit_stats(update, context, 'months')
@authorized_only
def _profit(self, update: Update, context: CallbackContext) -> None:
@ -744,12 +748,18 @@ class Telegram(RPCHandler):
f"*Total Trade Count:* `{trade_count}`\n"
f"*{'First Trade opened' if not timescale else 'Showing Profit since'}:* "
f"`{first_trade_date}`\n"
f"*Latest Trade opened:* `{latest_trade_date}\n`"
f"*Latest Trade opened:* `{latest_trade_date}`\n"
f"*Win / Loss:* `{stats['winning_trades']} / {stats['losing_trades']}`"
)
if stats['closed_trade_count'] > 0:
markdown_msg += (f"\n*Avg. Duration:* `{avg_duration}`\n"
f"*Best Performing:* `{best_pair}: {best_pair_profit_ratio:.2%}`")
markdown_msg += (
f"\n*Avg. Duration:* `{avg_duration}`\n"
f"*Best Performing:* `{best_pair}: {best_pair_profit_ratio:.2%}`\n"
f"*Trading volume:* `{round_coin_value(stats['trading_volume'], stake_cur)}`\n"
f"*Profit factor:* `{stats['profit_factor']:.2f}`\n"
f"*Max Drawdown:* `{stats['max_drawdown']:.2%} "
f"({round_coin_value(stats['max_drawdown_abs'], stake_cur)})`"
)
self._send_msg(markdown_msg, reload_able=True, callback_path="update_profit",
query=update.callback_query)
@ -785,7 +795,7 @@ class Telegram(RPCHandler):
headers=['Exit Reason', 'Exits', 'Wins', 'Losses']
)
if len(exit_reasons_tabulate) > 25:
self._send_msg(exit_reasons_msg, ParseMode.MARKDOWN)
self._send_msg(f"```\n{exit_reasons_msg}```", ParseMode.MARKDOWN)
exit_reasons_msg = ''
durations = stats['durations']
@ -889,7 +899,7 @@ class Telegram(RPCHandler):
:return: None
"""
msg = self._rpc._rpc_start()
self._send_msg('Status: `{status}`'.format(**msg))
self._send_msg(f"Status: `{msg['status']}`")
@authorized_only
def _stop(self, update: Update, context: CallbackContext) -> None:
@ -901,7 +911,7 @@ class Telegram(RPCHandler):
:return: None
"""
msg = self._rpc._rpc_stop()
self._send_msg('Status: `{status}`'.format(**msg))
self._send_msg(f"Status: `{msg['status']}`")
@authorized_only
def _reload_config(self, update: Update, context: CallbackContext) -> None:
@ -913,7 +923,7 @@ class Telegram(RPCHandler):
:return: None
"""
msg = self._rpc._rpc_reload_config()
self._send_msg('Status: `{status}`'.format(**msg))
self._send_msg(f"Status: `{msg['status']}`")
@authorized_only
def _stopbuy(self, update: Update, context: CallbackContext) -> None:
@ -925,7 +935,7 @@ class Telegram(RPCHandler):
:return: None
"""
msg = self._rpc._rpc_stopbuy()
self._send_msg('Status: `{status}`'.format(**msg))
self._send_msg(f"Status: `{msg['status']}`")
@authorized_only
def _force_exit(self, update: Update, context: CallbackContext) -> None:
@ -1087,9 +1097,9 @@ class Telegram(RPCHandler):
trade_id = int(context.args[0])
msg = self._rpc._rpc_delete(trade_id)
self._send_msg((
'`{result_msg}`\n'
f"`{msg['result_msg']}`\n"
'Please make sure to take care of this asset on the exchange manually.'
).format(**msg))
))
except RPCException as e:
self._send_msg(str(e))
@ -1410,14 +1420,14 @@ class Telegram(RPCHandler):
"Optionally takes a rate at which to sell "
"(only applies to limit orders).` \n")
message = (
"_BotControl_\n"
"_Bot Control_\n"
"------------\n"
"*/start:* `Starts the trader`\n"
"*/stop:* Stops the trader\n"
"*/stopbuy:* `Stops buying, but handles open trades gracefully` \n"
"*/forceexit <trade_id>|all:* `Instantly exits the given trade or all trades, "
"regardless of profit`\n"
"*/fe <trade_id>|all:* `Alias to /forceexit`"
"*/fx <trade_id>|all:* `Alias to /forceexit`\n"
f"{force_enter_text if self._config.get('force_entry_enable', False) else ''}"
"*/delete <trade_id>:* `Instantly delete the given trade in the database`\n"
"*/whitelist:* `Show current whitelist` \n"

14
freqtrade/rpc/webhook.py
View File

@ -45,21 +45,21 @@ class Webhook(RPCHandler):
try:
whconfig = self._config['webhook']
if msg['type'] in [RPCMessageType.ENTRY]:
valuedict = whconfig.get('webhookentry', None)
valuedict = whconfig.get('webhookentry')
elif msg['type'] in [RPCMessageType.ENTRY_CANCEL]:
valuedict = whconfig.get('webhookentrycancel', None)
valuedict = whconfig.get('webhookentrycancel')
elif msg['type'] in [RPCMessageType.ENTRY_FILL]:
valuedict = whconfig.get('webhookentryfill', None)
valuedict = whconfig.get('webhookentryfill')
elif msg['type'] == RPCMessageType.EXIT:
valuedict = whconfig.get('webhookexit', None)
valuedict = whconfig.get('webhookexit')
elif msg['type'] == RPCMessageType.EXIT_FILL:
valuedict = whconfig.get('webhookexitfill', None)
valuedict = whconfig.get('webhookexitfill')
elif msg['type'] == RPCMessageType.EXIT_CANCEL:
valuedict = whconfig.get('webhookexitcancel', None)
valuedict = whconfig.get('webhookexitcancel')
elif msg['type'] in (RPCMessageType.STATUS,
RPCMessageType.STARTUP,
RPCMessageType.WARNING):
valuedict = whconfig.get('webhookstatus', None)
valuedict = whconfig.get('webhookstatus')
else:
raise NotImplementedError('Unknown message type: {}'.format(msg['type']))
if not valuedict:

4
freqtrade/strategy/__init__.py
View File

@ -1,9 +1,9 @@
# flake8: noqa: F401
from freqtrade.exchange import (timeframe_to_minutes, timeframe_to_msecs, timeframe_to_next_date,
timeframe_to_prev_date, timeframe_to_seconds)
from freqtrade.strategy.hyper import (BooleanParameter, CategoricalParameter, DecimalParameter,
IntParameter, RealParameter)
from freqtrade.strategy.informative_decorator import informative
from freqtrade.strategy.interface import IStrategy
from freqtrade.strategy.parameters import (BooleanParameter, CategoricalParameter, DecimalParameter,
IntParameter, RealParameter)
from freqtrade.strategy.strategy_helper import (merge_informative_pair, stoploss_from_absolute,
stoploss_from_open)

388
freqtrade/strategy/hyper.py
View File

@ -3,295 +3,18 @@ IHyperStrategy interface, hyperoptable Parameter class.
This module defines a base class for auto-hyperoptable strategies.
"""
import logging
from abc import ABC, abstractmethod
from contextlib import suppress
from pathlib import Path
from typing import Any, Dict, Iterator, List, Optional, Sequence, Tuple, Union
from typing import Any, Dict, Iterator, List, Tuple, Type, Union
from freqtrade.exceptions import OperationalException
from freqtrade.misc import deep_merge_dicts, json_load
from freqtrade.optimize.hyperopt_tools import HyperoptTools
with suppress(ImportError):
from skopt.space import Integer, Real, Categorical
from freqtrade.optimize.space import SKDecimal
from freqtrade.enums import RunMode
from freqtrade.exceptions import OperationalException
from freqtrade.strategy.parameters import BaseParameter
logger = logging.getLogger(__name__)
class BaseParameter(ABC):
"""
Defines a parameter that can be optimized by hyperopt.
"""
category: Optional[str]
default: Any
value: Any
in_space: bool = False
name: str
def __init__(self, *, default: Any, space: Optional[str] = None,
optimize: bool = True, load: bool = True, **kwargs):
"""
Initialize hyperopt-optimizable parameter.
:param space: A parameter category. Can be 'buy' or 'sell'. This parameter is optional if
parameter field
name is prefixed with 'buy_' or 'sell_'.
:param optimize: Include parameter in hyperopt optimizations.
:param load: Load parameter value from {space}_params.
:param kwargs: Extra parameters to skopt.space.(Integer|Real|Categorical).
"""
if 'name' in kwargs:
raise OperationalException(
'Name is determined by parameter field name and can not be specified manually.')
self.category = space
self._space_params = kwargs
self.value = default
self.optimize = optimize
self.load = load
def __repr__(self):
return f'{self.__class__.__name__}({self.value})'
@abstractmethod
def get_space(self, name: str) -> Union['Integer', 'Real', 'SKDecimal', 'Categorical']:
"""
Get-space - will be used by Hyperopt to get the hyperopt Space
"""
class NumericParameter(BaseParameter):
""" Internal parameter used for Numeric purposes """
float_or_int = Union[int, float]
default: float_or_int
value: float_or_int
def __init__(self, low: Union[float_or_int, Sequence[float_or_int]],
high: Optional[float_or_int] = None, *, default: float_or_int,
space: Optional[str] = None, optimize: bool = True, load: bool = True, **kwargs):
"""
Initialize hyperopt-optimizable numeric parameter.
Cannot be instantiated, but provides the validation for other numeric parameters
:param low: Lower end (inclusive) of optimization space or [low, high].
:param high: Upper end (inclusive) of optimization space.
Must be none of entire range is passed first parameter.
:param default: A default value.
:param space: A parameter category. Can be 'buy' or 'sell'. This parameter is optional if
parameter fieldname is prefixed with 'buy_' or 'sell_'.
:param optimize: Include parameter in hyperopt optimizations.
:param load: Load parameter value from {space}_params.
:param kwargs: Extra parameters to skopt.space.*.
"""
if high is not None and isinstance(low, Sequence):
raise OperationalException(f'{self.__class__.__name__} space invalid.')
if high is None or isinstance(low, Sequence):
if not isinstance(low, Sequence) or len(low) != 2:
raise OperationalException(f'{self.__class__.__name__} space must be [low, high]')
self.low, self.high = low
else:
self.low = low
self.high = high
super().__init__(default=default, space=space, optimize=optimize,
load=load, **kwargs)
class IntParameter(NumericParameter):
default: int
value: int
def __init__(self, low: Union[int, Sequence[int]], high: Optional[int] = None, *, default: int,
space: Optional[str] = None, optimize: bool = True, load: bool = True, **kwargs):
"""
Initialize hyperopt-optimizable integer parameter.
:param low: Lower end (inclusive) of optimization space or [low, high].
:param high: Upper end (inclusive) of optimization space.
Must be none of entire range is passed first parameter.
:param default: A default value.
:param space: A parameter category. Can be 'buy' or 'sell'. This parameter is optional if
parameter fieldname is prefixed with 'buy_' or 'sell_'.
:param optimize: Include parameter in hyperopt optimizations.
:param load: Load parameter value from {space}_params.
:param kwargs: Extra parameters to skopt.space.Integer.
"""
super().__init__(low=low, high=high, default=default, space=space, optimize=optimize,
load=load, **kwargs)
def get_space(self, name: str) -> 'Integer':
"""
Create skopt optimization space.
:param name: A name of parameter field.
"""
return Integer(low=self.low, high=self.high, name=name, **self._space_params)
@property
def range(self):
"""
Get each value in this space as list.
Returns a List from low to high (inclusive) in Hyperopt mode.
Returns a List with 1 item (`value`) in "non-hyperopt" mode, to avoid
calculating 100ds of indicators.
"""
if self.in_space and self.optimize:
# Scikit-optimize ranges are "inclusive", while python's "range" is exclusive
return range(self.low, self.high + 1)
else:
return range(self.value, self.value + 1)
class RealParameter(NumericParameter):
default: float
value: float
def __init__(self, low: Union[float, Sequence[float]], high: Optional[float] = None, *,
default: float, space: Optional[str] = None, optimize: bool = True,
load: bool = True, **kwargs):
"""
Initialize hyperopt-optimizable floating point parameter with unlimited precision.
:param low: Lower end (inclusive) of optimization space or [low, high].
:param high: Upper end (inclusive) of optimization space.
Must be none if entire range is passed first parameter.
:param default: A default value.
:param space: A parameter category. Can be 'buy' or 'sell'. This parameter is optional if
parameter fieldname is prefixed with 'buy_' or 'sell_'.
:param optimize: Include parameter in hyperopt optimizations.
:param load: Load parameter value from {space}_params.
:param kwargs: Extra parameters to skopt.space.Real.
"""
super().__init__(low=low, high=high, default=default, space=space, optimize=optimize,
load=load, **kwargs)
def get_space(self, name: str) -> 'Real':
"""
Create skopt optimization space.
:param name: A name of parameter field.
"""
return Real(low=self.low, high=self.high, name=name, **self._space_params)
class DecimalParameter(NumericParameter):
default: float
value: float
def __init__(self, low: Union[float, Sequence[float]], high: Optional[float] = None, *,
default: float, decimals: int = 3, space: Optional[str] = None,
optimize: bool = True, load: bool = True, **kwargs):
"""
Initialize hyperopt-optimizable decimal parameter with a limited precision.
:param low: Lower end (inclusive) of optimization space or [low, high].
:param high: Upper end (inclusive) of optimization space.
Must be none if entire range is passed first parameter.
:param default: A default value.
:param decimals: A number of decimals after floating point to be included in testing.
:param space: A parameter category. Can be 'buy' or 'sell'. This parameter is optional if
parameter fieldname is prefixed with 'buy_' or 'sell_'.
:param optimize: Include parameter in hyperopt optimizations.
:param load: Load parameter value from {space}_params.
:param kwargs: Extra parameters to skopt.space.Integer.
"""
self._decimals = decimals
default = round(default, self._decimals)
super().__init__(low=low, high=high, default=default, space=space, optimize=optimize,
load=load, **kwargs)
def get_space(self, name: str) -> 'SKDecimal':
"""
Create skopt optimization space.
:param name: A name of parameter field.
"""
return SKDecimal(low=self.low, high=self.high, decimals=self._decimals, name=name,
**self._space_params)
@property
def range(self):
"""
Get each value in this space as list.
Returns a List from low to high (inclusive) in Hyperopt mode.
Returns a List with 1 item (`value`) in "non-hyperopt" mode, to avoid
calculating 100ds of indicators.
"""
if self.in_space and self.optimize:
low = int(self.low * pow(10, self._decimals))
high = int(self.high * pow(10, self._decimals)) + 1
return [round(n * pow(0.1, self._decimals), self._decimals) for n in range(low, high)]
else:
return [self.value]
class CategoricalParameter(BaseParameter):
default: Any
value: Any
opt_range: Sequence[Any]
def __init__(self, categories: Sequence[Any], *, default: Optional[Any] = None,
space: Optional[str] = None, optimize: bool = True, load: bool = True, **kwargs):
"""
Initialize hyperopt-optimizable parameter.
:param categories: Optimization space, [a, b, ...].
:param default: A default value. If not specified, first item from specified space will be
used.
:param space: A parameter category. Can be 'buy' or 'sell'. This parameter is optional if
parameter field
name is prefixed with 'buy_' or 'sell_'.
:param optimize: Include parameter in hyperopt optimizations.
:param load: Load parameter value from {space}_params.
:param kwargs: Extra parameters to skopt.space.Categorical.
"""
if len(categories) < 2:
raise OperationalException(
'CategoricalParameter space must be [a, b, ...] (at least two parameters)')
self.opt_range = categories
super().__init__(default=default, space=space, optimize=optimize,
load=load, **kwargs)
def get_space(self, name: str) -> 'Categorical':
"""
Create skopt optimization space.
:param name: A name of parameter field.
"""
return Categorical(self.opt_range, name=name, **self._space_params)
@property
def range(self):
"""
Get each value in this space as list.
Returns a List of categories in Hyperopt mode.
Returns a List with 1 item (`value`) in "non-hyperopt" mode, to avoid
calculating 100ds of indicators.
"""
if self.in_space and self.optimize:
return self.opt_range
else:
return [self.value]
class BooleanParameter(CategoricalParameter):
def __init__(self, *, default: Optional[Any] = None,
space: Optional[str] = None, optimize: bool = True, load: bool = True, **kwargs):
"""
Initialize hyperopt-optimizable Boolean Parameter.
It's a shortcut to `CategoricalParameter([True, False])`.
:param default: A default value. If not specified, first item from specified space will be
used.
:param space: A parameter category. Can be 'buy' or 'sell'. This parameter is optional if
parameter field
name is prefixed with 'buy_' or 'sell_'.
:param optimize: Include parameter in hyperopt optimizations.
:param load: Load parameter value from {space}_params.
:param kwargs: Extra parameters to skopt.space.Categorical.
"""
categories = [True, False]
super().__init__(categories=categories, default=default, space=space, optimize=optimize,
load=load, **kwargs)
class HyperStrategyMixin:
"""
A helper base class which allows HyperOptAuto class to reuse implementations of buy/sell
@ -307,7 +30,10 @@ class HyperStrategyMixin:
self.ft_sell_params: List[BaseParameter] = []
self.ft_protection_params: List[BaseParameter] = []
self._load_hyper_params(config.get('runmode') == RunMode.HYPEROPT)
params = self.load_params_from_file()
params = params.get('params', {})
self._ft_params_from_file = params
# Init/loading of parameters is done as part of ft_bot_start().
def enumerate_parameters(self, category: str = None) -> Iterator[Tuple[str, BaseParameter]]:
"""
@ -327,28 +53,13 @@ class HyperStrategyMixin:
for par in params:
yield par.name, par
@classmethod
def detect_parameters(cls, category: str) -> Iterator[Tuple[str, BaseParameter]]:
""" Detect all parameters for 'category' """
for attr_name in dir(cls):
if not attr_name.startswith('__'): # Ignore internals, not strictly necessary.
attr = getattr(cls, attr_name)
if issubclass(attr.__class__, BaseParameter):
if (attr_name.startswith(category + '_')
and attr.category is not None and attr.category != category):
raise OperationalException(
f'Inconclusive parameter name {attr_name}, category: {attr.category}.')
if (category == attr.category or
(attr_name.startswith(category + '_') and attr.category is None)):
yield attr_name, attr
@classmethod
def detect_all_parameters(cls) -> Dict:
""" Detect all parameters and return them as a list"""
params: Dict = {
'buy': list(cls.detect_parameters('buy')),
'sell': list(cls.detect_parameters('sell')),
'protection': list(cls.detect_parameters('protection')),
params: Dict[str, Any] = {
'buy': list(detect_parameters(cls, 'buy')),
'sell': list(detect_parameters(cls, 'sell')),
'protection': list(detect_parameters(cls, 'protection')),
}
params.update({
'count': len(params['buy'] + params['sell'] + params['protection'])
@ -356,21 +67,49 @@ class HyperStrategyMixin:
return params
def _load_hyper_params(self, hyperopt: bool = False) -> None:
def ft_load_params_from_file(self) -> None:
"""
Load Parameters from parameter file
Should/must run before config values are loaded in strategy_resolver.
"""
if self._ft_params_from_file:
# Set parameters from Hyperopt results file
params = self._ft_params_from_file
self.minimal_roi = params.get('roi', getattr(self, 'minimal_roi', {}))
self.stoploss = params.get('stoploss', {}).get(
'stoploss', getattr(self, 'stoploss', -0.1))
trailing = params.get('trailing', {})
self.trailing_stop = trailing.get(
'trailing_stop', getattr(self, 'trailing_stop', False))
self.trailing_stop_positive = trailing.get(
'trailing_stop_positive', getattr(self, 'trailing_stop_positive', None))
self.trailing_stop_positive_offset = trailing.get(
'trailing_stop_positive_offset',
getattr(self, 'trailing_stop_positive_offset', 0))
self.trailing_only_offset_is_reached = trailing.get(
'trailing_only_offset_is_reached',
getattr(self, 'trailing_only_offset_is_reached', 0.0))
def ft_load_hyper_params(self, hyperopt: bool = False) -> None:
"""
Load Hyperoptable parameters
Prevalence:
* Parameters from parameter file
* Parameters defined in parameters objects (buy_params, sell_params, ...)
* Parameter defaults
"""
params = self.load_params_from_file()
params = params.get('params', {})
self._ft_params_from_file = params
buy_params = deep_merge_dicts(params.get('buy', {}), getattr(self, 'buy_params', {}))
sell_params = deep_merge_dicts(params.get('sell', {}), getattr(self, 'sell_params', {}))
protection_params = deep_merge_dicts(params.get('protection', {}),
buy_params = deep_merge_dicts(self._ft_params_from_file.get('buy', {}),
getattr(self, 'buy_params', {}))
sell_params = deep_merge_dicts(self._ft_params_from_file.get('sell', {}),
getattr(self, 'sell_params', {}))
protection_params = deep_merge_dicts(self._ft_params_from_file.get('protection', {}),
getattr(self, 'protection_params', {}))
self._load_params(buy_params, 'buy', hyperopt)
self._load_params(sell_params, 'sell', hyperopt)
self._load_params(protection_params, 'protection', hyperopt)
self._ft_load_params(buy_params, 'buy', hyperopt)
self._ft_load_params(sell_params, 'sell', hyperopt)
self._ft_load_params(protection_params, 'protection', hyperopt)
def load_params_from_file(self) -> Dict:
filename_str = getattr(self, '__file__', '')
@ -393,7 +132,7 @@ class HyperStrategyMixin:
return {}
def _load_params(self, params: Dict, space: str, hyperopt: bool = False) -> None:
def _ft_load_params(self, params: Dict, space: str, hyperopt: bool = False) -> None:
"""
Set optimizable parameter values.
:param params: Dictionary with new parameter values.
@ -402,7 +141,7 @@ class HyperStrategyMixin:
logger.info(f"No params for {space} found, using default values.")
param_container: List[BaseParameter] = getattr(self, f"ft_{space}_params")
for attr_name, attr in self.detect_parameters(space):
for attr_name, attr in detect_parameters(self, space):
attr.name = attr_name
attr.in_space = hyperopt and HyperoptTools.has_space(self.config, space)
if not attr.category:
@ -424,7 +163,7 @@ class HyperStrategyMixin:
"""
Returns list of Parameters that are not part of the current optimize job
"""
params = {
params: Dict[str, Dict] = {
'buy': {},
'sell': {},
'protection': {},
@ -433,3 +172,26 @@ class HyperStrategyMixin:
if not p.optimize or not p.in_space:
params[p.category][name] = p.value
return params
def detect_parameters(
obj: Union[HyperStrategyMixin, Type[HyperStrategyMixin]],
category: str
) -> Iterator[Tuple[str, BaseParameter]]:
"""
Detect all parameters for 'category' for "obj"
:param obj: Strategy object or class
:param category: category - usually `'buy', 'sell', 'protection',...
"""
for attr_name in dir(obj):
if not attr_name.startswith('__'): # Ignore internals, not strictly necessary.
attr = getattr(obj, attr_name)
if issubclass(attr.__class__, BaseParameter):
if (attr_name.startswith(category + '_')
and attr.category is not None and attr.category != category):
raise OperationalException(
f'Inconclusive parameter name {attr_name}, category: {attr.category}.')
if (category == attr.category or
(attr_name.startswith(category + '_') and attr.category is None)):
yield attr_name, attr

114
freqtrade/strategy/interface.py
View File

@ -14,11 +14,10 @@ from freqtrade.constants import ListPairsWithTimeframes
from freqtrade.data.dataprovider import DataProvider
from freqtrade.enums import (CandleType, ExitCheckTuple, ExitType, SignalDirection, SignalTagType,
SignalType, TradingMode)
from freqtrade.enums.runmode import RunMode
from freqtrade.exceptions import OperationalException, StrategyError
from freqtrade.exchange import timeframe_to_minutes, timeframe_to_seconds
from freqtrade.exchange.exchange import timeframe_to_next_date
from freqtrade.persistence import PairLocks, Trade
from freqtrade.persistence.models import LocalTrade, Order
from freqtrade.exchange import timeframe_to_minutes, timeframe_to_next_date, timeframe_to_seconds
from freqtrade.persistence import Order, PairLocks, Trade
from freqtrade.strategy.hyper import HyperStrategyMixin
from freqtrade.strategy.informative_decorator import (InformativeData, PopulateIndicators,
_create_and_merge_informative_pair,
@ -84,7 +83,7 @@ class IStrategy(ABC, HyperStrategyMixin):
}
# run "populate_indicators" only for new candle
process_only_new_candles: bool = False
process_only_new_candles: bool = True
use_exit_signal: bool
exit_profit_only: bool
@ -146,6 +145,15 @@ class IStrategy(ABC, HyperStrategyMixin):
informative_data.candle_type = config['candle_type_def']
self._ft_informative.append((informative_data, cls_method))
def ft_bot_start(self, **kwargs) -> None:
"""
Strategy init - runs after dataprovider has been added.
Must call bot_start()
"""
strategy_safe_wrapper(self.bot_start)()
self.ft_load_hyper_params(self.config.get('runmode') == RunMode.HYPEROPT)
@abstractmethod
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
@ -279,8 +287,9 @@ class IStrategy(ABC, HyperStrategyMixin):
:param pair: Pair that's about to be bought/shorted.
: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 amount: Amount in target (base) currency that's going to be traded.
:param rate: Rate that's going to be used when using limit orders
or current rate for market orders.
:param time_in_force: Time in force. Defaults to GTC (Good-til-cancelled).
:param current_time: datetime object, containing the current datetime
:param entry_tag: Optional entry_tag (buy_tag) if provided with the buy signal.
@ -306,8 +315,9 @@ class IStrategy(ABC, HyperStrategyMixin):
:param pair: Pair for trade that's about to be exited.
:param trade: trade object.
:param order_type: Order type (as configured in order_types). usually limit or market.
:param amount: Amount in quote currency.
:param amount: Amount in base currency.
:param rate: Rate that's going to be used when using limit orders
or current rate for market orders.
:param time_in_force: Time in force. Defaults to GTC (Good-til-cancelled).
:param exit_reason: Exit reason.
Can be any of ['roi', 'stop_loss', 'stoploss_on_exchange', 'trailing_stop_loss',
@ -431,8 +441,9 @@ class IStrategy(ABC, HyperStrategyMixin):
return self.custom_sell(pair, trade, current_time, current_rate, current_profit, **kwargs)
def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float,
proposed_stake: float, min_stake: float, max_stake: float,
entry_tag: Optional[str], side: str, **kwargs) -> float:
proposed_stake: float, min_stake: Optional[float], max_stake: float,
leverage: float, entry_tag: Optional[str], side: str,
**kwargs) -> float:
"""
Customize stake size for each new trade.
@ -442,6 +453,7 @@ class IStrategy(ABC, HyperStrategyMixin):
:param proposed_stake: A stake amount proposed by the bot.
:param min_stake: Minimal stake size allowed by exchange.
:param max_stake: Balance available for trading.
:param leverage: Leverage selected for this trade.
:param entry_tag: Optional entry_tag (buy_tag) if provided with the buy signal.
:param side: 'long' or 'short' - indicating the direction of the proposed trade
:return: A stake size, which is between min_stake and max_stake.
@ -449,8 +461,9 @@ class IStrategy(ABC, HyperStrategyMixin):
return proposed_stake
def adjust_trade_position(self, trade: Trade, current_time: datetime,
current_rate: float, current_profit: float, min_stake: float,
max_stake: float, **kwargs) -> Optional[float]:
current_rate: float, current_profit: float,
min_stake: Optional[float], max_stake: float,
**kwargs) -> Optional[float]:
"""
Custom trade adjustment logic, returning the stake amount that a trade should be increased.
This means extra buy orders with additional fees.
@ -471,9 +484,37 @@ class IStrategy(ABC, HyperStrategyMixin):
"""
return None
def adjust_entry_price(self, trade: Trade, order: Optional[Order], pair: str,
current_time: datetime, proposed_rate: float, current_order_rate: float,
entry_tag: Optional[str], side: str, **kwargs) -> float:
"""
Entry price re-adjustment logic, returning the user desired limit price.
This only executes when a order was already placed, still open (unfilled fully or partially)
and not timed out on subsequent candles after entry trigger.
For full documentation please go to https://www.freqtrade.io/en/latest/strategy-callbacks/
When not implemented by a strategy, returns current_order_rate as default.
If current_order_rate is returned then the existing order is maintained.
If None is returned then order gets canceled but not replaced by a new one.
:param pair: Pair that's currently analyzed
:param trade: Trade object.
:param order: Order object
:param current_time: datetime object, containing the current datetime
:param proposed_rate: Rate, calculated based on pricing settings in entry_pricing.
:param current_order_rate: Rate of the existing order in place.
:param entry_tag: Optional entry_tag (buy_tag) if provided with the buy signal.
:param side: 'long' or 'short' - indicating the direction of the proposed trade
:param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
:return float: New entry price value if provided
"""
return current_order_rate
def leverage(self, pair: str, current_time: datetime, current_rate: float,
proposed_leverage: float, max_leverage: float, side: str,
**kwargs) -> float:
proposed_leverage: float, max_leverage: float, entry_tag: Optional[str],
side: str, **kwargs) -> float:
"""
Customize leverage for each new trade. This method is only called in futures mode.
@ -482,6 +523,7 @@ class IStrategy(ABC, HyperStrategyMixin):
:param current_rate: Rate, calculated based on pricing settings in exit_pricing.
:param proposed_leverage: A leverage proposed by the bot.
:param max_leverage: Max leverage allowed on this pair
:param entry_tag: Optional entry_tag (buy_tag) if provided with the buy signal.
:param side: 'long' or 'short' - indicating the direction of the proposed trade
:return: A leverage amount, which is between 1.0 and max_leverage.
"""
@ -852,16 +894,16 @@ class IStrategy(ABC, HyperStrategyMixin):
def should_exit(self, trade: Trade, rate: float, current_time: datetime, *,
enter: bool, exit_: bool,
low: float = None, high: float = None,
force_stoploss: float = 0) -> ExitCheckTuple:
force_stoploss: float = 0) -> List[ExitCheckTuple]:
"""
This function evaluates if one of the conditions required to trigger an exit order
has been reached, which can either be a stop-loss, ROI or exit-signal.
:param low: Only used during backtesting to simulate (long)stoploss/(short)ROI
:param high: Only used during backtesting, to simulate (short)stoploss/(long)ROI
:param force_stoploss: Externally provided stoploss
:return: True if trade should be exited, False otherwise
:return: List of exit reasons - or empty list.
"""
exits: List[ExitCheckTuple] = []
current_rate = rate
current_profit = trade.calc_profit_ratio(current_rate)
@ -891,19 +933,20 @@ class IStrategy(ABC, HyperStrategyMixin):
if exit_ and not enter:
exit_signal = ExitType.EXIT_SIGNAL
else:
custom_reason = strategy_safe_wrapper(self.custom_exit, default_retval=False)(
reason_cust = strategy_safe_wrapper(self.custom_exit, default_retval=False)(
pair=trade.pair, trade=trade, current_time=current_time,
current_rate=current_rate, current_profit=current_profit)
if custom_reason:
if reason_cust:
exit_signal = ExitType.CUSTOM_EXIT
if isinstance(custom_reason, str):
if len(custom_reason) > CUSTOM_EXIT_MAX_LENGTH:
if isinstance(reason_cust, str):
custom_reason = reason_cust
if len(reason_cust) > CUSTOM_EXIT_MAX_LENGTH:
logger.warning(f'Custom exit reason returned from '
f'custom_exit is too long and was trimmed'
f'to {CUSTOM_EXIT_MAX_LENGTH} characters.')
custom_reason = custom_reason[:CUSTOM_EXIT_MAX_LENGTH]
custom_reason = reason_cust[:CUSTOM_EXIT_MAX_LENGTH]
else:
custom_reason = None
custom_reason = ''
if (
exit_signal == ExitType.CUSTOM_EXIT
or (exit_signal == ExitType.EXIT_SIGNAL
@ -912,24 +955,29 @@ class IStrategy(ABC, HyperStrategyMixin):
logger.debug(f"{trade.pair} - Sell signal received. "
f"exit_type=ExitType.{exit_signal.name}" +
(f", custom_reason={custom_reason}" if custom_reason else ""))
return ExitCheckTuple(exit_type=exit_signal, exit_reason=custom_reason)
exits.append(ExitCheckTuple(exit_type=exit_signal, exit_reason=custom_reason))
# Sequence:
# Exit-signal
# ROI (if not stoploss)
# Stoploss
if roi_reached and stoplossflag.exit_type != ExitType.STOP_LOSS:
logger.debug(f"{trade.pair} - Required profit reached. exit_type=ExitType.ROI")
return ExitCheckTuple(exit_type=ExitType.ROI)
# ROI
# Trailing stoploss
if stoplossflag.exit_flag:
if stoplossflag.exit_type == ExitType.STOP_LOSS:
logger.debug(f"{trade.pair} - Stoploss hit. exit_type={stoplossflag.exit_type}")
return stoplossflag
exits.append(stoplossflag)
# This one is noisy, commented out...
# logger.debug(f"{trade.pair} - No exit signal.")
return ExitCheckTuple(exit_type=ExitType.NONE)
if roi_reached:
logger.debug(f"{trade.pair} - Required profit reached. exit_type=ExitType.ROI")
exits.append(ExitCheckTuple(exit_type=ExitType.ROI))
if stoplossflag.exit_type == ExitType.TRAILING_STOP_LOSS:
logger.debug(f"{trade.pair} - Trailing stoploss hit.")
exits.append(stoplossflag)
return exits
def stop_loss_reached(self, current_rate: float, trade: Trade,
current_time: datetime, current_profit: float,
@ -1044,7 +1092,7 @@ class IStrategy(ABC, HyperStrategyMixin):
else:
return current_profit > roi
def ft_check_timed_out(self, trade: LocalTrade, order: Order,
def ft_check_timed_out(self, trade: Trade, order: Order,
current_time: datetime) -> bool:
"""
FT Internal method.

289
freqtrade/strategy/parameters.py Normal file
View File

@ -0,0 +1,289 @@
"""
IHyperStrategy interface, hyperoptable Parameter class.
This module defines a base class for auto-hyperoptable strategies.
"""
import logging
from abc import ABC, abstractmethod
from contextlib import suppress
from typing import Any, Optional, Sequence, Union
with suppress(ImportError):
from skopt.space import Integer, Real, Categorical
from freqtrade.optimize.space import SKDecimal
from freqtrade.exceptions import OperationalException
logger = logging.getLogger(__name__)
class BaseParameter(ABC):
"""
Defines a parameter that can be optimized by hyperopt.
"""
category: Optional[str]
default: Any
value: Any
in_space: bool = False
name: str
def __init__(self, *, default: Any, space: Optional[str] = None,
optimize: bool = True, load: bool = True, **kwargs):
"""
Initialize hyperopt-optimizable parameter.
:param space: A parameter category. Can be 'buy' or 'sell'. This parameter is optional if
parameter field
name is prefixed with 'buy_' or 'sell_'.
:param optimize: Include parameter in hyperopt optimizations.
:param load: Load parameter value from {space}_params.
:param kwargs: Extra parameters to skopt.space.(Integer|Real|Categorical).
"""
if 'name' in kwargs:
raise OperationalException(
'Name is determined by parameter field name and can not be specified manually.')
self.category = space
self._space_params = kwargs
self.value = default
self.optimize = optimize
self.load = load
def __repr__(self):
return f'{self.__class__.__name__}({self.value})'
@abstractmethod
def get_space(self, name: str) -> Union['Integer', 'Real', 'SKDecimal', 'Categorical']:
"""
Get-space - will be used by Hyperopt to get the hyperopt Space
"""
class NumericParameter(BaseParameter):
""" Internal parameter used for Numeric purposes """
float_or_int = Union[int, float]
default: float_or_int
value: float_or_int
def __init__(self, low: Union[float_or_int, Sequence[float_or_int]],
high: Optional[float_or_int] = None, *, default: float_or_int,
space: Optional[str] = None, optimize: bool = True, load: bool = True, **kwargs):
"""
Initialize hyperopt-optimizable numeric parameter.
Cannot be instantiated, but provides the validation for other numeric parameters
:param low: Lower end (inclusive) of optimization space or [low, high].
:param high: Upper end (inclusive) of optimization space.
Must be none of entire range is passed first parameter.
:param default: A default value.
:param space: A parameter category. Can be 'buy' or 'sell'. This parameter is optional if
parameter fieldname is prefixed with 'buy_' or 'sell_'.
:param optimize: Include parameter in hyperopt optimizations.
:param load: Load parameter value from {space}_params.
:param kwargs: Extra parameters to skopt.space.*.
"""
if high is not None and isinstance(low, Sequence):
raise OperationalException(f'{self.__class__.__name__} space invalid.')
if high is None or isinstance(low, Sequence):
if not isinstance(low, Sequence) or len(low) != 2:
raise OperationalException(f'{self.__class__.__name__} space must be [low, high]')
self.low, self.high = low
else:
self.low = low
self.high = high
super().__init__(default=default, space=space, optimize=optimize,
load=load, **kwargs)
class IntParameter(NumericParameter):
default: int
value: int
low: int
high: int
def __init__(self, low: Union[int, Sequence[int]], high: Optional[int] = None, *, default: int,
space: Optional[str] = None, optimize: bool = True, load: bool = True, **kwargs):
"""
Initialize hyperopt-optimizable integer parameter.
:param low: Lower end (inclusive) of optimization space or [low, high].
:param high: Upper end (inclusive) of optimization space.
Must be none of entire range is passed first parameter.
:param default: A default value.
:param space: A parameter category. Can be 'buy' or 'sell'. This parameter is optional if
parameter fieldname is prefixed with 'buy_' or 'sell_'.
:param optimize: Include parameter in hyperopt optimizations.
:param load: Load parameter value from {space}_params.
:param kwargs: Extra parameters to skopt.space.Integer.
"""
super().__init__(low=low, high=high, default=default, space=space, optimize=optimize,
load=load, **kwargs)
def get_space(self, name: str) -> 'Integer':
"""
Create skopt optimization space.
:param name: A name of parameter field.
"""
return Integer(low=self.low, high=self.high, name=name, **self._space_params)
@property
def range(self):
"""
Get each value in this space as list.
Returns a List from low to high (inclusive) in Hyperopt mode.
Returns a List with 1 item (`value`) in "non-hyperopt" mode, to avoid
calculating 100ds of indicators.
"""
if self.in_space and self.optimize:
# Scikit-optimize ranges are "inclusive", while python's "range" is exclusive
return range(self.low, self.high + 1)
else:
return range(self.value, self.value + 1)
class RealParameter(NumericParameter):
default: float
value: float
def __init__(self, low: Union[float, Sequence[float]], high: Optional[float] = None, *,
default: float, space: Optional[str] = None, optimize: bool = True,
load: bool = True, **kwargs):
"""
Initialize hyperopt-optimizable floating point parameter with unlimited precision.
:param low: Lower end (inclusive) of optimization space or [low, high].
:param high: Upper end (inclusive) of optimization space.
Must be none if entire range is passed first parameter.
:param default: A default value.
:param space: A parameter category. Can be 'buy' or 'sell'. This parameter is optional if
parameter fieldname is prefixed with 'buy_' or 'sell_'.
:param optimize: Include parameter in hyperopt optimizations.
:param load: Load parameter value from {space}_params.
:param kwargs: Extra parameters to skopt.space.Real.
"""
super().__init__(low=low, high=high, default=default, space=space, optimize=optimize,
load=load, **kwargs)
def get_space(self, name: str) -> 'Real':
"""
Create skopt optimization space.
:param name: A name of parameter field.
"""
return Real(low=self.low, high=self.high, name=name, **self._space_params)
class DecimalParameter(NumericParameter):
default: float
value: float
def __init__(self, low: Union[float, Sequence[float]], high: Optional[float] = None, *,
default: float, decimals: int = 3, space: Optional[str] = None,
optimize: bool = True, load: bool = True, **kwargs):
"""
Initialize hyperopt-optimizable decimal parameter with a limited precision.
:param low: Lower end (inclusive) of optimization space or [low, high].
:param high: Upper end (inclusive) of optimization space.
Must be none if entire range is passed first parameter.
:param default: A default value.
:param decimals: A number of decimals after floating point to be included in testing.
:param space: A parameter category. Can be 'buy' or 'sell'. This parameter is optional if
parameter fieldname is prefixed with 'buy_' or 'sell_'.
:param optimize: Include parameter in hyperopt optimizations.
:param load: Load parameter value from {space}_params.
:param kwargs: Extra parameters to skopt.space.Integer.
"""
self._decimals = decimals
default = round(default, self._decimals)
super().__init__(low=low, high=high, default=default, space=space, optimize=optimize,
load=load, **kwargs)
def get_space(self, name: str) -> 'SKDecimal':
"""
Create skopt optimization space.
:param name: A name of parameter field.
"""
return SKDecimal(low=self.low, high=self.high, decimals=self._decimals, name=name,
**self._space_params)
@property
def range(self):
"""
Get each value in this space as list.
Returns a List from low to high (inclusive) in Hyperopt mode.
Returns a List with 1 item (`value`) in "non-hyperopt" mode, to avoid
calculating 100ds of indicators.
"""
if self.in_space and self.optimize:
low = int(self.low * pow(10, self._decimals))
high = int(self.high * pow(10, self._decimals)) + 1
return [round(n * pow(0.1, self._decimals), self._decimals) for n in range(low, high)]
else:
return [self.value]
class CategoricalParameter(BaseParameter):
default: Any
value: Any
opt_range: Sequence[Any]
def __init__(self, categories: Sequence[Any], *, default: Optional[Any] = None,
space: Optional[str] = None, optimize: bool = True, load: bool = True, **kwargs):
"""
Initialize hyperopt-optimizable parameter.
:param categories: Optimization space, [a, b, ...].
:param default: A default value. If not specified, first item from specified space will be
used.
:param space: A parameter category. Can be 'buy' or 'sell'. This parameter is optional if
parameter field
name is prefixed with 'buy_' or 'sell_'.
:param optimize: Include parameter in hyperopt optimizations.
:param load: Load parameter value from {space}_params.
:param kwargs: Extra parameters to skopt.space.Categorical.
"""
if len(categories) < 2:
raise OperationalException(
'CategoricalParameter space must be [a, b, ...] (at least two parameters)')
self.opt_range = categories
super().__init__(default=default, space=space, optimize=optimize,
load=load, **kwargs)
def get_space(self, name: str) -> 'Categorical':
"""
Create skopt optimization space.
:param name: A name of parameter field.
"""
return Categorical(self.opt_range, name=name, **self._space_params)
@property
def range(self):
"""
Get each value in this space as list.
Returns a List of categories in Hyperopt mode.
Returns a List with 1 item (`value`) in "non-hyperopt" mode, to avoid
calculating 100ds of indicators.
"""
if self.in_space and self.optimize:
return self.opt_range
else:
return [self.value]
class BooleanParameter(CategoricalParameter):
def __init__(self, *, default: Optional[Any] = None,
space: Optional[str] = None, optimize: bool = True, load: bool = True, **kwargs):
"""
Initialize hyperopt-optimizable Boolean Parameter.
It's a shortcut to `CategoricalParameter([True, False])`.
:param default: A default value. If not specified, first item from specified space will be
used.
:param space: A parameter category. Can be 'buy' or 'sell'. This parameter is optional if
parameter field
name is prefixed with 'buy_' or 'sell_'.
:param optimize: Include parameter in hyperopt optimizations.
:param load: Load parameter value from {space}_params.
:param kwargs: Extra parameters to skopt.space.Categorical.
"""
categories = [True, False]
super().__init__(categories=categories, default=default, space=space, optimize=optimize,
load=load, **kwargs)

10
freqtrade/strategy/strategy_wrapper.py
View File

@ -1,5 +1,7 @@
import logging
from copy import deepcopy
from functools import wraps
from typing import Any, Callable, TypeVar, cast
from freqtrade.exceptions import StrategyError
@ -7,12 +9,16 @@ from freqtrade.exceptions import StrategyError
logger = logging.getLogger(__name__)
def strategy_safe_wrapper(f, message: str = "", default_retval=None, supress_error=False):
F = TypeVar('F', bound=Callable[..., Any])
def strategy_safe_wrapper(f: F, message: str = "", default_retval=None, supress_error=False) -> F:
"""
Wrapper around user-provided methods and functions.
Caches all exceptions and returns either the default_retval (if it's not None) or raises
a StrategyError exception, which then needs to be handled by the calling method.
"""
@wraps(f)
def wrapper(*args, **kwargs):
try:
if 'trade' in kwargs:
@ -37,4 +43,4 @@ def strategy_safe_wrapper(f, message: str = "", default_retval=None, supress_err
raise StrategyError(str(error)) from error
return default_retval
return wrapper
return cast(F, wrapper)

6
freqtrade/templates/base_strategy.py.j2
View File

@ -4,7 +4,9 @@
# --- Do not remove these libs ---
import numpy as np # noqa
import pandas as pd # noqa
from pandas import DataFrame
from pandas import DataFrame # noqa
from datetime import datetime # noqa
from typing import Optional, Union # noqa
from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter,
IStrategy, IntParameter)
@ -62,7 +64,7 @@ class {{ strategy }}(IStrategy):
# trailing_stop_positive_offset = 0.0 # Disabled / not configured
# Run "populate_indicators()" only for new candle.
process_only_new_candles = False
process_only_new_candles = True
# These values can be overridden in the config.
use_exit_signal = True

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