Update freqtrade/freqai/data_drawer.py

Co-authored-by: Matthias <xmatthias@outlook.com>

.github/workflows/ci.yml

@@ -16,15 +16,16 @@ on:
 concurrency:
   group: ${{ github.workflow }}-${{ github.ref }}
   cancel-in-progress: true
-
+permissions:
+  repository-projects: read
 jobs:
   build_linux:
 
     runs-on: ${{ matrix.os }}
     strategy:
       matrix:
-        os: [ ubuntu-18.04, ubuntu-20.04, ubuntu-22.04 ]
-        python-version: ["3.8", "3.9", "3.10"]
+        os: [ ubuntu-20.04, ubuntu-22.04 ]
+        python-version: ["3.8", "3.9", "3.10", "3.11"]
 
     steps:
     - uses: actions/checkout@v3
@@ -90,14 +91,14 @@ jobs:
         freqtrade create-userdir --userdir user_data
         freqtrade hyperopt --datadir tests/testdata -e 6 --strategy SampleStrategy --hyperopt-loss SharpeHyperOptLossDaily --print-all
 
-    - name: Flake8
-      run: |
-        flake8
-
     - name: Sort imports (isort)
       run: |
         isort --check .
 
+    - name: Run Ruff
+      run: |
+        ruff check --format=github .
+
     - name: Mypy
       run: |
         mypy freqtrade scripts tests
@@ -115,7 +116,7 @@ jobs:
     strategy:
       matrix:
         os: [ macos-latest ]
-        python-version: ["3.8", "3.9", "3.10"]
+        python-version: ["3.8", "3.9", "3.10", "3.11"]
 
     steps:
     - uses: actions/checkout@v3
@@ -186,14 +187,14 @@ jobs:
         freqtrade create-userdir --userdir user_data
         freqtrade hyperopt --datadir tests/testdata -e 5 --strategy SampleStrategy --hyperopt-loss SharpeHyperOptLossDaily --print-all
 
-    - name: Flake8
-      run: |
-        flake8
-
     - name: Sort imports (isort)
       run: |
         isort --check .
 
+    - name: Run Ruff
+      run: |
+        ruff check --format=github .
+
     - name: Mypy
       run: |
         mypy freqtrade scripts
@@ -212,7 +213,7 @@ jobs:
     strategy:
       matrix:
         os: [ windows-latest ]
-        python-version: ["3.8", "3.9", "3.10"]
+        python-version: ["3.8", "3.9", "3.10", "3.11"]
 
     steps:
     - uses: actions/checkout@v3
@@ -248,9 +249,9 @@ jobs:
         freqtrade create-userdir --userdir user_data
         freqtrade hyperopt --datadir tests/testdata -e 5 --strategy SampleStrategy --hyperopt-loss SharpeHyperOptLossDaily --print-all
 
-    - name: Flake8
+    - name: Run Ruff
       run: |
-        flake8
+        ruff check --format=github .
 
     - name: Mypy
       run: |
@@ -321,7 +322,6 @@ jobs:
   build_linux_online:
     # Run pytest with "live" checks
     runs-on: ubuntu-22.04
-    # permissions:
     steps:
     - uses: actions/checkout@v3
 
@@ -425,7 +425,7 @@ jobs:
         python setup.py sdist bdist_wheel
 
     - name: Publish to PyPI (Test)
-      uses: pypa/gh-action-pypi-publish@v1.6.4
+      uses: pypa/gh-action-pypi-publish@v1.8.1
       if: (github.event_name == 'release')
       with:
         user: __token__
@@ -433,7 +433,7 @@ jobs:
         repository_url: https://test.pypi.org/legacy/
 
     - name: Publish to PyPI
-      uses: pypa/gh-action-pypi-publish@v1.6.4
+      uses: pypa/gh-action-pypi-publish@v1.8.1
       if: (github.event_name == 'release')
       with:
         user: __token__
@@ -466,12 +466,13 @@ jobs:
 
     - name: Build and test and push docker images
       env:
-        IMAGE_NAME: freqtradeorg/freqtrade
         BRANCH_NAME: ${{ steps.extract_branch.outputs.branch }}
       run: |
         build_helpers/publish_docker_multi.sh
 
   deploy_arm:
+    permissions:
+      packages: write
     needs: [ deploy ]
     # Only run on 64bit machines
     runs-on: [self-hosted, linux, ARM64]
@@ -494,8 +495,9 @@ jobs:
 
     - name: Build and test and push docker images
       env:
-        IMAGE_NAME: freqtradeorg/freqtrade
         BRANCH_NAME: ${{ steps.extract_branch.outputs.branch }}
+        GHCR_USERNAME: ${{ github.actor }}
+        GHCR_TOKEN: ${{ secrets.GITHUB_TOKEN }}
       run: |
         build_helpers/publish_docker_arm64.sh
 

.pre-commit-config.yaml

@@ -2,33 +2,40 @@
 # See https://pre-commit.com/hooks.html for more hooks
 repos:
   - repo: https://github.com/pycqa/flake8
-    rev: "4.0.1"
+    rev: "6.0.0"
     hooks:
       - id: flake8
         # stages: [push]
 
   - repo: https://github.com/pre-commit/mirrors-mypy
-    rev: "v0.991"
+    rev: "v1.0.1"
     hooks:
       - id: mypy
         exclude: build_helpers
         additional_dependencies:
-          - types-cachetools==5.2.1
+          - types-cachetools==5.3.0.4
           - types-filelock==3.2.7
-          - types-requests==2.28.11.8
-          - types-tabulate==0.9.0.0
-          - types-python-dateutil==2.8.19.6
+          - types-requests==2.28.11.15
+          - types-tabulate==0.9.0.1
+          - types-python-dateutil==2.8.19.10
+          - SQLAlchemy==2.0.7
         # stages: [push]
 
   - repo: https://github.com/pycqa/isort
-    rev: "5.10.1"
+    rev: "5.12.0"
     hooks:
       - id: isort
         name: isort (python)
         # stages: [push]
 
+  - repo: https://github.com/charliermarsh/ruff-pre-commit
+    # Ruff version.
+    rev: 'v0.0.255'
+    hooks:
+      - id: ruff
+
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v2.4.0
+    rev: v4.4.0
     hooks:
       - id: end-of-file-fixer
         exclude: |

CONTRIBUTING.md

@@ -45,16 +45,17 @@ pytest tests/test_<file_name>.py::test_<method_name>
 
 ### 2. Test if your code is PEP8 compliant
 
-#### Run Flake8
+#### Run Ruff
 
 ```bash
-flake8 freqtrade tests scripts
+ruff .
 ```
 
-We receive a lot of code that fails the `flake8` checks.
+We receive a lot of code that fails the `ruff` checks.
 To help with that, we encourage you to install the git pre-commit 
 hook that will warn you when you try to commit code that fails these checks.
-Guide for installing them is [here](http://flake8.pycqa.org/en/latest/user/using-hooks.html).
+
+you can manually run pre-commit with `pre-commit run -a`.
 
 ##### Additional styles applied
 

Dockerfile

@@ -1,4 +1,4 @@
-FROM python:3.10.7-slim-bullseye as base
+FROM python:3.10.10-slim-bullseye as base
 
 # Setup env
 ENV LANG C.UTF-8

README.md

@@ -40,6 +40,7 @@ Please read the [exchange specific notes](docs/exchanges.md) to learn about even
 - [X] [Binance](https://www.binance.com/)
 - [X] [Gate.io](https://www.gate.io/ref/6266643)
 - [X] [OKX](https://okx.com/)
+- [X] [Bybit](https://bybit.com/)
 
 Please make sure to read the [exchange specific notes](docs/exchanges.md), as well as the [trading with leverage](docs/leverage.md) documentation before diving in.
 
@@ -164,6 +165,10 @@ first. If it hasn't been reported, please
 ensure you follow the template guide so that the team can assist you as
 quickly as possible.
 
+For every [issue](https://github.com/freqtrade/freqtrade/issues/new/choose) created, kindly follow up and mark satisfaction or reminder to close issue when equilibrium ground is reached.
+
+--Maintain github's [community policy](https://docs.github.com/en/site-policy/github-terms/github-community-code-of-conduct)--
+
 ### [Feature Requests](https://github.com/freqtrade/freqtrade/labels/enhancement)
 
 Have you a great idea to improve the bot you want to share? Please,

build_helpers/install_ta-lib.sh

@@ -8,8 +8,8 @@ if [ -n "$2" ] || [ ! -f "${INSTALL_LOC}/lib/libta_lib.a" ]; then
   tar zxvf ta-lib-0.4.0-src.tar.gz
   cd ta-lib \
   && sed -i.bak "s|0.00000001|0.000000000000000001 |g" src/ta_func/ta_utility.h \
-  && curl 'http://git.savannah.gnu.org/gitweb/?p=config.git;a=blob_plain;f=config.guess;hb=HEAD' -o config.guess \
-  && curl 'http://git.savannah.gnu.org/gitweb/?p=config.git;a=blob_plain;f=config.sub;hb=HEAD' -o config.sub \
+  && curl 'https://raw.githubusercontent.com/gcc-mirror/gcc/master/config.guess' -o config.guess \
+  && curl 'https://raw.githubusercontent.com/gcc-mirror/gcc/master/config.sub' -o config.sub \
   && ./configure --prefix=${INSTALL_LOC}/ \
   && make
   if [ $? -ne 0 ]; then

build_helpers/install_windows.ps1

@@ -14,5 +14,8 @@ if ($pyv -eq '3.9') {
 if ($pyv -eq '3.10') {
     pip install build_helpers\TA_Lib-0.4.25-cp310-cp310-win_amd64.whl
 }
+if ($pyv -eq '3.11') {
+    pip install build_helpers\TA_Lib-0.4.25-cp311-cp311-win_amd64.whl
+}
 pip install -r requirements-dev.txt
 pip install -e .

build_helpers/pre_commit_update.py

@@ -8,12 +8,17 @@ import yaml
 
 pre_commit_file = Path('.pre-commit-config.yaml')
 require_dev = Path('requirements-dev.txt')
+require = Path('requirements.txt')
 
 with require_dev.open('r') as rfile:
     requirements = rfile.readlines()
 
+with require.open('r') as rfile:
+    requirements.extend(rfile.readlines())
+
 # Extract types only
-type_reqs = [r.strip('\n') for r in requirements if r.startswith('types-')]
+type_reqs = [r.strip('\n') for r in requirements if r.startswith(
+    'types-') or r.startswith('SQLAlchemy')]
 
 with pre_commit_file.open('r') as file:
     f = yaml.load(file, Loader=yaml.FullLoader)

build_helpers/publish_docker_arm64.sh

@@ -3,6 +3,10 @@
 # Use BuildKit, otherwise building on ARM fails
 export DOCKER_BUILDKIT=1
 
+IMAGE_NAME=freqtradeorg/freqtrade
+CACHE_IMAGE=freqtradeorg/freqtrade_cache
+GHCR_IMAGE_NAME=ghcr.io/freqtrade/freqtrade
+
 # Replace / with _ to create a valid tag
 TAG=$(echo "${BRANCH_NAME}" | sed -e "s/\//_/g")
 TAG_PLOT=${TAG}_plot
@@ -14,7 +18,6 @@ TAG_ARM=${TAG}_arm
 TAG_PLOT_ARM=${TAG_PLOT}_arm
 TAG_FREQAI_ARM=${TAG_FREQAI}_arm
 TAG_FREQAI_RL_ARM=${TAG_FREQAI_RL}_arm
-CACHE_IMAGE=freqtradeorg/freqtrade_cache
 
 echo "Running for ${TAG}"
 
@@ -38,13 +41,13 @@ if [ $? -ne 0 ]; then
     echo "failed building multiarch images"
     return 1
 fi
-# Tag image for upload and next build step
-docker tag freqtrade:$TAG_ARM ${CACHE_IMAGE}:$TAG_ARM
 
 docker build --cache-from freqtrade:${TAG_ARM} --build-arg sourceimage=${CACHE_IMAGE} --build-arg sourcetag=${TAG_ARM} -t freqtrade:${TAG_PLOT_ARM} -f docker/Dockerfile.plot .
 docker build --cache-from freqtrade:${TAG_ARM} --build-arg sourceimage=${CACHE_IMAGE} --build-arg sourcetag=${TAG_ARM} -t freqtrade:${TAG_FREQAI_ARM} -f docker/Dockerfile.freqai .
 docker build --cache-from freqtrade:${TAG_ARM} --build-arg sourceimage=${CACHE_IMAGE} --build-arg sourcetag=${TAG_ARM} -t freqtrade:${TAG_FREQAI_RL_ARM} -f docker/Dockerfile.freqai_rl .
 
+# Tag image for upload and next build step
+docker tag freqtrade:$TAG_ARM ${CACHE_IMAGE}:$TAG_ARM
 docker tag freqtrade:$TAG_PLOT_ARM ${CACHE_IMAGE}:$TAG_PLOT_ARM
 docker tag freqtrade:$TAG_FREQAI_ARM ${CACHE_IMAGE}:$TAG_FREQAI_ARM
 docker tag freqtrade:$TAG_FREQAI_RL_ARM ${CACHE_IMAGE}:$TAG_FREQAI_RL_ARM
@@ -59,7 +62,6 @@ fi
 
 docker images
 
-# docker push ${IMAGE_NAME}
 docker push ${CACHE_IMAGE}:$TAG_PLOT_ARM
 docker push ${CACHE_IMAGE}:$TAG_FREQAI_ARM
 docker push ${CACHE_IMAGE}:$TAG_FREQAI_RL_ARM
@@ -82,14 +84,30 @@ docker manifest push -p ${IMAGE_NAME}:${TAG_FREQAI}
 docker manifest create ${IMAGE_NAME}:${TAG_FREQAI_RL} ${CACHE_IMAGE}:${TAG_FREQAI_RL} ${CACHE_IMAGE}:${TAG_FREQAI_RL_ARM}
 docker manifest push -p ${IMAGE_NAME}:${TAG_FREQAI_RL}
 
+# copy images to ghcr.io
+
+alias crane="docker run --rm -i -v $(pwd)/.crane:/home/nonroot/.docker/ gcr.io/go-containerregistry/crane"
+mkdir .crane
+chmod a+rwx .crane
+
+echo "${GHCR_TOKEN}" | crane auth login ghcr.io -u "${GHCR_USERNAME}" --password-stdin
+
+crane copy ${IMAGE_NAME}:${TAG_FREQAI_RL} ${GHCR_IMAGE_NAME}:${TAG_FREQAI_RL}
+crane copy ${IMAGE_NAME}:${TAG_FREQAI} ${GHCR_IMAGE_NAME}:${TAG_FREQAI}
+crane copy ${IMAGE_NAME}:${TAG_PLOT} ${GHCR_IMAGE_NAME}:${TAG_PLOT}
+crane copy ${IMAGE_NAME}:${TAG} ${GHCR_IMAGE_NAME}:${TAG}
+
 # Tag as latest for develop builds
 if [ "${TAG}" = "develop" ]; then
     echo 'Tagging image as latest'
     docker manifest create ${IMAGE_NAME}:latest ${CACHE_IMAGE}:${TAG_ARM} ${IMAGE_NAME}:${TAG_PI} ${CACHE_IMAGE}:${TAG}
     docker manifest push -p ${IMAGE_NAME}:latest
+
+    crane copy ${IMAGE_NAME}:latest ${GHCR_IMAGE_NAME}:latest
 fi
 
 docker images
+rm -rf .crane
 
 # Cleanup old images from arm64 node.
 docker image prune -a --force --filter "until=24h"

build_helpers/publish_docker_multi.sh

@@ -2,6 +2,8 @@
 
 # The below assumes a correctly setup docker buildx environment
 
+IMAGE_NAME=freqtradeorg/freqtrade
+CACHE_IMAGE=freqtradeorg/freqtrade_cache
 # Replace / with _ to create a valid tag
 TAG=$(echo "${BRANCH_NAME}" | sed -e "s/\//_/g")
 TAG_PLOT=${TAG}_plot
@@ -11,7 +13,6 @@ TAG_PI="${TAG}_pi"
 
 PI_PLATFORM="linux/arm/v7"
 echo "Running for ${TAG}"
-CACHE_IMAGE=freqtradeorg/freqtrade_cache
 CACHE_TAG=${CACHE_IMAGE}:${TAG_PI}_cache
 
 # Add commit and commit_message to docker container

config_examples/config_freqai.example.json

@@ -48,7 +48,7 @@
     ],
     "freqai": {
         "enabled": true,
-        "purge_old_models": true,
+        "purge_old_models": 2,
         "train_period_days": 15,
         "backtest_period_days": 7,
         "live_retrain_hours": 0,

config_examples/config_full.example.json

@@ -60,6 +60,7 @@
         "force_entry": "market",
         "stoploss": "market",
         "stoploss_on_exchange": false,
+        "stoploss_price_type": "last",
         "stoploss_on_exchange_interval": 60,
         "stoploss_on_exchange_limit_ratio": 0.99
     },

docker/Dockerfile.armhf

@@ -1,4 +1,4 @@
-FROM python:3.9.12-slim-bullseye as base
+FROM python:3.9.16-slim-bullseye as base
 
 # Setup env
 ENV LANG C.UTF-8

docs/advanced-setup.md

@@ -192,7 +192,7 @@ $RepeatedMsgReduction on
 
 ### Logging to journald
 
-This needs the `systemd` python package installed as the dependency, which is not available on Windows. Hence, the whole journald logging functionality is not available for a bot running on Windows.
+This needs the `cysystemd` python package installed as dependency (`pip install cysystemd`), which is not available on Windows. Hence, the whole journald logging functionality is not available for a bot running on Windows.
 
 To send Freqtrade log messages to `journald` system service use the `--logfile` command line option with the value in the following format:
 

docs/bot-basics.md

@@ -12,6 +12,9 @@ This page provides you some basic concepts on how Freqtrade works and operates.
 * **Indicators**: Technical indicators (SMA, EMA, RSI, ...).
 * **Limit order**: Limit orders which execute at the defined limit price or better.
 * **Market order**: Guaranteed to fill, may move price depending on the order size.
+* **Current Profit**: Currently pending (unrealized) profit for this trade. This is mainly used throughout the bot and UI.
+* **Realized Profit**: Already realized profit. Only relevant in combination with [partial exits](strategy-callbacks.md#adjust-trade-position) - which also explains the calculation logic for this.
+* **Total Profit**: Combined realized and unrealized profit. The relative number (%) is calculated against the total investment in this trade.
 
 ## Fee handling
 

docs/configuration.md

@@ -666,7 +666,7 @@ You should also make sure to read the [Exchanges](exchanges.md) section of the d
 ### Using proxy with Freqtrade
 
 To use a proxy with freqtrade, export your proxy settings using the variables `"HTTP_PROXY"` and `"HTTPS_PROXY"` set to the appropriate values.
-This will have the proxy settings applied to everything (telegram, coingecko, ...) except exchange requests.
+This will have the proxy settings applied to everything (telegram, coingecko, ...) **except** for exchange requests.
 
 ``` bash
 export HTTP_PROXY="http://addr:port"
@@ -682,11 +682,12 @@ To use a proxy for exchange connections - you will have to define the proxies as
 { 
   "exchange": {
     "ccxt_config": {
-    "aiohttp_proxy": "http://addr:port",
-    "proxies": {
-      "http": "http://addr:port",
-      "https": "http://addr:port"
-    },
+      "aiohttp_proxy": "http://addr:port",
+      "proxies": {
+        "http": "http://addr:port",
+        "https": "http://addr:port"
+      },
+    }
   }
 }
 ```

docs/deprecated.md

@@ -74,3 +74,8 @@ Webhook terminology changed from "sell" to "exit", and from "buy" to "entry", re
 * `webhooksell`, `webhookexit` -> `exit`
 * `webhooksellfill`, `webhookexitfill` -> `exit_fill`
 * `webhooksellcancel`, `webhookexitcancel` -> `exit_cancel`
+
+
+## Removal of `populate_any_indicators`
+
+version 2023.3 saw the removal of `populate_any_indicators` in favor of split methods for feature engineering and targets. Please read the [migration document](strategy_migration.md#freqai-strategy) for full details.

docs/developer.md

@@ -24,7 +24,7 @@ This will spin up a local server (usually on port 8000) so you can see if everyt
 To configure a development environment, you can either use the provided [DevContainer](#devcontainer-setup), or use the `setup.sh` script and answer "y" when asked "Do you want to install dependencies for dev [y/N]? ".
 Alternatively (e.g. if your system is not supported by the setup.sh script), follow the manual installation process and run `pip3 install -e .[all]`.
 
-This will install all required tools for development, including `pytest`, `flake8`, `mypy`, and `coveralls`.
+This will install all required tools for development, including `pytest`, `ruff`, `mypy`, and `coveralls`.
 
 Then install the git hook scripts by running `pre-commit install`, so your changes will be verified locally before committing.
 This avoids a lot of waiting for CI already, as some basic formatting checks are done locally on your machine.
@@ -363,7 +363,7 @@ from pathlib import Path
 exchange = ccxt.binance({
     'apiKey': '<apikey>',
     'secret': '<secret>'
-    'options': {'defaultType': 'future'}
+    'options': {'defaultType': 'swap'}
     })
 _ = exchange.load_markets()
 

docs/exchanges.md

@@ -243,8 +243,8 @@ OKX requires a passphrase for each api key, you will therefore need to add this
     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 (we recommend to use net mode) - but changing the mode mid-trading is not supported and will lead to exceptions and failures to place trades.
+    OKX Futures has the concept of "position mode" - which can be "Buy/Sell" or long/short (hedge mode).
+    Freqtrade supports both modes (we recommend to use Buy/Sell mode) - 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
@@ -255,6 +255,18 @@ OKX requires a passphrase for each api key, you will therefore need to add this
 Gate.io allows the use of `POINT` to pay for fees. As this is not a tradable currency (no regular market available), automatic fee calculations will fail (and default to a fee of 0).
 The configuration parameter `exchange.unknown_fee_rate` can be used to specify the exchange rate between Point and the stake currency. Obviously, changing the stake-currency will also require changes to this value.
 
+## Bybit
+
+Futures trading on bybit is currently supported for USDT markets, and will use isolated futures mode.
+Users with unified accounts (there's no way back) can create a Sub-account which will start as "non-unified", and can therefore use isolated futures.
+On startup, freqtrade will set the position mode to "One-way Mode" for the whole (sub)account. This avoids making this call over and over again (slowing down bot operations), but means that changes to this setting may result in exceptions and errors.
+
+As bybit doesn't provide funding rate history, the dry-run calculation is used for live trades as well.
+
+!!! Tip "Stoploss on Exchange"
+    Bybit (futures only) supports `stoploss_on_exchange` and uses `stop-loss-limit` orders. It provides great advantages, so we recommend to benefit from it by enabling stoploss on exchange.
+    On futures, Bybit supports both `stop-limit` as well as `stop-market` orders. You can use either `"limit"` or `"market"` in the `order_types.stoploss` configuration setting to decide which type to use.
+
 ## All exchanges
 
 Should you experience constant errors with Nonce (like `InvalidNonce`), it is best to regenerate the API keys. Resetting Nonce is difficult and it's usually easier to regenerate the API keys.

docs/faq.md

@@ -2,7 +2,7 @@
 
 ## Supported Markets
 
-Freqtrade supports spot trading only.
+Freqtrade supports spot trading, as well as (isolated) futures trading for some selected exchanges. Please refer to the [documentation start page](index.md#supported-futures-exchanges-experimental) for an uptodate list of supported exchanges.
 
 ### Can my bot open short positions?
 
@@ -248,8 +248,26 @@ The Edge module is mostly a result of brainstorming of [@mishaker](https://githu
 You can find further info on expectancy, win rate, risk management and position size in the following sources:
 
 - https://www.tradeciety.com/ultimate-math-guide-for-traders/
-- http://www.vantharp.com/tharp-concepts/expectancy.asp
 - https://samuraitradingacademy.com/trading-expectancy/
 - https://www.learningmarkets.com/determining-expectancy-in-your-trading/
-- http://www.lonestocktrader.com/make-money-trading-positive-expectancy/
+- https://www.lonestocktrader.com/make-money-trading-positive-expectancy/
 - https://www.babypips.com/trading/trade-expectancy-matter
+
+## Official channels
+
+Freqtrade is using exclusively the following official channels:
+
+* [Freqtrade discord server](https://discord.gg/p7nuUNVfP7)
+* [Freqtrade documentation (https://freqtrade.io)](https://freqtrade.io)
+* [Freqtrade github organization](https://github.com/freqtrade)
+
+Nobody affiliated with the freqtrade project will ask you about your exchange keys or anything else exposing your funds to exploitation.
+Should you be asked to expose your exchange keys or send funds to some random wallet, then please don't follow these instructions.
+
+Failing to follow these guidelines will not be responsibility of freqtrade.
+
+## "Freqtrade token"
+
+Freqtrade does not have a Crypto token offering.
+
+Token offerings you find on the internet referring Freqtrade, FreqAI or freqUI must be considered to be a scam, trying to exploit freqtrade's popularity for their own, nefarious gains.

docs/freqai-configuration.md

@@ -9,7 +9,7 @@ FreqAI is configured through the typical [Freqtrade config file](configuration.m
 ```json
     "freqai": {
         "enabled": true,
-        "purge_old_models": true,
+        "purge_old_models": 2,
         "train_period_days": 30,
         "backtest_period_days": 7,
         "identifier" : "unique-id",
@@ -165,10 +165,10 @@ Below are the values you can expect to include/use inside a typical strategy dat
 
 ## Setting the `startup_candle_count`
 
-The `startup_candle_count` in the FreqAI strategy needs to be set up in the same way as in the standard Freqtrade strategy (see details [here](strategy-customization.md#strategy-startup-period)). This value is used by Freqtrade to ensure that a sufficient amount of data is provided when calling the `dataprovider`, to avoid any NaNs at the beginning of the first training. You can easily set this value by identifying the longest period (in candle units) which is passed to the indicator creation functions (e.g., Ta-Lib functions). In the presented example, `startup_candle_count` is 20 since this is the maximum value in `indicators_periods_candles`.
+The `startup_candle_count` in the FreqAI strategy needs to be set up in the same way as in the standard Freqtrade strategy (see details [here](strategy-customization.md#strategy-startup-period)). This value is used by Freqtrade to ensure that a sufficient amount of data is provided when calling the `dataprovider`, to avoid any NaNs at the beginning of the first training. You can easily set this value by identifying the longest period (in candle units) which is passed to the indicator creation functions (e.g., TA-Lib functions). In the presented example, `startup_candle_count` is 20 since this is the maximum value in `indicators_periods_candles`.
 
 !!! Note
-    There are instances where the Ta-Lib functions actually require more data than just the passed `period` or else the feature dataset gets populated with NaNs. Anecdotally, multiplying the `startup_candle_count` by 2 always leads to a fully NaN free training dataset. Hence, it is typically safest to multiply the expected `startup_candle_count` by 2. Look out for this log message to confirm that the data is clean:
+    There are instances where the TA-Lib functions actually require more data than just the passed `period` or else the feature dataset gets populated with NaNs. Anecdotally, multiplying the `startup_candle_count` by 2 always leads to a fully NaN free training dataset. Hence, it is typically safest to multiply the expected `startup_candle_count` by 2. Look out for this log message to confirm that the data is clean:
 
     ```
     2022-08-31 15:14:04 - freqtrade.freqai.data_kitchen - INFO - dropped 0 training points due to NaNs in populated dataset 4319.
@@ -205,7 +205,7 @@ All of the aforementioned model libraries implement gradient boosted decision tr
 * LightGBM: https://lightgbm.readthedocs.io/en/v3.3.2/#
 * XGBoost: https://xgboost.readthedocs.io/en/stable/#
 
-There are also numerous online articles describing and comparing the algorithms. Some relatively light-weight examples would be [CatBoost vs. LightGBM vs. XGBoost — Which is the best algorithm?](https://towardsdatascience.com/catboost-vs-lightgbm-vs-xgboost-c80f40662924#:~:text=In%20CatBoost%2C%20symmetric%20trees%2C%20or,the%20same%20depth%20can%20differ.) and [XGBoost, LightGBM or CatBoost — which boosting algorithm should I use?](https://medium.com/riskified-technology/xgboost-lightgbm-or-catboost-which-boosting-algorithm-should-i-use-e7fda7bb36bc). Keep in mind that the performance of each model is highly dependent on the application and so any reported metrics might not be true for your particular use of the model.
+There are also numerous online articles describing and comparing the algorithms. Some relatively lightweight examples would be [CatBoost vs. LightGBM vs. XGBoost — Which is the best algorithm?](https://towardsdatascience.com/catboost-vs-lightgbm-vs-xgboost-c80f40662924#:~:text=In%20CatBoost%2C%20symmetric%20trees%2C%20or,the%20same%20depth%20can%20differ.) and [XGBoost, LightGBM or CatBoost — which boosting algorithm should I use?](https://medium.com/riskified-technology/xgboost-lightgbm-or-catboost-which-boosting-algorithm-should-i-use-e7fda7bb36bc). Keep in mind that the performance of each model is highly dependent on the application and so any reported metrics might not be true for your particular use of the model.
 
 Apart from the models already available in FreqAI, it is also possible to customize and create your own prediction models using the `IFreqaiModel` class. You are encouraged to inherit `fit()`, `train()`, and `predict()` to customize various aspects of the training procedures. You can place custom FreqAI models in `user_data/freqaimodels` - and freqtrade will pick them up from there based on the provided `--freqaimodel` name - which has to correspond to the class name of your custom model.
 Make sure to use unique names to avoid overriding built-in models.

docs/freqai-feature-engineering.md

@@ -8,7 +8,7 @@ Low level feature engineering is performed in the user strategy within a set of
 |---------------|-------------|
 | `feature_engineering__expand_all()` | This optional function will automatically expand the defined features on the config defined `indicator_periods_candles`, `include_timeframes`, `include_shifted_candles`, and `include_corr_pairs`.
 | `feature_engineering__expand_basic()` | This optional function will automatically expand the defined features on the config defined `include_timeframes`, `include_shifted_candles`, and `include_corr_pairs`. Note: this function does *not* expand across `include_periods_candles`.
-| `feature_engineering_standard()` | This optional function will be called once with the dataframe of the base timeframe. This is the final function to be called, which means that the dataframe entering this function will contain all the features and columns from the base asset created by the other `feature_engineering_expand` functions. This function is a good place to do custom exotic feature extractions (e.g. tsfresh). This function is also a good place for any feature that should not be auto-expanded upon (e.g. day of the week).
+| `feature_engineering_standard()` | This optional function will be called once with the dataframe of the base timeframe. This is the final function to be called, which means that the dataframe entering this function will contain all the features and columns from the base asset created by the other `feature_engineering_expand` functions. This function is a good place to do custom exotic feature extractions (e.g. tsfresh). This function is also a good place for any feature that should not be auto-expanded upon (e.g., day of the week).
 | `set_freqai_targets()` | Required function to set the targets for the model. All targets must be prepended with `&` to be recognized by the FreqAI internals.
 
 Meanwhile, high level feature engineering is handled within `"feature_parameters":{}` in the FreqAI config. Within this file, it is possible to decide large scale feature expansions on top of the `base_features` such as "including correlated pairs" or "including informative timeframes" or even "including recent candles."
@@ -16,7 +16,7 @@ Meanwhile, high level feature engineering is handled within `"feature_parameters
 It is advisable to start from the template `feature_engineering_*` functions in the source provided example strategy (found in `templates/FreqaiExampleStrategy.py`) to ensure that the feature definitions are following the correct conventions. Here is an example of how to set the indicators and labels in the strategy:
 
 ```python
-    def feature_engineering_expand_all(self, dataframe, period, **kwargs):
+    def feature_engineering_expand_all(self, dataframe, period, metadata, **kwargs):
         """
         *Only functional with FreqAI enabled strategies*
         This function will automatically expand the defined features on the config defined
@@ -28,8 +28,13 @@ It is advisable to start from the template `feature_engineering_*` functions in
 
         All features must be prepended with `%` to be recognized by FreqAI internals.
 
+        Access metadata such as the current pair/timeframe/period with:
+
+        `metadata["pair"]` `metadata["tf"]`  `metadata["period"]`
+
         :param df: strategy dataframe which will receive the features
         :param period: period of the indicator - usage example:
+        :param metadata: metadata of current pair
         dataframe["%-ema-period"] = ta.EMA(dataframe, timeperiod=period)
         """
 
@@ -62,7 +67,7 @@ It is advisable to start from the template `feature_engineering_*` functions in
 
         return dataframe
 
-    def feature_engineering_expand_basic(self, dataframe, **kwargs):
+    def feature_engineering_expand_basic(self, dataframe, metadata, **kwargs):
         """
         *Only functional with FreqAI enabled strategies*
         This function will automatically expand the defined features on the config defined
@@ -75,9 +80,14 @@ It is advisable to start from the template `feature_engineering_*` functions in
         Features defined here will *not* be automatically duplicated on user defined
         `indicator_periods_candles`
 
+        Access metadata such as the current pair/timeframe with:
+
+        `metadata["pair"]` `metadata["tf"]`
+
         All features must be prepended with `%` to be recognized by FreqAI internals.
 
         :param df: strategy dataframe which will receive the features
+        :param metadata: metadata of current pair
         dataframe["%-pct-change"] = dataframe["close"].pct_change()
         dataframe["%-ema-200"] = ta.EMA(dataframe, timeperiod=200)
         """
@@ -86,7 +96,7 @@ It is advisable to start from the template `feature_engineering_*` functions in
         dataframe["%-raw_price"] = dataframe["close"]
         return dataframe
 
-    def feature_engineering_standard(self, dataframe, **kwargs):
+    def feature_engineering_standard(self, dataframe, metadata, **kwargs):
         """
         *Only functional with FreqAI enabled strategies*
         This optional function will be called once with the dataframe of the base timeframe.
@@ -98,22 +108,32 @@ It is advisable to start from the template `feature_engineering_*` functions in
         This function is a good place for any feature that should not be auto-expanded upon
         (e.g. day of the week).
 
+        Access metadata such as the current pair with:
+
+        `metadata["pair"]`
+
         All features must be prepended with `%` to be recognized by FreqAI internals.
 
         :param df: strategy dataframe which will receive the features
+        :param metadata: metadata of current pair
         usage example: dataframe["%-day_of_week"] = (dataframe["date"].dt.dayofweek + 1) / 7
         """
         dataframe["%-day_of_week"] = (dataframe["date"].dt.dayofweek + 1) / 7
         dataframe["%-hour_of_day"] = (dataframe["date"].dt.hour + 1) / 25
         return dataframe
 
-    def set_freqai_targets(self, dataframe, **kwargs):
+    def set_freqai_targets(self, dataframe, metadata, **kwargs):
         """
         *Only functional with FreqAI enabled strategies*
         Required function to set the targets for the model.
         All targets must be prepended with `&` to be recognized by the FreqAI internals.
 
+        Access metadata such as the current pair with:
+
+        `metadata["pair"]`
+
         :param df: strategy dataframe which will receive the targets
+        :param metadata: metadata of current pair
         usage example: dataframe["&-target"] = dataframe["close"].shift(-1) / dataframe["close"]
         """
         dataframe["&-s_close"] = (
@@ -161,6 +181,19 @@ You can ask for each of the defined features to be included also for informative
 In total, the number of features the user of the presented example strat has created is: length of `include_timeframes` * no. features in `feature_engineering_expand_*()` * length of `include_corr_pairlist` * no. `include_shifted_candles` * length of `indicator_periods_candles`
  $= 3 * 3 * 3 * 2 * 2 = 108$.
 
+
+ ### Gain finer control over `feature_engineering_*` functions with `metadata`
+
+ All `feature_engineering_*` and `set_freqai_targets()` functions are passed a `metadata` dictionary which contains information about the `pair`, `tf` (timeframe), and `period` that FreqAI is automating for feature building. As such, a user can use `metadata` inside `feature_engineering_*` functions as criteria for blocking/reserving features for certain timeframes, periods, pairs etc.
+
+ ```py
+def feature_engineering_expand_all(self, dataframe, period, metadata, **kwargs):
+ if metadata["tf"] == "1h":
+    dataframe["%-roc-period"] = ta.ROC(dataframe, timeperiod=period)
+```
+
+This will block `ta.ROC()` from being added to any timeframes other than `"1h"`.
+
 ### Returning additional info from training
 
 Important metrics can be returned to the strategy at the end of each model training by assigning them to `dk.data['extra_returns_per_train']['my_new_value'] = XYZ` inside the custom prediction model class. 
@@ -201,7 +234,7 @@ This will perform PCA on the features and reduce their dimensionality so that th
 
 ## Inlier metric
 
-The `inlier_metric` is a metric aimed at quantifying how similar a the features of a data point are to the most recent historic data points. 
+The `inlier_metric` is a metric aimed at quantifying how similar the features of a data point are to the most recent historical data points. 
 
 You define the lookback window by setting `inlier_metric_window` and FreqAI computes the distance between the present time point and each of the previous `inlier_metric_window` lookback points. A Weibull function is fit to each of the lookback distributions and its cumulative distribution function (CDF) is used to produce a quantile for each lookback point. The `inlier_metric` is then computed for each time point as the average of the corresponding lookback quantiles. The figure below explains the concept for an `inlier_metric_window` of 5.
 

docs/freqai-parameter-table.md

@@ -15,10 +15,9 @@ Mandatory parameters are marked as **Required** and have to be set in one of the
 | `identifier` | **Required.** <br> A unique ID for the current model. If models are saved to disk, the `identifier` allows for reloading specific pre-trained models/data. <br> **Datatype:** String.
 | `live_retrain_hours` | Frequency of retraining during dry/live runs. <br> **Datatype:** Float > 0. <br> Default: `0` (models retrain as often as possible).
 | `expiration_hours` | Avoid making predictions if a model is more than `expiration_hours` old. <br> **Datatype:** Positive integer. <br> Default: `0` (models never expire).
-| `purge_old_models` | Delete all unused models during live runs (not relevant to backtesting). If set to false (not default), dry/live runs will accumulate all unused models to disk. If <br> **Datatype:** Boolean. <br> Default: `True`.
+| `purge_old_models` | Number of models to keep on disk (not relevant to backtesting). Default is 2, which means that dry/live runs will keep the latest 2 models on disk. Setting to 0 keeps all models. This parameter also accepts a boolean to maintain backwards compatibility. <br> **Datatype:** Integer. <br> Default: `2`.
 | `save_backtest_models` | Save models to disk when running backtesting. Backtesting operates most efficiently by saving the prediction data and reusing them directly for subsequent runs (when you wish to tune entry/exit parameters). Saving backtesting models to disk also allows to use the same model files for starting a dry/live instance with the same model `identifier`. <br> **Datatype:** Boolean. <br> Default: `False` (no models are saved).
 | `fit_live_predictions_candles` | Number of historical candles to use for computing target (label) statistics from prediction data, instead of from the training dataset (more information can be found [here](freqai-configuration.md#creating-a-dynamic-target-threshold)). <br> **Datatype:** Positive integer.
-| `follow_mode` | Use a `follower` that will look for models associated with a specific `identifier` and load those for inferencing. A `follower` will **not** train new models. <br> **Datatype:** Boolean. <br> Default: `False`.
 | `continual_learning` | Use the final state of the most recently trained model as starting point for the new model, allowing for incremental learning (more information can be found [here](freqai-running.md#continual-learning)). <br> **Datatype:** Boolean. <br> Default: `False`.
 | `write_metrics_to_disk` | Collect train timings, inference timings and cpu usage in json file. <br> **Datatype:** Boolean. <br> Default: `False`
 | `data_kitchen_thread_count` | <br> Designate the number of threads you want to use for data processing (outlier methods, normalization, etc.). This has no impact on the number of threads used for training. If user does not set it (default), FreqAI will use max number of threads - 2 (leaving 1 physical core available for Freqtrade bot and FreqUI) <br> **Datatype:** Positive integer.
@@ -46,13 +45,15 @@ Mandatory parameters are marked as **Required** and have to be set in one of the
 | `noise_standard_deviation` | If set, FreqAI adds noise to the training features with the aim of preventing overfitting. FreqAI generates random deviates from a gaussian distribution with a standard deviation of `noise_standard_deviation` and adds them to all data points. `noise_standard_deviation` should be kept relative to the normalized space, i.e., between -1 and 1. In other words, since data in FreqAI is always normalized to be between -1 and 1, `noise_standard_deviation: 0.05` would result in 32% of the data being randomly increased/decreased by more than 2.5% (i.e., the percent of data falling within the first standard deviation). <br> **Datatype:** Integer. <br> Default: `0`.
 | `outlier_protection_percentage` | Enable to prevent outlier detection methods from discarding too much data. If more than `outlier_protection_percentage` % of points are detected as outliers by the SVM or DBSCAN, FreqAI will log a warning message and ignore outlier detection, i.e., the original dataset will be kept intact. If the outlier protection is triggered, no predictions will be made based on the training dataset. <br> **Datatype:** Float. <br> Default: `30`.
 | `reverse_train_test_order` | Split the feature dataset (see below) and use the latest data split for training and test on historical split of the data. This allows the model to be trained up to the most recent data point, while avoiding overfitting. However, you should be careful to understand the unorthodox nature of this parameter before employing it. <br> **Datatype:** Boolean. <br> Default: `False` (no reversal).
+| `shuffle_after_split` | Split the data into train and test sets, and then shuffle both sets individually. <br> **Datatype:** Boolean. <br> Default: `False`.
+| `buffer_train_data_candles` | Cut `buffer_train_data_candles` off the beginning and end of the training data *after* the indicators were populated. The main example use is when predicting maxima and minima, the argrelextrema function  cannot know the maxima/minima at the edges of the timerange. To improve model accuracy, it is best to compute argrelextrema on the full timerange and then use this function to cut off the edges (buffer) by the kernel. In another case, if the targets are set to a shifted price movement, this buffer is unnecessary because the shifted candles at the end of the timerange will be NaN and FreqAI will automatically cut those off of the training dataset.<br> **Datatype:** Boolean. <br> Default: `False`.
 
 ### Data split parameters
 
 |  Parameter | Description |
 |------------|-------------|
 |  |  **Data split parameters within the `freqai.data_split_parameters` sub dictionary**
-| `data_split_parameters` | Include any additional parameters available from Scikit-learn `test_train_split()`, which are shown [here](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html) (external website). <br> **Datatype:** Dictionary.
+| `data_split_parameters` | Include any additional parameters available from scikit-learn `test_train_split()`, which are shown [here](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html) (external website). <br> **Datatype:** Dictionary.
 | `test_size` | The fraction of data that should be used for testing instead of training. <br> **Datatype:** Positive float < 1.
 | `shuffle` | Shuffle the training data points during training. Typically, to not remove the chronological order of data in time-series forecasting, this is set to `False`. <br> **Datatype:** Boolean. <br> Defaut: `False`.
 
@@ -83,12 +84,13 @@ Mandatory parameters are marked as **Required** and have to be set in one of the
 | `add_state_info` | Tell FreqAI to include state information in the feature set for training and inferencing. The current state variables include trade duration, current profit, trade position. This is only available in dry/live runs, and is automatically switched to false for backtesting. <br> **Datatype:** bool. <br> Default: `False`.
 | `net_arch` | Network architecture which is well described in [`stable_baselines3` doc](https://stable-baselines3.readthedocs.io/en/master/guide/custom_policy.html#examples). In summary: `[<shared layers>, dict(vf=[<non-shared value network layers>], pi=[<non-shared policy network layers>])]`. By default this is set to `[128, 128]`, which defines 2 shared hidden layers with 128 units each.
 | `randomize_starting_position` | Randomize the starting point of each episode to avoid overfitting. <br> **Datatype:** bool. <br> Default: `False`.
+| `drop_ohlc_from_features` | Do not include the normalized ohlc data in the feature set passed to the agent during training (ohlc will still be used for driving the environment in all cases) <br> **Datatype:** Boolean. <br> **Default:** `False`
 
 ### Additional parameters
 
 |  Parameter | Description |
 |------------|-------------|
 |  |  **Extraneous parameters**
-| `freqai.keras` | If the selected model makes use of Keras (typical for Tensorflow-based prediction models), this flag needs to be activated so that the model save/loading follows Keras standards. <br> **Datatype:** Boolean. <br> Default: `False`.
+| `freqai.keras` | If the selected model makes use of Keras (typical for TensorFlow-based prediction models), this flag needs to be activated so that the model save/loading follows Keras standards. <br> **Datatype:** Boolean. <br> Default: `False`.
 | `freqai.conv_width` | The width of a convolutional neural network input tensor. This replaces the need for shifting candles (`include_shifted_candles`) by feeding in historical data points as the second dimension of the tensor. Technically, this parameter can also be used for regressors, but it only adds computational overhead and does not change the model training/prediction. <br> **Datatype:** Integer. <br> Default: `2`.
 | `freqai.reduce_df_footprint` | Recast all numeric columns to float32/int32, with the objective of reducing ram/disk usage and decreasing train/inference timing. This parameter is set in the main level of the Freqtrade configuration file (not inside FreqAI). <br> **Datatype:** Boolean. <br> Default: `False`.

docs/freqai-reinforcement-learning.md

@@ -24,7 +24,7 @@ The framework is built on stable_baselines3 (torch) and OpenAI gym for the base
 
 ### Important considerations
 
-As explained above, the agent is "trained" in an artificial trading "environment". In our case, that environment may seem quite similar to a real Freqtrade backtesting environment, but it is *NOT*. In fact, the RL training environment is much more simplified. It does not incorporate any of the complicated strategy logic, such as callbacks like `custom_exit`, `custom_stoploss`, leverage controls, etc. The RL environment is instead a very "raw" representation of the true market, where the agent has free-will to learn the policy (read: stoploss, take profit, etc.) which is enforced by the `calculate_reward()`. Thus, it is important to consider that the agent training environment is not identical to the real world.
+As explained above, the agent is "trained" in an artificial trading "environment". In our case, that environment may seem quite similar to a real Freqtrade backtesting environment, but it is *NOT*. In fact, the RL training environment is much more simplified. It does not incorporate any of the complicated strategy logic, such as callbacks like `custom_exit`, `custom_stoploss`, leverage controls, etc. The RL environment is instead a very "raw" representation of the true market, where the agent has free will to learn the policy (read: stoploss, take profit, etc.) which is enforced by the `calculate_reward()`. Thus, it is important to consider that the agent training environment is not identical to the real world.
 
 ## Running Reinforcement Learning
 
@@ -175,10 +175,23 @@ As you begin to modify the strategy and the prediction model, you will quickly r
                 pnl = self.get_unrealized_profit()
 
                 factor = 100
+
+                pair = self.pair.replace(':', '')
+
+                # you can use feature values from dataframe
+                # Assumes the shifted RSI indicator has been generated in the strategy.
+                rsi_now = self.raw_features[f"%-rsi-period-10_shift-1_{pair}_"
+                                f"{self.config['timeframe']}"].iloc[self._current_tick]
+
                 # reward agent for entering trades
-                if action in (Actions.Long_enter.value, Actions.Short_enter.value) \
-                        and self._position == Positions.Neutral:
-                    return 25
+                if (action in (Actions.Long_enter.value, Actions.Short_enter.value)
+                        and self._position == Positions.Neutral):
+                    if rsi_now < 40:
+                        factor = 40 / rsi_now
+                    else:
+                        factor = 1
+                    return 25 * factor
+
                 # discourage agent from not entering trades
                 if action == Actions.Neutral.value and self._position == Positions.Neutral:
                     return -1
@@ -235,13 +248,13 @@ FreqAI also provides a built in episodic summary logger called `self.tensorboard
             """
             def calculate_reward(self, action: int) -> float:
                 if not self._is_valid(action):
-                    self.tensorboard_log("is_valid")
+                    self.tensorboard_log("invalid")
                     return -2
 
 ```
 
 !!! Note
-    The `self.tensorboard_log()` function is designed for tracking incremented objects only i.e. events, actions inside the training environment. If the event of interest is a float, the float can be passed as the second argument e.g. `self.tensorboard_log("float_metric1", 0.23)` would add 0.23 to `float_metric`. In this case you can also disable incrementing using `inc=False` parameter.
+    The `self.tensorboard_log()` function is designed for tracking incremented objects only i.e. events, actions inside the training environment. If the event of interest is a float, the float can be passed as the second argument e.g. `self.tensorboard_log("float_metric1", 0.23)`. In this case the metric values are not incremented.
 
 ### Choosing a base environment
 

docs/freqai-running.md

@@ -120,7 +120,7 @@ In the presented example config, the user will only allow predictions on models
 
 Model training parameters are unique to the selected machine learning library. FreqAI allows you to set any parameter for any library using the `model_training_parameters` dictionary in the config. The example config (found in `config_examples/config_freqai.example.json`) shows some of the example parameters associated with `Catboost` and `LightGBM`, but you can add any parameters available in those libraries or any other machine learning library you choose to implement.
 
-Data split parameters are defined in `data_split_parameters` which can be any parameters associated with Scikit-learn's `train_test_split()` function. `train_test_split()` has a parameters called `shuffle` which allows to shuffle the data or keep it unshuffled. This is particularly useful to avoid biasing training with temporally auto-correlated data. More details about these parameters can be found the [Scikit-learn website](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html) (external website).
+Data split parameters are defined in `data_split_parameters` which can be any parameters associated with scikit-learn's `train_test_split()` function. `train_test_split()` has a parameters called `shuffle` which allows to shuffle the data or keep it unshuffled. This is particularly useful to avoid biasing training with temporally auto-correlated data. More details about these parameters can be found the [scikit-learn website](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html) (external website).
 
 The FreqAI specific parameter `label_period_candles` defines the offset (number of candles into the future) used for the `labels`. In the presented [example config](freqai-configuration.md#setting-up-the-configuration-file), the user is asking for `labels` that are 24 candles in the future.
 
@@ -165,20 +165,3 @@ tensorboard --logdir user_data/models/unique-id
 where `unique-id` is the `identifier` set in the `freqai` configuration file. This command must be run in a separate shell if you wish to view the output in your browser at 127.0.0.1:6060 (6060 is the default port used by Tensorboard).
 
 ![tensorboard](assets/tensorboard.jpg)
-
-## Setting up a follower
-
-You can indicate to the bot that it should not train models, but instead should look for models trained by a leader with a specific `identifier` by defining:
-
-```json
-    "freqai": {
-        "enabled": true,
-        "follow_mode": true,
-        "identifier": "example",
-        "feature_parameters": {
-        // leader bots feature_parameters inserted here 
-        },
-    }
-```
-
-In this example, the user has a leader bot with the `"identifier": "example"`. The leader bot is already running or is launched simultaneously with the follower. The follower will load models created by the leader and inference them to obtain predictions instead of training its own models. The user will also need to duplicate the `feature_parameters` parameters from from the leaders freqai configuration file into the freqai section of the followers config. 

docs/freqai.md

@@ -4,7 +4,10 @@
 
 ## Introduction
 
-FreqAI is a software designed to automate a variety of tasks associated with training a predictive machine learning model to generate market forecasts given a set of input signals. In general, the FreqAI aims to be a sand-box for easily deploying robust machine-learning libraries on real-time data ([details])(#freqai-position-in-open-source-machine-learning-landscape).
+FreqAI is a software designed to automate a variety of tasks associated with training a predictive machine learning model to generate market forecasts given a set of input signals. In general, FreqAI aims to be a sandbox for easily deploying robust machine learning libraries on real-time data ([details](#freqai-position-in-open-source-machine-learning-landscape)).
+
+!!! Note
+    FreqAI is, and always will be, a not-for-profit, open-source project. FreqAI does *not* have a crypto token, FreqAI does *not* sell signals, and FreqAI does not have a domain besides the present [freqtrade documentation](https://www.freqtrade.io/en/latest/freqai/).
 
 Features include:
 
@@ -19,7 +22,7 @@ Features include:
 * **Automatic data download** - Compute timeranges for data downloads and update historic data (in live deployments)
 * **Cleaning of incoming data** - Handle NaNs safely before training and model inferencing
 * **Dimensionality reduction** - Reduce the size of the training data via [Principal Component Analysis](freqai-feature-engineering.md#data-dimensionality-reduction-with-principal-component-analysis)
-* **Deploying bot fleets** - Set one bot to train models while a fleet of [follower bots](freqai-running.md#setting-up-a-follower) inference the models and handle trades
+* **Deploying bot fleets** - Set one bot to train models while a fleet of [consumers](producer-consumer.md) use signals.
 
 ## Quick start
 
@@ -68,13 +71,17 @@ pip install -r requirements-freqai.txt
 !!! Note
     Catboost will not be installed on arm devices (raspberry, Mac M1, ARM based VPS, ...), since it does not provide wheels for this platform.
 
+!!! Note "python 3.11"
+    Some dependencies (Catboost, Torch) currently don't support python 3.11. Freqtrade therefore only supports python 3.10 for these models/dependencies.
+    Tests involving these dependencies are skipped on 3.11.
+
 ### Usage with docker
 
-If you are using docker, a dedicated tag with FreqAI dependencies is available as `:freqai`. As such - you can replace the image line in your docker-compose file with `image: freqtradeorg/freqtrade:develop_freqai`. This image contains the regular FreqAI dependencies. Similar to native installs, Catboost will not be available on ARM based devices.
+If you are using docker, a dedicated tag with FreqAI dependencies is available as `:freqai`. As such - you can replace the image line in your docker compose file with `image: freqtradeorg/freqtrade:develop_freqai`. This image contains the regular FreqAI dependencies. Similar to native installs, Catboost will not be available on ARM based devices.
 
 ### FreqAI position in open-source machine learning landscape
 
-Forecasting chaotic time-series based systems, such as equity/cryptocurrency markets, requires a broad set of tools geared toward testing a wide range of hypotheses. Fortunately, a recent maturation of robust machine learning libraries (e.g. `scikit-learn`) has opened up a wide range of research possibilities. Scientists from a diverse range of fields can now easily prototype their studies on an abundance of established machine learning algorithms. Similarly, these user-friendly libraries enable "citzen scientists" to use their basic Python skills for data-exploration. However, leveraging these machine learning libraries on historical and live chaotic data sources can be logistically difficult and expensive. Additionally, robust data-collection, storage, and handling presents a disparate challenge. [`FreqAI`](#freqai) aims to provide a generalized and extensible open-sourced framework geared toward live deployments of adaptive modeling for market forecasting. The `FreqAI` framework is effectively a sandbox for the rich world of open-source machine learning libraries. Inside the `FreqAI` sandbox, users find they can combine a wide variety of third-party libraries to test creative hypotheses on a free live 24/7 chaotic data source - cryptocurrency exchange data. 
+Forecasting chaotic time-series based systems, such as equity/cryptocurrency markets, requires a broad set of tools geared toward testing a wide range of hypotheses. Fortunately, a recent maturation of robust machine learning libraries (e.g. `scikit-learn`) has opened up a wide range of research possibilities. Scientists from a diverse range of fields can now easily prototype their studies on an abundance of established machine learning algorithms. Similarly, these user-friendly libraries enable "citzen scientists" to use their basic Python skills for data exploration. However, leveraging these machine learning libraries on historical and live chaotic data sources can be logistically difficult and expensive. Additionally, robust data collection, storage, and handling presents a disparate challenge. [`FreqAI`](#freqai) aims to provide a generalized and extensible open-sourced framework geared toward live deployments of adaptive modeling for market forecasting. The `FreqAI` framework is effectively a sandbox for the rich world of open-source machine learning libraries. Inside the `FreqAI` sandbox, users find they can combine a wide variety of third-party libraries to test creative hypotheses on a free live 24/7 chaotic data source - cryptocurrency exchange data. 
 
 ### Citing FreqAI
 

docs/index.md

@@ -52,6 +52,7 @@ Please read the [exchange specific notes](exchanges.md) to learn about eventual,
 - [X] [Binance](https://www.binance.com/)
 - [X] [Gate.io](https://www.gate.io/ref/6266643)
 - [X] [OKX](https://okx.com/)
+- [X] [Bybit](https://bybit.com/)
 
 Please make sure to read the [exchange specific notes](exchanges.md), as well as the [trading with leverage](leverage.md) documentation before diving in.
 

docs/installation.md

@@ -30,6 +30,12 @@ The easiest way to install and run Freqtrade is to clone the bot Github reposito
 !!! Warning "Up-to-date clock"
     The clock on the system running the bot must be accurate, synchronized to a NTP server frequently enough to avoid problems with communication to the exchanges.
 
+!!! Error "Running setup.py install for gym did not run successfully."
+    If you get an error related with gym we suggest you to downgrade setuptools it to version 65.5.0 you can do it with the following command:
+    ```bash
+    pip install setuptools==65.5.0
+    ```
+
 ------
 
 ## Requirements
@@ -284,10 +290,8 @@ cd freqtrade
 
 #### Freqtrade install: Conda Environment
 
-Prepare conda-freqtrade environment, using file `environment.yml`, which exist in main freqtrade directory
-
 ```bash
-conda env create -n freqtrade-conda -f environment.yml
+conda create --name freqtrade python=3.10
 ```
 
 !!! Note "Creating Conda Environment"
@@ -296,12 +300,9 @@ conda env create -n freqtrade-conda -f environment.yml
     ```bash
     # choose your own packages
     conda env create -n [name of the environment] [python version] [packages]
-
-    # point to file with packages
-    conda env create -n [name of the environment] -f [file]
     ```
 
-#### Enter/exit freqtrade-conda environment
+#### Enter/exit freqtrade environment
 
 To check available environments, type
 
@@ -313,7 +314,7 @@ Enter installed environment
 
 ```bash
 # enter conda environment
-conda activate freqtrade-conda
+conda activate freqtrade
 
 # exit conda environment - don't do it now
 conda deactivate
@@ -323,6 +324,7 @@ Install last python dependencies with pip
 
 ```bash
 python3 -m pip install --upgrade pip
+python3 -m pip install -r requirements.txt
 python3 -m pip install -e .
 ```
 
@@ -330,7 +332,7 @@ Patch conda libta-lib (Linux only)
 
 ```bash
 # Ensure that the environment is active!
-conda activate freqtrade-conda
+conda activate freqtrade
 
 cd build_helpers
 bash install_ta-lib.sh ${CONDA_PREFIX} nosudo
@@ -349,8 +351,8 @@ conda env list
 # activate base environment
 conda activate
 
-# activate freqtrade-conda environment
-conda activate freqtrade-conda
+# activate freqtrade environment
+conda activate freqtrade
 
 #deactivate any conda environments
 conda deactivate                              

docs/requirements-docs.txt

@@ -1,6 +1,6 @@
 markdown==3.3.7
 mkdocs==1.4.2
-mkdocs-material==9.0.5
+mkdocs-material==9.1.3
 mdx_truly_sane_lists==1.3
-pymdown-extensions==9.9.1
+pymdown-extensions==9.10
 jinja2==3.1.2

docs/rest-api.md

@@ -163,7 +163,7 @@ python3 scripts/rest_client.py --config rest_config.json <command> [optional par
 | `strategy <strategy>` | Get specific Strategy content. **Alpha**
 | `available_pairs` | List available backtest data. **Alpha**
 | `version` | Show version.
-| `sysinfo` | Show informations about the system load.
+| `sysinfo` | Show information about the system load.
 | `health` | Show bot health (last bot loop).
 
 !!! Warning "Alpha status"
@@ -192,6 +192,11 @@ blacklist
 
         :param add: List of coins to add (example: "BNB/BTC")
 
+cancel_open_order
+	Cancel open order for trade.
+
+        :param trade_id: Cancels open orders for this trade.
+
 count
 	Return the amount of open trades.
 
@@ -274,7 +279,6 @@ reload_config
 	Reload configuration.
 
 show_config
-
         Returns part of the configuration, relevant for trading operations.
 
 start
@@ -320,6 +324,7 @@ version
 whitelist
 	Show the current whitelist.
 
+
 ```
 
 ### Message WebSocket

docs/stoploss.md

@@ -24,7 +24,7 @@ These modes can be configured with these values:
 ```
 
 !!! Note
-    Stoploss on exchange is only supported for Binance (stop-loss-limit), Huobi (stop-limit), Kraken (stop-loss-market, stop-loss-limit), Gateio (stop-limit), and Kucoin (stop-limit and stop-market) as of now.  
+    Stoploss on exchange is only supported for Binance (stop-loss-limit), Huobi (stop-limit), Kraken (stop-loss-market, stop-loss-limit), Gate (stop-limit), and Kucoin (stop-limit and stop-market) as of now.  
     <ins>Do not set too low/tight stoploss value if using stop loss on exchange!</ins>  
     If set to low/tight then you have greater risk of missing fill on the order and stoploss will not work.
 
@@ -52,6 +52,18 @@ The bot cannot do these every 5 seconds (at each iteration), otherwise it would
 So this parameter will tell the bot how often it should update the stoploss order. The default value is 60 (1 minute).
 This same logic will reapply a stoploss order on the exchange should you cancel it accidentally.
 
+### stoploss_price_type
+
+!!! Warning "Only applies to futures"
+    `stoploss_price_type` only applies to futures markets (on exchanges where it's available).
+    Freqtrade will perform a validation of this setting on startup, failing to start if an invalid setting for your exchange has been selected.
+    Supported price types are gonna differs between each exchanges. Please check with your exchange on which price types it supports.
+
+Stoploss on exchange on futures markets can trigger on different price types.
+The naming for these prices in exchange terminology often varies, but is usually something around "last" (or "contract price" ), "mark" and "index".
+
+Acceptable values for this setting are `"last"`, `"mark"` and `"index"` - which freqtrade will transfer automatically to the corresponding API type, and place the [stoploss on exchange](#stoploss_on_exchange-and-stoploss_on_exchange_limit_ratio) order correspondingly.
+
 ### force_exit
 
 `force_exit` is an optional value, which defaults to the same value as `exit` and is used when sending a `/forceexit` command from Telegram or from the Rest API.

docs/strategy-callbacks.md

@@ -316,11 +316,11 @@ class AwesomeStrategy(IStrategy):
 
         # evaluate highest to lowest, so that highest possible stop is used
         if current_profit > 0.40:
-            return stoploss_from_open(0.25, current_profit, is_short=trade.is_short)
+            return stoploss_from_open(0.25, current_profit, is_short=trade.is_short, leverage=trade.leverage)
         elif current_profit > 0.25:
-            return stoploss_from_open(0.15, current_profit, is_short=trade.is_short)
+            return stoploss_from_open(0.15, current_profit, is_short=trade.is_short, leverage=trade.leverage)
         elif current_profit > 0.20:
-            return stoploss_from_open(0.07, current_profit, is_short=trade.is_short)
+            return stoploss_from_open(0.07, current_profit, is_short=trade.is_short, leverage=trade.leverage)
 
         # return maximum stoploss value, keeping current stoploss price unchanged
         return 1

docs/strategy-customization.md

@@ -881,7 +881,7 @@ All columns of the informative dataframe will be available on the returning data
 
 ### *stoploss_from_open()*
 
-Stoploss values returned from `custom_stoploss` must specify a percentage relative to `current_rate`, but sometimes you may want to specify a stoploss relative to the open price instead. `stoploss_from_open()` is a helper function to calculate a stoploss value that can be returned from `custom_stoploss` which will be equivalent to the desired percentage above the open price.
+Stoploss values returned from `custom_stoploss` must specify a percentage relative to `current_rate`, but sometimes you may want to specify a stoploss relative to the entry point instead. `stoploss_from_open()` is a helper function to calculate a stoploss value that can be returned from `custom_stoploss` which will be equivalent to the desired trade profit above the entry point.
 
 ??? Example "Returning a stoploss relative to the open price from the custom stoploss function"
 
@@ -889,6 +889,8 @@ Stoploss values returned from `custom_stoploss` must specify a percentage relati
 
     If we want a stop price at 7% above the open price we can call `stoploss_from_open(0.07, current_profit, False)` which will return `0.1157024793`.  11.57% below $121 is $107, which is the same as 7% above $100.
 
+    This function will consider leverage - so at 10x leverage, the actual stoploss would be 0.7% above $100 (0.7% * 10x = 7%).
+
 
     ``` python
 
@@ -907,7 +909,7 @@ Stoploss values returned from `custom_stoploss` must specify a percentage relati
 
             # once the profit has risen above 10%, keep the stoploss at 7% above the open price
             if current_profit > 0.10:
-                return stoploss_from_open(0.07, current_profit, is_short=trade.is_short)
+                return stoploss_from_open(0.07, current_profit, is_short=trade.is_short, leverage=trade.leverage)
 
             return 1
 
@@ -954,12 +956,14 @@ In some situations it may be confusing to deal with stops relative to current ra
 
 ## Additional data (Wallets)
 
-The strategy provides access to the `Wallets` object. This contains the current balances on the exchange.
+The strategy provides access to the `wallets` object. This contains the current balances on the exchange.
 
-!!! Note
-    Wallets is not available during backtesting / hyperopt.
+!!! Note "Backtesting / Hyperopt"
+    Wallets behaves differently depending on the function it's called.
+    Within `populate_*()` methods, it'll return the full wallet as configured.
+    Within [callbacks](strategy-callbacks.md), you'll get the wallet state corresponding to the actual simulated wallet at that point in the simulation process.
 
-Please always check if `Wallets` is available to avoid failures during backtesting.
+Please always check if `wallets` is available to avoid failures during backtesting.
 
 ``` python
 if self.wallets:
@@ -1036,11 +1040,10 @@ from datetime import timedelta, datetime, timezone
 
 # Within populate indicators (or populate_buy):
 if self.config['runmode'].value in ('live', 'dry_run'):
-   # fetch closed trades for the last 2 days
-    trades = Trade.get_trades([Trade.pair == metadata['pair'],
-                               Trade.open_date > datetime.utcnow() - timedelta(days=2),
-                               Trade.is_open.is_(False),
-                ]).all()
+    # fetch closed trades for the last 2 days
+    trades = Trade.get_trades_proxy(
+        pair=metadata['pair'], is_open=False, 
+        open_date=datetime.now(timezone.utc) - timedelta(days=2))
     # Analyze the conditions you'd like to lock the pair .... will probably be different for every strategy
     sumprofit = sum(trade.close_profit for trade in trades)
     if sumprofit < 0:

docs/strategy_analysis_example.md

@@ -80,6 +80,7 @@ from freqtrade.resolvers import StrategyResolver
 from freqtrade.data.dataprovider import DataProvider
 strategy = StrategyResolver.load_strategy(config)
 strategy.dp = DataProvider(config, None, None)
+strategy.ft_bot_start()
 
 # Generate buy/sell signals using strategy
 df = strategy.analyze_ticker(candles, {'pair': pair})

docs/telegram-usage.md

@@ -152,7 +152,7 @@ You can create your own keyboard in `config.json`:
 !!! Note "Supported Commands"
     Only the following commands are allowed. Command arguments are not supported!
 
-    `/start`, `/stop`, `/status`, `/status table`, `/trades`, `/profit`, `/performance`, `/daily`, `/stats`, `/count`, `/locks`, `/balance`, `/stopentry`, `/reload_config`, `/show_config`, `/logs`, `/whitelist`, `/blacklist`, `/edge`, `/help`, `/version`
+    `/start`, `/stop`, `/status`, `/status table`, `/trades`, `/profit`, `/performance`, `/daily`, `/stats`, `/count`, `/locks`, `/balance`, `/stopentry`, `/reload_config`, `/show_config`, `/logs`, `/whitelist`, `/blacklist`, `/edge`, `/help`, `/version`, `/marketdir`
 
 ## Telegram commands
 
@@ -162,26 +162,34 @@ official commands. You can ask at any moment for help with `/help`.
 
 |  Command | Description |
 |----------|-------------|
+| **System commands**
 | `/start` | Starts the trader
 | `/stop` | Stops the trader
 | `/stopbuy | /stopentry` | Stops the trader from opening new trades. Gracefully closes open trades according to their rules.
 | `/reload_config` | Reloads the configuration file
 | `/show_config` | Shows part of the current configuration with relevant settings to operation
 | `/logs [limit]` | Show last log messages.
+| `/help` | Show help message
+| `/version` | Show version
+| **Status** |
 | `/status` | Lists all open trades
 | `/status <trade_id>` | Lists one or more specific trade. Separate multiple <trade_id> with a blank space.
 | `/status table` | List all open trades in a table format. Pending buy orders are marked with an asterisk (*) Pending sell orders are marked with a double asterisk (**)
 | `/trades [limit]` | List all recently closed trades in a table format.
-| `/delete <trade_id>` | Delete a specific trade from the Database. Tries to close open orders. Requires manual handling of this trade on the exchange.
 | `/count` | Displays number of trades used and available
 | `/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)
+| `/marketdir [long | short | even | none]` | Updates the user managed variable that represents the current market direction. If no direction is provided, the currently set direction will be displayed.
+| **Modify Trade states** |
 | `/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)
+| `/delete <trade_id>` | Delete a specific trade from the Database. Tries to close open orders. Requires manual handling of this trade on the exchange.
+| `/cancel_open_order <trade_id> | /coo <trade_id>` | Cancel an open order for a trade.
+| **Metrics** |
+| `/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)
 | `/performance` | Show performance of each finished trade grouped by pair
 | `/balance` | Show account balance per currency
 | `/daily <n>` | Shows profit or loss per day, over the last n days (n defaults to 7)
@@ -193,8 +201,7 @@ official commands. You can ask at any moment for help with `/help`.
 | `/whitelist [sorted] [baseonly]` | Show the current whitelist. Optionally display in alphabetical order and/or with just the base currency of each pairing.
 | `/blacklist [pair]` | Show the current blacklist, or adds a pair to the blacklist.
 | `/edge` | Show validated pairs by Edge if it is enabled.
-| `/help` | Show help message
-| `/version` | Show version
+
 
 ## Telegram commands in action
 
@@ -236,7 +243,7 @@ Enter Tag is configurable via Strategy.
 > **Enter Tag:** Awesome Long Signal
 > **Open Rate:** `0.00007489`
 > **Current Rate:** `0.00007489`
-> **Current Profit:** `12.95%`
+> **Unrealized Profit:** `12.95%`
 > **Stoploss:** `0.00007389 (-0.02%)`
 
 ### /status table
@@ -410,3 +417,27 @@ ARDR/ETH   0.366667      0.143059       -0.01
 ### /version
 
 > **Version:** `0.14.3`
+
+### /marketdir
+
+If a market direction is provided the command updates the user managed variable that represents the current market direction.
+This variable is not set to any valid market direction on bot startup and must be set by the user. The example below is for `/marketdir long`:
+
+```
+Successfully updated marketdirection from none to long.
+```
+
+If no market direction is provided the command outputs the currently set market directions. The example below is for `/marketdir`:
+
+```
+Currently set marketdirection: even
+```
+
+You can use the market direction in your strategy via `self.market_direction`.
+
+!!! Warning "Bot restarts"
+    Please note that the market direction is not persisted, and will be reset after a bot restart/reload.
+
+!!! Danger "Backtesting"
+    As this value/variable is intended to be changed manually in dry/live trading.
+    Strategies using `market_direction` will probably not produce reliable, reproducible results (changes to this variable will not be reflected for backtesting). Use at your own risk.

docs/utils.md

@@ -955,3 +955,47 @@ Print trades with id 2 and 3 as json
 ``` bash
 freqtrade show-trades --db-url sqlite:///tradesv3.sqlite --trade-ids 2 3 --print-json
 ```
+
+### Strategy-Updater
+
+Updates listed strategies or all strategies within the strategies folder to be v3 compliant.
+If the command runs without --strategy-list then all strategies inside the strategies folder will be converted.
+Your original strategy will remain available in the `user_data/strategies_orig_updater/` directory.
+
+!!! Warning "Conversion results"
+    Strategy updater will work on a "best effort" approach. Please do your due diligence and verify the results of the conversion.
+    We also recommend to run a python formatter (e.g. `black`) to format results in a sane manner.
+
+```
+usage: freqtrade strategy-updater [-h] [-v] [--logfile FILE] [-V] [-c PATH]
+                                  [-d PATH] [--userdir PATH]
+                                  [--strategy-list STRATEGY_LIST [STRATEGY_LIST ...]]
+
+options:
+  -h, --help            show this help message and exit
+  --strategy-list STRATEGY_LIST [STRATEGY_LIST ...]
+                        Provide a space-separated list of strategies to
+                        backtest. Please note that timeframe needs to be set
+                        either in config or via command line. When using this
+                        together with `--export trades`, the strategy-name is
+                        injected into the filename (so `backtest-data.json`
+                        becomes `backtest-data-SampleStrategy.json`
+
+Common arguments:
+  -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
+  --logfile FILE, --log-file 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, --data-dir PATH
+                        Path to directory with historical backtesting data.
+  --userdir PATH, --user-data-dir PATH
+                        Path to userdata directory.
+
+```

docs/windows_installation.md

@@ -26,7 +26,7 @@ Install ta-lib according to the [ta-lib documentation](https://github.com/mrjbq7
 
 As compiling from source on windows has heavy dependencies (requires a partial visual studio installation), there is also a repository of unofficial pre-compiled windows Wheels [here](https://www.lfd.uci.edu/~gohlke/pythonlibs/#ta-lib), which need to be downloaded and installed using `pip install TA_Lib-0.4.25-cp38-cp38-win_amd64.whl` (make sure to use the version matching your python version).
 
-Freqtrade provides these dependencies for the latest 3 Python versions (3.8, 3.9 and 3.10) and for 64bit Windows.
+Freqtrade provides these dependencies for the latest 3 Python versions (3.8, 3.9, 3.10 and 3.11) and for 64bit Windows.
 Other versions must be downloaded from the above link.
 
 ``` powershell

environment.yml

@@ -1,75 +0,0 @@
-name: freqtrade
-channels:
-  - conda-forge
-# - defaults
-dependencies:
-# 1/4 req main
-    - python>=3.8,<=3.10
-    - numpy
-    - pandas
-    - pip
-
-    - py-find-1st
-    - aiohttp
-    - SQLAlchemy
-    - python-telegram-bot
-    - arrow
-    - cachetools
-    - requests
-    - urllib3
-    - jsonschema
-    - TA-Lib
-    - tabulate
-    - jinja2
-    - blosc
-    - sdnotify
-    - fastapi
-    - uvicorn
-    - pyjwt
-    - aiofiles
-    - psutil
-    - colorama
-    - questionary
-    - prompt-toolkit
-    - schedule
-    - python-dateutil
-    - joblib
-    - pyarrow
-
-
-    # ============================
-    # 2/4 req dev
-
-    - coveralls
-    - flake8
-    - mypy
-    - pytest
-    - pytest-asyncio
-    - pytest-cov
-    - pytest-mock
-    - isort
-    - nbconvert
-
-    # ============================
-    # 3/4 req hyperopt
-
-    - scipy
-    - scikit-learn
-    - filelock
-    - scikit-optimize
-    - progressbar2
-    # ============================
-    # 4/4 req plot
-
-    - plotly
-    - jupyter
-
-    - pip:
-        - pycoingecko
-        # - py_find_1st
-        - tables
-        - pytest-random-order
-        - ccxt
-        - flake8-tidy-imports
-        - -e .
-    # - python-rapidjso

freqtrade/__init__.py

@@ -1,5 +1,5 @@
 """ Freqtrade bot """
-__version__ = '2023.1'
+__version__ = '2023.3.dev'
 
 if 'dev' in __version__:
     from pathlib import Path

freqtrade/commands/__init__.py

@@ -22,5 +22,6 @@ from freqtrade.commands.optimize_commands import (start_backtesting, start_backt
                                                   start_edge, start_hyperopt)
 from freqtrade.commands.pairlist_commands import start_test_pairlist
 from freqtrade.commands.plot_commands import start_plot_dataframe, start_plot_profit
+from freqtrade.commands.strategy_utils_commands import start_strategy_update
 from freqtrade.commands.trade_commands import start_trading
 from freqtrade.commands.webserver_commands import start_webserver

freqtrade/commands/analyze_commands.py

@@ -40,8 +40,8 @@ def setup_analyze_configuration(args: Dict[str, Any], method: RunMode) -> Dict[s
 
         if (not Path(signals_file).exists()):
             raise OperationalException(
-                (f"Cannot find latest backtest signals file: {signals_file}."
-                  "Run backtesting with `--export signals`.")
+                f"Cannot find latest backtest signals file: {signals_file}."
+                "Run backtesting with `--export signals`."
             )
 
     return config

freqtrade/commands/arguments.py

@@ -111,10 +111,13 @@ ARGS_ANALYZE_ENTRIES_EXITS = ["exportfilename", "analysis_groups", "enter_reason
 NO_CONF_REQURIED = ["convert-data", "convert-trade-data", "download-data", "list-timeframes",
                     "list-markets", "list-pairs", "list-strategies", "list-freqaimodels",
                     "list-data", "hyperopt-list", "hyperopt-show", "backtest-filter",
-                    "plot-dataframe", "plot-profit", "show-trades", "trades-to-ohlcv"]
+                    "plot-dataframe", "plot-profit", "show-trades", "trades-to-ohlcv",
+                    "strategy-updater"]
 
 NO_CONF_ALLOWED = ["create-userdir", "list-exchanges", "new-strategy"]
 
+ARGS_STRATEGY_UTILS = ["strategy_list", "strategy_path", "recursive_strategy_search"]
+
 
 class Arguments:
     """
@@ -198,8 +201,8 @@ class Arguments:
                                         start_list_freqAI_models, start_list_markets,
                                         start_list_strategies, start_list_timeframes,
                                         start_new_config, start_new_strategy, start_plot_dataframe,
-                                        start_plot_profit, start_show_trades, start_test_pairlist,
-                                        start_trading, start_webserver)
+                                        start_plot_profit, start_show_trades, start_strategy_update,
+                                        start_test_pairlist, start_trading, start_webserver)
 
         subparsers = self.parser.add_subparsers(dest='command',
                                                 # Use custom message when no subhandler is added
@@ -440,3 +443,11 @@ class Arguments:
                                               parents=[_common_parser])
         webserver_cmd.set_defaults(func=start_webserver)
         self._build_args(optionlist=ARGS_WEBSERVER, parser=webserver_cmd)
+
+        # Add strategy_updater subcommand
+        strategy_updater_cmd = subparsers.add_parser('strategy-updater',
+                                                     help='updates outdated strategy'
+                                                          'files to the current version',
+                                                     parents=[_common_parser])
+        strategy_updater_cmd.set_defaults(func=start_strategy_update)
+        self._build_args(optionlist=ARGS_STRATEGY_UTILS, parser=strategy_updater_cmd)

freqtrade/commands/build_config_commands.py

@@ -108,7 +108,7 @@ def ask_user_config() -> Dict[str, Any]:
                 "binance",
                 "binanceus",
                 "bittrex",
-                "gateio",
+                "gate",
                 "huobi",
                 "kraken",
                 "kucoin",
@@ -123,7 +123,7 @@ def ask_user_config() -> Dict[str, Any]:
             "message": "Do you want to trade Perpetual Swaps (perpetual futures)?",
             "default": False,
             "filter": lambda val: 'futures' if val else 'spot',
-            "when": lambda x: x["exchange_name"] in ['binance', 'gateio', 'okx'],
+            "when": lambda x: x["exchange_name"] in ['binance', 'gate', 'okx'],
         },
         {
             "type": "autocomplete",

freqtrade/commands/data_commands.py

@@ -5,7 +5,7 @@ from datetime import datetime, timedelta
 from typing import Any, Dict, List
 
 from freqtrade.configuration import TimeRange, setup_utils_configuration
-from freqtrade.constants import DATETIME_PRINT_FORMAT
+from freqtrade.constants import DATETIME_PRINT_FORMAT, Config
 from freqtrade.data.converter import convert_ohlcv_format, convert_trades_format
 from freqtrade.data.history import (convert_trades_to_ohlcv, refresh_backtest_ohlcv_data,
                                     refresh_backtest_trades_data)
@@ -20,15 +20,24 @@ from freqtrade.util.binance_mig import migrate_binance_futures_data
 logger = logging.getLogger(__name__)
 
 
+def _data_download_sanity(config: Config) -> None:
+    if 'days' in config and 'timerange' in config:
+        raise OperationalException("--days and --timerange are mutually exclusive. "
+                                   "You can only specify one or the other.")
+
+    if 'pairs' not in config:
+        raise OperationalException(
+            "Downloading data requires a list of pairs. "
+            "Please check the documentation on how to configure this.")
+
+
 def start_download_data(args: Dict[str, Any]) -> None:
     """
     Download data (former download_backtest_data.py script)
     """
     config = setup_utils_configuration(args, RunMode.UTIL_EXCHANGE)
 
-    if 'days' in config and 'timerange' in config:
-        raise OperationalException("--days and --timerange are mutually exclusive. "
-                                   "You can only specify one or the other.")
+    _data_download_sanity(config)
     timerange = TimeRange()
     if 'days' in config:
         time_since = (datetime.now() - timedelta(days=config['days'])).strftime("%Y%m%d")
@@ -40,11 +49,6 @@ def start_download_data(args: Dict[str, Any]) -> None:
     # Remove stake-currency to skip checks which are not relevant for datadownload
     config['stake_currency'] = ''
 
-    if 'pairs' not in config:
-        raise OperationalException(
-            "Downloading data requires a list of pairs. "
-            "Please check the documentation on how to configure this.")
-
     pairs_not_available: List[str] = []
 
     # Init exchange

freqtrade/commands/db_commands.py

@@ -1,7 +1,7 @@
 import logging
 from typing import Any, Dict
 
-from sqlalchemy import func
+from sqlalchemy import func, select
 
 from freqtrade.configuration.config_setup import setup_utils_configuration
 from freqtrade.enums import RunMode
@@ -20,7 +20,7 @@ def start_convert_db(args: Dict[str, Any]) -> None:
     config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
 
     init_db(config['db_url'])
-    session_target = Trade._session
+    session_target = Trade.session
     init_db(config['db_url_from'])
     logger.info("Starting db migration.")
 
@@ -36,16 +36,16 @@ def start_convert_db(args: Dict[str, Any]) -> None:
 
     session_target.commit()
 
-    for pairlock in PairLock.query:
+    for pairlock in PairLock.get_all_locks():
         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()
+    max_trade_id = session_target.scalar(select(func.max(Trade.id)))
+    max_order_id = session_target.scalar(select(func.max(Order.id)))
+    max_pairlock_id = session_target.scalar(select(func.max(PairLock.id)))
 
     set_sequence_ids(session_target.get_bind(),
                      trade_id=max_trade_id,

freqtrade/commands/strategy_utils_commands.py

@@ -0,0 +1,55 @@
+import logging
+import sys
+import time
+from pathlib import Path
+from typing import Any, Dict
+
+from freqtrade.configuration import setup_utils_configuration
+from freqtrade.enums import RunMode
+from freqtrade.resolvers import StrategyResolver
+from freqtrade.strategy.strategyupdater import StrategyUpdater
+
+
+logger = logging.getLogger(__name__)
+
+
+def start_strategy_update(args: Dict[str, Any]) -> None:
+    """
+    Start the strategy updating script
+    :param args: Cli args from Arguments()
+    :return: None
+    """
+
+    if sys.version_info == (3, 8):  # pragma: no cover
+        sys.exit("Freqtrade strategy updater requires Python version >= 3.9")
+
+    config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
+
+    strategy_objs = StrategyResolver.search_all_objects(
+        config, enum_failed=False, recursive=config.get('recursive_strategy_search', False))
+
+    filtered_strategy_objs = []
+    if args['strategy_list']:
+        filtered_strategy_objs = [
+            strategy_obj for strategy_obj in strategy_objs
+            if strategy_obj['name'] in args['strategy_list']
+        ]
+
+    else:
+        # Use all available entries.
+        filtered_strategy_objs = strategy_objs
+
+    processed_locations = set()
+    for strategy_obj in filtered_strategy_objs:
+        if strategy_obj['location'] not in processed_locations:
+            processed_locations.add(strategy_obj['location'])
+            start_conversion(strategy_obj, config)
+
+
+def start_conversion(strategy_obj, config):
+    print(f"Conversion of {Path(strategy_obj['location']).name} started.")
+    instance_strategy_updater = StrategyUpdater()
+    start = time.perf_counter()
+    instance_strategy_updater.start(config, strategy_obj)
+    elapsed = time.perf_counter() - start
+    print(f"Conversion of {Path(strategy_obj['location']).name} took {elapsed:.1f} seconds.")

freqtrade/commands/trade_commands.py

@@ -1,4 +1,5 @@
 import logging
+import signal
 from typing import Any, Dict
 
 
@@ -12,15 +13,20 @@ def start_trading(args: Dict[str, Any]) -> int:
     # Import here to avoid loading worker module when it's not used
     from freqtrade.worker import Worker
 
+    def term_handler(signum, frame):
+        # Raise KeyboardInterrupt - so we can handle it in the same way as Ctrl-C
+        raise KeyboardInterrupt()
+
     # Create and run worker
     worker = None
     try:
+        signal.signal(signal.SIGTERM, term_handler)
         worker = Worker(args)
         worker.run()
     except Exception as e:
         logger.error(str(e))
         logger.exception("Fatal exception!")
-    except KeyboardInterrupt:
+    except (KeyboardInterrupt):
         logger.info('SIGINT received, aborting ...')
     finally:
         if worker:

freqtrade/configuration/config_validation.py

@@ -27,10 +27,7 @@ def _extend_validator(validator_class):
             if 'default' in subschema:
                 instance.setdefault(prop, subschema['default'])
 
-        for error in validate_properties(
-            validator, properties, instance, schema,
-        ):
-            yield error
+        yield from validate_properties(validator, properties, instance, schema)
 
     return validators.extend(
         validator_class, {'properties': set_defaults}

freqtrade/configuration/environment_vars.py

@@ -32,7 +32,7 @@ def flat_vars_to_nested_dict(env_dict: Dict[str, Any], prefix: str) -> Dict[str,
     :param prefix: Prefix to consider (usually FREQTRADE__)
     :return: Nested dict based on available and relevant variables.
     """
-    no_convert = ['CHAT_ID']
+    no_convert = ['CHAT_ID', 'PASSWORD']
     relevant_vars: Dict[str, Any] = {}
 
     for env_var, val in sorted(env_dict.items()):

freqtrade/configuration/load_config.py

@@ -58,7 +58,7 @@ def load_config_file(path: str) -> Dict[str, Any]:
     """
     try:
         # Read config from stdin if requested in the options
-        with open(path) if path != '-' else sys.stdin as file:
+        with Path(path).open() if path != '-' else sys.stdin as file:
             config = rapidjson.load(file, parse_mode=CONFIG_PARSE_MODE)
     except FileNotFoundError:
         raise OperationalException(

freqtrade/constants.py

@@ -5,7 +5,7 @@ bot constants
 """
 from typing import Any, Dict, List, Literal, Tuple
 
-from freqtrade.enums import CandleType, RPCMessageType
+from freqtrade.enums import CandleType, PriceType, RPCMessageType
 
 
 DEFAULT_CONFIG = 'config.json'
@@ -25,6 +25,7 @@ PRICING_SIDES = ['ask', 'bid', 'same', 'other']
 ORDERTYPE_POSSIBILITIES = ['limit', 'market']
 _ORDERTIF_POSSIBILITIES = ['GTC', 'FOK', 'IOC', 'PO']
 ORDERTIF_POSSIBILITIES = _ORDERTIF_POSSIBILITIES + [t.lower() for t in _ORDERTIF_POSSIBILITIES]
+STOPLOSS_PRICE_TYPES = [p for p in PriceType]
 HYPEROPT_LOSS_BUILTIN = ['ShortTradeDurHyperOptLoss', 'OnlyProfitHyperOptLoss',
                          'SharpeHyperOptLoss', 'SharpeHyperOptLossDaily',
                          'SortinoHyperOptLoss', 'SortinoHyperOptLossDaily',
@@ -229,6 +230,7 @@ CONF_SCHEMA = {
                     'default': 'market'},
                 'stoploss': {'type': 'string', 'enum': ORDERTYPE_POSSIBILITIES},
                 'stoploss_on_exchange': {'type': 'boolean'},
+                'stoploss_price_type': {'type': 'string', 'enum': STOPLOSS_PRICE_TYPES},
                 'stoploss_on_exchange_interval': {'type': 'number'},
                 'stoploss_on_exchange_limit_ratio': {'type': 'number', 'minimum': 0.0,
                                                      'maximum': 1.0}
@@ -544,7 +546,7 @@ CONF_SCHEMA = {
                 "enabled": {"type": "boolean", "default": False},
                 "keras": {"type": "boolean", "default": False},
                 "write_metrics_to_disk": {"type": "boolean", "default": False},
-                "purge_old_models": {"type": "boolean", "default": True},
+                "purge_old_models": {"type": ["boolean", "number"], "default": 2},
                 "conv_width": {"type": "integer", "default": 1},
                 "train_period_days": {"type": "integer", "default": 0},
                 "backtest_period_days": {"type": "number", "default": 7},
@@ -566,7 +568,9 @@ CONF_SCHEMA = {
                                            "shuffle": {"type": "boolean", "default": False},
                                            "nu": {"type": "number", "default": 0.1}
                                            },
-                                       }
+                                       },
+                        "shuffle_after_split": {"type": "boolean", "default": False},
+                        "buffer_train_data_candles": {"type": "integer", "default": 0}
                     },
                     "required": ["include_timeframes", "include_corr_pairlist", ]
                 },
@@ -584,6 +588,7 @@ CONF_SCHEMA = {
                 "rl_config": {
                     "type": "object",
                     "properties": {
+                        "drop_ohlc_from_features": {"type": "boolean", "default": False},
                         "train_cycles": {"type": "integer"},
                         "max_trade_duration_candles": {"type": "integer"},
                         "add_state_info": {"type": "boolean", "default": False},
@@ -679,6 +684,7 @@ EntryExit = Literal['entry', 'exit']
 BuySell = Literal['buy', 'sell']
 MakerTaker = Literal['maker', 'taker']
 BidAsk = Literal['bid', 'ask']
+OBLiteral = Literal['asks', 'bids']
 
 Config = Dict[str, Any]
 IntOrInf = float

freqtrade/data/btanalysis.py

@@ -346,7 +346,7 @@ def evaluate_result_multi(results: pd.DataFrame, timeframe: str,
     return df_final[df_final['open_trades'] > max_open_trades]
 
 
-def trade_list_to_dataframe(trades: List[LocalTrade]) -> pd.DataFrame:
+def trade_list_to_dataframe(trades: Union[List[Trade], List[LocalTrade]]) -> pd.DataFrame:
     """
     Convert list of Trade objects to pandas Dataframe
     :param trades: List of trade objects
@@ -373,7 +373,7 @@ def load_trades_from_db(db_url: str, strategy: Optional[str] = None) -> pd.DataF
     filters = []
     if strategy:
         filters.append(Trade.strategy == strategy)
-    trades = trade_list_to_dataframe(Trade.get_trades(filters).all())
+    trades = trade_list_to_dataframe(list(Trade.get_trades(filters).all()))
 
     return trades
 

freqtrade/data/dataprovider.py

@@ -9,7 +9,7 @@ from collections import deque
 from datetime import datetime, timezone
 from typing import Any, Dict, List, Optional, Tuple
 
-from pandas import DataFrame, to_timedelta
+from pandas import DataFrame, Timedelta, Timestamp, to_timedelta
 
 from freqtrade.configuration import TimeRange
 from freqtrade.constants import (FULL_DATAFRAME_THRESHOLD, Config, ListPairsWithTimeframes,
@@ -18,6 +18,7 @@ from freqtrade.data.history import load_pair_history
 from freqtrade.enums import CandleType, RPCMessageType, RunMode
 from freqtrade.exceptions import ExchangeError, OperationalException
 from freqtrade.exchange import Exchange, timeframe_to_seconds
+from freqtrade.exchange.types import OrderBook
 from freqtrade.misc import append_candles_to_dataframe
 from freqtrade.rpc import RPCManager
 from freqtrade.util import PeriodicCache
@@ -206,9 +207,11 @@ class DataProvider:
         existing_df, _ = self.__producer_pairs_df[producer_name][pair_key]
 
         # CHECK FOR MISSING CANDLES
-        timeframe_delta = to_timedelta(timeframe)  # Convert the timeframe to a timedelta for pandas
-        local_last = existing_df.iloc[-1]['date']  # We want the last date from our copy
-        incoming_first = dataframe.iloc[0]['date']  # We want the first date from the incoming
+        # Convert the timeframe to a timedelta for pandas
+        timeframe_delta: Timedelta = to_timedelta(timeframe)
+        local_last: Timestamp = existing_df.iloc[-1]['date']  # We want the last date from our copy
+        # We want the first date from the incoming
+        incoming_first: Timestamp = dataframe.iloc[0]['date']
 
         # Remove existing candles that are newer than the incoming first candle
         existing_df1 = existing_df[existing_df['date'] < incoming_first]
@@ -221,7 +224,7 @@ class DataProvider:
         # we missed some candles between our data and the incoming
         # so return False and candle_difference.
         if candle_difference > 1:
-            return (False, candle_difference)
+            return (False, int(candle_difference))
         if existing_df1.empty:
             appended_df = dataframe
         else:
@@ -421,10 +424,8 @@ class DataProvider:
         """
         if self._exchange is None:
             raise OperationalException(NO_EXCHANGE_EXCEPTION)
-        if helping_pairs:
-            self._exchange.refresh_latest_ohlcv(pairlist + helping_pairs)
-        else:
-            self._exchange.refresh_latest_ohlcv(pairlist)
+        final_pairs = (pairlist + helping_pairs) if helping_pairs else pairlist
+        self._exchange.refresh_latest_ohlcv(final_pairs)
 
     @property
     def available_pairs(self) -> ListPairsWithTimeframes:
@@ -487,7 +488,7 @@ class DataProvider:
         except ExchangeError:
             return {}
 
-    def orderbook(self, pair: str, maximum: int) -> Dict[str, List]:
+    def orderbook(self, pair: str, maximum: int) -> OrderBook:
         """
         Fetch latest l2 orderbook data
         Warning: Does a network request - so use with common sense.

freqtrade/data/entryexitanalysis.py

@@ -24,9 +24,9 @@ def _load_signal_candles(backtest_dir: Path):
         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)}")
+        with scpf.open("rb") as scp:
+            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)
 

freqtrade/data/history/idatahandler.py

@@ -308,7 +308,7 @@ class IDataHandler(ABC):
             timerange=timerange_startup,
             candle_type=candle_type
         )
-        if self._check_empty_df(pairdf, pair, timeframe, candle_type, warn_no_data, True):
+        if self._check_empty_df(pairdf, pair, timeframe, candle_type, warn_no_data):
             return pairdf
         else:
             enddate = pairdf.iloc[-1]['date']
@@ -316,7 +316,7 @@ class IDataHandler(ABC):
             if timerange_startup:
                 self._validate_pairdata(pair, pairdf, timeframe, candle_type, timerange_startup)
                 pairdf = trim_dataframe(pairdf, timerange_startup)
-                if self._check_empty_df(pairdf, pair, timeframe, candle_type, warn_no_data):
+                if self._check_empty_df(pairdf, pair, timeframe, candle_type, warn_no_data, True):
                     return pairdf
 
             # incomplete candles should only be dropped if we didn't trim the end beforehand.

freqtrade/edge/edge_positioning.py

@@ -195,7 +195,7 @@ class Edge:
 
     def stake_amount(self, pair: str, free_capital: float,
                      total_capital: float, capital_in_trade: float) -> float:
-        stoploss = self.stoploss(pair)
+        stoploss = self.get_stoploss(pair)
         available_capital = (total_capital + capital_in_trade) * self._capital_ratio
         allowed_capital_at_risk = available_capital * self._allowed_risk
         max_position_size = abs(allowed_capital_at_risk / stoploss)
@@ -214,7 +214,7 @@ class Edge:
             )
         return round(position_size, 15)
 
-    def stoploss(self, pair: str) -> float:
+    def get_stoploss(self, pair: str) -> float:
         if pair in self._cached_pairs:
             return self._cached_pairs[pair].stoploss
         else:

freqtrade/enums/__init__.py

@@ -5,7 +5,9 @@ from freqtrade.enums.exitchecktuple import ExitCheckTuple
 from freqtrade.enums.exittype import ExitType
 from freqtrade.enums.hyperoptstate import HyperoptState
 from freqtrade.enums.marginmode import MarginMode
+from freqtrade.enums.marketstatetype import MarketDirection
 from freqtrade.enums.ordertypevalue import OrderTypeValues
+from freqtrade.enums.pricetype import PriceType
 from freqtrade.enums.rpcmessagetype import NO_ECHO_MESSAGES, RPCMessageType, RPCRequestType
 from freqtrade.enums.runmode import NON_UTIL_MODES, OPTIMIZE_MODES, TRADING_MODES, RunMode
 from freqtrade.enums.signaltype import SignalDirection, SignalTagType, SignalType

freqtrade/enums/candletype.py

@@ -13,6 +13,9 @@ class CandleType(str, Enum):
     FUNDING_RATE = "funding_rate"
     # BORROW_RATE = "borrow_rate"  # * unimplemented
 
+    def __str__(self):
+        return f"{self.name.lower()}"
+
     @staticmethod
     def from_string(value: str) -> 'CandleType':
         if not value:

freqtrade/enums/marketstatetype.py

@@ -0,0 +1,15 @@
+from enum import Enum
+
+
+class MarketDirection(Enum):
+    """
+    Enum for various market directions.
+    """
+    LONG = "long"
+    SHORT = "short"
+    EVEN = "even"
+    NONE = "none"
+
+    def __str__(self):
+        # convert to string
+        return self.value

freqtrade/enums/pricetype.py

@@ -0,0 +1,8 @@
+from enum import Enum
+
+
+class PriceType(str, Enum):
+    """Enum to distinguish possible trigger prices for stoplosses"""
+    LAST = "last"
+    MARK = "mark"
+    INDEX = "index"

freqtrade/enums/rpcmessagetype.py

@@ -4,6 +4,7 @@ from enum import Enum
 class RPCMessageType(str, Enum):
     STATUS = 'status'
     WARNING = 'warning'
+    EXCEPTION = 'exception'
     STARTUP = 'startup'
 
     ENTRY = 'entry'
@@ -37,5 +38,8 @@ class RPCRequestType(str, Enum):
     WHITELIST = 'whitelist'
     ANALYZED_DF = 'analyzed_df'
 
+    def __str__(self):
+        return self.value
+
 
 NO_ECHO_MESSAGES = (RPCMessageType.ANALYZED_DF, RPCMessageType.WHITELIST, RPCMessageType.NEW_CANDLE)

freqtrade/enums/signaltype.py

@@ -10,6 +10,9 @@ class SignalType(Enum):
     ENTER_SHORT = "enter_short"
     EXIT_SHORT = "exit_short"
 
+    def __str__(self):
+        return f"{self.name.lower()}"
+
 
 class SignalTagType(Enum):
     """
@@ -18,7 +21,13 @@ class SignalTagType(Enum):
     ENTER_TAG = "enter_tag"
     EXIT_TAG = "exit_tag"
 
+    def __str__(self):
+        return f"{self.name.lower()}"
+
 
 class SignalDirection(str, Enum):
     LONG = 'long'
     SHORT = 'short'
+
+    def __str__(self):
+        return f"{self.name.lower()}"

freqtrade/exchange/__init__.py

@@ -17,7 +17,7 @@ from freqtrade.exchange.exchange_utils import (amount_to_contract_precision, amo
                                                timeframe_to_next_date, timeframe_to_prev_date,
                                                timeframe_to_seconds, validate_exchange,
                                                validate_exchanges)
-from freqtrade.exchange.gateio import Gateio
+from freqtrade.exchange.gate import Gate
 from freqtrade.exchange.hitbtc import Hitbtc
 from freqtrade.exchange.huobi import Huobi
 from freqtrade.exchange.kraken import Kraken

freqtrade/exchange/binance.py

@@ -7,7 +7,8 @@ from typing import Dict, List, Optional, Tuple
 import arrow
 import ccxt
 
-from freqtrade.enums import CandleType, MarginMode, TradingMode
+from freqtrade.constants import BuySell
+from freqtrade.enums import CandleType, MarginMode, PriceType, TradingMode
 from freqtrade.exceptions import DDosProtection, OperationalException, TemporaryError
 from freqtrade.exchange import Exchange
 from freqtrade.exchange.common import retrier
@@ -23,16 +24,22 @@ class Binance(Exchange):
     _ft_has: Dict = {
         "stoploss_on_exchange": True,
         "stoploss_order_types": {"limit": "stop_loss_limit"},
-        "order_time_in_force": ['GTC', 'FOK', 'IOC'],
+        "order_time_in_force": ["GTC", "FOK", "IOC", "PO"],
         "ohlcv_candle_limit": 1000,
         "trades_pagination": "id",
         "trades_pagination_arg": "fromId",
         "l2_limit_range": [5, 10, 20, 50, 100, 500, 1000],
-        "ccxt_futures_name": "swap"
     }
     _ft_has_futures: Dict = {
         "stoploss_order_types": {"limit": "stop", "market": "stop_market"},
+        "order_time_in_force": ["GTC", "FOK", "IOC"],
         "tickers_have_price": False,
+        "floor_leverage": True,
+        "stop_price_type_field": "workingType",
+        "stop_price_type_value_mapping": {
+            PriceType.LAST: "CONTRACT_PRICE",
+            PriceType.MARK: "MARK_PRICE",
+        },
     }
 
     _supported_trading_mode_margin_pairs: List[Tuple[TradingMode, MarginMode]] = [
@@ -42,6 +49,26 @@ class Binance(Exchange):
         (TradingMode.FUTURES, MarginMode.ISOLATED)
     ]
 
+    def _get_params(
+        self,
+        side: BuySell,
+        ordertype: str,
+        leverage: float,
+        reduceOnly: bool,
+        time_in_force: str = 'GTC',
+    ) -> Dict:
+        params = super()._get_params(side, ordertype, leverage, reduceOnly, time_in_force)
+        if (
+            time_in_force == 'PO'
+            and ordertype != 'market'
+            and self.trading_mode == TradingMode.SPOT
+            # Only spot can do post only orders
+        ):
+            params.pop('timeInForce')
+            params['postOnly'] = True
+
+        return params
+
     def get_tickers(self, symbols: Optional[List[str]] = None, cached: bool = False) -> Tickers:
         tickers = super().get_tickers(symbols=symbols, cached=cached)
         if self.trading_mode == TradingMode.FUTURES:
@@ -78,33 +105,9 @@ class Binance(Exchange):
             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
+                f'Error in additional_exchange_init due to {e.__class__.__name__}. Message: {e}'
+                ) from e
 
-    @retrier
-    def _set_leverage(
-        self,
-        leverage: float,
-        pair: Optional[str] = None,
-        trading_mode: Optional[TradingMode] = None
-    ):
-        """
-        Set's the leverage before making a trade, in order to not
-        have the same leverage on every trade
-        """
-        trading_mode = trading_mode or self.trading_mode
-
-        if self._config['dry_run'] or trading_mode != TradingMode.FUTURES:
-            return
-
-        try:
-            self._api.set_leverage(symbol=pair, leverage=round(leverage))
-        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
 
@@ -150,6 +153,7 @@ class Binance(Exchange):
         is_short: bool,
         amount: float,
         stake_amount: float,
+        leverage: float,
         wallet_balance: float,  # Or margin balance
         mm_ex_1: float = 0.0,  # (Binance) Cross only
         upnl_ex_1: float = 0.0,  # (Binance) Cross only
@@ -159,11 +163,12 @@ class Binance(Exchange):
         MARGIN: https://www.binance.com/en/support/faq/f6b010588e55413aa58b7d63ee0125ed
         PERPETUAL: https://www.binance.com/en/support/faq/b3c689c1f50a44cabb3a84e663b81d93
 
-        :param exchange_name:
+        :param pair: Pair to calculate liquidation price for
         :param open_rate: Entry price of position
         :param is_short: True if the trade is a short, false otherwise
         :param amount: Absolute value of position size incl. leverage (in base currency)
         :param stake_amount: Stake amount - Collateral in settle currency.
+        :param leverage: Leverage used for this position.
         :param trading_mode: SPOT, MARGIN, FUTURES, etc.
         :param margin_mode: Either ISOLATED or CROSS
         :param wallet_balance: Amount of margin_mode in the wallet being used to trade
@@ -212,7 +217,7 @@ class Binance(Exchange):
                 leverage_tiers_path = (
                     Path(__file__).parent / 'binance_leverage_tiers.json'
                 )
-                with open(leverage_tiers_path) as json_file:
+                with leverage_tiers_path.open() as json_file:
                     return json_load(json_file)
             else:
                 try:

freqtrade/exchange/bybit.py

@@ -1,9 +1,16 @@
 """ Bybit exchange subclass """
 import logging
-from typing import Dict, List, Tuple
+from datetime import datetime
+from typing import Any, Dict, List, Optional, Tuple
 
-from freqtrade.enums import MarginMode, TradingMode
+import ccxt
+
+from freqtrade.constants import BuySell
+from freqtrade.enums import MarginMode, PriceType, TradingMode
+from freqtrade.exceptions import DDosProtection, OperationalException, TemporaryError
 from freqtrade.exchange import Exchange
+from freqtrade.exchange.common import retrier
+from freqtrade.exchange.exchange_utils import timeframe_to_msecs
 
 
 logger = logging.getLogger(__name__)
@@ -20,18 +27,27 @@ class Bybit(Exchange):
     """
 
     _ft_has: Dict = {
-        "ohlcv_candle_limit": 1000,
-        "ccxt_futures_name": "linear",
+        "ohlcv_candle_limit": 200,
         "ohlcv_has_history": False,
     }
     _ft_has_futures: Dict = {
         "ohlcv_has_history": True,
+        "mark_ohlcv_timeframe": "4h",
+        "funding_fee_timeframe": "8h",
+        "stoploss_on_exchange": True,
+        "stoploss_order_types": {"limit": "limit", "market": "market"},
+        "stop_price_type_field": "triggerBy",
+        "stop_price_type_value_mapping": {
+            PriceType.LAST: "LastPrice",
+            PriceType.MARK: "MarkPrice",
+            PriceType.INDEX: "IndexPrice",
+        },
     }
 
     _supported_trading_mode_margin_pairs: List[Tuple[TradingMode, MarginMode]] = [
         # TradingMode.SPOT always supported and not required in this list
         # (TradingMode.FUTURES, MarginMode.CROSS),
-        # (TradingMode.FUTURES, MarginMode.ISOLATED)
+        (TradingMode.FUTURES, MarginMode.ISOLATED)
     ]
 
     @property
@@ -47,3 +63,158 @@ class Bybit(Exchange):
             })
         config.update(super()._ccxt_config)
         return config
+
+    def market_is_future(self, market: Dict[str, Any]) -> bool:
+        main = super().market_is_future(market)
+        # For ByBit, we'll only support USDT markets for now.
+        return (
+            main and market['settle'] == 'USDT'
+        )
+
+    @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']:
+                position_mode = self._api.set_position_mode(False)
+                self._log_exchange_response('set_position_mode', position_mode)
+        except ccxt.DDoSProtection as e:
+            raise DDosProtection(e) from e
+        except (ccxt.NetworkError, ccxt.ExchangeError) as e:
+            raise TemporaryError(
+                f'Error in additional_exchange_init due to {e.__class__.__name__}. Message: {e}'
+                ) from e
+        except ccxt.BaseError as e:
+            raise OperationalException(e) from e
+
+    async def _fetch_funding_rate_history(
+        self,
+        pair: str,
+        timeframe: str,
+        limit: int,
+        since_ms: Optional[int] = None,
+    ) -> List[List]:
+        """
+        Fetch funding rate history
+        Necessary workaround until https://github.com/ccxt/ccxt/issues/15990 is fixed.
+        """
+        params = {}
+        if since_ms:
+            until = since_ms + (timeframe_to_msecs(timeframe) * self._ft_has['ohlcv_candle_limit'])
+            params.update({'until': until})
+        # Funding rate
+        data = await self._api_async.fetch_funding_rate_history(
+            pair, since=since_ms,
+            params=params)
+        # Convert funding rate to candle pattern
+        data = [[x['timestamp'], x['fundingRate'], 0, 0, 0, 0] for x in data]
+        return data
+
+    def _lev_prep(self, pair: str, leverage: float, side: BuySell, accept_fail: bool = False):
+        if self.trading_mode != TradingMode.SPOT:
+            params = {'leverage': leverage}
+            self.set_margin_mode(pair, self.margin_mode, accept_fail=True, params=params)
+            self._set_leverage(leverage, pair, accept_fail=True)
+
+    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 self.trading_mode == TradingMode.FUTURES and self.margin_mode:
+            params['position_idx'] = 0
+        return params
+
+    def dry_run_liquidation_price(
+        self,
+        pair: str,
+        open_rate: float,   # Entry price of position
+        is_short: bool,
+        amount: float,
+        stake_amount: float,
+        leverage: float,
+        wallet_balance: float,  # Or margin balance
+        mm_ex_1: float = 0.0,  # (Binance) Cross only
+        upnl_ex_1: float = 0.0,  # (Binance) Cross only
+    ) -> Optional[float]:
+        """
+        Important: Must be fetching data from cached values as this is used by backtesting!
+        PERPETUAL:
+         bybit:
+          https://www.bybithelp.com/HelpCenterKnowledge/bybitHC_Article?language=en_US&id=000001067
+
+        Long:
+        Liquidation Price = (
+            Entry Price * (1 - Initial Margin Rate + Maintenance Margin Rate)
+            - Extra Margin Added/ Contract)
+        Short:
+        Liquidation Price = (
+            Entry Price * (1 + Initial Margin Rate - Maintenance Margin Rate)
+            + Extra Margin Added/ Contract)
+
+        Implementation Note: Extra margin is currently not used.
+
+        :param pair: Pair to calculate liquidation price for
+        :param open_rate: Entry price of position
+        :param is_short: True if the trade is a short, false otherwise
+        :param amount: Absolute value of position size incl. leverage (in base currency)
+        :param stake_amount: Stake amount - Collateral in settle currency.
+        :param leverage: Leverage used for this position.
+        :param trading_mode: SPOT, MARGIN, FUTURES, etc.
+        :param margin_mode: Either ISOLATED or CROSS
+        :param wallet_balance: Amount of margin_mode in the wallet being used to trade
+            Cross-Margin Mode: crossWalletBalance
+            Isolated-Margin Mode: isolatedWalletBalance
+        """
+
+        market = self.markets[pair]
+        mm_ratio, _ = self.get_maintenance_ratio_and_amt(pair, stake_amount)
+
+        if self.trading_mode == TradingMode.FUTURES and self.margin_mode == MarginMode.ISOLATED:
+
+            if market['inverse']:
+                raise OperationalException(
+                    "Freqtrade does not yet support inverse contracts")
+            initial_margin_rate = 1 / leverage
+
+            # See docstring - ignores extra margin!
+            if is_short:
+                return open_rate * (1 + initial_margin_rate - mm_ratio)
+            else:
+                return open_rate * (1 - initial_margin_rate + mm_ratio)
+
+        else:
+            raise OperationalException(
+                "Freqtrade only supports isolated futures for leverage trading")
+
+    def get_funding_fees(
+            self, pair: str, amount: float, is_short: bool, open_date: datetime) -> float:
+        """
+        Fetch funding fees, either from the exchange (live) or calculates them
+        based on funding rate/mark price history
+        :param pair: The quote/base pair of the trade
+        :param is_short: trade direction
+        :param amount: Trade amount
+        :param open_date: Open date of the trade
+        :return: funding fee since open_date
+        :raises: ExchangeError if something goes wrong.
+        """
+        # Bybit does not provide "applied" funding fees per position.
+        if self.trading_mode == TradingMode.FUTURES:
+            return self._fetch_and_calculate_funding_fees(
+                    pair, amount, is_short, open_date)
+        return 0.0

freqtrade/exchange/common.py

@@ -46,13 +46,13 @@ MAP_EXCHANGE_CHILDCLASS = {
     'binanceje': 'binance',
     'binanceusdm': 'binance',
     'okex': 'okx',
-    'gate': 'gateio',
+    'gateio': 'gate',
 }
 
 SUPPORTED_EXCHANGES = [
     'binance',
     'bittrex',
-    'gateio',
+    'gate',
     'huobi',
     'kraken',
     'okx',

freqtrade/exchange/exchange.py

@@ -7,6 +7,7 @@ import inspect
 import logging
 from copy import deepcopy
 from datetime import datetime, timedelta, timezone
+from math import floor
 from threading import Lock
 from typing import Any, Coroutine, Dict, List, Literal, Optional, Tuple, Union
 
@@ -20,9 +21,10 @@ from pandas import DataFrame, concat
 
 from freqtrade.constants import (DEFAULT_AMOUNT_RESERVE_PERCENT, NON_OPEN_EXCHANGE_STATES, BidAsk,
                                  BuySell, Config, EntryExit, ListPairsWithTimeframes, MakerTaker,
-                                 PairWithTimeframe)
+                                 OBLiteral, PairWithTimeframe)
 from freqtrade.data.converter import clean_ohlcv_dataframe, ohlcv_to_dataframe, trades_dict_to_list
 from freqtrade.enums import OPTIMIZE_MODES, CandleType, MarginMode, TradingMode
+from freqtrade.enums.pricetype import PriceType
 from freqtrade.exceptions import (DDosProtection, ExchangeError, InsufficientFundsError,
                                   InvalidOrderException, OperationalException, PricingError,
                                   RetryableOrderError, TemporaryError)
@@ -35,7 +37,7 @@ from freqtrade.exchange.exchange_utils import (CcxtModuleType, amount_to_contrac
                                                price_to_precision, timeframe_to_minutes,
                                                timeframe_to_msecs, timeframe_to_next_date,
                                                timeframe_to_prev_date, timeframe_to_seconds)
-from freqtrade.exchange.types import OHLCVResponse, Ticker, Tickers
+from freqtrade.exchange.types import OHLCVResponse, OrderBook, Ticker, Tickers
 from freqtrade.misc import (chunks, deep_merge_dicts, file_dump_json, file_load_json,
                             safe_value_fallback2)
 from freqtrade.plugins.pairlist.pairlist_helpers import expand_pairlist
@@ -58,7 +60,6 @@ class Exchange:
     _ft_has_default: Dict = {
         "stoploss_on_exchange": False,
         "order_time_in_force": ["GTC"],
-        "time_in_force_parameter": "timeInForce",
         "ohlcv_params": {},
         "ohlcv_candle_limit": 500,
         "ohlcv_has_history": True,  # Some exchanges (Kraken) don't provide history via ohlcv
@@ -67,6 +68,7 @@ class Exchange:
         # Check https://github.com/ccxt/ccxt/issues/10767 for removal of ohlcv_volume_currency
         "ohlcv_volume_currency": "base",  # "base" or "quote"
         "tickers_have_quoteVolume": True,
+        "tickers_have_bid_ask": True,  # bid / ask empty for fetch_tickers
         "tickers_have_price": True,
         "trades_pagination": "time",  # Possible are "time" or "id"
         "trades_pagination_arg": "since",
@@ -599,12 +601,27 @@ class Exchange:
             if not self.exchange_has('createMarketOrder'):
                 raise OperationalException(
                     f'Exchange {self.name} does not support market orders.')
+        self.validate_stop_ordertypes(order_types)
 
+    def validate_stop_ordertypes(self, order_types: Dict) -> None:
+        """
+        Validate stoploss order types
+        """
         if (order_types.get("stoploss_on_exchange")
                 and not self._ft_has.get("stoploss_on_exchange", False)):
             raise OperationalException(
                 f'On exchange stoploss is not supported for {self.name}.'
             )
+        if self.trading_mode == TradingMode.FUTURES:
+            price_mapping = self._ft_has.get('stop_price_type_value_mapping', {}).keys()
+            if (
+                order_types.get("stoploss_on_exchange", False) is True
+                and 'stoploss_price_type' in order_types
+                and order_types['stoploss_price_type'] not in price_mapping
+            ):
+                raise OperationalException(
+                    f'On exchange stoploss price type is not supported for {self.name}.'
+                )
 
     def validate_pricing(self, pricing: Dict) -> None:
         if pricing.get('use_order_book', False) and not self.exchange_has('fetchL2OrderBook'):
@@ -833,7 +850,7 @@ class Exchange:
             'remaining': _amount,
             'datetime': arrow.utcnow().strftime('%Y-%m-%dT%H:%M:%S.%fZ'),
             'timestamp': arrow.utcnow().int_timestamp * 1000,
-            'status': "closed" if ordertype == "market" and not stop_loss else "open",
+            'status': "open",
             'fee': None,
             'info': {},
             'leverage': leverage
@@ -843,20 +860,33 @@ class Exchange:
             dry_order["stopPrice"] = dry_order["price"]
             # Workaround to avoid filling stoploss orders immediately
             dry_order["ft_order_type"] = "stoploss"
+        orderbook: Optional[OrderBook] = None
+        if self.exchange_has('fetchL2OrderBook'):
+            orderbook = self.fetch_l2_order_book(pair, 20)
+        if ordertype == "limit" and orderbook:
+            # Allow a 3% price difference
+            allowed_diff = 0.03
+            if self._dry_is_price_crossed(pair, side, rate, orderbook, allowed_diff):
+                logger.info(
+                    f"Converted order {pair} to market order due to price {rate} crossing spread "
+                    f"by more than {allowed_diff:.2%}.")
+                dry_order["type"] = "market"
 
         if dry_order["type"] == "market" and not dry_order.get("ft_order_type"):
             # Update market order pricing
-            average = self.get_dry_market_fill_price(pair, side, amount, rate)
+            average = self.get_dry_market_fill_price(pair, side, amount, rate, orderbook)
             dry_order.update({
                 'average': average,
                 'filled': _amount,
                 'remaining': 0.0,
+                'status': "closed",
                 'cost': (dry_order['amount'] * average) / leverage
             })
             # market orders will always incurr taker fees
             dry_order = self.add_dry_order_fee(pair, dry_order, 'taker')
 
-        dry_order = self.check_dry_limit_order_filled(dry_order, immediate=True)
+        dry_order = self.check_dry_limit_order_filled(
+            dry_order, immediate=True, orderbook=orderbook)
 
         self._dry_run_open_orders[dry_order["id"]] = dry_order
         # Copy order and close it - so the returned order is open unless it's a market order
@@ -878,20 +908,22 @@ class Exchange:
         })
         return dry_order
 
-    def get_dry_market_fill_price(self, pair: str, side: str, amount: float, rate: float) -> float:
+    def get_dry_market_fill_price(self, pair: str, side: str, amount: float, rate: float,
+                                  orderbook: Optional[OrderBook]) -> float:
         """
         Get the market order fill price based on orderbook interpolation
         """
         if self.exchange_has('fetchL2OrderBook'):
-            ob = self.fetch_l2_order_book(pair, 20)
-            ob_type = 'asks' if side == 'buy' else 'bids'
+            if not orderbook:
+                orderbook = self.fetch_l2_order_book(pair, 20)
+            ob_type: OBLiteral = 'asks' if side == 'buy' else 'bids'
             slippage = 0.05
             max_slippage_val = rate * ((1 + slippage) if side == 'buy' else (1 - slippage))
 
             remaining_amount = amount
             filled_amount = 0.0
             book_entry_price = 0.0
-            for book_entry in ob[ob_type]:
+            for book_entry in orderbook[ob_type]:
                 book_entry_price = book_entry[0]
                 book_entry_coin_volume = book_entry[1]
                 if remaining_amount > 0:
@@ -919,20 +951,20 @@ class Exchange:
 
         return rate
 
-    def _is_dry_limit_order_filled(self, pair: str, side: str, limit: float) -> bool:
+    def _dry_is_price_crossed(self, pair: str, side: str, limit: float,
+                              orderbook: Optional[OrderBook] = None, offset: float = 0.0) -> bool:
         if not self.exchange_has('fetchL2OrderBook'):
             return True
-        ob = self.fetch_l2_order_book(pair, 1)
+        if not orderbook:
+            orderbook = self.fetch_l2_order_book(pair, 1)
         try:
             if side == 'buy':
-                price = ob['asks'][0][0]
-                logger.debug(f"{pair} checking dry buy-order: price={price}, limit={limit}")
-                if limit >= price:
+                price = orderbook['asks'][0][0]
+                if limit * (1 - offset) >= price:
                     return True
             else:
-                price = ob['bids'][0][0]
-                logger.debug(f"{pair} checking dry sell-order: price={price}, limit={limit}")
-                if limit <= price:
+                price = orderbook['bids'][0][0]
+                if limit * (1 + offset) <= price:
                     return True
         except IndexError:
             # Ignore empty orderbooks when filling - can be filled with the next iteration.
@@ -940,7 +972,8 @@ class Exchange:
         return False
 
     def check_dry_limit_order_filled(
-            self, order: Dict[str, Any], immediate: bool = False) -> Dict[str, Any]:
+            self, order: Dict[str, Any], immediate: bool = False,
+            orderbook: Optional[OrderBook] = None) -> Dict[str, Any]:
         """
         Check dry-run limit order fill and update fee (if it filled).
         """
@@ -948,7 +981,7 @@ class Exchange:
                 and order['type'] in ["limit"]
                 and not order.get('ft_order_type')):
             pair = order['symbol']
-            if self._is_dry_limit_order_filled(pair, order['side'], order['price']):
+            if self._dry_is_price_crossed(pair, order['side'], order['price'], orderbook):
                 order.update({
                     'status': 'closed',
                     'filled': order['amount'],
@@ -985,10 +1018,10 @@ class Exchange:
 
     # Order handling
 
-    def _lev_prep(self, pair: str, leverage: float, side: BuySell):
+    def _lev_prep(self, pair: str, leverage: float, side: BuySell, accept_fail: bool = False):
         if self.trading_mode != TradingMode.SPOT:
-            self.set_margin_mode(pair, self.margin_mode)
-            self._set_leverage(leverage, pair)
+            self.set_margin_mode(pair, self.margin_mode, accept_fail)
+            self._set_leverage(leverage, pair, accept_fail)
 
     def _get_params(
         self,
@@ -1000,8 +1033,7 @@ class Exchange:
     ) -> Dict:
         params = self._params.copy()
         if time_in_force != 'GTC' and ordertype != 'market':
-            param = self._ft_has.get('time_in_force_parameter', '')
-            params.update({param: time_in_force.upper()})
+            params.update({'timeInForce': time_in_force.upper()})
         if reduceOnly:
             params.update({'reduceOnly': True})
         return params
@@ -1053,7 +1085,7 @@ class Exchange:
                 f'Tried to {side} amount {amount} at rate {rate}.'
                 f'Message: {e}') from e
         except ccxt.InvalidOrder as e:
-            raise ExchangeError(
+            raise InvalidOrderException(
                 f'Could not create {ordertype} {side} order on market {pair}. '
                 f'Tried to {side} amount {amount} at rate {rate}. '
                 f'Message: {e}') from e
@@ -1103,8 +1135,15 @@ class Exchange:
                           "sell" else (stop_price >= limit_rate))
         # Ensure rate is less than stop price
         if bad_stop_price:
-            raise OperationalException(
-                'In stoploss limit order, stop price should be more than limit price')
+            # This can for example happen if the stop / liquidation price is set to 0
+            # Which is possible if a market-order closes right away.
+            # The InvalidOrderException will bubble up to exit_positions, where it will be
+            # handled gracefully.
+            raise InvalidOrderException(
+                "In stoploss limit order, stop price should be more than limit price. "
+                f"Stop price: {stop_price}, Limit price: {limit_rate}, "
+                f"Limit Price pct: {limit_price_pct}"
+                )
         return limit_rate
 
     def _get_stop_params(self, side: BuySell, ordertype: str, stop_price: float) -> Dict:
@@ -1114,8 +1153,8 @@ class Exchange:
         return params
 
     @retrier(retries=0)
-    def stoploss(self, pair: str, amount: float, stop_price: float, order_types: Dict,
-                 side: BuySell, leverage: float) -> Dict:
+    def create_stoploss(self, pair: str, amount: float, stop_price: float, order_types: 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
@@ -1160,10 +1199,14 @@ class Exchange:
                                            stop_price=stop_price_norm)
             if self.trading_mode == TradingMode.FUTURES:
                 params['reduceOnly'] = True
+                if 'stoploss_price_type' in order_types and 'stop_price_type_field' in self._ft_has:
+                    price_type = self._ft_has['stop_price_type_value_mapping'][
+                        order_types.get('stoploss_price_type', PriceType.LAST)]
+                    params[self._ft_has['stop_price_type_field']] = price_type
 
             amount = self.amount_to_precision(pair, self._amount_to_contracts(pair, amount))
 
-            self._lev_prep(pair, leverage, side)
+            self._lev_prep(pair, leverage, side, accept_fail=True)
             order = self._api.create_order(symbol=pair, type=ordertype, side=side,
                                            amount=amount, price=limit_rate, params=params)
             self._log_exchange_response('create_stoploss_order', order)
@@ -1490,7 +1533,7 @@ class Exchange:
         return result
 
     @retrier
-    def fetch_l2_order_book(self, pair: str, limit: int = 100) -> dict:
+    def fetch_l2_order_book(self, pair: str, limit: int = 100) -> OrderBook:
         """
         Get L2 order book from exchange.
         Can be limited to a certain amount (if supported).
@@ -1533,7 +1576,7 @@ class Exchange:
 
     def get_rate(self, pair: str, refresh: bool,
                  side: EntryExit, is_short: bool,
-                 order_book: Optional[dict] = None, ticker: Optional[Ticker] = None) -> float:
+                 order_book: Optional[OrderBook] = None, ticker: Optional[Ticker] = None) -> float:
         """
         Calculates bid/ask target
         bid rate - between current ask price and last price
@@ -1571,7 +1614,8 @@ class Exchange:
             logger.debug('order_book %s', order_book)
             # top 1 = index 0
             try:
-                rate = order_book[f"{price_side}s"][order_book_top - 1][0]
+                obside: OBLiteral = 'bids' if price_side == 'bid' else 'asks'
+                rate = order_book[obside][order_book_top - 1][0]
             except (IndexError, KeyError) as e:
                 logger.warning(
                     f"{pair} - {name} Price at location {order_book_top} from orderbook "
@@ -1923,7 +1967,8 @@ class Exchange:
                           cache: bool, drop_incomplete: bool) -> DataFrame:
         # keeping last candle time as last refreshed time of the pair
         if ticks and cache:
-            self._pairs_last_refresh_time[(pair, timeframe, c_type)] = ticks[-1][0] // 1000
+            idx = -2 if drop_incomplete and len(ticks) > 1 else -1
+            self._pairs_last_refresh_time[(pair, timeframe, c_type)] = ticks[idx][0] // 1000
         # keeping parsed dataframe in cache
         ohlcv_df = ohlcv_to_dataframe(ticks, timeframe, pair=pair, fill_missing=True,
                                       drop_incomplete=drop_incomplete)
@@ -1977,9 +2022,9 @@ class Exchange:
                     continue
                 # Deconstruct tuple (has 5 elements)
                 pair, timeframe, c_type, ticks, drop_hint = res
-                drop_incomplete = drop_hint if drop_incomplete is None else drop_incomplete
+                drop_incomplete_ = drop_hint if drop_incomplete is None else drop_incomplete
                 ohlcv_df = self._process_ohlcv_df(
-                    pair, timeframe, c_type, ticks, cache, drop_incomplete)
+                    pair, timeframe, c_type, ticks, cache, drop_incomplete_)
 
                 results_df[(pair, timeframe, c_type)] = ohlcv_df
 
@@ -1996,7 +2041,9 @@ class Exchange:
         # Timeframe in seconds
         interval_in_sec = timeframe_to_seconds(timeframe)
         plr = self._pairs_last_refresh_time.get((pair, timeframe, candle_type), 0) + interval_in_sec
-        return plr < arrow.utcnow().int_timestamp
+        # current,active candle open date
+        now = int(timeframe_to_prev_date(timeframe).timestamp())
+        return plr < now
 
     @retrier_async
     async def _async_get_candle_history(
@@ -2484,7 +2531,7 @@ class Exchange:
         self,
         leverage: float,
         pair: Optional[str] = None,
-        trading_mode: Optional[TradingMode] = None
+        accept_fail: bool = False,
     ):
         """
         Set's the leverage before making a trade, in order to not
@@ -2493,12 +2540,18 @@ class Exchange:
         if self._config['dry_run'] or not self.exchange_has("setLeverage"):
             # Some exchanges only support one margin_mode type
             return
-
+        if self._ft_has.get('floor_leverage', False) is True:
+            # Rounding for binance ...
+            leverage = floor(leverage)
         try:
             res = self._api.set_leverage(symbol=pair, leverage=leverage)
             self._log_exchange_response('set_leverage', res)
         except ccxt.DDoSProtection as e:
             raise DDosProtection(e) from e
+        except (ccxt.BadRequest, ccxt.InsufficientFunds) as e:
+            if not accept_fail:
+                raise TemporaryError(
+                    f'Could not set leverage due to {e.__class__.__name__}. Message: {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
@@ -2520,7 +2573,8 @@ class Exchange:
         return open_date.minute > 0 or open_date.second > 0
 
     @retrier
-    def set_margin_mode(self, pair: str, margin_mode: MarginMode, params: dict = {}):
+    def set_margin_mode(self, pair: str, margin_mode: MarginMode, accept_fail: bool = False,
+                        params: dict = {}):
         """
         Set's the margin mode on the exchange to cross or isolated for a specific pair
         :param pair: base/quote currency pair (e.g. "ADA/USDT")
@@ -2534,6 +2588,10 @@ class Exchange:
             self._log_exchange_response('set_margin_mode', res)
         except ccxt.DDoSProtection as e:
             raise DDosProtection(e) from e
+        except ccxt.BadRequest as e:
+            if not accept_fail:
+                raise TemporaryError(
+                    f'Could not set margin mode due to {e.__class__.__name__}. Message: {e}') from e
         except (ccxt.NetworkError, ccxt.ExchangeError) as e:
             raise TemporaryError(
                 f'Could not set margin mode due to {e.__class__.__name__}. Message: {e}') from e
@@ -2687,6 +2745,7 @@ class Exchange:
         is_short: bool,
         amount: float,  # Absolute value of position size
         stake_amount: float,
+        leverage: float,
         wallet_balance: float,
         mm_ex_1: float = 0.0,  # (Binance) Cross only
         upnl_ex_1: float = 0.0,  # (Binance) Cross only
@@ -2700,14 +2759,15 @@ class Exchange:
             raise OperationalException(
                 f"{self.name} does not support {self.margin_mode} {self.trading_mode}")
 
-        isolated_liq = None
+        liquidation_price = None
         if self._config['dry_run'] or not self.exchange_has("fetchPositions"):
 
-            isolated_liq = self.dry_run_liquidation_price(
+            liquidation_price = self.dry_run_liquidation_price(
                 pair=pair,
                 open_rate=open_rate,
                 is_short=is_short,
                 amount=amount,
+                leverage=leverage,
                 stake_amount=stake_amount,
                 wallet_balance=wallet_balance,
                 mm_ex_1=mm_ex_1,
@@ -2717,16 +2777,16 @@ class Exchange:
             positions = self.fetch_positions(pair)
             if len(positions) > 0:
                 pos = positions[0]
-                isolated_liq = pos['liquidationPrice']
+                liquidation_price = pos['liquidationPrice']
 
-        if isolated_liq:
-            buffer_amount = abs(open_rate - isolated_liq) * self.liquidation_buffer
-            isolated_liq = (
-                isolated_liq - buffer_amount
+        if liquidation_price is not None:
+            buffer_amount = abs(open_rate - liquidation_price) * self.liquidation_buffer
+            liquidation_price_buffer = (
+                liquidation_price - buffer_amount
                 if is_short else
-                isolated_liq + buffer_amount
+                liquidation_price + buffer_amount
             )
-            return isolated_liq
+            return max(liquidation_price_buffer, 0.0)
         else:
             return None
 
@@ -2737,6 +2797,7 @@ class Exchange:
         is_short: bool,
         amount: float,
         stake_amount: float,
+        leverage: float,
         wallet_balance: float,  # Or margin balance
         mm_ex_1: float = 0.0,  # (Binance) Cross only
         upnl_ex_1: float = 0.0,  # (Binance) Cross only
@@ -2744,7 +2805,7 @@ class Exchange:
         """
         Important: Must be fetching data from cached values as this is used by backtesting!
         PERPETUAL:
-         gateio: https://www.gate.io/help/futures/futures/27724/liquidation-price-bankruptcy-price
+         gate: https://www.gate.io/help/futures/futures/27724/liquidation-price-bankruptcy-price
          > Liquidation Price = (Entry Price ± Margin / Contract Multiplier / Size) /
                                 [ 1 ± (Maintenance Margin Ratio + Taker Rate)]
             Wherein, "+" or "-" depends on whether the contract goes long or short:
@@ -2758,13 +2819,14 @@ class Exchange:
         :param is_short: True if the trade is a short, false otherwise
         :param amount: Absolute value of position size incl. leverage (in base currency)
         :param stake_amount: Stake amount - Collateral in settle currency.
+        :param leverage: Leverage used for this position.
         :param trading_mode: SPOT, MARGIN, FUTURES, etc.
         :param margin_mode: Either ISOLATED or CROSS
         :param wallet_balance: Amount of margin_mode in the wallet being used to trade
             Cross-Margin Mode: crossWalletBalance
             Isolated-Margin Mode: isolatedWalletBalance
 
-        # * Not required by Gateio or OKX
+        # * Not required by Gate or OKX
         :param mm_ex_1:
         :param upnl_ex_1:
         """

freqtrade/exchange/gate.py

@@ -4,7 +4,7 @@ from datetime import datetime
 from typing import Any, Dict, List, Optional, Tuple
 
 from freqtrade.constants import BuySell
-from freqtrade.enums import MarginMode, TradingMode
+from freqtrade.enums import MarginMode, PriceType, TradingMode
 from freqtrade.exceptions import OperationalException
 from freqtrade.exchange import Exchange
 from freqtrade.misc import safe_value_fallback2
@@ -13,7 +13,7 @@ from freqtrade.misc import safe_value_fallback2
 logger = logging.getLogger(__name__)
 
 
-class Gateio(Exchange):
+class Gate(Exchange):
     """
     Gate.io exchange class. Contains adjustments needed for Freqtrade to work
     with this exchange.
@@ -32,8 +32,15 @@ class Gateio(Exchange):
 
     _ft_has_futures: Dict = {
         "needs_trading_fees": True,
+        "tickers_have_bid_ask": False,
         "fee_cost_in_contracts": False,  # Set explicitly to false for clarity
         "order_props_in_contracts": ['amount', 'filled', 'remaining'],
+        "stop_price_type_field": "price_type",
+        "stop_price_type_value_mapping": {
+            PriceType.LAST: 0,
+            PriceType.MARK: 1,
+            PriceType.INDEX: 2,
+        },
     }
 
     _supported_trading_mode_margin_pairs: List[Tuple[TradingMode, MarginMode]] = [
@@ -49,6 +56,7 @@ class Gateio(Exchange):
             if any(v == 'market' for k, v in order_types.items()):
                 raise OperationalException(
                     f'Exchange {self.name} does not support market orders.')
+        super().validate_stop_ordertypes(order_types)
 
     def _get_params(
             self,
@@ -67,8 +75,7 @@ class Gateio(Exchange):
         )
         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'})
+            params.update({'timeInForce': 'IOC'})
         return params
 
     def get_trades_for_order(self, order_id: str, pair: str, since: datetime,
@@ -77,7 +84,7 @@ class Gateio(Exchange):
 
         if self.trading_mode == TradingMode.FUTURES:
             # Futures usually don't contain fees in the response.
-            # As such, futures orders on gateio will not contain a fee, which causes
+            # As such, futures orders on gate will not contain a fee, which causes
             # a repeated "update fee" cycle and wrong calculations.
             # Therefore we patch the response with fees if it's not available.
             # An alternative also contianing fees would be

freqtrade/exchange/hitbtc.py

@@ -19,5 +19,4 @@ class Hitbtc(Exchange):
 
     _ft_has: Dict = {
         "ohlcv_candle_limit": 1000,
-        "ohlcv_params": {"sort": "DESC"}
     }

freqtrade/exchange/kraken.py

@@ -97,8 +97,8 @@ class Kraken(Exchange):
                 ))
 
     @retrier(retries=0)
-    def stoploss(self, pair: str, amount: float, stop_price: float,
-                 order_types: Dict, side: BuySell, leverage: float) -> Dict:
+    def create_stoploss(self, pair: str, amount: float, stop_price: float,
+                        order_types: Dict, side: BuySell, leverage: float) -> Dict:
         """
         Creates a stoploss market order.
         Stoploss market orders is the only stoploss type supported by kraken.
@@ -158,7 +158,7 @@ class Kraken(Exchange):
         self,
         leverage: float,
         pair: Optional[str] = None,
-        trading_mode: Optional[TradingMode] = None
+        accept_fail: bool = False,
     ):
         """
         Kraken set's the leverage as an option in the order object, so we need to

freqtrade/exchange/kucoin.py

@@ -36,3 +36,35 @@ class Kucoin(Exchange):
             'stop': 'loss'
             })
         return params
+
+    def create_order(
+            self,
+            *,
+            pair: str,
+            ordertype: str,
+            side: BuySell,
+            amount: float,
+            rate: float,
+            leverage: float,
+            reduceOnly: bool = False,
+            time_in_force: str = 'GTC',
+            ) -> Dict:
+
+        res = super().create_order(
+            pair=pair,
+            ordertype=ordertype,
+            side=side,
+            amount=amount,
+            rate=rate,
+            leverage=leverage,
+            reduceOnly=reduceOnly,
+            time_in_force=time_in_force,
+        )
+        # Kucoin returns only the order-id.
+        # ccxt returns status = 'closed' at the moment - which is information ccxt invented.
+        # Since we rely on status heavily, we must set it to 'open' here.
+        # ref: https://github.com/ccxt/ccxt/pull/16674, (https://github.com/ccxt/ccxt/pull/16553)
+        if not self._config['dry_run']:
+            res['type'] = ordertype
+            res['status'] = 'open'
+        return res

freqtrade/exchange/okx.py

@@ -1,13 +1,16 @@
 import logging
-from typing import Dict, List, Optional, Tuple
+from typing import Any, Dict, List, Optional, Tuple
 
 import ccxt
 
 from freqtrade.constants import BuySell
 from freqtrade.enums import CandleType, MarginMode, TradingMode
-from freqtrade.exceptions import DDosProtection, OperationalException, TemporaryError
+from freqtrade.enums.pricetype import PriceType
+from freqtrade.exceptions import (DDosProtection, OperationalException, RetryableOrderError,
+                                  TemporaryError)
 from freqtrade.exchange import Exchange, date_minus_candles
 from freqtrade.exchange.common import retrier
+from freqtrade.misc import safe_value_fallback2
 
 
 logger = logging.getLogger(__name__)
@@ -23,10 +26,18 @@ class Okx(Exchange):
         "ohlcv_candle_limit": 100,  # Warning, special case with data prior to X months
         "mark_ohlcv_timeframe": "4h",
         "funding_fee_timeframe": "8h",
+        "stoploss_order_types": {"limit": "limit"},
+        "stoploss_on_exchange": True,
     }
     _ft_has_futures: Dict = {
         "tickers_have_quoteVolume": False,
         "fee_cost_in_contracts": True,
+        "stop_price_type_field": "slTriggerPxType",
+        "stop_price_type_value_mapping": {
+            PriceType.LAST: "last",
+            PriceType.MARK: "index",
+            PriceType.INDEX: "mark",
+            },
     }
 
     _supported_trading_mode_margin_pairs: List[Tuple[TradingMode, MarginMode]] = [
@@ -114,17 +125,18 @@ class Okx(Exchange):
         return params
 
     @retrier
-    def _lev_prep(self, pair: str, leverage: float, side: BuySell):
+    def _lev_prep(self, pair: str, leverage: float, side: BuySell, accept_fail: bool = False):
         if self.trading_mode != TradingMode.SPOT and self.margin_mode is not None:
             try:
-                # TODO-lev: Test me properly (check mgnMode passed)
-                self._api.set_leverage(
+                res = self._api.set_leverage(
                     leverage=leverage,
                     symbol=pair,
                     params={
                         "mgnMode": self.margin_mode.value,
                         "posSide": self._get_posSide(side, False),
                     })
+                self._log_exchange_response('set_leverage', res)
+
             except ccxt.DDoSProtection as e:
                 raise DDosProtection(e) from e
             except (ccxt.NetworkError, ccxt.ExchangeError) as e:
@@ -148,3 +160,78 @@ class Okx(Exchange):
 
         pair_tiers = self._leverage_tiers[pair]
         return pair_tiers[-1]['maxNotional'] / leverage
+
+    def _get_stop_params(self, side: BuySell, ordertype: str, stop_price: float) -> Dict:
+
+        params = self._params.copy()
+        # Verify if stopPrice works for your exchange!
+        params.update({'stopLossPrice': stop_price})
+
+        if self.trading_mode == TradingMode.FUTURES and self.margin_mode:
+            params['tdMode'] = self.margin_mode.value
+            params['posSide'] = self._get_posSide(side, True)
+        return params
+
+    def stoploss_adjust(self, stop_loss: float, order: Dict, side: str) -> bool:
+        """
+        OKX uses non-default stoploss price naming.
+        """
+        if not self._ft_has.get('stoploss_on_exchange'):
+            raise OperationalException(f"stoploss is not implemented for {self.name}.")
+
+        return (
+            order.get('stopLossPrice', None) is None
+            or ((side == "sell" and stop_loss > float(order['stopLossPrice'])) or
+                (side == "buy" and stop_loss < float(order['stopLossPrice'])))
+        )
+
+    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)
+
+        try:
+            params1 = {'stop': True}
+            order_reg = self._api.fetch_order(order_id, pair, params=params1)
+            self._log_exchange_response('fetch_stoploss_order', order_reg)
+            return order_reg
+        except ccxt.OrderNotFound:
+            pass
+        params2 = {'stop': True, 'ordType': 'conditional'}
+        for method in (self._api.fetch_open_orders, self._api.fetch_closed_orders,
+                       self._api.fetch_canceled_orders):
+            try:
+                orders = method(pair, params=params2)
+                orders_f = [order for order in orders if order['id'] == order_id]
+                if orders_f:
+                    order = orders_f[0]
+                    if (order['status'] == 'closed'
+                            and (real_order_id := order.get('info', {}).get('ordId')) is not None):
+                        # Once a order triggered, we fetch the regular followup order.
+                        order_reg = self.fetch_order(real_order_id, pair)
+                        self._log_exchange_response('fetch_stoploss_order1', order_reg)
+                        order_reg['id_stop'] = order_reg['id']
+                        order_reg['id'] = order_id
+                        order_reg['type'] = 'stoploss'
+                        order_reg['status_stop'] = 'triggered'
+                        return order_reg
+                    order['type'] = 'stoploss'
+                    return order
+            except ccxt.BaseError:
+                pass
+        raise RetryableOrderError(
+                f'StoplossOrder not found (pair: {pair} id: {order_id}).')
+
+    def get_order_id_conditional(self, order: Dict[str, Any]) -> str:
+        if order['type'] == 'stop':
+            return safe_value_fallback2(order, order, 'id_stop', 'id')
+        return order['id']
+
+    def cancel_stoploss_order(self, order_id: str, pair: str, params: Dict = {}) -> Dict:
+        params1 = {'stop': True}
+        # 'ordType': 'conditional'
+        #
+        return self.cancel_order(
+            order_id=order_id,
+            pair=pair,
+            params=params1,
+        )

freqtrade/exchange/types.py

@@ -15,6 +15,15 @@ class Ticker(TypedDict):
     # Several more - only listing required.
 
 
+class OrderBook(TypedDict):
+    symbol: str
+    bids: List[Tuple[float, float]]
+    asks: List[Tuple[float, float]]
+    timestamp: Optional[int]
+    datetime: Optional[str]
+    nonce: Optional[int]
+
+
 Tickers = Dict[str, Ticker]
 
 # pair, timeframe, candleType, OHLCV, drop last?,

freqtrade/freqai/RL/Base3ActionRLEnv.py

@@ -47,7 +47,7 @@ class Base3ActionRLEnv(BaseEnvironment):
         self._update_unrealized_total_profit()
         step_reward = self.calculate_reward(action)
         self.total_reward += step_reward
-        self.tensorboard_log(self.actions._member_names_[action])
+        self.tensorboard_log(self.actions._member_names_[action], category="actions")
 
         trade_type = None
         if self.is_tradesignal(action):

freqtrade/freqai/RL/Base4ActionRLEnv.py

@@ -48,7 +48,7 @@ class Base4ActionRLEnv(BaseEnvironment):
         self._update_unrealized_total_profit()
         step_reward = self.calculate_reward(action)
         self.total_reward += step_reward
-        self.tensorboard_log(self.actions._member_names_[action])
+        self.tensorboard_log(self.actions._member_names_[action], category="actions")
 
         trade_type = None
         if self.is_tradesignal(action):

freqtrade/freqai/RL/Base5ActionRLEnv.py

@@ -49,7 +49,7 @@ class Base5ActionRLEnv(BaseEnvironment):
         self._update_unrealized_total_profit()
         step_reward = self.calculate_reward(action)
         self.total_reward += step_reward
-        self.tensorboard_log(self.actions._member_names_[action])
+        self.tensorboard_log(self.actions._member_names_[action], category="actions")
 
         trade_type = None
         if self.is_tradesignal(action):

freqtrade/freqai/RL/BaseEnvironment.py

@@ -45,7 +45,8 @@ class BaseEnvironment(gym.Env):
     def __init__(self, df: DataFrame = DataFrame(), prices: DataFrame = DataFrame(),
                  reward_kwargs: dict = {}, window_size=10, starting_point=True,
                  id: str = 'baseenv-1', seed: int = 1, config: dict = {}, live: bool = False,
-                 fee: float = 0.0015, can_short: bool = False):
+                 fee: float = 0.0015, can_short: bool = False, pair: str = "",
+                 df_raw: DataFrame = DataFrame()):
         """
         Initializes the training/eval environment.
         :param df: dataframe of features
@@ -60,12 +61,14 @@ class BaseEnvironment(gym.Env):
         :param fee: The fee to use for environmental interactions.
         :param can_short: Whether or not the environment can short
         """
-        self.config = config
-        self.rl_config = config['freqai']['rl_config']
-        self.add_state_info = self.rl_config.get('add_state_info', False)
-        self.id = id
-        self.max_drawdown = 1 - self.rl_config.get('max_training_drawdown_pct', 0.8)
-        self.compound_trades = config['stake_amount'] == 'unlimited'
+        self.config: dict = config
+        self.rl_config: dict = config['freqai']['rl_config']
+        self.add_state_info: bool = self.rl_config.get('add_state_info', False)
+        self.id: str = id
+        self.max_drawdown: float = 1 - self.rl_config.get('max_training_drawdown_pct', 0.8)
+        self.compound_trades: bool = config['stake_amount'] == 'unlimited'
+        self.pair: str = pair
+        self.raw_features: DataFrame = df_raw
         if self.config.get('fee', None) is not None:
             self.fee = self.config['fee']
         else:
@@ -74,8 +77,8 @@ class BaseEnvironment(gym.Env):
         # set here to default 5Ac, but all children envs can override this
         self.actions: Type[Enum] = BaseActions
         self.tensorboard_metrics: dict = {}
-        self.can_short = can_short
-        self.live = live
+        self.can_short: bool = can_short
+        self.live: bool = live
         if not self.live and self.add_state_info:
             self.add_state_info = False
             logger.warning("add_state_info is not available in backtesting. Deactivating.")
@@ -93,13 +96,12 @@ class BaseEnvironment(gym.Env):
         :param reward_kwargs: extra config settings assigned by user in `rl_config`
         :param starting_point: start at edge of window or not
         """
-        self.df = df
-        self.signal_features = self.df
-        self.prices = prices
-        self.window_size = window_size
-        self.starting_point = starting_point
-        self.rr = reward_kwargs["rr"]
-        self.profit_aim = reward_kwargs["profit_aim"]
+        self.signal_features: DataFrame = df
+        self.prices: DataFrame = prices
+        self.window_size: int = window_size
+        self.starting_point: bool = starting_point
+        self.rr: float = reward_kwargs["rr"]
+        self.profit_aim: float = reward_kwargs["profit_aim"]
 
         # # spaces
         if self.add_state_info:
@@ -135,7 +137,8 @@ class BaseEnvironment(gym.Env):
         self.np_random, seed = seeding.np_random(seed)
         return [seed]
 
-    def tensorboard_log(self, metric: str, value: Union[int, float] = 1, inc: bool = True):
+    def tensorboard_log(self, metric: str, value: Optional[Union[int, float]] = None,
+                        inc: Optional[bool] = None, category: str = "custom"):
         """
         Function builds the tensorboard_metrics dictionary
         to be parsed by the TensorboardCallback. This
@@ -147,17 +150,24 @@ class BaseEnvironment(gym.Env):
 
         def calculate_reward(self, action: int) -> float:
             if not self._is_valid(action):
-                self.tensorboard_log("is_valid")
+                self.tensorboard_log("invalid")
                 return -2
 
         :param metric: metric to be tracked and incremented
-        :param value: value to increment `metric` by
-        :param inc: sets whether the `value` is incremented or not
+        :param value: `metric` value
+        :param inc: (deprecated) sets whether the `value` is incremented or not
+        :param category: `metric` category
         """
-        if not inc or metric not in self.tensorboard_metrics:
-            self.tensorboard_metrics[metric] = value
+        increment = True if value is None else False
+        value = 1 if increment else value
+
+        if category not in self.tensorboard_metrics:
+            self.tensorboard_metrics[category] = {}
+
+        if not increment or metric not in self.tensorboard_metrics[category]:
+            self.tensorboard_metrics[category][metric] = value
         else:
-            self.tensorboard_metrics[metric] += value
+            self.tensorboard_metrics[category][metric] += value
 
     def reset_tensorboard_log(self):
         self.tensorboard_metrics = {}

freqtrade/freqai/RL/BaseReinforcementLearningModel.py

@@ -1,3 +1,4 @@
+import copy
 import importlib
 import logging
 from abc import abstractmethod
@@ -50,6 +51,7 @@ class BaseReinforcementLearningModel(IFreqaiModel):
         self.eval_callback: Optional[EvalCallback] = None
         self.model_type = self.freqai_info['rl_config']['model_type']
         self.rl_config = self.freqai_info['rl_config']
+        self.df_raw: DataFrame = DataFrame()
         self.continual_learning = self.freqai_info.get('continual_learning', False)
         if self.model_type in SB3_MODELS:
             import_str = 'stable_baselines3'
@@ -107,10 +109,12 @@ class BaseReinforcementLearningModel(IFreqaiModel):
 
         data_dictionary: Dict[str, Any] = dk.make_train_test_datasets(
             features_filtered, labels_filtered)
+        self.df_raw = copy.deepcopy(data_dictionary["train_features"])
         dk.fit_labels()  # FIXME useless for now, but just satiating append methods
 
         # normalize all data based on train_dataset only
         prices_train, prices_test = self.build_ohlc_price_dataframes(dk.data_dictionary, pair, dk)
+
         data_dictionary = dk.normalize_data(data_dictionary)
 
         # data cleaning/analysis
@@ -143,14 +147,10 @@ class BaseReinforcementLearningModel(IFreqaiModel):
         train_df = data_dictionary["train_features"]
         test_df = data_dictionary["test_features"]
 
-        env_info = self.pack_env_dict()
+        env_info = self.pack_env_dict(dk.pair)
 
-        self.train_env = self.MyRLEnv(df=train_df,
-                                      prices=prices_train,
-                                      **env_info)
-        self.eval_env = Monitor(self.MyRLEnv(df=test_df,
-                                             prices=prices_test,
-                                             **env_info))
+        self.train_env = self.MyRLEnv(df=train_df, prices=prices_train, **env_info)
+        self.eval_env = Monitor(self.MyRLEnv(df=test_df, prices=prices_test, **env_info))
         self.eval_callback = EvalCallback(self.eval_env, deterministic=True,
                                           render=False, eval_freq=len(train_df),
                                           best_model_save_path=str(dk.data_path))
@@ -158,7 +158,7 @@ class BaseReinforcementLearningModel(IFreqaiModel):
         actions = self.train_env.get_actions()
         self.tensorboard_callback = TensorboardCallback(verbose=1, actions=actions)
 
-    def pack_env_dict(self) -> Dict[str, Any]:
+    def pack_env_dict(self, pair: str) -> Dict[str, Any]:
         """
         Create dictionary of environment arguments
         """
@@ -166,7 +166,9 @@ class BaseReinforcementLearningModel(IFreqaiModel):
                     "reward_kwargs": self.reward_params,
                     "config": self.config,
                     "live": self.live,
-                    "can_short": self.can_short}
+                    "can_short": self.can_short,
+                    "pair": pair,
+                    "df_raw": self.df_raw}
         if self.data_provider:
             env_info["fee"] = self.data_provider._exchange \
                 .get_fee(symbol=self.data_provider.current_whitelist()[0])  # type: ignore
@@ -233,6 +235,9 @@ class BaseReinforcementLearningModel(IFreqaiModel):
         filtered_dataframe, _ = dk.filter_features(
             unfiltered_df, dk.training_features_list, training_filter=False
         )
+
+        filtered_dataframe = self.drop_ohlc_from_df(filtered_dataframe, dk)
+
         filtered_dataframe = dk.normalize_data_from_metadata(filtered_dataframe)
         dk.data_dictionary["prediction_features"] = filtered_dataframe
 
@@ -280,7 +285,6 @@ class BaseReinforcementLearningModel(IFreqaiModel):
         train_df = data_dictionary["train_features"]
         test_df = data_dictionary["test_features"]
 
-        # %-raw_volume_gen_shift-2_ETH/USDT_1h
         # price data for model training and evaluation
         tf = self.config['timeframe']
         rename_dict = {'%-raw_open': 'open', '%-raw_low': 'low',
@@ -313,8 +317,24 @@ class BaseReinforcementLearningModel(IFreqaiModel):
         prices_test.rename(columns=rename_dict, inplace=True)
         prices_test.reset_index(drop=True)
 
+        train_df = self.drop_ohlc_from_df(train_df, dk)
+        test_df = self.drop_ohlc_from_df(test_df, dk)
+
         return prices_train, prices_test
 
+    def drop_ohlc_from_df(self, df: DataFrame, dk: FreqaiDataKitchen):
+        """
+        Given a dataframe, drop the ohlc data
+        """
+        drop_list = ['%-raw_open', '%-raw_low', '%-raw_high', '%-raw_close']
+
+        if self.rl_config["drop_ohlc_from_features"]:
+            df.drop(drop_list, axis=1, inplace=True)
+            feature_list = dk.training_features_list
+            dk.training_features_list = [e for e in feature_list if e not in drop_list]
+
+        return df
+
     def load_model_from_disk(self, dk: FreqaiDataKitchen) -> Any:
         """
         Can be used by user if they are trying to limit_ram_usage *and*
@@ -347,7 +367,7 @@ class BaseReinforcementLearningModel(IFreqaiModel):
         sets a custom reward based on profit and trade duration.
         """
 
-        def calculate_reward(self, action: int) -> float:
+        def calculate_reward(self, action: int) -> float:  # noqa: C901
             """
             An example reward function. This is the one function that users will likely
             wish to inject their own creativity into.
@@ -363,10 +383,19 @@ class BaseReinforcementLearningModel(IFreqaiModel):
             pnl = self.get_unrealized_profit()
             factor = 100.
 
+            # you can use feature values from dataframe
+            rsi_now = self.raw_features[f"%-rsi-period-10_shift-1_{self.pair}_"
+                                        f"{self.config['timeframe']}"].iloc[self._current_tick]
+
             # reward agent for entering trades
             if (action in (Actions.Long_enter.value, Actions.Short_enter.value)
                     and self._position == Positions.Neutral):
-                return 25
+                if rsi_now < 40:
+                    factor = 40 / rsi_now
+                else:
+                    factor = 1
+                return 25 * factor
+
             # discourage agent from not entering trades
             if action == Actions.Neutral.value and self._position == Positions.Neutral:
                 return -1

freqtrade/freqai/RL/TensorboardCallback.py

@@ -13,7 +13,7 @@ class TensorboardCallback(BaseCallback):
     episodic summary reports.
     """
     def __init__(self, verbose=1, actions: Type[Enum] = BaseActions):
-        super(TensorboardCallback, self).__init__(verbose)
+        super().__init__(verbose)
         self.model: Any = None
         self.logger = None  # type: Any
         self.training_env: BaseEnvironment = None  # type: ignore
@@ -46,14 +46,12 @@ class TensorboardCallback(BaseCallback):
         local_info = self.locals["infos"][0]
         tensorboard_metrics = self.training_env.get_attr("tensorboard_metrics")[0]
 
-        for info in local_info:
-            if info not in ["episode", "terminal_observation"]:
-                self.logger.record(f"_info/{info}", local_info[info])
+        for metric in local_info:
+            if metric not in ["episode", "terminal_observation"]:
+                self.logger.record(f"info/{metric}", local_info[metric])
 
-        for info in tensorboard_metrics:
-            if info in [action.name for action in self.actions]:
-                self.logger.record(f"_actions/{info}", tensorboard_metrics[info])
-            else:
-                self.logger.record(f"_custom/{info}", tensorboard_metrics[info])
+        for category in tensorboard_metrics:
+            for metric in tensorboard_metrics[category]:
+                self.logger.record(f"{category}/{metric}", tensorboard_metrics[category][metric])
 
         return True

freqtrade/freqai/data_drawer.py

@@ -59,7 +59,7 @@ class FreqaiDataDrawer:
     Juha Nykänen @suikula, Wagner Costa @wagnercosta, Johan Vlugt @Jooopieeert
     """
 
-    def __init__(self, full_path: Path, config: Config, follow_mode: bool = False):
+    def __init__(self, full_path: Path, config: Config):
 
         self.config = config
         self.freqai_info = config.get("freqai", {})
@@ -72,21 +72,13 @@ class FreqaiDataDrawer:
         self.model_return_values: Dict[str, DataFrame] = {}
         self.historic_data: Dict[str, Dict[str, DataFrame]] = {}
         self.historic_predictions: Dict[str, DataFrame] = {}
-        self.follower_dict: Dict[str, pair_info] = {}
         self.full_path = full_path
-        self.follower_name: str = self.config.get("bot_name", "follower1")
-        self.follower_dict_path = Path(
-            self.full_path / f"follower_dictionary-{self.follower_name}.json"
-        )
         self.historic_predictions_path = Path(self.full_path / "historic_predictions.pkl")
-        self.historic_predictions_bkp_path = Path(
-            self.full_path / "historic_predictions.backup.pkl")
+        self.historic_predictions_folder = Path(self.full_path / "historic_predictions")
+        self.historic_predictions_bkp_folder = Path(self.full_path / "historic_predictions_backup")
         self.pair_dictionary_path = Path(self.full_path / "pair_dictionary.json")
         self.global_metadata_path = Path(self.full_path / "global_metadata.json")
         self.metric_tracker_path = Path(self.full_path / "metric_tracker.json")
-        self.follow_mode = follow_mode
-        if follow_mode:
-            self.create_follower_dict()
         self.load_drawer_from_disk()
         self.load_historic_predictions_from_disk()
         self.metric_tracker: Dict[str, Dict[str, Dict[str, list]]] = {}
@@ -134,7 +126,7 @@ class FreqaiDataDrawer:
         """
         exists = self.global_metadata_path.is_file()
         if exists:
-            with open(self.global_metadata_path, "r") as fp:
+            with self.global_metadata_path.open("r") as fp:
                 metatada_dict = rapidjson.load(fp, number_mode=rapidjson.NM_NATIVE)
                 return metatada_dict
         return {}
@@ -147,15 +139,10 @@ class FreqaiDataDrawer:
         """
         exists = self.pair_dictionary_path.is_file()
         if exists:
-            with open(self.pair_dictionary_path, "r") as fp:
+            with self.pair_dictionary_path.open("r") as fp:
                 self.pair_dict = rapidjson.load(fp, number_mode=rapidjson.NM_NATIVE)
-        elif not self.follow_mode:
-            logger.info("Could not find existing datadrawer, starting from scratch")
         else:
-            logger.warning(
-                f"Follower could not find pair_dictionary at {self.full_path} "
-                "sending null values back to strategy"
-            )
+            logger.info("Could not find existing datadrawer, starting from scratch")
 
     def load_metric_tracker_from_disk(self):
         """
@@ -165,7 +152,7 @@ class FreqaiDataDrawer:
         if self.freqai_info.get('write_metrics_to_disk', False):
             exists = self.metric_tracker_path.is_file()
             if exists:
-                with open(self.metric_tracker_path, "r") as fp:
+                with self.metric_tracker_path.open("r") as fp:
                     self.metric_tracker = rapidjson.load(fp, number_mode=rapidjson.NM_NATIVE)
                 logger.info("Loading existing metric tracker from disk.")
             else:
@@ -176,11 +163,12 @@ class FreqaiDataDrawer:
         Locate and load a previously saved historic predictions.
         :return: bool - whether or not the drawer was located
         """
-        exists = self.historic_predictions_path.is_file()
+        exists = self.historic_predictions_folder.exists()
+        convert = self.historic_predictions_path.is_file()
+
         if exists:
             try:
-                with open(self.historic_predictions_path, "rb") as fp:
-                    self.historic_predictions = cloudpickle.load(fp)
+                self.load_historic_predictions_from_folder()
                 logger.info(
                     f"Found existing historic predictions at {self.full_path}, but beware "
                     "that statistics may be inaccurate if the bot has been offline for "
@@ -188,13 +176,19 @@ class FreqaiDataDrawer:
                 )
             except EOFError:
                 logger.warning(
-                    'Historical prediction file was corrupted. Trying to load backup file.')
-                with open(self.historic_predictions_bkp_path, "rb") as fp:
-                    self.historic_predictions = cloudpickle.load(fp)
-                logger.warning('FreqAI successfully loaded the backup historical predictions file.')
+                    'Historical prediction files were corrupted. Trying to load backup files.')
+                self.load_historic_predictions_from_folder()
+                logger.warning('FreqAI successfully loaded the backup '
+                               'historical predictions files.')
+
+        elif not exists and convert:
+            logger.info("Converting your historic predictions pkl to parquet"
+                        "to improve performance.")
+            with Path.open(self.historic_predictions_path, "rb") as fp:
+                self.historic_predictions = cloudpickle.load(fp)
+            self.save_historic_predictions_to_disk()
+            exists = True
 
-        elif not self.follow_mode:
-            logger.info("Could not find existing historic_predictions, starting from scratch")
         else:
             logger.warning(
                 f"Follower could not find historic predictions at {self.full_path} "
@@ -203,73 +197,66 @@ class FreqaiDataDrawer:
 
         return exists
 
+    def load_historic_predictions_from_folder(self):
+        """
+        Try to build the historic_predictions dictionary from parquet
+        files in the historic_predictions_folder
+        """
+        for file_path in self.historic_predictions_folder.glob("*.parquet"):
+            key = file_path.stem
+            key.replace("_", "/")
+            self.historic_predictions[key] = pd.read_parquet(file_path)
+
+        return
+
     def save_historic_predictions_to_disk(self):
         """
         Save historic predictions pickle to disk
         """
-        with open(self.historic_predictions_path, "wb") as fp:
-            cloudpickle.dump(self.historic_predictions, fp, protocol=cloudpickle.DEFAULT_PROTOCOL)
+
+        self.historic_predictions_folder.mkdir(parents=True, exist_ok=True)
+        for key, value in self.historic_predictions.items():
+            key = key.replace("/", "_")
+            # pytest.set_trace()
+            filename = Path(self.historic_predictions_folder / f"{key}.parquet")
+            value.to_parquet(filename)
 
         # create a backup
-        shutil.copy(self.historic_predictions_path, self.historic_predictions_bkp_path)
+        shutil.copytree(self.historic_predictions_folder,
+                        self.historic_predictions_bkp_folder, dirs_exist_ok=True)
 
     def save_metric_tracker_to_disk(self):
         """
         Save metric tracker of all pair metrics collected.
         """
         with self.save_lock:
-            with open(self.metric_tracker_path, 'w') as fp:
+            with self.metric_tracker_path.open('w') as fp:
                 rapidjson.dump(self.metric_tracker, fp, default=self.np_encoder,
                                number_mode=rapidjson.NM_NATIVE)
 
-    def save_drawer_to_disk(self):
+    def save_drawer_to_disk(self) -> None:
         """
         Save data drawer full of all pair model metadata in present model folder.
         """
         with self.save_lock:
-            with open(self.pair_dictionary_path, 'w') as fp:
+            with self.pair_dictionary_path.open('w') as fp:
                 rapidjson.dump(self.pair_dict, fp, default=self.np_encoder,
                                number_mode=rapidjson.NM_NATIVE)
 
-    def save_follower_dict_to_disk(self):
-        """
-        Save follower dictionary to disk (used by strategy for persistent prediction targets)
-        """
-        with open(self.follower_dict_path, "w") as fp:
-            rapidjson.dump(self.follower_dict, fp, default=self.np_encoder,
-                           number_mode=rapidjson.NM_NATIVE)
-
     def save_global_metadata_to_disk(self, metadata: Dict[str, Any]):
         """
         Save global metadata json to disk
         """
         with self.save_lock:
-            with open(self.global_metadata_path, 'w') as fp:
+            with self.global_metadata_path.open('w') as fp:
                 rapidjson.dump(metadata, fp, default=self.np_encoder,
                                number_mode=rapidjson.NM_NATIVE)
 
-    def create_follower_dict(self):
-        """
-        Create or dictionary for each follower to maintain unique persistent prediction targets
-        """
-
-        whitelist_pairs = self.config.get("exchange", {}).get("pair_whitelist")
-
-        exists = self.follower_dict_path.is_file()
-
-        if exists:
-            logger.info("Found an existing follower dictionary")
-
-        for pair in whitelist_pairs:
-            self.follower_dict[pair] = {}
-
-        self.save_follower_dict_to_disk()
-
     def np_encoder(self, object):
         if isinstance(object, np.generic):
             return object.item()
 
-    def get_pair_dict_info(self, pair: str) -> Tuple[str, int, bool]:
+    def get_pair_dict_info(self, pair: str) -> Tuple[str, int]:
         """
         Locate and load existing model metadata from persistent storage. If not located,
         create a new one and append the current pair to it and prepare it for its first
@@ -278,32 +265,19 @@ class FreqaiDataDrawer:
         :return:
             model_filename: str = unique filename used for loading persistent objects from disk
             trained_timestamp: int = the last time the coin was trained
-            return_null_array: bool = Follower could not find pair metadata
         """
 
         pair_dict = self.pair_dict.get(pair)
-        data_path_set = self.pair_dict.get(pair, self.empty_pair_dict).get("data_path", "")
-        return_null_array = False
 
         if pair_dict:
             model_filename = pair_dict["model_filename"]
             trained_timestamp = pair_dict["trained_timestamp"]
-        elif not self.follow_mode:
+        else:
             self.pair_dict[pair] = self.empty_pair_dict.copy()
             model_filename = ""
             trained_timestamp = 0
 
-        if not data_path_set and self.follow_mode:
-            logger.warning(
-                f"Follower could not find current pair {pair} in "
-                f"pair_dictionary at path {self.full_path}, sending null values "
-                "back to strategy."
-            )
-            trained_timestamp = 0
-            model_filename = ''
-            return_null_array = True
-
-        return model_filename, trained_timestamp, return_null_array
+        return model_filename, trained_timestamp
 
     def set_pair_dict_info(self, metadata: dict) -> None:
         pair_in_dict = self.pair_dict.get(metadata["pair"])
@@ -311,7 +285,6 @@ class FreqaiDataDrawer:
             return
         else:
             self.pair_dict[metadata["pair"]] = self.empty_pair_dict.copy()
-
             return
 
     def set_initial_return_values(self, pair: str, pred_df: DataFrame) -> None:
@@ -423,6 +396,12 @@ class FreqaiDataDrawer:
 
     def purge_old_models(self) -> None:
 
+        num_keep = self.freqai_info["purge_old_models"]
+        if not num_keep:
+            return
+        elif type(num_keep) == bool:
+            num_keep = 2
+
         model_folders = [x for x in self.full_path.iterdir() if x.is_dir()]
 
         pattern = re.compile(r"sub-train-(\w+)_(\d{10})")
@@ -445,11 +424,11 @@ class FreqaiDataDrawer:
                 delete_dict[coin]["timestamps"][int(timestamp)] = dir
 
         for coin in delete_dict:
-            if delete_dict[coin]["num_folders"] > 2:
+            if delete_dict[coin]["num_folders"] > num_keep:
                 sorted_dict = collections.OrderedDict(
                     sorted(delete_dict[coin]["timestamps"].items())
                 )
-                num_delete = len(sorted_dict) - 2
+                num_delete = len(sorted_dict) - num_keep
                 deleted = 0
                 for k, v in sorted_dict.items():
                     if deleted >= num_delete:
@@ -458,12 +437,6 @@ class FreqaiDataDrawer:
                     shutil.rmtree(v)
                     deleted += 1
 
-    def update_follower_metadata(self):
-        # follower needs to load from disk to get any changes made by leader to pair_dict
-        self.load_drawer_from_disk()
-        if self.config.get("freqai", {}).get("purge_old_models", False):
-            self.purge_old_models()
-
     def save_metadata(self, dk: FreqaiDataKitchen) -> None:
         """
         Saves only metadata for backtesting studies if user prefers
@@ -481,7 +454,7 @@ class FreqaiDataDrawer:
         dk.data["training_features_list"] = list(dk.data_dictionary["train_features"].columns)
         dk.data["label_list"] = dk.label_list
 
-        with open(save_path / f"{dk.model_filename}_metadata.json", "w") as fp:
+        with (save_path / f"{dk.model_filename}_metadata.json").open("w") as fp:
             rapidjson.dump(dk.data, fp, default=self.np_encoder, number_mode=rapidjson.NM_NATIVE)
 
         return
@@ -514,7 +487,7 @@ class FreqaiDataDrawer:
         dk.data["training_features_list"] = dk.training_features_list
         dk.data["label_list"] = dk.label_list
         # store the metadata
-        with open(save_path / f"{dk.model_filename}_metadata.json", "w") as fp:
+        with (save_path / f"{dk.model_filename}_metadata.json").open("w") as fp:
             rapidjson.dump(dk.data, fp, default=self.np_encoder, number_mode=rapidjson.NM_NATIVE)
 
         # save the train data to file so we can check preds for area of applicability later
@@ -528,7 +501,7 @@ class FreqaiDataDrawer:
 
         if self.freqai_info["feature_parameters"].get("principal_component_analysis"):
             cloudpickle.dump(
-                dk.pca, open(dk.data_path / f"{dk.model_filename}_pca_object.pkl", "wb")
+                dk.pca, (dk.data_path / f"{dk.model_filename}_pca_object.pkl").open("wb")
             )
 
         self.model_dictionary[coin] = model
@@ -548,7 +521,7 @@ class FreqaiDataDrawer:
         Load only metadata into datakitchen to increase performance during
         presaved backtesting (prediction file loading).
         """
-        with open(dk.data_path / f"{dk.model_filename}_metadata.json", "r") as fp:
+        with (dk.data_path / f"{dk.model_filename}_metadata.json").open("r") as fp:
             dk.data = rapidjson.load(fp, number_mode=rapidjson.NM_NATIVE)
             dk.training_features_list = dk.data["training_features_list"]
             dk.label_list = dk.data["label_list"]
@@ -571,7 +544,7 @@ class FreqaiDataDrawer:
             dk.data = self.meta_data_dictionary[coin]["meta_data"]
             dk.data_dictionary["train_features"] = self.meta_data_dictionary[coin]["train_df"]
         else:
-            with open(dk.data_path / f"{dk.model_filename}_metadata.json", "r") as fp:
+            with (dk.data_path / f"{dk.model_filename}_metadata.json").open("r") as fp:
                 dk.data = rapidjson.load(fp, number_mode=rapidjson.NM_NATIVE)
 
             dk.data_dictionary["train_features"] = pd.read_pickle(
@@ -609,7 +582,7 @@ class FreqaiDataDrawer:
 
         if self.config["freqai"]["feature_parameters"]["principal_component_analysis"]:
             dk.pca = cloudpickle.load(
-                open(dk.data_path / f"{dk.model_filename}_pca_object.pkl", "rb")
+                (dk.data_path / f"{dk.model_filename}_pca_object.pkl").open("rb")
             )
 
         return model
@@ -627,12 +600,12 @@ class FreqaiDataDrawer:
 
             for pair in dk.all_pairs:
                 for tf in feat_params.get("include_timeframes"):
-
+                    hist_df = history_data[pair][tf]
                     # check if newest candle is already appended
                     df_dp = strategy.dp.get_pair_dataframe(pair, tf)
                     if len(df_dp.index) == 0:
                         continue
-                    if str(history_data[pair][tf].iloc[-1]["date"]) == str(
+                    if str(hist_df.iloc[-1]["date"]) == str(
                         df_dp.iloc[-1:]["date"].iloc[-1]
                     ):
                         continue
@@ -640,21 +613,30 @@ class FreqaiDataDrawer:
                     try:
                         index = (
                             df_dp.loc[
-                                df_dp["date"] == history_data[pair][tf].iloc[-1]["date"]
+                                df_dp["date"] == hist_df.iloc[-1]["date"]
                             ].index[0]
                             + 1
                         )
                     except IndexError:
-                        logger.warning(
-                            f"Unable to update pair history for {pair}. "
-                            "If this does not resolve itself after 1 additional candle, "
-                            "please report the error to #freqai discord channel"
-                        )
-                        return
+                        if hist_df.iloc[-1]['date'] < df_dp['date'].iloc[0]:
+                            raise OperationalException("In memory historical data is older than "
+                                                       f"oldest DataProvider candle for {pair} on "
+                                                       f"timeframe {tf}")
+                        else:
+                            index = -1
+                            logger.warning(
+                                f"No common dates in historical data and dataprovider for {pair}. "
+                                f"Appending latest dataprovider candle to historical data "
+                                "but please be aware that there is likely a gap in the historical "
+                                "data. \n"
+                                f"Historical data ends at {hist_df.iloc[-1]['date']} "
+                                f"while dataprovider starts at {df_dp['date'].iloc[0]} and"
+                                f"ends at {df_dp['date'].iloc[0]}."
+                            )
 
                     history_data[pair][tf] = pd.concat(
                         [
-                            history_data[pair][tf],
+                            hist_df,
                             df_dp.iloc[index:],
                         ],
                         ignore_index=True,
@@ -723,7 +705,7 @@ class FreqaiDataDrawer:
         Returns timerange information based on historic predictions file
         :return: timerange calculated from saved live data
         """
-        if not self.historic_predictions_path.is_file():
+        if not self.historic_predictions_folder.exists():
             raise OperationalException(
                 'Historic predictions not found. Historic predictions data is required '
                 'to run backtest with the freqai-backtest-live-models option '

freqtrade/freqai/data_kitchen.py

@@ -1,6 +1,7 @@
 import copy
 import inspect
 import logging
+import random
 import shutil
 from datetime import datetime, timezone
 from math import cos, sin
@@ -170,6 +171,19 @@ class FreqaiDataKitchen:
             train_labels = labels
             train_weights = weights
 
+        if feat_dict["shuffle_after_split"]:
+            rint1 = random.randint(0, 100)
+            rint2 = random.randint(0, 100)
+            train_features = train_features.sample(
+                frac=1, random_state=rint1).reset_index(drop=True)
+            train_labels = train_labels.sample(frac=1, random_state=rint1).reset_index(drop=True)
+            train_weights = pd.DataFrame(train_weights).sample(
+                frac=1, random_state=rint1).reset_index(drop=True).to_numpy()[:, 0]
+            test_features = test_features.sample(frac=1, random_state=rint2).reset_index(drop=True)
+            test_labels = test_labels.sample(frac=1, random_state=rint2).reset_index(drop=True)
+            test_weights = pd.DataFrame(test_weights).sample(
+                frac=1, random_state=rint2).reset_index(drop=True).to_numpy()[:, 0]
+
         # Simplest way to reverse the order of training and test data:
         if self.freqai_config['feature_parameters'].get('reverse_train_test_order', False):
             return self.build_data_dictionary(
@@ -237,7 +251,7 @@ class FreqaiDataKitchen:
                 (drop_index == 0) & (drop_index_labels == 0)
             ]
             logger.info(
-                f"dropped {len(unfiltered_df) - len(filtered_df)} training points"
+                f"{self.pair}: dropped {len(unfiltered_df) - len(filtered_df)} training points"
                 f" due to NaNs in populated dataset {len(unfiltered_df)}."
             )
             if (1 - len(filtered_df) / len(unfiltered_df)) > 0.1 and self.live:
@@ -661,7 +675,7 @@ class FreqaiDataKitchen:
                 ]
 
             logger.info(
-                f"SVM tossed {len(y_pred) - kept_points.sum()}"
+                f"{self.pair}: SVM tossed {len(y_pred) - kept_points.sum()}"
                 f" test points from {len(y_pred)} total points."
             )
 
@@ -935,7 +949,7 @@ class FreqaiDataKitchen:
 
         if (len(do_predict) - do_predict.sum()) > 0:
             logger.info(
-                f"DI tossed {len(do_predict) - do_predict.sum()} predictions for "
+                f"{self.pair}: DI tossed {len(do_predict) - do_predict.sum()} predictions for "
                 "being too far from training data."
             )
 
@@ -1247,17 +1261,19 @@ class FreqaiDataKitchen:
         tfs: List[str] = self.freqai_config["feature_parameters"].get("include_timeframes")
 
         for tf in tfs:
+            metadata = {"pair": pair, "tf": tf}
             informative_df = self.get_pair_data_for_features(
                 pair, tf, strategy, corr_dataframes, base_dataframes, is_corr_pairs)
             informative_copy = informative_df.copy()
 
             for t in self.freqai_config["feature_parameters"]["indicator_periods_candles"]:
                 df_features = strategy.feature_engineering_expand_all(
-                    informative_copy.copy(), t)
+                    informative_copy.copy(), t, metadata=metadata)
                 suffix = f"{t}"
                 informative_df = self.merge_features(informative_df, df_features, tf, tf, suffix)
 
-            generic_df = strategy.feature_engineering_expand_basic(informative_copy.copy())
+            generic_df = strategy.feature_engineering_expand_basic(
+                informative_copy.copy(), metadata=metadata)
             suffix = "gen"
 
             informative_df = self.merge_features(informative_df, generic_df, tf, tf, suffix)
@@ -1299,123 +1315,54 @@ class FreqaiDataKitchen:
         dataframe: DataFrame = dataframe containing populated indicators
         """
 
-        # this is a hack to check if the user is using the populate_any_indicators function
+        # check if the user is using the deprecated populate_any_indicators function
         new_version = inspect.getsource(strategy.populate_any_indicators) == (
             inspect.getsource(IStrategy.populate_any_indicators))
 
-        if new_version:
-            tfs: List[str] = self.freqai_config["feature_parameters"].get("include_timeframes")
-            pairs: List[str] = self.freqai_config["feature_parameters"].get(
-                "include_corr_pairlist", [])
+        if not new_version:
+            raise OperationalException(
+                "You are using the `populate_any_indicators()` function"
+                " which was deprecated on March 1, 2023. Please refer "
+                "to the strategy migration guide to use the new "
+                "feature_engineering_* methods: \n"
+                "https://www.freqtrade.io/en/stable/strategy_migration/#freqai-strategy \n"
+                "And the feature_engineering_* documentation: \n"
+                "https://www.freqtrade.io/en/latest/freqai-feature-engineering/"
+                )
 
-            for tf in tfs:
-                if tf not in base_dataframes:
-                    base_dataframes[tf] = pd.DataFrame()
-                for p in pairs:
-                    if p not in corr_dataframes:
-                        corr_dataframes[p] = {}
-                    if tf not in corr_dataframes[p]:
-                        corr_dataframes[p][tf] = pd.DataFrame()
-
-            if not prediction_dataframe.empty:
-                dataframe = prediction_dataframe.copy()
-            else:
-                dataframe = base_dataframes[self.config["timeframe"]].copy()
-
-            corr_pairs: List[str] = self.freqai_config["feature_parameters"].get(
-                "include_corr_pairlist", [])
-            dataframe = self.populate_features(dataframe.copy(), pair, strategy,
-                                               corr_dataframes, base_dataframes)
-
-            dataframe = strategy.feature_engineering_standard(dataframe.copy())
-            # ensure corr pairs are always last
-            for corr_pair in corr_pairs:
-                if pair == corr_pair:
-                    continue  # dont repeat anything from whitelist
-                if corr_pairs and do_corr_pairs:
-                    dataframe = self.populate_features(dataframe.copy(), corr_pair, strategy,
-                                                       corr_dataframes, base_dataframes, True)
-
-            dataframe = strategy.set_freqai_targets(dataframe.copy())
-
-            self.get_unique_classes_from_labels(dataframe)
-
-            dataframe = self.remove_special_chars_from_feature_names(dataframe)
-
-            if self.config.get('reduce_df_footprint', False):
-                dataframe = reduce_dataframe_footprint(dataframe)
-
-            return dataframe
-
-        else:
-            # the user is using the populate_any_indicators functions which is deprecated
-
-            df = self.use_strategy_to_populate_indicators_old_version(
-                strategy, corr_dataframes, base_dataframes, pair,
-                prediction_dataframe, do_corr_pairs)
-            return df
-
-    def use_strategy_to_populate_indicators_old_version(
-        self,
-        strategy: IStrategy,
-        corr_dataframes: dict = {},
-        base_dataframes: dict = {},
-        pair: str = "",
-        prediction_dataframe: DataFrame = pd.DataFrame(),
-        do_corr_pairs: bool = True,
-    ) -> DataFrame:
-        """
-        Use the user defined strategy for populating indicators during retrain
-        :param strategy: IStrategy = user defined strategy object
-        :param corr_dataframes: dict = dict containing the df pair dataframes
-                                (for user defined timeframes)
-        :param base_dataframes: dict = dict containing the current pair dataframes
-                                (for user defined timeframes)
-        :param metadata: dict = strategy furnished pair metadata
-        :return:
-        dataframe: DataFrame = dataframe containing populated indicators
-        """
-
-        # for prediction dataframe creation, we let dataprovider handle everything in the strategy
-        # so we create empty dictionaries, which allows us to pass None to
-        # `populate_any_indicators()`. Signaling we want the dp to give us the live dataframe.
         tfs: List[str] = self.freqai_config["feature_parameters"].get("include_timeframes")
-        pairs: List[str] = self.freqai_config["feature_parameters"].get("include_corr_pairlist", [])
+        pairs: List[str] = self.freqai_config["feature_parameters"].get(
+            "include_corr_pairlist", [])
+
+        for tf in tfs:
+            if tf not in base_dataframes:
+                base_dataframes[tf] = pd.DataFrame()
+            for p in pairs:
+                if p not in corr_dataframes:
+                    corr_dataframes[p] = {}
+                if tf not in corr_dataframes[p]:
+                    corr_dataframes[p][tf] = pd.DataFrame()
+
         if not prediction_dataframe.empty:
             dataframe = prediction_dataframe.copy()
-            for tf in tfs:
-                base_dataframes[tf] = None
-                for p in pairs:
-                    if p not in corr_dataframes:
-                        corr_dataframes[p] = {}
-                    corr_dataframes[p][tf] = None
         else:
             dataframe = base_dataframes[self.config["timeframe"]].copy()
 
-        sgi = False
-        for tf in tfs:
-            if tf == tfs[-1]:
-                sgi = True  # doing this last allows user to use all tf raw prices in labels
-            dataframe = strategy.populate_any_indicators(
-                pair,
-                dataframe.copy(),
-                tf,
-                informative=base_dataframes[tf],
-                set_generalized_indicators=sgi
-            )
-
+        corr_pairs: List[str] = self.freqai_config["feature_parameters"].get(
+            "include_corr_pairlist", [])
+        dataframe = self.populate_features(dataframe.copy(), pair, strategy,
+                                           corr_dataframes, base_dataframes)
+        metadata = {"pair": pair}
+        dataframe = strategy.feature_engineering_standard(dataframe.copy(), metadata=metadata)
         # ensure corr pairs are always last
-        for corr_pair in pairs:
+        for corr_pair in corr_pairs:
             if pair == corr_pair:
                 continue  # dont repeat anything from whitelist
-            for tf in tfs:
-                if pairs and do_corr_pairs:
-                    dataframe = strategy.populate_any_indicators(
-                        corr_pair,
-                        dataframe.copy(),
-                        tf,
-                        informative=corr_dataframes[corr_pair][tf]
-                    )
+            if corr_pairs and do_corr_pairs:
+                dataframe = self.populate_features(dataframe.copy(), corr_pair, strategy,
+                                                   corr_dataframes, base_dataframes, True)
+
+        dataframe = strategy.set_freqai_targets(dataframe.copy(), metadata=metadata)
 
         self.get_unique_classes_from_labels(dataframe)
 
@@ -1546,3 +1493,25 @@ class FreqaiDataKitchen:
             dataframe.columns = dataframe.columns.str.replace(c, "")
 
         return dataframe
+
+    def buffer_timerange(self, timerange: TimeRange):
+        """
+        Buffer the start and end of the timerange. This is used *after* the indicators
+        are populated.
+
+        The main example use is when predicting maxima and minima, the argrelextrema
+        function  cannot know the maxima/minima at the edges of the timerange. To improve
+        model accuracy, it is best to compute argrelextrema on the full timerange
+        and then use this function to cut off the edges (buffer) by the kernel.
+
+        In another case, if the targets are set to a shifted price movement, this
+        buffer is unnecessary because the shifted candles at the end of the timerange
+        will be NaN and FreqAI will automatically cut those off of the training
+        dataset.
+        """
+        buffer = self.freqai_config["feature_parameters"]["buffer_train_data_candles"]
+        if buffer:
+            timerange.stopts -= buffer * timeframe_to_seconds(self.config["timeframe"])
+            timerange.startts += buffer * timeframe_to_seconds(self.config["timeframe"])
+
+        return timerange

freqtrade/freqai/freqai_interface.py

@@ -1,4 +1,3 @@
-import inspect
 import logging
 import threading
 import time
@@ -66,12 +65,11 @@ class IFreqaiModel(ABC):
         self.retrain = False
         self.first = True
         self.set_full_path()
-        self.follow_mode: bool = self.freqai_info.get("follow_mode", False)
         self.save_backtest_models: bool = self.freqai_info.get("save_backtest_models", True)
         if self.save_backtest_models:
             logger.info('Backtesting module configured to save all models.')
 
-        self.dd = FreqaiDataDrawer(Path(self.full_path), self.config, self.follow_mode)
+        self.dd = FreqaiDataDrawer(Path(self.full_path), self.config)
         # set current candle to arbitrary historical date
         self.current_candle: datetime = datetime.fromtimestamp(637887600, tz=timezone.utc)
         self.dd.current_candle = self.current_candle
@@ -106,8 +104,7 @@ class IFreqaiModel(ABC):
         self.data_provider: Optional[DataProvider] = None
         self.max_system_threads = max(int(psutil.cpu_count() * 2 - 2), 1)
         self.can_short = True  # overridden in start() with strategy.can_short
-
-        self.warned_deprecated_populate_any_indicators = False
+        self.model: Any = None
 
         record_params(config, self.full_path)
 
@@ -139,9 +136,6 @@ class IFreqaiModel(ABC):
         self.data_provider = strategy.dp
         self.can_short = strategy.can_short
 
-        # check if the strategy has deprecated populate_any_indicators function
-        self.check_deprecated_populate_any_indicators(strategy)
-
         if self.live:
             self.inference_timer('start')
             self.dk = FreqaiDataKitchen(self.config, self.live, metadata["pair"])
@@ -153,7 +147,7 @@ class IFreqaiModel(ABC):
         # (backtest window, i.e. window immediately following the training window).
         # FreqAI slides the window and sequentially builds the backtesting results before returning
         # the concatenated results for the full backtesting period back to the strategy.
-        elif not self.follow_mode:
+        else:
             self.dk = FreqaiDataKitchen(self.config, self.live, metadata["pair"])
             if not self.config.get("freqai_backtest_live_models", False):
                 logger.info(f"Training {len(self.dk.training_timeranges)} timeranges")
@@ -228,7 +222,7 @@ class IFreqaiModel(ABC):
                 logger.warning(f'{pair} not in current whitelist, removing from train queue.')
                 continue
 
-            (_, trained_timestamp, _) = self.dd.get_pair_dict_info(pair)
+            (_, trained_timestamp) = self.dd.get_pair_dict_info(pair)
 
             dk = FreqaiDataKitchen(self.config, self.live, pair)
             (
@@ -286,7 +280,7 @@ class IFreqaiModel(ABC):
         # following tr_train. Both of these windows slide through the
         # entire backtest
         for tr_train, tr_backtest in zip(dk.training_timeranges, dk.backtesting_timeranges):
-            (_, _, _) = self.dd.get_pair_dict_info(pair)
+            (_, _) = self.dd.get_pair_dict_info(pair)
             train_it += 1
             total_trains = len(dk.backtesting_timeranges)
             self.training_timerange = tr_train
@@ -325,9 +319,13 @@ class IFreqaiModel(ABC):
                     populate_indicators = False
 
                 dataframe_base_train = dataframe.loc[dataframe["date"] < tr_train.stopdt, :]
-                dataframe_base_train = strategy.set_freqai_targets(dataframe_base_train)
+                dataframe_base_train = strategy.set_freqai_targets(
+                    dataframe_base_train, metadata=metadata)
                 dataframe_base_backtest = dataframe.loc[dataframe["date"] < tr_backtest.stopdt, :]
-                dataframe_base_backtest = strategy.set_freqai_targets(dataframe_base_backtest)
+                dataframe_base_backtest = strategy.set_freqai_targets(
+                    dataframe_base_backtest, metadata=metadata)
+
+                tr_train = dk.buffer_timerange(tr_train)
 
                 dataframe_train = dk.slice_dataframe(tr_train, dataframe_base_train)
                 dataframe_backtest = dk.slice_dataframe(tr_backtest, dataframe_base_backtest)
@@ -341,13 +339,14 @@ class IFreqaiModel(ABC):
                     except Exception as msg:
                         logger.warning(
                             f"Training {pair} raised exception {msg.__class__.__name__}. "
-                            f"Message: {msg}, skipping.")
+                            f"Message: {msg}, skipping.", exc_info=True)
+                        self.model = None
 
                     self.dd.pair_dict[pair]["trained_timestamp"] = int(
                         tr_train.stopts)
-                    if self.plot_features:
+                    if self.plot_features and self.model is not None:
                         plot_feature_importance(self.model, pair, dk, self.plot_features)
-                    if self.save_backtest_models:
+                    if self.save_backtest_models and self.model is not None:
                         logger.info('Saving backtest model to disk.')
                         self.dd.save_data(self.model, pair, dk)
                     else:
@@ -379,18 +378,9 @@ class IFreqaiModel(ABC):
         :returns:
         dk: FreqaiDataKitchen = Data management/analysis tool associated to present pair only
         """
-        # update follower
-        if self.follow_mode:
-            self.dd.update_follower_metadata()
 
         # get the model metadata associated with the current pair
-        (_, trained_timestamp, return_null_array) = self.dd.get_pair_dict_info(metadata["pair"])
-
-        # if the metadata doesn't exist, the follower returns null arrays to strategy
-        if self.follow_mode and return_null_array:
-            logger.info("Returning null array from follower to strategy")
-            self.dd.return_null_values_to_strategy(dataframe, dk)
-            return dk
+        (_, trained_timestamp) = self.dd.get_pair_dict_info(metadata["pair"])
 
         # append the historic data once per round
         if self.dd.historic_data:
@@ -398,27 +388,18 @@ class IFreqaiModel(ABC):
             logger.debug(f'Updating historic data on pair {metadata["pair"]}')
             self.track_current_candle()
 
-        if not self.follow_mode:
+        (_, new_trained_timerange, data_load_timerange) = dk.check_if_new_training_required(
+            trained_timestamp
+        )
+        dk.set_paths(metadata["pair"], new_trained_timerange.stopts)
 
-            (_, new_trained_timerange, data_load_timerange) = dk.check_if_new_training_required(
-                trained_timestamp
-            )
-            dk.set_paths(metadata["pair"], new_trained_timerange.stopts)
+        # load candle history into memory if it is not yet.
+        if not self.dd.historic_data:
+            self.dd.load_all_pair_histories(data_load_timerange, dk)
 
-            # load candle history into memory if it is not yet.
-            if not self.dd.historic_data:
-                self.dd.load_all_pair_histories(data_load_timerange, dk)
-
-            if not self.scanning:
-                self.scanning = True
-                self.start_scanning(strategy)
-
-        elif self.follow_mode:
-            dk.set_paths(metadata["pair"], trained_timestamp)
-            logger.info(
-                "FreqAI instance set to follow_mode, finding existing pair "
-                f"using { self.identifier }"
-            )
+        if not self.scanning:
+            self.scanning = True
+            self.start_scanning(strategy)
 
         # load the model and associated data into the data kitchen
         self.model = self.dd.load_data(metadata["pair"], dk)
@@ -506,7 +487,7 @@ class IFreqaiModel(ABC):
                 "strategy is furnishing the same features as the pretrained"
                 "model. In case of --strategy-list, please be aware that FreqAI "
                 "requires all strategies to maintain identical "
-                "populate_any_indicator() functions"
+                "feature_engineering_* functions"
             )
 
     def data_cleaning_train(self, dk: FreqaiDataKitchen) -> None:
@@ -580,7 +561,13 @@ class IFreqaiModel(ABC):
         :return:
         :boolean: whether the model file exists or not.
         """
-        path_to_modelfile = Path(dk.data_path / f"{dk.model_filename}_model.joblib")
+        if self.dd.model_type == 'joblib':
+            file_type = ".joblib"
+        elif self.dd.model_type == 'keras':
+            file_type = ".h5"
+        elif 'stable_baselines' in self.dd.model_type or 'sb3_contrib' == self.dd.model_type:
+            file_type = ".zip"
+        path_to_modelfile = Path(dk.data_path / f"{dk.model_filename}_model{file_type}")
         file_exists = path_to_modelfile.is_file()
         if file_exists:
             logger.info("Found model at %s", dk.data_path / dk.model_filename)
@@ -612,7 +599,7 @@ class IFreqaiModel(ABC):
         :param strategy: IStrategy = user defined strategy object
         :param dk: FreqaiDataKitchen = non-persistent data container for current coin/loop
         :param data_load_timerange: TimeRange = the amount of data to be loaded
-                                    for populate_any_indicators
+                                    for populating indicators
                                     (larger than new_trained_timerange so that
                                     new_trained_timerange does not contain any NaNs)
         """
@@ -625,6 +612,8 @@ class IFreqaiModel(ABC):
             strategy, corr_dataframes, base_dataframes, pair
         )
 
+        new_trained_timerange = dk.buffer_timerange(new_trained_timerange)
+
         unfiltered_dataframe = dk.slice_dataframe(new_trained_timerange, unfiltered_dataframe)
 
         # find the features indicated by strategy and store in datakitchen
@@ -640,8 +629,7 @@ class IFreqaiModel(ABC):
         if self.plot_features:
             plot_feature_importance(model, pair, dk, self.plot_features)
 
-        if self.freqai_info.get("purge_old_models", False):
-            self.dd.purge_old_models()
+        self.dd.purge_old_models()
 
     def set_initial_historic_predictions(
         self, pred_df: DataFrame, dk: FreqaiDataKitchen, pair: str, strat_df: DataFrame
@@ -817,7 +805,7 @@ class IFreqaiModel(ABC):
                 logger.warning("Couldn't cache corr_pair dataframes for improved performance. "
                                "Consider ensuring that the full coin/stake, e.g. XYZ/USD, "
                                "is included in the column names when you are creating features "
-                               "in `populate_any_indicators()`.")
+                               "in `feature_engineering_*` functions.")
             self.get_corr_dataframes = not bool(self.corr_dataframes)
         elif self.corr_dataframes:
             dataframe = dk.attach_corr_pair_columns(
@@ -944,26 +932,6 @@ class IFreqaiModel(ABC):
             dk.return_dataframe, saved_dataframe, how='left', left_on='date', right_on="date_pred")
         return dk
 
-    def check_deprecated_populate_any_indicators(self, strategy: IStrategy):
-        """
-        Check and warn if the deprecated populate_any_indicators function is used.
-        :param strategy: strategy object
-        """
-
-        if not self.warned_deprecated_populate_any_indicators:
-            self.warned_deprecated_populate_any_indicators = True
-            old_version = inspect.getsource(strategy.populate_any_indicators) != (
-                inspect.getsource(IStrategy.populate_any_indicators))
-
-            if old_version:
-                logger.warning("DEPRECATION WARNING: "
-                               "You are using the deprecated populate_any_indicators function. "
-                               "This function will raise an error on March 1 2023. "
-                               "Please update your strategy by using "
-                               "the new feature_engineering functions. See \n"
-                               "https://www.freqtrade.io/en/latest/freqai-feature-engineering/"
-                               "for details.")
-
     # Following methods which are overridden by user made prediction models.
     # See freqai/prediction_models/CatboostPredictionModel.py for an example.
 

freqtrade/freqai/prediction_models/ReinforcementLearner.py

@@ -100,7 +100,7 @@ class ReinforcementLearner(BaseReinforcementLearningModel):
             """
             # first, penalize if the action is not valid
             if not self._is_valid(action):
-                self.tensorboard_log("is_valid")
+                self.tensorboard_log("invalid", category="actions")
                 return -2
 
             pnl = self.get_unrealized_profit()

freqtrade/freqai/prediction_models/ReinforcementLearner_multiproc.py

@@ -34,7 +34,12 @@ class ReinforcementLearner_multiproc(ReinforcementLearner):
         train_df = data_dictionary["train_features"]
         test_df = data_dictionary["test_features"]
 
-        env_info = self.pack_env_dict()
+        if self.train_env:
+            self.train_env.close()
+        if self.eval_env:
+            self.eval_env.close()
+
+        env_info = self.pack_env_dict(dk.pair)
 
         env_id = "train_env"
         self.train_env = SubprocVecEnv([make_env(self.MyRLEnv, env_id, i, 1,

freqtrade/freqai/utils.py

@@ -211,7 +211,7 @@ def record_params(config: Dict[str, Any], full_path: Path) -> None:
         "pairs": config.get('exchange', {}).get('pair_whitelist')
     }
 
-    with open(params_record_path, "w") as handle:
+    with params_record_path.open("w") as handle:
         rapidjson.dump(
             run_params,
             handle,

freqtrade/freqtradebot.py

@@ -127,19 +127,19 @@ class FreqtradeBot(LoggingMixin):
                 for minutes in [0, 15, 30, 45]:
                     t = str(time(time_slot, minutes, 2))
                     self._schedule.every().day.at(t).do(update)
-        self.last_process = datetime(1970, 1, 1, tzinfo=timezone.utc)
+        self.last_process: Optional[datetime] = None
 
         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:
+    def notify_status(self, msg: str, msg_type=RPCMessageType.STATUS) -> None:
         """
         Public method for users of this class (worker, etc.) to send notifications
         via RPC about changes in the bot status.
         """
         self.rpc.send_msg({
-            'type': RPCMessageType.STATUS,
+            'type': msg_type,
             'status': msg
         })
 
@@ -344,7 +344,15 @@ class FreqtradeBot(LoggingMixin):
             try:
                 fo = self.exchange.fetch_order_or_stoploss_order(order.order_id, order.ft_pair,
                                                                  order.ft_order_side == 'stoploss')
-
+                if not order.trade:
+                    # This should not happen, but it does if trades were deleted manually.
+                    # This can only incur on sqlite, which doesn't enforce foreign constraints.
+                    logger.warning(
+                        f"Order {order.order_id} has no trade attached. "
+                        "This may suggest a database corruption. "
+                        f"The expected trade ID is {order.ft_trade_id}. Ignoring this order."
+                    )
+                    continue
                 self.update_trade_state(order.trade, order.order_id, fo,
                                         stoploss_order=(order.ft_order_side == 'stoploss'))
 
@@ -355,7 +363,7 @@ class FreqtradeBot(LoggingMixin):
                         "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)
+                    self.handle_cancel_order(fo, order.trade, constants.CANCEL_REASON['TIMEOUT'])
 
             except ExchangeError as e:
 
@@ -578,7 +586,7 @@ class FreqtradeBot(LoggingMixin):
 
         min_entry_stake = self.exchange.get_min_pair_stake_amount(trade.pair,
                                                                   current_entry_rate,
-                                                                  self.strategy.stoploss)
+                                                                  0.0)
         min_exit_stake = self.exchange.get_min_pair_stake_amount(trade.pair,
                                                                  current_exit_rate,
                                                                  self.strategy.stoploss)
@@ -586,7 +594,7 @@ class FreqtradeBot(LoggingMixin):
         stake_available = self.wallets.get_available_stake_amount()
         logger.debug(f"Calling adjust_trade_position for pair {trade.pair}")
         stake_amount = strategy_safe_wrapper(self.strategy.adjust_trade_position,
-                                             default_retval=None)(
+                                             default_retval=None, supress_error=True)(
             trade=trade,
             current_time=datetime.now(timezone.utc), current_rate=current_entry_rate,
             current_profit=current_entry_profit, min_stake=min_entry_stake,
@@ -625,7 +633,7 @@ class FreqtradeBot(LoggingMixin):
                 return
 
             remaining = (trade.amount - amount) * current_exit_rate
-            if remaining < min_exit_stake:
+            if min_exit_stake and remaining < min_exit_stake:
                 logger.info(f"Remaining amount of {remaining} would be smaller "
                             f"than the minimum of {min_exit_stake}.")
                 return
@@ -692,7 +700,8 @@ class FreqtradeBot(LoggingMixin):
         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, order_adjust, leverage_)
+            pair, price, stake_amount, trade_side, enter_tag, trade, order_adjust, leverage_,
+            pos_adjust)
 
         if not stake_amount:
             return False
@@ -750,13 +759,15 @@ class FreqtradeBot(LoggingMixin):
                                self.exchange.name, order['filled'], order['amount'],
                                order['remaining']
                                )
-                amount = safe_value_fallback(order, 'filled', 'amount')
-                enter_limit_filled_price = safe_value_fallback(order, 'average', 'price')
+                amount = safe_value_fallback(order, 'filled', 'amount', amount)
+                enter_limit_filled_price = safe_value_fallback(
+                    order, 'average', 'price', enter_limit_filled_price)
 
         # in case of FOK the order may be filled immediately and fully
         elif order_status == 'closed':
-            amount = safe_value_fallback(order, 'filled', 'amount')
-            enter_limit_filled_price = safe_value_fallback(order, 'average', 'price')
+            amount = safe_value_fallback(order, 'filled', 'amount', amount)
+            enter_limit_filled_price = safe_value_fallback(
+                order, 'average', 'price', enter_limit_requested)
 
         # Fee is applied twice because we make a LIMIT_BUY and LIMIT_SELL
         fee = self.exchange.get_fee(symbol=pair, taker_or_maker='maker')
@@ -799,6 +810,9 @@ class FreqtradeBot(LoggingMixin):
                 precision_mode=self.exchange.precisionMode,
                 contract_size=self.exchange.get_contract_size(pair),
             )
+            stoploss = self.strategy.stoploss if not self.edge else self.edge.get_stoploss(pair)
+            trade.adjust_stop_loss(trade.open_rate, stoploss, initial=True)
+
         else:
             # This is additional buy, we reset fee_open_currency so timeout checking can work
             trade.is_open = True
@@ -808,7 +822,7 @@ class FreqtradeBot(LoggingMixin):
 
         trade.orders.append(order_obj)
         trade.recalc_trade_from_orders()
-        Trade.query.session.add(trade)
+        Trade.session.add(trade)
         Trade.commit()
 
         # Updating wallets
@@ -831,7 +845,7 @@ class FreqtradeBot(LoggingMixin):
 
     def cancel_stoploss_on_exchange(self, trade: Trade) -> Trade:
         # First cancelling stoploss on exchange ...
-        if self.strategy.order_types.get('stoploss_on_exchange') and trade.stoploss_order_id:
+        if trade.stoploss_order_id:
             try:
                 logger.info(f"Canceling stoploss on exchange for {trade}")
                 co = self.exchange.cancel_stoploss_order_with_result(
@@ -850,7 +864,12 @@ class FreqtradeBot(LoggingMixin):
         trade: Optional[Trade],
         order_adjust: bool,
         leverage_: Optional[float],
+        pos_adjust: bool,
     ) -> Tuple[float, float, float]:
+        """
+        Validate and eventually adjust (within limits) limit, amount and leverage
+        :return: Tuple with (price, amount, leverage)
+        """
 
         if price:
             enter_limit_requested = price
@@ -896,7 +915,9 @@ class FreqtradeBot(LoggingMixin):
         # We do however also need min-stake to determine leverage, therefore this is ignored as
         # edge-case for now.
         min_stake_amount = self.exchange.get_min_pair_stake_amount(
-            pair, enter_limit_requested, self.strategy.stoploss, leverage)
+            pair, enter_limit_requested,
+            self.strategy.stoploss if not pos_adjust else 0.0,
+            leverage)
         max_stake_amount = self.exchange.get_max_pair_stake_amount(
             pair, enter_limit_requested, leverage)
 
@@ -1003,12 +1024,16 @@ class FreqtradeBot(LoggingMixin):
         trades_closed = 0
         for trade in trades:
             try:
+                try:
+                    if (self.strategy.order_types.get('stoploss_on_exchange') and
+                            self.handle_stoploss_on_exchange(trade)):
+                        trades_closed += 1
+                        Trade.commit()
+                        continue
 
-                if (self.strategy.order_types.get('stoploss_on_exchange') and
-                        self.handle_stoploss_on_exchange(trade)):
-                    trades_closed += 1
-                    Trade.commit()
-                    continue
+                except InvalidOrderException as exception:
+                    logger.warning(
+                        f'Unable to handle stoploss on exchange for {trade.pair}: {exception}')
                 # Check if we can sell our current pair
                 if trade.open_order_id is None and trade.is_open and self.handle_trade(trade):
                     trades_closed += 1
@@ -1068,7 +1093,7 @@ class FreqtradeBot(LoggingMixin):
             datetime.now(timezone.utc),
             enter=enter,
             exit_=exit_,
-            force_stoploss=self.edge.stoploss(trade.pair) if self.edge else 0
+            force_stoploss=self.edge.get_stoploss(trade.pair) if self.edge else 0
         )
         for should_exit in exits:
             if should_exit.exit_flag:
@@ -1088,7 +1113,7 @@ class FreqtradeBot(LoggingMixin):
         :return: True if the order succeeded, and False in case of problems.
         """
         try:
-            stoploss_order = self.exchange.stoploss(
+            stoploss_order = self.exchange.create_stoploss(
                 pair=trade.pair,
                 amount=trade.amount,
                 stop_price=stop_price,
@@ -1112,8 +1137,7 @@ class FreqtradeBot(LoggingMixin):
             trade.stoploss_order_id = None
             logger.error(f'Unable to place a stoploss order on exchange. {e}')
             logger.warning('Exiting the trade forcefully')
-            self.execute_trade_exit(trade, stop_price, exit_check=ExitCheckTuple(
-                exit_type=ExitType.EMERGENCY_EXIT))
+            self.emergency_exit(trade, stop_price)
 
         except ExchangeError:
             trade.stoploss_order_id = None
@@ -1160,15 +1184,13 @@ class FreqtradeBot(LoggingMixin):
 
         # If enter order is fulfilled but there is no stoploss, we add a stoploss on exchange
         if not stoploss_order:
-            stoploss = (
-                self.edge.stoploss(pair=trade.pair)
-                if self.edge else
-                trade.stop_loss_pct / trade.leverage
-            )
-            if trade.is_short:
-                stop_price = trade.open_rate * (1 - stoploss)
-            else:
-                stop_price = trade.open_rate * (1 + stoploss)
+            stop_price = trade.stoploss_or_liquidation
+            if self.edge:
+                stoploss = self.edge.get_stoploss(pair=trade.pair)
+                stop_price = (
+                    trade.open_rate * (1 - stoploss) if trade.is_short
+                    else trade.open_rate * (1 + stoploss)
+                )
 
             if self.create_stoploss_order(trade=trade, stop_price=stop_price):
                 # The above will return False if the placement failed and the trade was force-sold.
@@ -1253,11 +1275,11 @@ class FreqtradeBot(LoggingMixin):
             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)
+                    self.handle_cancel_order(order, trade, constants.CANCEL_REASON['TIMEOUT'])
                 else:
                     self.replace_order(order, order_obj, trade)
 
-    def handle_timedout_order(self, order: Dict, trade: Trade) -> None:
+    def handle_cancel_order(self, order: Dict, trade: Trade, reason: str) -> None:
         """
         Check if current analyzed order timed out and cancel if necessary.
         :param order: Order dict grabbed with exchange.fetch_order()
@@ -1265,22 +1287,24 @@ class FreqtradeBot(LoggingMixin):
         :return: None
         """
         if order['side'] == trade.entry_side:
-            self.handle_cancel_enter(trade, order, constants.CANCEL_REASON['TIMEOUT'])
+            self.handle_cancel_enter(trade, order, reason)
         else:
-            canceled = self.handle_cancel_exit(
-                trade, order, constants.CANCEL_REASON['TIMEOUT'])
+            canceled = self.handle_cancel_exit(trade, order, reason)
             canceled_count = trade.get_exit_order_count()
             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['price'],
-                        exit_check=ExitCheckTuple(exit_type=ExitType.EMERGENCY_EXIT))
-                except DependencyException as exception:
-                    logger.warning(
-                        f'Unable to emergency sell trade {trade.pair}: {exception}')
+                self.emergency_exit(trade, order['price'])
+
+    def emergency_exit(self, trade: Trade, price: float) -> None:
+        try:
+            self.execute_trade_exit(
+                trade, price,
+                exit_check=ExitCheckTuple(exit_type=ExitType.EMERGENCY_EXIT))
+        except DependencyException as exception:
+            logger.warning(
+                f'Unable to emergency exit trade {trade.pair}: {exception}')
 
     def replace_order(self, order: Dict, order_obj: Optional[Order], trade: Trade) -> None:
         """
@@ -1307,7 +1331,7 @@ class FreqtradeBot(LoggingMixin):
                                                          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,
+                current_order_rate=order_obj.safe_price, entry_tag=trade.enter_tag,
                 side=trade.entry_side)
 
             replacing = True
@@ -1323,7 +1347,8 @@ class FreqtradeBot(LoggingMixin):
                     # place new order only if new price is supplied
                     self.execute_entry(
                         pair=trade.pair,
-                        stake_amount=(order_obj.remaining * order_obj.price / trade.leverage),
+                        stake_amount=(
+                            order_obj.safe_remaining * order_obj.safe_price / trade.leverage),
                         price=adjusted_entry_price,
                         trade=trade,
                         is_short=trade.is_short,
@@ -1337,6 +1362,8 @@ class FreqtradeBot(LoggingMixin):
         """
 
         for trade in Trade.get_open_order_trades():
+            if not trade.open_order_id:
+                continue
             try:
                 order = self.exchange.fetch_order(trade.open_order_id, trade.pair)
             except (ExchangeError):
@@ -1361,6 +1388,9 @@ class FreqtradeBot(LoggingMixin):
         """
         was_trade_fully_canceled = False
         side = trade.entry_side.capitalize()
+        if not trade.open_order_id:
+            logger.warning(f"No open order for {trade}.")
+            return False
 
         # Cancelled orders may have the status of 'canceled' or 'closed'
         if order['status'] not in constants.NON_OPEN_EXCHANGE_STATES:
@@ -1447,34 +1477,32 @@ class FreqtradeBot(LoggingMixin):
                     return False
 
             try:
-                co = self.exchange.cancel_order_with_result(trade.open_order_id, trade.pair,
-                                                            trade.amount)
+                order = self.exchange.cancel_order_with_result(order['id'], trade.pair,
+                                                               trade.amount)
             except InvalidOrderException:
                 logger.exception(
                     f"Could not cancel {trade.exit_side} order {trade.open_order_id}")
                 return False
-            trade.close_rate = None
-            trade.close_rate_requested = None
-            trade.close_profit = None
-            trade.close_profit_abs = None
+
             # Set exit_reason for fill message
             exit_reason_prev = trade.exit_reason
             trade.exit_reason = trade.exit_reason + f", {reason}" if trade.exit_reason else reason
-            self.update_trade_state(trade, trade.open_order_id, co)
             # Order might be filled above in odd timing issues.
-            if co.get('status') in ('canceled', 'cancelled'):
+            if order.get('status') in ('canceled', 'cancelled'):
                 trade.exit_reason = None
-                trade.open_order_id = None
             else:
                 trade.exit_reason = exit_reason_prev
-
-            logger.info(f'{trade.exit_side.capitalize()} order {reason} for {trade}.')
             cancelled = True
         else:
             reason = constants.CANCEL_REASON['CANCELLED_ON_EXCHANGE']
-            logger.info(f'{trade.exit_side.capitalize()} order {reason} for {trade}.')
-            self.update_trade_state(trade, trade.open_order_id, order)
-            trade.open_order_id = None
+            trade.exit_reason = None
+
+        self.update_trade_state(trade, trade.open_order_id, order)
+
+        logger.info(f'{trade.exit_side.capitalize()} order {reason} for {trade}.')
+        trade.open_order_id = None
+        trade.close_rate = None
+        trade.close_rate_requested = None
 
         self._notify_exit_cancel(
             trade,
@@ -1626,13 +1654,13 @@ class FreqtradeBot(LoggingMixin):
 
         # second condition is for mypy only; order will always be passed during sub trade
         if sub_trade and order is not None:
-            amount = order.safe_filled if fill else order.amount
+            amount = order.safe_filled if fill else order.safe_amount
             order_rate: float = order.safe_price
 
             profit = trade.calc_profit(rate=order_rate, amount=amount, open_rate=trade.open_rate)
             profit_ratio = trade.calc_profit_ratio(order_rate, amount, trade.open_rate)
         else:
-            order_rate = trade.close_rate if trade.close_rate else trade.close_rate_requested
+            order_rate = trade.safe_close_rate
             profit = trade.calc_profit(rate=order_rate) + (0.0 if fill else trade.realized_profit)
             profit_ratio = trade.calc_profit_ratio(order_rate)
             amount = trade.amount
@@ -1687,7 +1715,7 @@ class FreqtradeBot(LoggingMixin):
             raise DependencyException(
                 f"Order_obj not found for {order_id}. This should not have happened.")
 
-        profit_rate = trade.close_rate if trade.close_rate else trade.close_rate_requested
+        profit_rate: float = trade.safe_close_rate
         profit_trade = trade.calc_profit(rate=profit_rate)
         current_rate = self.exchange.get_rate(
             trade.pair, side='exit', is_short=trade.is_short, refresh=False)
@@ -1730,7 +1758,8 @@ class FreqtradeBot(LoggingMixin):
 #
 
     def update_trade_state(
-            self, trade: Trade, order_id: str, action_order: Optional[Dict[str, Any]] = None,
+            self, trade: Trade, order_id: Optional[str],
+            action_order: Optional[Dict[str, Any]] = None,
             stoploss_order: bool = False, send_msg: bool = True) -> bool:
         """
         Checks trades with open orders and updates the amount if necessary
@@ -1789,6 +1818,7 @@ class FreqtradeBot(LoggingMixin):
                     is_short=trade.is_short,
                     amount=trade.amount,
                     stake_amount=trade.stake_amount,
+                    leverage=trade.leverage,
                     wallet_balance=trade.stake_amount,
                 ))
 

freqtrade/leverage/__init__.py

@@ -1,2 +1 @@
-# flake8: noqa: F401
-from freqtrade.leverage.interest import interest
+from freqtrade.leverage.interest import interest  # noqa: F401

freqtrade/loggers.py

@@ -103,9 +103,9 @@ def setup_logging(config: Config) -> None:
             logging.root.addHandler(handler_sl)
         elif s[0] == 'journald':  # pragma: no cover
             try:
-                from systemd.journal import JournaldLogHandler
+                from cysystemd.journal import JournaldLogHandler
             except ImportError:
-                raise OperationalException("You need the systemd python package be installed in "
+                raise OperationalException("You need the cysystemd python package be installed in "
                                            "order to use logging to journald.")
             handler_jd = get_existing_handlers(JournaldLogHandler)
             if handler_jd:

freqtrade/misc.py

@@ -6,8 +6,7 @@ import logging
 import re
 from datetime import datetime
 from pathlib import Path
-from typing import Any, Dict, Iterator, List, Mapping, Optional, Union
-from typing.io import IO
+from typing import Any, Dict, Iterator, List, Mapping, Optional, TextIO, Union
 from urllib.parse import urlparse
 
 import orjson
@@ -81,7 +80,7 @@ def file_dump_json(filename: Path, data: Any, is_zip: bool = False, log: bool =
     else:
         if log:
             logger.info(f'dumping json to "{filename}"')
-        with open(filename, 'w') as fp:
+        with filename.open('w') as fp:
             rapidjson.dump(data, fp, default=str, number_mode=rapidjson.NM_NATIVE)
 
     logger.debug(f'done json to "{filename}"')
@@ -98,12 +97,12 @@ def file_dump_joblib(filename: Path, data: Any, log: bool = True) -> None:
 
     if log:
         logger.info(f'dumping joblib to "{filename}"')
-    with open(filename, 'wb') as fp:
+    with filename.open('wb') as fp:
         joblib.dump(data, fp)
     logger.debug(f'done joblib dump to "{filename}"')
 
 
-def json_load(datafile: IO) -> Any:
+def json_load(datafile: Union[gzip.GzipFile, TextIO]) -> Any:
     """
     load data with rapidjson
     Use this to have a consistent experience,
@@ -112,7 +111,7 @@ def json_load(datafile: IO) -> Any:
     return rapidjson.load(datafile, number_mode=rapidjson.NM_NATIVE)
 
 
-def file_load_json(file):
+def file_load_json(file: Path):
 
     if file.suffix != ".gz":
         gzipfile = file.with_suffix(file.suffix + '.gz')
@@ -125,7 +124,7 @@ def file_load_json(file):
             pairdata = json_load(datafile)
     elif file.is_file():
         logger.debug(f"Loading historical data from file {file}")
-        with open(file) as datafile:
+        with file.open() as datafile:
             pairdata = json_load(datafile)
     else:
         return None

freqtrade/mixins/__init__.py

@@ -1,2 +1 @@
-# flake8: noqa: F401
-from freqtrade.mixins.logging_mixin import LoggingMixin
+from freqtrade.mixins.logging_mixin import LoggingMixin  # noqa: F401

freqtrade/optimize/backtest_caching.py

@@ -29,7 +29,7 @@ def get_strategy_run_id(strategy) -> str:
     # Include _ft_params_from_file - so changing parameter files cause cache eviction
     digest.update(rapidjson.dumps(
         strategy._ft_params_from_file, default=str, number_mode=rapidjson.NM_NAN).encode('utf-8'))
-    with open(strategy.__file__, 'rb') as fp:
+    with Path(strategy.__file__).open('rb') as fp:
         digest.update(fp.read())
     return digest.hexdigest().lower()
 

freqtrade/optimize/backtesting.py

@@ -93,7 +93,7 @@ class Backtesting:
         if self.config.get('strategy_list'):
             if self.config.get('freqai', {}).get('enabled', False):
                 logger.warning("Using --strategy-list with FreqAI REQUIRES all strategies "
-                               "to have identical populate_any_indicators.")
+                               "to have identical feature_engineering_* functions.")
             for strat in list(self.config['strategy_list']):
                 stratconf = deepcopy(self.config)
                 stratconf['strategy'] = strat
@@ -440,11 +440,8 @@ class Backtesting:
                               side_1 * abs(self.strategy.trailing_stop_positive / leverage)))
             else:
                 # Worst case: price ticks tiny bit above open and dives down.
-                stop_rate = row[OPEN_IDX] * (1 - side_1 * abs(trade.stop_loss_pct / leverage))
-                if is_short:
-                    assert stop_rate > row[LOW_IDX]
-                else:
-                    assert stop_rate < row[HIGH_IDX]
+                stop_rate = row[OPEN_IDX] * (1 - side_1 * abs(
+                    (trade.stop_loss_pct or 0.0) / leverage))
 
             # Limit lower-end to candle low to avoid exits below the low.
             # This still remains "worst case" - but "worst realistic case".
@@ -472,7 +469,7 @@ class Backtesting:
             # - (Expected abs profit - open_rate - open_fee) / (fee_close -1)
             roi_rate = trade.open_rate * roi / leverage
             open_fee_rate = side_1 * trade.open_rate * (1 + side_1 * trade.fee_open)
-            close_rate = -(roi_rate + open_fee_rate) / (trade.fee_close - side_1 * 1)
+            close_rate = -(roi_rate + open_fee_rate) / ((trade.fee_close or 0.0) - side_1 * 1)
             if is_short:
                 is_new_roi = row[OPEN_IDX] < close_rate
             else:
@@ -525,7 +522,7 @@ class Backtesting:
         max_stake = self.exchange.get_max_pair_stake_amount(trade.pair, current_rate)
         stake_available = self.wallets.get_available_stake_amount()
         stake_amount = strategy_safe_wrapper(self.strategy.adjust_trade_position,
-                                             default_retval=None)(
+                                             default_retval=None, supress_error=True)(
             trade=trade,  # type: ignore[arg-type]
             current_time=current_date, current_rate=current_rate,
             current_profit=current_profit, min_stake=min_stake,
@@ -563,7 +560,7 @@ class Backtesting:
             pos_trade = self._get_exit_for_signal(trade, row, exit_, amount)
             if pos_trade is not None:
                 order = pos_trade.orders[-1]
-                if self._get_order_filled(order.price, row):
+                if self._get_order_filled(order.ft_price, row):
                     order.close_bt_order(current_date, trade)
                     trade.recalc_trade_from_orders()
                 self.wallets.update()
@@ -664,6 +661,7 @@ class Backtesting:
             side=trade.exit_side,
             order_type=order_type,
             status="open",
+            ft_price=close_rate,
             price=close_rate,
             average=close_rate,
             amount=amount,
@@ -747,7 +745,7 @@ class Backtesting:
             leverage = min(max(leverage, 1.0), max_leverage)
 
         min_stake_amount = self.exchange.get_min_pair_stake_amount(
-            pair, propose_rate, -0.05, leverage=leverage) or 0
+            pair, propose_rate, -0.05 if not pos_adjust else 0.0, leverage=leverage) or 0
         max_stake_amount = self.exchange.get_max_pair_stake_amount(
             pair, propose_rate, leverage=leverage)
         stake_available = self.wallets.get_available_stake_amount()
@@ -868,6 +866,7 @@ class Backtesting:
                 open_rate=propose_rate,
                 amount=amount,
                 stake_amount=trade.stake_amount,
+                leverage=trade.leverage,
                 wallet_balance=trade.stake_amount,
                 is_short=is_short,
             ))
@@ -886,6 +885,7 @@ class Backtesting:
                 order_date=current_time,
                 order_filled_date=current_time,
                 order_update_date=current_time,
+                ft_price=propose_rate,
                 price=propose_rate,
                 average=propose_rate,
                 amount=amount,
@@ -894,7 +894,7 @@ class Backtesting:
                 cost=stake_amount + trade.fee_open,
             )
             trade.orders.append(order)
-            if pos_adjust and self._get_order_filled(order.price, row):
+            if pos_adjust and self._get_order_filled(order.ft_price, row):
                 order.close_bt_order(current_time, trade)
             else:
                 trade.open_order_id = str(self.order_id_counter)
@@ -1007,15 +1007,15 @@ class Backtesting:
         # 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)(
+                                                   default_retval=order.ft_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,
+                proposed_rate=row[OPEN_IDX], current_order_rate=order.ft_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:
+            if requested_rate == order.ft_price:
                 # assumption: there can't be multiple open entry orders at any given time
                 return False
             else:
@@ -1027,7 +1027,8 @@ class Backtesting:
             if requested_rate:
                 self._enter_trade(pair=trade.pair, row=row, trade=trade,
                                   requested_rate=requested_rate,
-                                  requested_stake=(order.remaining * order.price / trade.leverage),
+                                  requested_stake=(
+                                    order.safe_remaining * order.ft_price / trade.leverage),
                                   direction='short' if trade.is_short else 'long')
                 self.replaced_entry_orders += 1
             else:
@@ -1094,7 +1095,7 @@ class Backtesting:
         for trade in list(LocalTrade.bt_trades_open_pp[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):
+            if order and self._get_order_filled(order.ft_price, row):
                 order.close_bt_order(current_time, trade)
                 trade.open_order_id = None
                 self.wallets.update()
@@ -1105,7 +1106,7 @@ 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):
+            if order and self._get_order_filled(order.ft_price, row):
                 order.close_bt_order(current_time, trade)
                 trade.open_order_id = None
                 sub_trade = order.safe_amount_after_fee != trade.amount
@@ -1114,7 +1115,7 @@ class Backtesting:
                     trade.recalc_trade_from_orders()
                 else:
                     trade.close_date = current_time
-                    trade.close(order.price, show_msg=False)
+                    trade.close(order.ft_price, show_msg=False)
 
                     # logger.debug(f"{pair} - Backtesting exit {trade}")
                     LocalTrade.close_bt_trade(trade)

freqtrade/optimize/hyperopt_loss/hyperopt_loss_sharpe_daily.py

@@ -44,7 +44,7 @@ class SharpeHyperOptLossDaily(IHyperOptLoss):
 
         sum_daily = (
             results.resample(resample_freq, on='close_date').agg(
-                {"profit_ratio_after_slippage": sum}).reindex(t_index).fillna(0)
+                {"profit_ratio_after_slippage": 'sum'}).reindex(t_index).fillna(0)
         )
 
         total_profit = sum_daily["profit_ratio_after_slippage"] - risk_free_rate

freqtrade/optimize/hyperopt_loss/hyperopt_loss_sortino_daily.py

@@ -46,7 +46,7 @@ class SortinoHyperOptLossDaily(IHyperOptLoss):
 
         sum_daily = (
             results.resample(resample_freq, on='close_date').agg(
-                {"profit_ratio_after_slippage": sum}).reindex(t_index).fillna(0)
+                {"profit_ratio_after_slippage": 'sum'}).reindex(t_index).fillna(0)
         )
 
         total_profit = sum_daily["profit_ratio_after_slippage"] - minimum_acceptable_return

freqtrade/optimize/hyperopt_tools.py

@@ -1,4 +1,3 @@
-import io
 import logging
 from copy import deepcopy
 from datetime import datetime, timezone
@@ -464,8 +463,8 @@ class HyperoptTools():
             return
 
         try:
-            io.open(csv_file, 'w+').close()
-        except IOError:
+            Path(csv_file).open('w+').close()
+        except OSError:
             logger.error(f"Failed to create CSV file: {csv_file}")
             return