Merge pull request #8224 from freqtrade/new_release

New release 2023.2

.github/workflows/ci.yml

@@ -23,7 +23,7 @@ jobs:
     runs-on: ${{ matrix.os }}
     strategy:
       matrix:
-        os: [ ubuntu-18.04, ubuntu-20.04, ubuntu-22.04 ]
+        os: [ ubuntu-20.04, ubuntu-22.04 ]
         python-version: ["3.8", "3.9", "3.10"]
 
     steps:
@@ -360,6 +360,8 @@ jobs:
         pip install -e .
 
     - name: Tests incl. ccxt compatibility tests
+      env:
+        CI_WEB_PROXY: http://152.67.78.211:13128
       run: |
         pytest --random-order --cov=freqtrade --cov-config=.coveragerc --longrun
 

.pre-commit-config.yaml

@@ -2,33 +2,33 @@
 # 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: "v0.942"
+    rev: "v0.991"
     hooks:
       - id: mypy
         exclude: build_helpers
         additional_dependencies:
-          - types-cachetools==5.2.1
+          - types-cachetools==5.3.0.0
           - types-filelock==3.2.7
-          - types-requests==2.28.11.7
+          - types-requests==2.28.11.13
           - types-tabulate==0.9.0.0
-          - types-python-dateutil==2.8.19.5
+          - types-python-dateutil==2.8.19.6
         # 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/pre-commit/pre-commit-hooks
-    rev: v2.4.0
+    rev: v4.4.0
     hooks:
       - id: end-of-file-fixer
         exclude: |

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_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/publish_docker_arm64.sh

@@ -70,20 +70,21 @@ docker push ${CACHE_IMAGE}:$TAG_ARM
 # Otherwise installation might fail.
 echo "create manifests"
 
-docker manifest create --amend ${IMAGE_NAME}:${TAG} ${CACHE_IMAGE}:${TAG_ARM} ${IMAGE_NAME}:${TAG_PI} ${CACHE_IMAGE}:${TAG}
+docker manifest create ${IMAGE_NAME}:${TAG} ${CACHE_IMAGE}:${TAG} ${CACHE_IMAGE}:${TAG_ARM} ${IMAGE_NAME}:${TAG_PI}
 docker manifest push -p ${IMAGE_NAME}:${TAG}
 
-docker manifest create ${IMAGE_NAME}:${TAG_PLOT} ${CACHE_IMAGE}:${TAG_PLOT_ARM} ${CACHE_IMAGE}:${TAG_PLOT}
+docker manifest create ${IMAGE_NAME}:${TAG_PLOT} ${CACHE_IMAGE}:${TAG_PLOT} ${CACHE_IMAGE}:${TAG_PLOT_ARM}
 docker manifest push -p ${IMAGE_NAME}:${TAG_PLOT}
 
-docker manifest create ${IMAGE_NAME}:${TAG_FREQAI} ${CACHE_IMAGE}:${TAG_FREQAI_ARM} ${CACHE_IMAGE}:${TAG_FREQAI}
+docker manifest create ${IMAGE_NAME}:${TAG_FREQAI} ${CACHE_IMAGE}:${TAG_FREQAI} ${CACHE_IMAGE}:${TAG_FREQAI_ARM}
 docker manifest push -p ${IMAGE_NAME}:${TAG_FREQAI}
 
-docker manifest create ${IMAGE_NAME}:${TAG_FREQAI_RL} ${CACHE_IMAGE}:${TAG_FREQAI_RL_ARM} ${CACHE_IMAGE}:${TAG_FREQAI_RL}
+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}
 
 # 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
 fi

build_helpers/publish_docker_multi.sh

@@ -26,7 +26,10 @@ if [ "${GITHUB_EVENT_NAME}" = "schedule" ]; then
         --cache-to=type=registry,ref=${CACHE_TAG} \
         -f docker/Dockerfile.armhf \
         --platform ${PI_PLATFORM} \
-        -t ${IMAGE_NAME}:${TAG_PI} --push .
+        -t ${IMAGE_NAME}:${TAG_PI} \
+        --push \
+        --provenance=false \
+        .
 else
     echo "event ${GITHUB_EVENT_NAME}: building with cache"
     # Build regular image
@@ -35,12 +38,16 @@ else
 
     # Pull last build to avoid rebuilding the whole image
     # docker pull --platform ${PI_PLATFORM} ${IMAGE_NAME}:${TAG}
+    # disable provenance due to https://github.com/docker/buildx/issues/1509
     docker buildx build \
         --cache-from=type=registry,ref=${CACHE_TAG} \
         --cache-to=type=registry,ref=${CACHE_TAG} \
         -f docker/Dockerfile.armhf \
         --platform ${PI_PLATFORM} \
-        -t ${IMAGE_NAME}:${TAG_PI} --push .
+        -t ${IMAGE_NAME}:${TAG_PI} \
+        --push \
+        --provenance=false \
+        .
 fi
 
 if [ $? -ne 0 ]; then
@@ -68,12 +75,10 @@ fi
 
 docker images
 
-docker push ${CACHE_IMAGE}
+docker push ${CACHE_IMAGE}:$TAG
 docker push ${CACHE_IMAGE}:$TAG_PLOT
 docker push ${CACHE_IMAGE}:$TAG_FREQAI
 docker push ${CACHE_IMAGE}:$TAG_FREQAI_RL
-docker push ${CACHE_IMAGE}:$TAG
-
 
 docker images
 

config_examples/config_binance.example.json

@@ -59,20 +59,6 @@
     "pairlists": [
         {"method": "StaticPairList"}
     ],
-    "edge": {
-        "enabled": false,
-        "process_throttle_secs": 3600,
-        "calculate_since_number_of_days": 7,
-        "allowed_risk": 0.01,
-        "stoploss_range_min": -0.01,
-        "stoploss_range_max": -0.1,
-        "stoploss_range_step": -0.01,
-        "minimum_winrate": 0.60,
-        "minimum_expectancy": 0.20,
-        "min_trade_number": 10,
-        "max_trade_duration_minute": 1440,
-        "remove_pumps": false
-    },
     "telegram": {
         "enabled": false,
         "token": "your_telegram_token",

config_examples/config_bittrex.example.json

@@ -56,20 +56,6 @@
     "pairlists": [
         {"method": "StaticPairList"}
     ],
-    "edge": {
-        "enabled": false,
-        "process_throttle_secs": 3600,
-        "calculate_since_number_of_days": 7,
-        "allowed_risk": 0.01,
-        "stoploss_range_min": -0.01,
-        "stoploss_range_max": -0.1,
-        "stoploss_range_step": -0.01,
-        "minimum_winrate": 0.60,
-        "minimum_expectancy": 0.20,
-        "min_trade_number": 10,
-        "max_trade_duration_minute": 1440,
-        "remove_pumps": false
-    },
     "telegram": {
         "enabled": false,
         "token": "your_telegram_token",

config_examples/config_freqai.example.json

@@ -21,8 +21,8 @@
         "ccxt_config": {},
         "ccxt_async_config": {},
         "pair_whitelist": [
-            "1INCH/USDT",
-            "ALGO/USDT"
+            "1INCH/USDT:USDT",
+            "ALGO/USDT:USDT"
         ],
         "pair_blacklist": []
     },
@@ -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,
@@ -60,8 +60,8 @@
                 "1h"
             ],
             "include_corr_pairlist": [
-                "BTC/USDT",
-                "ETH/USDT"
+                "BTC/USDT:USDT",
+                "ETH/USDT:USDT"
             ],
             "label_period_candles": 20,
             "include_shifted_candles": 2,

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
     },

config_examples/config_kraken.example.json

@@ -64,20 +64,6 @@
     "pairlists": [
         {"method": "StaticPairList"}
     ],
-    "edge": {
-        "enabled": false,
-        "process_throttle_secs": 3600,
-        "calculate_since_number_of_days": 7,
-        "allowed_risk": 0.01,
-        "stoploss_range_min": -0.01,
-        "stoploss_range_max": -0.1,
-        "stoploss_range_step": -0.01,
-        "minimum_winrate": 0.60,
-        "minimum_expectancy": 0.20,
-        "min_trade_number": 10,
-        "max_trade_duration_minute": 1440,
-        "remove_pumps": false
-    },
     "telegram": {
         "enabled": false,
         "token": "your_telegram_token",

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-backtesting.md

@@ -32,7 +32,7 @@ To analyze the entry/exit tags, we now need to use the `freqtrade backtesting-an
 with `--analysis-groups` option provided with space-separated arguments (default `0 1 2`):
 
 ``` bash
-freqtrade backtesting-analysis -c <config.json> --analysis-groups 0 1 2 3 4
+freqtrade backtesting-analysis -c <config.json> --analysis-groups 0 1 2 3 4 5
 ```
 
 This command will read from the last backtesting results. The `--analysis-groups` option is
@@ -43,6 +43,7 @@ ranging from the simplest (0) to the most detailed per pair, per buy and per sel
 * 2: profit summaries grouped by enter_tag and exit_tag
 * 3: profit summaries grouped by pair and enter_tag
 * 4: profit summaries grouped by pair, enter_ and exit_tag (this can get quite large)
+* 5: profit summaries grouped by exit_tag
 
 More options are available by running with the `-h` option.
 

docs/advanced-hyperopt.md

@@ -75,7 +75,7 @@ This function needs to return a floating point number (`float`). Smaller numbers
 
 ## Overriding pre-defined spaces
 
-To override a pre-defined space (`roi_space`, `generate_roi_table`, `stoploss_space`, `trailing_space`), define a nested class called Hyperopt and define the required spaces as follows:
+To override a pre-defined space (`roi_space`, `generate_roi_table`, `stoploss_space`, `trailing_space`, `max_open_trades_space`), define a nested class called Hyperopt and define the required spaces as follows:
 
 ```python
 from freqtrade.optimize.space import Categorical, Dimension, Integer, SKDecimal
@@ -123,6 +123,12 @@ class MyAwesomeStrategy(IStrategy):
 
                 Categorical([True, False], name='trailing_only_offset_is_reached'),
         ]
+
+        # Define a custom max_open_trades space
+        def max_open_trades_space(self) -> List[Dimension]:
+            return [
+                Integer(-1, 10, name='max_open_trades'),
+            ]
 ```
 
 !!! Note

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/backtesting.md

@@ -300,7 +300,11 @@ A backtesting result will look like that:
 | Absolute profit             | 0.00762792 BTC      |
 | Total profit %              | 76.2%               |
 | CAGR %                      | 460.87%             |
+| Sortino                     | 1.88                |
+| Sharpe                      | 2.97                |
+| Calmar                      | 6.29                |
 | Profit factor               | 1.11                |
+| Expectancy                  | -0.15               |
 | Avg. stake amount           | 0.001      BTC      |
 | Total trade volume          | 0.429      BTC      |
 |                             |                     |
@@ -400,7 +404,11 @@ It contains some useful key metrics about performance of your strategy on backte
 | Absolute profit             | 0.00762792 BTC      |
 | Total profit %              | 76.2%               |
 | CAGR %                      | 460.87%             |
+| Sortino                     | 1.88                |
+| Sharpe                      | 2.97                |
+| Calmar                      | 6.29                |
 | Profit factor               | 1.11                |
+| Expectancy                  | -0.15               |
 | Avg. stake amount           | 0.001      BTC      |
 | Total trade volume          | 0.429      BTC      |
 |                             |                     |
@@ -447,6 +455,9 @@ It contains some useful key metrics about performance of your strategy on backte
 - `Absolute profit`: Profit made in stake currency.
 - `Total profit %`: Total profit. Aligned to the `TOTAL` row's `Tot Profit %` from the first table. Calculated as `(End capital − Starting capital) / Starting capital`.
 - `CAGR %`: Compound annual growth rate.
+- `Sortino`: Annualized Sortino ratio.
+- `Sharpe`: Annualized Sharpe ratio.
+- `Calmar`: Annualized Calmar ratio.
 - `Profit factor`: profit / loss.
 - `Avg. stake amount`: Average stake amount, either `stake_amount` or the average when using dynamic stake amount.
 - `Total trade volume`: Volume generated on the exchange to reach the above profit.

docs/bot-basics.md

@@ -75,3 +75,7 @@ This loop will be repeated again and again until the bot is stopped.
 
 !!! Note
     Both Backtesting and Hyperopt include exchange default Fees in the calculation. Custom fees can be passed to backtesting / hyperopt by specifying the `--fee` argument.
+
+!!! Warning "Callback call frequency"
+    Backtesting will call each callback at max. once per candle (`--timeframe-detail` modifies this behavior to once per detailed candle).
+    Most callbacks will be called once per iteration in live (usually every ~5s) - which can cause backtesting mismatches.

docs/configuration.md

@@ -11,7 +11,7 @@ Per default, the bot loads the configuration from the `config.json` file, locate
 
 You can specify a different configuration file used by the bot with the `-c/--config` command-line option.
 
-If you used the [Quick start](installation.md/#quick-start) method for installing
+If you used the [Quick start](docker_quickstart.md#docker-quick-start) method for installing
 the bot, the installation script should have already created the default configuration file (`config.json`) for you.
 
 If the default configuration file is not created we recommend to use `freqtrade new-config --config config.json` to generate a basic configuration file.
@@ -134,7 +134,7 @@ Mandatory parameters are marked as **Required**, which means that they are requi
 
 |  Parameter | Description |
 |------------|-------------|
-| `max_open_trades` | **Required.** Number of open trades your bot is allowed to have. Only one open trade per pair is possible, so the length of your pairlist is another limitation that can apply. If -1 then it is ignored (i.e. potentially unlimited open trades, limited by the pairlist). [More information below](#configuring-amount-per-trade).<br> **Datatype:** Positive integer or -1.
+| `max_open_trades` | **Required.** Number of open trades your bot is allowed to have. Only one open trade per pair is possible, so the length of your pairlist is another limitation that can apply. If -1 then it is ignored (i.e. potentially unlimited open trades, limited by the pairlist). [More information below](#configuring-amount-per-trade). [Strategy Override](#parameters-in-the-strategy).<br> **Datatype:** Positive integer or -1.
 | `stake_currency` | **Required.** Crypto-currency used for trading. <br> **Datatype:** String
 | `stake_amount` | **Required.** Amount of crypto-currency your bot will use for each trade. Set it to `"unlimited"` to allow the bot to use all available balance. [More information below](#configuring-amount-per-trade). <br> **Datatype:** Positive float or `"unlimited"`.
 | `tradable_balance_ratio` | Ratio of the total account balance the bot is allowed to trade. [More information below](#configuring-amount-per-trade). <br>*Defaults to `0.99` 99%).*<br> **Datatype:** Positive float between `0.1` and `1.0`.
@@ -263,6 +263,7 @@ Values set in the configuration file always overwrite values set in the strategy
 * `minimal_roi`
 * `timeframe`
 * `stoploss`
+* `max_open_trades`
 * `trailing_stop`
 * `trailing_stop_positive`
 * `trailing_stop_positive_offset`
@@ -665,7 +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"
@@ -681,11 +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/developer.md

@@ -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

@@ -75,6 +75,25 @@ Binance has been split into 2, and users must use the correct ccxt exchange ID f
 * [binance.com](https://www.binance.com/) - International users. Use exchange id: `binance`.
 * [binance.us](https://www.binance.us/) - US based users. Use exchange id: `binanceus`.
 
+### Binance RSA keys
+
+Freqtrade supports binance RSA API keys.
+
+We recommend to use them as environment variable.
+
+``` bash
+export FREQTRADE__EXCHANGE__SECRET="$(cat ./rsa_binance.private)"
+```
+
+They can however also be configured via configuration file. Since json doesn't support multi-line strings, you'll have to replace all newlines with `\n` to have a valid json file.
+
+``` json
+// ...
+ "key": "<someapikey>",
+ "secret": "-----BEGIN PRIVATE KEY-----\nMIIEvQIBABACAFQA<...>s8KX8=\n-----END PRIVATE KEY-----"
+// ...
+```
+
 ### Binance Futures
 
 Binance has specific (unfortunately complex) [Futures Trading Quantitative Rules](https://www.binance.com/en/support/faq/4f462ebe6ff445d4a170be7d9e897272) which need to be followed, and which prohibit a too low stake-amount (among others) for too many orders.
@@ -224,8 +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
@@ -236,6 +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",
@@ -43,116 +43,113 @@ The FreqAI strategy requires including the following lines of code in the standa
 
     def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
 
-        # the model will return all labels created by user in `populate_any_indicators`
+        # the model will return all labels created by user in `set_freqai_labels()`
         # (& appended targets), an indication of whether or not the prediction should be accepted,
         # the target mean/std values for each of the labels created by user in
-        # `populate_any_indicators()` for each training period.
+        # `feature_engineering_*` for each training period.
 
         dataframe = self.freqai.start(dataframe, metadata, self)
 
         return dataframe
 
-    def populate_any_indicators(
-        self, pair, df, tf, informative=None, set_generalized_indicators=False
-    ):
+    def feature_engineering_expand_all(self, dataframe, period, **kwargs):
         """
-        Function designed to automatically generate, name and merge features
-        from user indicated timeframes in the configuration file. User controls the indicators
-        passed to the training/prediction by prepending indicators with `'%-' + pair `
-        (see convention below). I.e. user should not prepend any supporting metrics
-        (e.g. bb_lowerband below) with % unless they explicitly want to pass that metric to the
-        model.
-        :param pair: pair to be used as informative
-        :param df: strategy dataframe which will receive merges from informatives
-        :param tf: timeframe of the dataframe which will modify the feature names
-        :param informative: the dataframe associated with the informative pair
+        *Only functional with FreqAI enabled strategies*
+        This function will automatically expand the defined features on the config defined
+        `indicator_periods_candles`, `include_timeframes`, `include_shifted_candles`, and
+        `include_corr_pairs`. In other words, a single feature defined in this function
+        will automatically expand to a total of
+        `indicator_periods_candles` * `include_timeframes` * `include_shifted_candles` *
+        `include_corr_pairs` numbers of features added to the model.
+
+        All features must be prepended with `%` to be recognized by FreqAI internals.
+
+        :param df: strategy dataframe which will receive the features
+        :param period: period of the indicator - usage example:
+        dataframe["%-ema-period"] = ta.EMA(dataframe, timeperiod=period)
         """
 
-        if informative is None:
-            informative = self.dp.get_pair_dataframe(pair, tf)
+        dataframe["%-rsi-period"] = ta.RSI(dataframe, timeperiod=period)
+        dataframe["%-mfi-period"] = ta.MFI(dataframe, timeperiod=period)
+        dataframe["%-adx-period"] = ta.ADX(dataframe, timeperiod=period)
+        dataframe["%-sma-period"] = ta.SMA(dataframe, timeperiod=period)
+        dataframe["%-ema-period"] = ta.EMA(dataframe, timeperiod=period)
 
-        # first loop is automatically duplicating indicators for time periods
-        for t in self.freqai_info["feature_parameters"]["indicator_periods_candles"]:
-            t = int(t)
-            informative[f"%-{pair}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t)
-            informative[f"%-{pair}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
-            informative[f"%-{pair}adx-period_{t}"] = ta.ADX(informative, window=t)
+        return dataframe
 
-        indicators = [col for col in informative if col.startswith("%")]
-        # This loop duplicates and shifts all indicators to add a sense of recency to data
-        for n in range(self.freqai_info["feature_parameters"]["include_shifted_candles"] + 1):
-            if n == 0:
-                continue
-            informative_shift = informative[indicators].shift(n)
-            informative_shift = informative_shift.add_suffix("_shift-" + str(n))
-            informative = pd.concat((informative, informative_shift), axis=1)
+    def feature_engineering_expand_basic(self, dataframe, **kwargs):
+        """
+        *Only functional with FreqAI enabled strategies*
+        This function will automatically expand the defined features on the config defined
+        `include_timeframes`, `include_shifted_candles`, and `include_corr_pairs`.
+        In other words, a single feature defined in this function
+        will automatically expand to a total of
+        `include_timeframes` * `include_shifted_candles` * `include_corr_pairs`
+        numbers of features added to the model.
 
-        df = merge_informative_pair(df, informative, self.config["timeframe"], tf, ffill=True)
-        skip_columns = [
-            (s + "_" + tf) for s in ["date", "open", "high", "low", "close", "volume"]
-        ]
-        df = df.drop(columns=skip_columns)
+        Features defined here will *not* be automatically duplicated on user defined
+        `indicator_periods_candles`
 
-        # Add generalized indicators here (because in live, it will call this
-        # function to populate indicators during training). Notice how we ensure not to
-        # add them multiple times
-        if set_generalized_indicators:
+        All features must be prepended with `%` to be recognized by FreqAI internals.
 
-            # user adds targets here by prepending them with &- (see convention below)
-            # If user wishes to use multiple targets, a multioutput prediction model
-            # needs to be used such as templates/CatboostPredictionMultiModel.py
-            df["&-s_close"] = (
-                df["close"]
-                .shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
-                .rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
-                .mean()
-                / df["close"]
-                - 1
+        :param df: strategy dataframe which will receive the features
+        dataframe["%-pct-change"] = dataframe["close"].pct_change()
+        dataframe["%-ema-200"] = ta.EMA(dataframe, timeperiod=200)
+        """
+        dataframe["%-pct-change"] = dataframe["close"].pct_change()
+        dataframe["%-raw_volume"] = dataframe["volume"]
+        dataframe["%-raw_price"] = dataframe["close"]
+        return dataframe
+
+    def feature_engineering_standard(self, dataframe, **kwargs):
+        """
+        *Only functional with FreqAI enabled strategies*
+        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 created by all other
+        freqai_feature_engineering_* functions.
+
+        This function is a good place to do custom exotic feature extractions (e.g. tsfresh).
+        This function is a good place for any feature that should not be auto-expanded upon
+        (e.g. day of the week).
+
+        All features must be prepended with `%` to be recognized by FreqAI internals.
+
+        :param df: strategy dataframe which will receive the features
+        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):
+        """
+        *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.
+
+        :param df: strategy dataframe which will receive the targets
+        usage example: dataframe["&-target"] = dataframe["close"].shift(-1) / dataframe["close"]
+        """
+        dataframe["&-s_close"] = (
+            dataframe["close"]
+            .shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
+            .rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
+            .mean()
+            / dataframe["close"]
+            - 1
             )
-
-        return df
-
-
 ```
 
-Notice how the `populate_any_indicators()` is where [features](freqai-feature-engineering.md#feature-engineering) and labels/targets are added. A full example strategy is available in `templates/FreqaiExampleStrategy.py`.
-
-Notice also the location of the labels under `if set_generalized_indicators:` at the bottom of the example. This is where single features and labels/targets should be added to the feature set to avoid duplication of them from various configuration parameters that multiply the feature set, such as `include_timeframes`.
+Notice how the `feature_engineering_*()` is where [features](freqai-feature-engineering.md#feature-engineering) are added. Meanwhile `set_freqai_targets()` adds the labels/targets. A full example strategy is available in `templates/FreqaiExampleStrategy.py`.
 
 !!! Note
     The `self.freqai.start()` function cannot be called outside the `populate_indicators()`.
 
 !!! Note
-    Features **must** be defined in `populate_any_indicators()`. Defining FreqAI features in `populate_indicators()`
-    will cause the algorithm to fail in live/dry mode. In order to add generalized features that are not associated with a specific pair or timeframe, the following structure inside `populate_any_indicators()` should be used
-    (as exemplified in `freqtrade/templates/FreqaiExampleStrategy.py`):
-
-    ```python
-        def populate_any_indicators(self, pair, df, tf, informative=None, set_generalized_indicators=False):
-
-            ...
-
-            # Add generalized indicators here (because in live, it will call only this function to populate
-            # indicators for retraining). Notice how we ensure not to add them multiple times by associating
-            # these generalized indicators to the basepair/timeframe
-            if set_generalized_indicators:
-                df['%-day_of_week'] = (df["date"].dt.dayofweek + 1) / 7
-                df['%-hour_of_day'] = (df['date'].dt.hour + 1) / 25
-
-                # user adds targets here by prepending them with &- (see convention below)
-                # If user wishes to use multiple targets, a multioutput prediction model
-                # needs to be used such as templates/CatboostPredictionMultiModel.py
-                df["&-s_close"] = (
-                    df["close"]
-                    .shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
-                    .rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
-                    .mean()
-                    / df["close"]
-                    - 1
-                    )
-    ```
-
-    Please see the example script located in `freqtrade/templates/FreqaiExampleStrategy.py` for a full example of `populate_any_indicators()`.
+    Features **must** be defined in `feature_engineering_*()`. Defining FreqAI features in `populate_indicators()`
+    will cause the algorithm to fail in live/dry mode. In order to add generalized features that are not associated with a specific pair or timeframe, you should use `feature_engineering_standard()`
+    (as exemplified in `freqtrade/templates/FreqaiExampleStrategy.py`).
 
 ## Important dataframe key patterns
 
@@ -160,18 +157,18 @@ Below are the values you can expect to include/use inside a typical strategy dat
 
 |  DataFrame Key | Description |
 |------------|-------------|
-| `df['&*']` | Any dataframe column prepended with `&` in `populate_any_indicators()` is treated as a training target (label) inside FreqAI (typically following the naming convention `&-s*`). For example, to predict the close price 40 candles into the future, you would set `df['&-s_close'] = df['close'].shift(-self.freqai_info["feature_parameters"]["label_period_candles"])` with `"label_period_candles": 40` in the config. FreqAI makes the predictions and gives them back under the same key (`df['&-s_close']`) to be used in `populate_entry/exit_trend()`. <br> **Datatype:** Depends on the output of the model.
+| `df['&*']` | Any dataframe column prepended with `&` in `set_freqai_targets()` is treated as a training target (label) inside FreqAI (typically following the naming convention `&-s*`). For example, to predict the close price 40 candles into the future, you would set `df['&-s_close'] = df['close'].shift(-self.freqai_info["feature_parameters"]["label_period_candles"])` with `"label_period_candles": 40` in the config. FreqAI makes the predictions and gives them back under the same key (`df['&-s_close']`) to be used in `populate_entry/exit_trend()`. <br> **Datatype:** Depends on the output of the model.
 | `df['&*_std/mean']` | Standard deviation and mean values of the defined labels during training (or live tracking with `fit_live_predictions_candles`). Commonly used to understand the rarity of a prediction (use the z-score as shown in `templates/FreqaiExampleStrategy.py` and explained [here](#creating-a-dynamic-target-threshold) to evaluate how often a particular prediction was observed during training or historically with `fit_live_predictions_candles`). <br> **Datatype:** Float.
 | `df['do_predict']` | Indication of an outlier data point. The return value is integer between -2 and 2, which lets you know if the prediction is trustworthy or not. `do_predict==1` means that the prediction is trustworthy. If the Dissimilarity Index (DI, see details [here](freqai-feature-engineering.md#identifying-outliers-with-the-dissimilarity-index-di)) of the input data point is above the threshold defined in the config, FreqAI will subtract 1 from `do_predict`, resulting in `do_predict==0`. If `use_SVM_to_remove_outliers()` is active, the Support Vector Machine (SVM, see details [here](freqai-feature-engineering.md#identifying-outliers-using-a-support-vector-machine-svm)) may also detect outliers in training and prediction data. In this case, the SVM will also subtract 1 from `do_predict`. If the input data point was considered an outlier by the SVM but not by the DI, or vice versa, the result will be `do_predict==0`. If both the DI and the SVM considers the input data point to be an outlier, the result will be `do_predict==-1`. As with the SVM, if `use_DBSCAN_to_remove_outliers` is active, DBSCAN (see details [here](freqai-feature-engineering.md#identifying-outliers-with-dbscan)) may also detect outliers and subtract 1 from `do_predict`. Hence, if both the SVM and DBSCAN are active and identify a datapoint that was above the DI threshold as an outlier, the result will be `do_predict==-2`. A particular case is when `do_predict == 2`, which means that the model has expired due to exceeding `expired_hours`. <br> **Datatype:** Integer between -2 and 2.
 | `df['DI_values']` | Dissimilarity Index (DI) values are proxies for the level of confidence FreqAI has in the prediction. A lower DI means the prediction is close to the training data, i.e., higher prediction confidence. See details about the DI [here](freqai-feature-engineering.md#identifying-outliers-with-the-dissimilarity-index-di). <br> **Datatype:** Float.
-| `df['%*']` | Any dataframe column prepended with `%` in `populate_any_indicators()` is treated as a training feature. For example, you can include the RSI in the training feature set (similar to in `templates/FreqaiExampleStrategy.py`) by setting `df['%-rsi']`. See more details on how this is done [here](freqai-feature-engineering.md). <br> **Note:** Since the number of features prepended with `%` can multiply very quickly (10s of thousands of features are easily engineered using the multiplictative functionality of, e.g., `include_shifted_candles` and `include_timeframes` as described in the [parameter table](freqai-parameter-table.md)), these features are removed from the dataframe that is returned from FreqAI to the strategy. To keep a particular type of feature for plotting purposes, you would prepend it with `%%`. <br> **Datatype:** Depends on the output of the model.
+| `df['%*']` | Any dataframe column prepended with `%` in `feature_engineering_*()` is treated as a training feature. For example, you can include the RSI in the training feature set (similar to in `templates/FreqaiExampleStrategy.py`) by setting `df['%-rsi']`. See more details on how this is done [here](freqai-feature-engineering.md). <br> **Note:** Since the number of features prepended with `%` can multiply very quickly (10s of thousands of features are easily engineered using the multiplictative functionality of, e.g., `include_shifted_candles` and `include_timeframes` as described in the [parameter table](freqai-parameter-table.md)), these features are removed from the dataframe that is returned from FreqAI to the strategy. To keep a particular type of feature for plotting purposes, you would prepend it with `%%`. <br> **Datatype:** Depends on the output of the model.
 
 ## 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.
@@ -208,7 +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

@@ -2,96 +2,150 @@
 
 ## Defining the features
 
-Low level feature engineering is performed in the user strategy within a function called `populate_any_indicators()`. That function sets the `base features` such as, `RSI`, `MFI`, `EMA`, `SMA`, time of day, volume, etc. The `base features` can be custom indicators or they can be imported from any technical-analysis library that you can find. One important syntax rule is that all `base features` string names are prepended with `%-{pair}`, while labels/targets are prepended with `&`.
+Low level feature engineering is performed in the user strategy within a set of functions called `feature_engineering_*`. These function set the `base features` such as, `RSI`, `MFI`, `EMA`, `SMA`, time of day, volume, etc. The `base features` can be custom indicators or they can be imported from any technical-analysis library that you can find. FreqAI is equipped with a set of functions to simplify rapid large-scale feature engineering:
 
-!!! Note
-    Adding the full pair string, e.g. XYZ/USD, in the feature name enables improved performance for dataframe caching on the backend. If you decide *not* to add the full pair string in the feature string, FreqAI will operate in a reduced performance mode.
+|  Function | Description |
+|---------------|-------------|
+| `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).
+| `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."
 
-It is advisable to start from the template `populate_any_indicators()` 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:
+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 populate_any_indicators(
-        self, pair, df, tf, informative=None, set_generalized_indicators=False
-    ):
+    def feature_engineering_expand_all(self, dataframe, period, metadata, **kwargs):
         """
-        Function designed to automatically generate, name, and merge features
-        from user-indicated timeframes in the configuration file. The user controls the indicators
-        passed to the training/prediction by prepending indicators with `'%-' + pair `
-        (see convention below). I.e., the user should not prepend any supporting metrics
-        (e.g., bb_lowerband below) with % unless they explicitly want to pass that metric to the
-        model.
-        :param pair: pair to be used as informative
-        :param df: strategy dataframe which will receive merges from informatives
-        :param tf: timeframe of the dataframe which will modify the feature names
-        :param informative: the dataframe associated with the informative pair
+        *Only functional with FreqAI enabled strategies*
+        This function will automatically expand the defined features on the config defined
+        `indicator_periods_candles`, `include_timeframes`, `include_shifted_candles`, and
+        `include_corr_pairs`. In other words, a single feature defined in this function
+        will automatically expand to a total of
+        `indicator_periods_candles` * `include_timeframes` * `include_shifted_candles` *
+        `include_corr_pairs` numbers of features added to the model.
+
+        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)
         """
 
-        if informative is None:
-            informative = self.dp.get_pair_dataframe(pair, tf)
+        dataframe["%-rsi-period"] = ta.RSI(dataframe, timeperiod=period)
+        dataframe["%-mfi-period"] = ta.MFI(dataframe, timeperiod=period)
+        dataframe["%-adx-period"] = ta.ADX(dataframe, timeperiod=period)
+        dataframe["%-sma-period"] = ta.SMA(dataframe, timeperiod=period)
+        dataframe["%-ema-period"] = ta.EMA(dataframe, timeperiod=period)
 
-        # first loop is automatically duplicating indicators for time periods
-        for t in self.freqai_info["feature_parameters"]["indicator_periods_candles"]:
-            t = int(t)
-            informative[f"%-{pair}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t)
-            informative[f"%-{pair}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
-            informative[f"%-{pair}adx-period_{t}"] = ta.ADX(informative, window=t)
+        bollinger = qtpylib.bollinger_bands(
+            qtpylib.typical_price(dataframe), window=period, stds=2.2
+        )
+        dataframe["bb_lowerband-period"] = bollinger["lower"]
+        dataframe["bb_middleband-period"] = bollinger["mid"]
+        dataframe["bb_upperband-period"] = bollinger["upper"]
 
-            bollinger = qtpylib.bollinger_bands(
-                qtpylib.typical_price(informative), window=t, stds=2.2
+        dataframe["%-bb_width-period"] = (
+            dataframe["bb_upperband-period"]
+            - dataframe["bb_lowerband-period"]
+        ) / dataframe["bb_middleband-period"]
+        dataframe["%-close-bb_lower-period"] = (
+            dataframe["close"] / dataframe["bb_lowerband-period"]
+        )
+
+        dataframe["%-roc-period"] = ta.ROC(dataframe, timeperiod=period)
+
+        dataframe["%-relative_volume-period"] = (
+            dataframe["volume"] / dataframe["volume"].rolling(period).mean()
+        )
+
+        return dataframe
+
+    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
+        `include_timeframes`, `include_shifted_candles`, and `include_corr_pairs`.
+        In other words, a single feature defined in this function
+        will automatically expand to a total of
+        `include_timeframes` * `include_shifted_candles` * `include_corr_pairs`
+        numbers of features added to the model.
+
+        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)
+        """
+        dataframe["%-pct-change"] = dataframe["close"].pct_change()
+        dataframe["%-raw_volume"] = dataframe["volume"]
+        dataframe["%-raw_price"] = dataframe["close"]
+        return dataframe
+
+    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.
+        This is the final function to be called, which means that the dataframe entering this
+        function will contain all the features and columns created by all other
+        freqai_feature_engineering_* functions.
+
+        This function is a good place to do custom exotic feature extractions (e.g. tsfresh).
+        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, 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"] = (
+            dataframe["close"]
+            .shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
+            .rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
+            .mean()
+            / dataframe["close"]
+            - 1
             )
-            informative[f"{pair}bb_lowerband-period_{t}"] = bollinger["lower"]
-            informative[f"{pair}bb_middleband-period_{t}"] = bollinger["mid"]
-            informative[f"{pair}bb_upperband-period_{t}"] = bollinger["upper"]
-
-            informative[f"%-{pair}bb_width-period_{t}"] = (
-                informative[f"{pair}bb_upperband-period_{t}"]
-                - informative[f"{pair}bb_lowerband-period_{t}"]
-            ) / informative[f"{pair}bb_middleband-period_{t}"]
-            informative[f"%-{pair}close-bb_lower-period_{t}"] = (
-                informative["close"] / informative[f"{pair}bb_lowerband-period_{t}"]
-            )
-
-            informative[f"%-{pair}relative_volume-period_{t}"] = (
-                informative["volume"] / informative["volume"].rolling(t).mean()
-            )
-
-        indicators = [col for col in informative if col.startswith("%")]
-        # This loop duplicates and shifts all indicators to add a sense of recency to data
-        for n in range(self.freqai_info["feature_parameters"]["include_shifted_candles"] + 1):
-            if n == 0:
-                continue
-            informative_shift = informative[indicators].shift(n)
-            informative_shift = informative_shift.add_suffix("_shift-" + str(n))
-            informative = pd.concat((informative, informative_shift), axis=1)
-
-        df = merge_informative_pair(df, informative, self.config["timeframe"], tf, ffill=True)
-        skip_columns = [
-            (s + "_" + tf) for s in ["date", "open", "high", "low", "close", "volume"]
-        ]
-        df = df.drop(columns=skip_columns)
-
-        # Add generalized indicators here (because in live, it will call this
-        # function to populate indicators during training). Notice how we ensure not to
-        # add them multiple times
-        if set_generalized_indicators:
-            df["%-day_of_week"] = (df["date"].dt.dayofweek + 1) / 7
-            df["%-hour_of_day"] = (df["date"].dt.hour + 1) / 25
-
-            # user adds targets here by prepending them with &- (see convention below)
-            # If user wishes to use multiple targets, a multioutput prediction model
-            # needs to be used such as templates/CatboostPredictionMultiModel.py
-            df["&-s_close"] = (
-                df["close"]
-                .shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
-                .rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
-                .mean()
-                / df["close"]
-                - 1
-            )
-
-        return df
+        
+        return dataframe
 ```
 
 In the presented example, the user does not wish to pass the `bb_lowerband` as a feature to the model,
@@ -118,15 +172,28 @@ After having defined the `base features`, the next step is to expand upon them u
     }
 ```
 
-The `include_timeframes` in the config above are the timeframes (`tf`) of each call to `populate_any_indicators()` in the strategy. In the presented case, the user is asking for the `5m`, `15m`, and `4h` timeframes of the `rsi`, `mfi`, `roc`, and `bb_width` to be included in the feature set.
+The `include_timeframes` in the config above are the timeframes (`tf`) of each call to `feature_engineering_expand_*()` in the strategy. In the presented case, the user is asking for the `5m`, `15m`, and `4h` timeframes of the `rsi`, `mfi`, `roc`, and `bb_width` to be included in the feature set.
 
-You can ask for each of the defined features to be included also for informative pairs using the `include_corr_pairlist`. This means that the feature set will include all the features from `populate_any_indicators` on all the `include_timeframes` for each of the correlated pairs defined in the config (`ETH/USD`, `LINK/USD`, and `BNB/USD` in the presented example).
+You can ask for each of the defined features to be included also for informative pairs using the `include_corr_pairlist`. This means that the feature set will include all the features from `feature_engineering_expand_*()` on all the `include_timeframes` for each of the correlated pairs defined in the config (`ETH/USD`, `LINK/USD`, and `BNB/USD` in the presented example).
 
 `include_shifted_candles` indicates the number of previous candles to include in the feature set. For example, `include_shifted_candles: 2` tells FreqAI to include the past 2 candles for each of the features in the feature set.
 
-In total, the number of features the user of the presented example strat has created is: length of `include_timeframes` * no. features in `populate_any_indicators()` * length of `include_corr_pairlist` * no. `include_shifted_candles` * length of `indicator_periods_candles`
+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. 
@@ -167,7 +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.
@@ -29,12 +28,12 @@ Mandatory parameters are marked as **Required** and have to be set in one of the
 |------------|-------------|
 |  |  **Feature parameters within the `freqai.feature_parameters` sub dictionary**
 | `feature_parameters` | A dictionary containing the parameters used to engineer the feature set. Details and examples are shown [here](freqai-feature-engineering.md). <br> **Datatype:** Dictionary.
-| `include_timeframes` | A list of timeframes that all indicators in `populate_any_indicators` will be created for. The list is added as features to the base indicators dataset. <br> **Datatype:** List of timeframes (strings).
-| `include_corr_pairlist` | A list of correlated coins that FreqAI will add as additional features to all `pair_whitelist` coins. All indicators set in `populate_any_indicators` during feature engineering (see details [here](freqai-feature-engineering.md)) will be created for each correlated coin. The correlated coins features are added to the base indicators dataset. <br> **Datatype:** List of assets (strings).
-| `label_period_candles` | Number of candles into the future that the labels are created for. This is used in `populate_any_indicators` (see `templates/FreqaiExampleStrategy.py` for detailed usage). You can create custom labels and choose whether to make use of this parameter or not. <br> **Datatype:** Positive integer.
+| `include_timeframes` | A list of timeframes that all indicators in `feature_engineering_expand_*()` will be created for. The list is added as features to the base indicators dataset. <br> **Datatype:** List of timeframes (strings).
+| `include_corr_pairlist` | A list of correlated coins that FreqAI will add as additional features to all `pair_whitelist` coins. All indicators set in `feature_engineering_expand_*()` during feature engineering (see details [here](freqai-feature-engineering.md)) will be created for each correlated coin. The correlated coins features are added to the base indicators dataset. <br> **Datatype:** List of assets (strings).
+| `label_period_candles` | Number of candles into the future that the labels are created for. This is used in `feature_engineering_expand_all()` (see `templates/FreqaiExampleStrategy.py` for detailed usage). You can create custom labels and choose whether to make use of this parameter or not. <br> **Datatype:** Positive integer.
 | `include_shifted_candles` | Add features from previous candles to subsequent candles with the intent of adding historical information. If used, FreqAI will duplicate and shift all features from the `include_shifted_candles` previous candles so that the information is available for the subsequent candle. <br> **Datatype:** Positive integer.
 | `weight_factor` | Weight training data points according to their recency (see details [here](freqai-feature-engineering.md#weighting-features-for-temporal-importance)). <br> **Datatype:** Positive float (typically < 1).
-| `indicator_max_period_candles` | **No longer used (#7325)**. Replaced by `startup_candle_count` which is set in the [strategy](freqai-configuration.md#building-a-freqai-strategy). `startup_candle_count` is timeframe independent and defines the maximum *period* used in `populate_any_indicators()` for indicator creation. FreqAI uses this parameter together with the maximum timeframe in `include_time_frames` to calculate how many data points to download such that the first data point does not include a NaN. <br> **Datatype:** Positive integer.
+| `indicator_max_period_candles` | **No longer used (#7325)**. Replaced by `startup_candle_count` which is set in the [strategy](freqai-configuration.md#building-a-freqai-strategy). `startup_candle_count` is timeframe independent and defines the maximum *period* used in `feature_engineering_*()` for indicator creation. FreqAI uses this parameter together with the maximum timeframe in `include_time_frames` to calculate how many data points to download such that the first data point does not include a NaN. <br> **Datatype:** Positive integer.
 | `indicator_periods_candles` | Time periods to calculate indicators for. The indicators are added to the base indicator dataset. <br> **Datatype:** List of positive integers.
 | `principal_component_analysis` | Automatically reduce the dimensionality of the data set using Principal Component Analysis. See details about how it works [here](#reducing-data-dimensionality-with-principal-component-analysis) <br> **Datatype:** Boolean. <br> Default: `False`.
 | `plot_feature_importances` | Create a feature importance plot for each model for the top/bottom `plot_feature_importances` number of features. Plot is stored in `user_data/models/<identifier>/sub-train-<COIN>_<timestamp>.html`. <br> **Datatype:** Integer. <br> Default: `0`.
@@ -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`.
 
@@ -89,6 +90,6 @@ Mandatory parameters are marked as **Required** and have to be set in one of the
 |  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
 
@@ -34,65 +34,36 @@ Setting up and running a Reinforcement Learning model is the same as running a R
 freqtrade trade --freqaimodel ReinforcementLearner --strategy MyRLStrategy --config config.json
 ```
 
-where `ReinforcementLearner` will use the templated `ReinforcementLearner` from `freqai/prediction_models/ReinforcementLearner` (or a custom user defined one located in `user_data/freqaimodels`). The strategy, on the other hand, follows the same base [feature engineering](freqai-feature-engineering.md) with `populate_any_indicators` as a typical Regressor:
+where `ReinforcementLearner` will use the templated `ReinforcementLearner` from `freqai/prediction_models/ReinforcementLearner` (or a custom user defined one located in `user_data/freqaimodels`). The strategy, on the other hand, follows the same base [feature engineering](freqai-feature-engineering.md) with `feature_engineering_*` as a typical Regressor. The difference lies in the creation of the targets, Reinforcement Learning doesn't require them. However, FreqAI requires a default (neutral) value to be set in the action column:
 
 ```python
-    def populate_any_indicators(
-        self, pair, df, tf, informative=None, set_generalized_indicators=False
-    ):
+    def set_freqai_targets(self, dataframe, **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.
 
-        if informative is None:
-            informative = self.dp.get_pair_dataframe(pair, tf)
+        More details about feature engineering available:
 
-        # first loop is automatically duplicating indicators for time periods
-        for t in self.freqai_info["feature_parameters"]["indicator_periods_candles"]:
+        https://www.freqtrade.io/en/latest/freqai-feature-engineering
 
-            t = int(t)
-            informative[f"%-{pair}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t)
-            informative[f"%-{pair}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
-            informative[f"%-{pair}adx-period_{t}"] = ta.ADX(informative, window=t)
-
-        # The following raw price values are necessary for RL models
-        informative[f"%-{pair}raw_close"] = informative["close"]
-        informative[f"%-{pair}raw_open"] = informative["open"]
-        informative[f"%-{pair}raw_high"] = informative["high"]
-        informative[f"%-{pair}raw_low"] = informative["low"]
-
-        indicators = [col for col in informative if col.startswith("%")]
-        # This loop duplicates and shifts all indicators to add a sense of recency to data
-        for n in range(self.freqai_info["feature_parameters"]["include_shifted_candles"] + 1):
-            if n == 0:
-                continue
-            informative_shift = informative[indicators].shift(n)
-            informative_shift = informative_shift.add_suffix("_shift-" + str(n))
-            informative = pd.concat((informative, informative_shift), axis=1)
-
-        df = merge_informative_pair(df, informative, self.config["timeframe"], tf, ffill=True)
-        skip_columns = [
-            (s + "_" + tf) for s in ["date", "open", "high", "low", "close", "volume"]
-        ]
-        df = df.drop(columns=skip_columns)
-
-        # Add generalized indicators here (because in live, it will call this
-        # function to populate indicators during training). Notice how we ensure not to
-        # add them multiple times
-        if set_generalized_indicators:
-
-            # For RL, there are no direct targets to set. This is filler (neutral)
-            # until the agent sends an action.
-            df["&-action"] = 0
-
-        return df
+        :param df: strategy dataframe which will receive the targets
+        usage example: dataframe["&-target"] = dataframe["close"].shift(-1) / dataframe["close"]
+        """
+        # For RL, there are no direct targets to set. This is filler (neutral)
+        # until the agent sends an action.
+        dataframe["&-action"] = 0
 ```
 
 Most of the function remains the same as for typical Regressors, however, the function above shows how the strategy must pass the raw price data to the agent so that it has access to raw OHLCV in the training environment:
 
 ```python
+    def feature_engineering_standard(self, dataframe, **kwargs):
         # The following features are necessary for RL models
-        informative[f"%-{pair}raw_close"] = informative["close"]
-        informative[f"%-{pair}raw_open"] = informative["open"]
-        informative[f"%-{pair}raw_high"] = informative["high"]
-        informative[f"%-{pair}raw_low"] = informative["low"]
+        dataframe[f"%-raw_close"] = dataframe["close"]
+        dataframe[f"%-raw_open"] = dataframe["open"]
+        dataframe[f"%-raw_high"] = dataframe["high"]
+        dataframe[f"%-raw_low"] = dataframe["low"]
 ```
 
 Finally, there is no explicit "label" to make - instead it is necessary to assign the `&-action` column which will contain the agent's actions when accessed in `populate_entry/exit_trends()`. In the present example, the neutral action to 0. This value should align with the environment used. FreqAI provides two environments, both use 0 as the neutral action.
@@ -204,10 +175,21 @@ As you begin to modify the strategy and the prediction model, you will quickly r
                 pnl = self.get_unrealized_profit()
 
                 factor = 100
+
+                # 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_{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 (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
@@ -272,7 +254,6 @@ FreqAI also provides a built in episodic summary logger called `self.tensorboard
 !!! 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.
 
-
 ### Choosing a base environment
 
 FreqAI provides three base environments, `Base3ActionRLEnvironment`, `Base4ActionEnvironment` and `Base5ActionEnvironment`. As the names imply, the environments are customized for agents that can select from 3, 4 or 5 actions. The `Base3ActionEnvironment` is the simplest, the agent can select from hold, long, or short. This environment can also be used for long-only bots (it automatically follows the `can_short` flag from the strategy), where long is the enter condition and short is the exit condition. Meanwhile, in the `Base4ActionEnvironment`, the agent can enter long, enter short, hold neutral, or exit position. Finally, in the `Base5ActionEnvironment`, the agent has the same actions as Base4, but instead of a single exit action, it separates exit long and exit short. The main changes stemming from the environment selection include:

docs/freqai-running.md

@@ -67,6 +67,10 @@ Backtesting mode requires [downloading the necessary data](#downloading-data-to-
     *want* to retrain a new model with the same config file, you should simply change the `identifier`.
     This way, you can return to using any model you wish by simply specifying the `identifier`.
 
+!!! Note
+    Backtesting calls `set_freqai_targets()` one time for each backtest window (where the number of windows is the full backtest timerange divided by the `backtest_period_days` parameter). Doing this means that the targets simulate dry/live behavior without look ahead bias. However, the definition of the features in `feature_engineering_*()` is performed once on the entire backtest timerange. This means that you should be sure that features do look-ahead into the future.
+    More details about look-ahead bias can be found in [Common Mistakes](strategy-customization.md#common-mistakes-when-developing-strategies).
+
 ---
 
 ### Saving prediction data
@@ -116,7 +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.
 
@@ -135,7 +139,7 @@ freqtrade hyperopt --hyperopt-loss SharpeHyperOptLoss --strategy FreqaiExampleSt
 `hyperopt` requires you to have the data pre-downloaded in the same fashion as if you were doing [backtesting](#backtesting). In addition, you must consider some restrictions when trying to hyperopt FreqAI strategies:
 
 - The `--analyze-per-epoch` hyperopt parameter is not compatible with FreqAI.
-- It's not possible to hyperopt indicators in the `populate_any_indicators()` function. This means that you cannot optimize model parameters using hyperopt. Apart from this exception, it is possible to optimize all other [spaces](hyperopt.md#running-hyperopt-with-smaller-search-space).
+- It's not possible to hyperopt indicators in the `feature_engineering_*()` and `set_freqai_targets()` functions. This means that you cannot optimize model parameters using hyperopt. Apart from this exception, it is possible to optimize all other [spaces](hyperopt.md#running-hyperopt-with-smaller-search-space).
 - The backtesting instructions also apply to hyperopt.
 
 The best method for combining hyperopt and FreqAI is to focus on hyperopting entry/exit thresholds/criteria. You need to focus on hyperopting parameters that are not used in your features. For example, you should not try to hyperopt rolling window lengths in the feature creation, or any part of the FreqAI config which changes predictions. In order to efficiently hyperopt the FreqAI strategy, FreqAI stores predictions as dataframes and reuses them. Hence the requirement to hyperopt entry/exit thresholds/criteria only.
@@ -161,20 +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
 
@@ -70,11 +73,11 @@ pip install -r requirements-freqai.txt
 
 ### 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/hyperopt.md

@@ -50,7 +50,7 @@ usage: freqtrade hyperopt [-h] [-v] [--logfile FILE] [-V] [-c PATH] [-d PATH]
                           [--eps] [--dmmp] [--enable-protections]
                           [--dry-run-wallet DRY_RUN_WALLET]
                           [--timeframe-detail TIMEFRAME_DETAIL] [-e INT]
-                          [--spaces {all,buy,sell,roi,stoploss,trailing,protection,default} [{all,buy,sell,roi,stoploss,trailing,protection,default} ...]]
+                          [--spaces {all,buy,sell,roi,stoploss,trailing,protection,trades,default} [{all,buy,sell,roi,stoploss,trailing,protection,trades,default} ...]]
                           [--print-all] [--no-color] [--print-json] [-j JOBS]
                           [--random-state INT] [--min-trades INT]
                           [--hyperopt-loss NAME] [--disable-param-export]
@@ -96,7 +96,7 @@ optional arguments:
                         Specify detail timeframe for backtesting (`1m`, `5m`,
                         `30m`, `1h`, `1d`).
   -e INT, --epochs INT  Specify number of epochs (default: 100).
-  --spaces {all,buy,sell,roi,stoploss,trailing,protection,default} [{all,buy,sell,roi,stoploss,trailing,protection,default} ...]
+  --spaces {all,buy,sell,roi,stoploss,trailing,protection,trades,default} [{all,buy,sell,roi,stoploss,trailing,protection,trades,default} ...]
                         Specify which parameters to hyperopt. Space-separated
                         list.
   --print-all           Print all results, not only the best ones.
@@ -180,6 +180,7 @@ Rarely you may also need to create a [nested class](advanced-hyperopt.md#overrid
 * `generate_roi_table` - for custom ROI optimization (if you need the ranges for the values in the ROI table that differ from default or the number of entries (steps) in the ROI table which differs from the default 4 steps)
 * `stoploss_space` - for custom stoploss optimization (if you need the range for the stoploss parameter in the optimization hyperspace that differs from default)
 * `trailing_space` - for custom trailing stop optimization (if you need the ranges for the trailing stop parameters in the optimization hyperspace that differ from default)
+* `max_open_trades_space` - for custom max_open_trades optimization (if you need the ranges for the max_open_trades parameter in the optimization hyperspace that differ from default)
 
 !!! Tip "Quickly optimize ROI, stoploss and trailing stoploss"
     You can quickly optimize the spaces `roi`, `stoploss` and `trailing` without changing anything in your strategy.
@@ -365,7 +366,7 @@ class MyAwesomeStrategy(IStrategy):
     timeframe = '15m'
     minimal_roi = {
         "0":  0.10
-    },
+    }
     # Define the parameter spaces
     buy_ema_short = IntParameter(3, 50, default=5)
     buy_ema_long = IntParameter(15, 200, default=50)
@@ -400,7 +401,7 @@ class MyAwesomeStrategy(IStrategy):
         return dataframe
 
     def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
-          conditions = []
+        conditions = []
         conditions.append(qtpylib.crossed_above(
                 dataframe[f'ema_long_{self.buy_ema_long.value}'], dataframe[f'ema_short_{self.buy_ema_short.value}']
             ))
@@ -643,6 +644,7 @@ Legal values are:
 * `roi`: just optimize the minimal profit table for your strategy
 * `stoploss`: search for the best stoploss value
 * `trailing`: search for the best trailing stop values
+* `trades`: search for the best max open trades values
 * `protection`: search for the best protection parameters (read the [protections section](#optimizing-protections) on how to properly define these)
 * `default`: `all` except `trailing` and `protection`
 * space-separated list of any of the above values for example `--spaces roi stoploss`
@@ -916,5 +918,5 @@ Once the optimized strategy has been implemented into your strategy, you should
 To achieve same the results (number of trades, their durations, profit, etc.) as during Hyperopt, please use the same configuration and parameters (timerange, timeframe, ...) used for hyperopt `--dmmp`/`--disable-max-market-positions` and `--eps`/`--enable-position-stacking` for Backtesting.
 
 Should results not match, please double-check to make sure you transferred all conditions correctly.
-Pay special care to the stoploss (and trailing stoploss) parameters, as these are often set in configuration files, which override changes to the strategy.
-You should also carefully review the log of your backtest to ensure that there were no parameters inadvertently set by the configuration (like `stoploss` or `trailing_stop`).
+Pay special care to the stoploss, max_open_trades and trailing stoploss parameters, as these are often set in configuration files, which override changes to the strategy.
+You should also carefully review the log of your backtest to ensure that there were no parameters inadvertently set by the configuration (like `stoploss`, `max_open_trades` or `trailing_stop`).

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

docs/leverage.md

@@ -67,8 +67,6 @@ You will also have to pick a "margin mode" (explanation below) - with freqtrade
 Freqtrade follows the [ccxt naming conventions for futures](https://docs.ccxt.com/en/latest/manual.html?#perpetual-swap-perpetual-future).
 A futures pair will therefore have the naming of `base/quote:settle` (e.g. `ETH/USDT:USDT`).
 
-Binance is currently still an exception to this naming scheme, where pairs are named `ETH/USDT` also for futures markets, but will be aligned as soon as CCXT is ready.
-
 ### Margin mode
 
 On top of `trading_mode` - you will also have to configure your `margin_mode`.
@@ -92,6 +90,8 @@ One account is used to share collateral between markets (trading pairs). Margin
 "margin_mode": "cross"
 ```
 
+Please read the [exchange specific notes](exchanges.md) for exchanges that support this mode and how they differ.
+
 ## Set leverage to use
 
 Different strategies and risk profiles will require different levels of leverage.

docs/requirements-docs.txt

@@ -1,6 +1,6 @@
 markdown==3.3.7
 mkdocs==1.4.2
-mkdocs-material==8.5.11
+mkdocs-material==9.0.13
 mdx_truly_sane_lists==1.3
-pymdown-extensions==9.9
+pymdown-extensions==9.9.2
 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-advanced.md

@@ -80,7 +80,7 @@ class AwesomeStrategy(IStrategy):
 ## Enter Tag
 
 When your strategy has multiple buy signals, you can name the signal that triggered.
-Then you can access you buy signal on `custom_exit`
+Then you can access your buy signal on `custom_exit`
 
 ```python
 def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:

docs/strategy-callbacks.md

@@ -659,6 +659,7 @@ Position adjustments will always be applied in the direction of the trade, so a
 
 !!! Warning "Backtesting"
     During backtesting this callback is called for each candle in `timeframe` or `timeframe_detail`, so run-time performance will be affected.
+    This can also cause deviating results between live and backtesting, since backtesting can adjust the trade only once per candle, whereas live could adjust the trade multiple times per candle.
 
 ``` python
 from freqtrade.persistence import Trade
@@ -827,7 +828,7 @@ class AwesomeStrategy(IStrategy):
 
         """
         # Limit orders to use and follow SMA200 as price target for the first 10 minutes since entry trigger for BTC/USDT pair.
-        if pair == 'BTC/USDT' and entry_tag == 'long_sma200' and side == 'long' and (current_time - timedelta(minutes=10) > trade.open_date_utc:
+        if pair == 'BTC/USDT' and entry_tag == 'long_sma200' and side == 'long' and (current_time - timedelta(minutes=10)) > trade.open_date_utc:
             # just cancel the order if it has been filled more than half of the amount
             if order.filled > order.remaining:
                 return None

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/strategy_migration.md

@@ -477,3 +477,254 @@ after:
     "ignore_buying_expired_candle_after": 120
 }
 ```
+
+## FreqAI strategy
+
+The `populate_any_indicators()` method has been split into `feature_engineering_expand_all()`, `feature_engineering_expand_basic()`, `feature_engineering_standard()` and`set_freqai_targets()`.
+
+For each new function, the pair (and timeframe where necessary) will be automatically added to the column.
+As such, the definition of features becomes much simpler with the new logic.
+
+For a full explanation of each method, please go to the corresponding [freqAI documentation page](freqai-feature-engineering.md#defining-the-features)
+
+``` python linenums="1" hl_lines="12-37 39-42 63-65 67-75"
+
+def populate_any_indicators(
+        self, pair, df, tf, informative=None, set_generalized_indicators=False
+    ):
+
+        if informative is None:
+            informative = self.dp.get_pair_dataframe(pair, tf)
+
+        # first loop is automatically duplicating indicators for time periods
+        for t in self.freqai_info["feature_parameters"]["indicator_periods_candles"]:
+
+            t = int(t)
+            informative[f"%-{pair}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t)
+            informative[f"%-{pair}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
+            informative[f"%-{pair}adx-period_{t}"] = ta.ADX(informative, timeperiod=t)
+            informative[f"%-{pair}sma-period_{t}"] = ta.SMA(informative, timeperiod=t)
+            informative[f"%-{pair}ema-period_{t}"] = ta.EMA(informative, timeperiod=t)
+
+            bollinger = qtpylib.bollinger_bands(
+                qtpylib.typical_price(informative), window=t, stds=2.2
+            )
+            informative[f"{pair}bb_lowerband-period_{t}"] = bollinger["lower"]
+            informative[f"{pair}bb_middleband-period_{t}"] = bollinger["mid"]
+            informative[f"{pair}bb_upperband-period_{t}"] = bollinger["upper"]
+
+            informative[f"%-{pair}bb_width-period_{t}"] = (
+                informative[f"{pair}bb_upperband-period_{t}"]
+                - informative[f"{pair}bb_lowerband-period_{t}"]
+            ) / informative[f"{pair}bb_middleband-period_{t}"]
+            informative[f"%-{pair}close-bb_lower-period_{t}"] = (
+                informative["close"] / informative[f"{pair}bb_lowerband-period_{t}"]
+            )
+
+            informative[f"%-{pair}roc-period_{t}"] = ta.ROC(informative, timeperiod=t)
+
+            informative[f"%-{pair}relative_volume-period_{t}"] = (
+                informative["volume"] / informative["volume"].rolling(t).mean()
+            ) # (1)
+
+        informative[f"%-{pair}pct-change"] = informative["close"].pct_change()
+        informative[f"%-{pair}raw_volume"] = informative["volume"]
+        informative[f"%-{pair}raw_price"] = informative["close"]
+        # (2)
+
+        indicators = [col for col in informative if col.startswith("%")]
+        # This loop duplicates and shifts all indicators to add a sense of recency to data
+        for n in range(self.freqai_info["feature_parameters"]["include_shifted_candles"] + 1):
+            if n == 0:
+                continue
+            informative_shift = informative[indicators].shift(n)
+            informative_shift = informative_shift.add_suffix("_shift-" + str(n))
+            informative = pd.concat((informative, informative_shift), axis=1)
+
+        df = merge_informative_pair(df, informative, self.config["timeframe"], tf, ffill=True)
+        skip_columns = [
+            (s + "_" + tf) for s in ["date", "open", "high", "low", "close", "volume"]
+        ]
+        df = df.drop(columns=skip_columns)
+
+        # Add generalized indicators here (because in live, it will call this
+        # function to populate indicators during training). Notice how we ensure not to
+        # add them multiple times
+        if set_generalized_indicators:
+            df["%-day_of_week"] = (df["date"].dt.dayofweek + 1) / 7
+            df["%-hour_of_day"] = (df["date"].dt.hour + 1) / 25
+            # (3)
+
+            # user adds targets here by prepending them with &- (see convention below)
+            df["&-s_close"] = (
+                df["close"]
+                .shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
+                .rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
+                .mean()
+                / df["close"]
+                - 1
+            )  # (4)
+
+        return df
+```
+
+1. Features - Move to `feature_engineering_expand_all`
+2. Basic features, not expanded across `include_periods_candles` - move to`feature_engineering_expand_basic()`.
+3. Standard features which should not be expanded - move to `feature_engineering_standard()`.
+4. Targets - Move this part to `set_freqai_targets()`.
+
+### freqai - feature engineering expand all
+
+Features will now expand automatically. As such, the expansion loops, as well as the `{pair}` / `{timeframe}` parts will need to be removed.
+
+``` python linenums="1"
+    def feature_engineering_expand_all(self, dataframe, period, **kwargs):
+        """
+        *Only functional with FreqAI enabled strategies*
+        This function will automatically expand the defined features on the config defined
+        `indicator_periods_candles`, `include_timeframes`, `include_shifted_candles`, and
+        `include_corr_pairs`. In other words, a single feature defined in this function
+        will automatically expand to a total of
+        `indicator_periods_candles` * `include_timeframes` * `include_shifted_candles` *
+        `include_corr_pairs` numbers of features added to the model.
+
+        All features must be prepended with `%` to be recognized by FreqAI internals.
+
+        More details on how these config defined parameters accelerate feature engineering
+        in the documentation at:
+
+        https://www.freqtrade.io/en/latest/freqai-parameter-table/#feature-parameters
+
+        https://www.freqtrade.io/en/latest/freqai-feature-engineering/#defining-the-features
+
+        :param df: strategy dataframe which will receive the features
+        :param period: period of the indicator - usage example:
+        dataframe["%-ema-period"] = ta.EMA(dataframe, timeperiod=period)
+        """
+
+        dataframe["%-rsi-period"] = ta.RSI(dataframe, timeperiod=period)
+        dataframe["%-mfi-period"] = ta.MFI(dataframe, timeperiod=period)
+        dataframe["%-adx-period"] = ta.ADX(dataframe, timeperiod=period)
+        dataframe["%-sma-period"] = ta.SMA(dataframe, timeperiod=period)
+        dataframe["%-ema-period"] = ta.EMA(dataframe, timeperiod=period)
+
+        bollinger = qtpylib.bollinger_bands(
+            qtpylib.typical_price(dataframe), window=period, stds=2.2
+        )
+        dataframe["bb_lowerband-period"] = bollinger["lower"]
+        dataframe["bb_middleband-period"] = bollinger["mid"]
+        dataframe["bb_upperband-period"] = bollinger["upper"]
+
+        dataframe["%-bb_width-period"] = (
+            dataframe["bb_upperband-period"]
+            - dataframe["bb_lowerband-period"]
+        ) / dataframe["bb_middleband-period"]
+        dataframe["%-close-bb_lower-period"] = (
+            dataframe["close"] / dataframe["bb_lowerband-period"]
+        )
+
+        dataframe["%-roc-period"] = ta.ROC(dataframe, timeperiod=period)
+
+        dataframe["%-relative_volume-period"] = (
+            dataframe["volume"] / dataframe["volume"].rolling(period).mean()
+        )
+
+        return dataframe
+
+```
+
+### Freqai - feature engineering basic
+
+Basic features. Make sure to remove the `{pair}` part from your features.
+
+``` python linenums="1"
+    def feature_engineering_expand_basic(self, dataframe, **kwargs):
+        """
+        *Only functional with FreqAI enabled strategies*
+        This function will automatically expand the defined features on the config defined
+        `include_timeframes`, `include_shifted_candles`, and `include_corr_pairs`.
+        In other words, a single feature defined in this function
+        will automatically expand to a total of
+        `include_timeframes` * `include_shifted_candles` * `include_corr_pairs`
+        numbers of features added to the model.
+
+        Features defined here will *not* be automatically duplicated on user defined
+        `indicator_periods_candles`
+
+        All features must be prepended with `%` to be recognized by FreqAI internals.
+
+        More details on how these config defined parameters accelerate feature engineering
+        in the documentation at:
+
+        https://www.freqtrade.io/en/latest/freqai-parameter-table/#feature-parameters
+
+        https://www.freqtrade.io/en/latest/freqai-feature-engineering/#defining-the-features
+
+        :param df: strategy dataframe which will receive the features
+        dataframe["%-pct-change"] = dataframe["close"].pct_change()
+        dataframe["%-ema-200"] = ta.EMA(dataframe, timeperiod=200)
+        """
+        dataframe["%-pct-change"] = dataframe["close"].pct_change()
+        dataframe["%-raw_volume"] = dataframe["volume"]
+        dataframe["%-raw_price"] = dataframe["close"]
+        return dataframe
+```
+
+### FreqAI - feature engineering standard
+
+``` python linenums="1"
+    def feature_engineering_standard(self, dataframe, **kwargs):
+        """
+        *Only functional with FreqAI enabled strategies*
+        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 created by all other
+        freqai_feature_engineering_* functions.
+
+        This function is a good place to do custom exotic feature extractions (e.g. tsfresh).
+        This function is a good place for any feature that should not be auto-expanded upon
+        (e.g. day of the week).
+
+        All features must be prepended with `%` to be recognized by FreqAI internals.
+
+        More details about feature engineering available:
+
+        https://www.freqtrade.io/en/latest/freqai-feature-engineering
+
+        :param df: strategy dataframe which will receive the features
+        usage example: dataframe["%-day_of_week"] = (dataframe["date"].dt.dayofweek + 1) / 7
+        """
+        dataframe["%-day_of_week"] = dataframe["date"].dt.dayofweek
+        dataframe["%-hour_of_day"] = dataframe["date"].dt.hour
+        return dataframe
+```
+
+### FreqAI - set Targets
+
+Targets now get their own, dedicated method.
+
+``` python linenums="1"
+    def set_freqai_targets(self, dataframe, **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.
+
+        More details about feature engineering available:
+
+        https://www.freqtrade.io/en/latest/freqai-feature-engineering
+
+        :param df: strategy dataframe which will receive the targets
+        usage example: dataframe["&-target"] = dataframe["close"].shift(-1) / dataframe["close"]
+        """
+        dataframe["&-s_close"] = (
+            dataframe["close"]
+            .shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
+            .rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
+            .mean()
+            / dataframe["close"]
+            - 1
+            )
+
+        return dataframe
+```

docs/telegram-usage.md

@@ -162,26 +162,33 @@ 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)
+| **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 +200,7 @@ 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
 

environment.yml

@@ -12,7 +12,7 @@ dependencies:
     - py-find-1st
     - aiohttp
     - SQLAlchemy
-    - python-telegram-bot
+    - python-telegram-bot<20.0.0
     - arrow
     - cachetools
     - requests
@@ -54,7 +54,7 @@ dependencies:
     # 3/4 req hyperopt
 
     - scipy
-    - scikit-learn
+    - scikit-learn<1.2.0
     - filelock
     - scikit-optimize
     - progressbar2

freqtrade/__init__.py

@@ -1,19 +1,20 @@
 """ Freqtrade bot """
-__version__ = '2022.12'
+__version__ = '2023.2'
 
 if 'dev' in __version__:
+    from pathlib import Path
     try:
         import subprocess
+        freqtrade_basedir = Path(__file__).parent
 
         __version__ = __version__ + '-' + subprocess.check_output(
             ['git', 'log', '--format="%h"', '-n 1'],
-            stderr=subprocess.DEVNULL).decode("utf-8").rstrip().strip('"')
+            stderr=subprocess.DEVNULL, cwd=freqtrade_basedir).decode("utf-8").rstrip().strip('"')
 
     except Exception:  # pragma: no cover
         # git not available, ignore
         try:
             # Try Fallback to freqtrade_commit file (created by CI while building docker image)
-            from pathlib import Path
             versionfile = Path('./freqtrade_commit')
             if versionfile.is_file():
                 __version__ = f"docker-{__version__}-{versionfile.read_text()[:8]}"

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/cli_options.py

@@ -251,7 +251,8 @@ AVAILABLE_CLI_OPTIONS = {
     "spaces": Arg(
         '--spaces',
         help='Specify which parameters to hyperopt. Space-separated list.',
-        choices=['all', 'buy', 'sell', 'roi', 'stoploss', 'trailing', 'protection', 'default'],
+        choices=['all', 'buy', 'sell', 'roi', 'stoploss',
+                 'trailing', 'protection', 'trades', 'default'],
         nargs='+',
         default='default',
     ),
@@ -632,10 +633,11 @@ AVAILABLE_CLI_OPTIONS = {
               "1: by enter_tag, "
               "2: by enter_tag and exit_tag, "
               "3: by pair and enter_tag, "
-              "4: by pair, enter_ and exit_tag (this can get quite large)"),
+              "4: by pair, enter_ and exit_tag (this can get quite large), "
+              "5: by exit_tag"),
         nargs='+',
         default=['0', '1', '2'],
-        choices=['0', '1', '2', '3', '4'],
+        choices=['0', '1', '2', '3', '4', '5'],
     ),
     "enter_reason_list": Arg(
         "--enter-reason-list",

freqtrade/commands/data_commands.py

@@ -14,6 +14,7 @@ from freqtrade.exceptions import OperationalException
 from freqtrade.exchange import market_is_active, timeframe_to_minutes
 from freqtrade.plugins.pairlist.pairlist_helpers import dynamic_expand_pairlist, expand_pairlist
 from freqtrade.resolvers import ExchangeResolver
+from freqtrade.util.binance_mig import migrate_binance_futures_data
 
 
 logger = logging.getLogger(__name__)
@@ -86,6 +87,7 @@ def start_download_data(args: Dict[str, Any]) -> None:
                     "Please use `--dl-trades` instead for this exchange "
                     "(will unfortunately take a long time)."
                     )
+            migrate_binance_futures_data(config)
             pairs_not_available = refresh_backtest_ohlcv_data(
                 exchange, pairs=expanded_pairs, timeframes=config['timeframes'],
                 datadir=config['datadir'], timerange=timerange,
@@ -145,6 +147,7 @@ def start_convert_data(args: Dict[str, Any], ohlcv: bool = True) -> None:
     """
     config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
     if ohlcv:
+        migrate_binance_futures_data(config)
         candle_types = [CandleType.from_string(ct) for ct in config.get('candle_types', ['spot'])]
         for candle_type in candle_types:
             convert_ohlcv_format(config,

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/configuration.py

@@ -28,7 +28,7 @@ class Configuration:
     Reuse this class for the bot, backtesting, hyperopt and every script that required configuration
     """
 
-    def __init__(self, args: Dict[str, Any], runmode: RunMode = None) -> None:
+    def __init__(self, args: Dict[str, Any], runmode: Optional[RunMode] = None) -> None:
         self.args = args
         self.config: Optional[Config] = None
         self.runmode = runmode

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

@@ -6,7 +6,7 @@ import re
 import sys
 from copy import deepcopy
 from pathlib import Path
-from typing import Any, Dict, List
+from typing import Any, Dict, List, Optional
 
 import rapidjson
 
@@ -75,7 +75,8 @@ def load_config_file(path: str) -> Dict[str, Any]:
     return config
 
 
-def load_from_files(files: List[str], base_path: Path = None, level: int = 0) -> Dict[str, Any]:
+def load_from_files(
+        files: List[str], base_path: Optional[Path] = None, level: int = 0) -> Dict[str, Any]:
     """
     Recursively load configuration files if specified.
     Sub-files are assumed to be relative to the initial config.

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", ]
                 },
@@ -636,7 +640,6 @@ SCHEMA_TRADE_REQUIRED = [
 
 SCHEMA_BACKTEST_REQUIRED = [
     'exchange',
-    'max_open_trades',
     'stake_currency',
     'stake_amount',
     'dry_run_wallet',
@@ -646,6 +649,7 @@ SCHEMA_BACKTEST_REQUIRED = [
 SCHEMA_BACKTEST_REQUIRED_FINAL = SCHEMA_BACKTEST_REQUIRED + [
     'stoploss',
     'minimal_roi',
+    'max_open_trades'
 ]
 
 SCHEMA_MINIMAL_REQUIRED = [
@@ -679,5 +683,7 @@ 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

@@ -10,7 +10,7 @@ from typing import Any, Dict, List, Optional, Union
 import numpy as np
 import pandas as pd
 
-from freqtrade.constants import LAST_BT_RESULT_FN
+from freqtrade.constants import LAST_BT_RESULT_FN, IntOrInf
 from freqtrade.exceptions import OperationalException
 from freqtrade.misc import json_load
 from freqtrade.optimize.backtest_caching import get_backtest_metadata_filename
@@ -90,7 +90,8 @@ def get_latest_hyperopt_filename(directory: Union[Path, str]) -> str:
         return 'hyperopt_results.pickle'
 
 
-def get_latest_hyperopt_file(directory: Union[Path, str], predef_filename: str = None) -> Path:
+def get_latest_hyperopt_file(
+        directory: Union[Path, str], predef_filename: Optional[str] = None) -> Path:
     """
     Get latest hyperopt export based on '.last_result.json'.
     :param directory: Directory to search for last result
@@ -193,7 +194,7 @@ def get_backtest_resultlist(dirname: Path):
 
 
 def find_existing_backtest_stats(dirname: Union[Path, str], run_ids: Dict[str, str],
-                                 min_backtest_date: datetime = None) -> Dict[str, Any]:
+                                 min_backtest_date: Optional[datetime] = None) -> Dict[str, Any]:
     """
     Find existing backtest stats that match specified run IDs and load them.
     :param dirname: pathlib.Path object, or string pointing to the file.
@@ -332,7 +333,7 @@ def analyze_trade_parallelism(results: pd.DataFrame, timeframe: str) -> pd.DataF
 
 
 def evaluate_result_multi(results: pd.DataFrame, timeframe: str,
-                          max_open_trades: int) -> pd.DataFrame:
+                          max_open_trades: IntOrInf) -> pd.DataFrame:
     """
     Find overlapping trades by expanding each trade once per period it was open
     and then counting overlaps

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:
@@ -281,7 +284,7 @@ class DataProvider:
     def historic_ohlcv(
         self,
         pair: str,
-        timeframe: str = None,
+        timeframe: Optional[str] = None,
         candle_type: str = ''
     ) -> DataFrame:
         """
@@ -333,7 +336,7 @@ class DataProvider:
     def get_pair_dataframe(
         self,
         pair: str,
-        timeframe: str = None,
+        timeframe: Optional[str] = None,
         candle_type: str = ''
     ) -> DataFrame:
         """
@@ -415,16 +418,14 @@ class DataProvider:
 
     def refresh(self,
                 pairlist: ListPairsWithTimeframes,
-                helping_pairs: ListPairsWithTimeframes = None) -> None:
+                helping_pairs: Optional[ListPairsWithTimeframes] = None) -> None:
         """
         Refresh data, called with each cycle
         """
         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:
@@ -439,7 +440,7 @@ class DataProvider:
     def ohlcv(
         self,
         pair: str,
-        timeframe: str = None,
+        timeframe: Optional[str] = None,
         copy: bool = True,
         candle_type: str = ''
     ) -> DataFrame:
@@ -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

@@ -52,7 +52,7 @@ def _process_candles_and_indicators(pairlist, strategy_name, trades, signal_cand
     return analysed_trades_dict
 
 
-def _analyze_candles_and_indicators(pair, trades, signal_candles):
+def _analyze_candles_and_indicators(pair, trades: pd.DataFrame, signal_candles: pd.DataFrame):
     buyf = signal_candles
 
     if len(buyf) > 0:
@@ -120,7 +120,7 @@ def _do_group_table_output(bigdf, glist):
 
         else:
             agg_mask = {'profit_abs': ['count', 'sum', 'median', 'mean'],
-                        'profit_ratio': ['sum', 'median', 'mean']}
+                        'profit_ratio': ['median', 'mean', 'sum']}
             agg_cols = ['num_buys', 'profit_abs_sum', 'profit_abs_median',
                         'profit_abs_mean', 'median_profit_pct', 'mean_profit_pct',
                         'total_profit_pct']
@@ -141,6 +141,12 @@ def _do_group_table_output(bigdf, glist):
             # 4: profit summaries grouped by pair, enter_ and exit_tag (this can get quite large)
             if g == "4":
                 group_mask = ['pair', 'enter_reason', 'exit_reason']
+
+            # 5: profit summaries grouped by exit_tag
+            if g == "5":
+                group_mask = ['exit_reason']
+                sortcols = ['exit_reason']
+
             if group_mask:
                 new = bigdf.groupby(group_mask).agg(agg_mask).reset_index()
                 new.columns = group_mask + agg_cols

freqtrade/data/history/history_utils.py

@@ -28,8 +28,8 @@ def load_pair_history(pair: str,
                       fill_up_missing: bool = True,
                       drop_incomplete: bool = False,
                       startup_candles: int = 0,
-                      data_format: str = None,
-                      data_handler: IDataHandler = None,
+                      data_format: Optional[str] = None,
+                      data_handler: Optional[IDataHandler] = None,
                       candle_type: CandleType = CandleType.SPOT
                       ) -> DataFrame:
     """
@@ -69,7 +69,7 @@ def load_data(datadir: Path,
               fail_without_data: bool = False,
               data_format: str = 'json',
               candle_type: CandleType = CandleType.SPOT,
-              user_futures_funding_rate: int = None,
+              user_futures_funding_rate: Optional[int] = None,
               ) -> Dict[str, DataFrame]:
     """
     Load ohlcv history data for a list of pairs.
@@ -116,7 +116,7 @@ def refresh_data(*, datadir: Path,
                  timeframe: str,
                  pairs: List[str],
                  exchange: Exchange,
-                 data_format: str = None,
+                 data_format: Optional[str] = None,
                  timerange: Optional[TimeRange] = None,
                  candle_type: CandleType,
                  ) -> None:
@@ -189,7 +189,7 @@ def _download_pair_history(pair: str, *,
                            timeframe: str = '5m',
                            process: str = '',
                            new_pairs_days: int = 30,
-                           data_handler: IDataHandler = None,
+                           data_handler: Optional[IDataHandler] = None,
                            timerange: Optional[TimeRange] = None,
                            candle_type: CandleType,
                            erase: bool = False,
@@ -272,7 +272,7 @@ def refresh_backtest_ohlcv_data(exchange: Exchange, pairs: List[str], timeframes
                                 datadir: Path, trading_mode: str,
                                 timerange: Optional[TimeRange] = None,
                                 new_pairs_days: int = 30, erase: bool = False,
-                                data_format: str = None,
+                                data_format: Optional[str] = None,
                                 prepend: bool = False,
                                 ) -> List[str]:
     """

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.
@@ -374,6 +374,21 @@ class IDataHandler(ABC):
                 logger.warning(f"{pair}, {candle_type}, {timeframe}, "
                                f"data ends at {pairdata.iloc[-1]['date']:%Y-%m-%d %H:%M:%S}")
 
+    def rename_futures_data(
+            self, pair: str, new_pair: str, timeframe: str, candle_type: CandleType):
+        """
+        Temporary method to migrate data from old naming to new naming (BTC/USDT -> BTC/USDT:USDT)
+        Only used for binance to support the binance futures naming unification.
+        """
+
+        file_old = self._pair_data_filename(self._datadir, pair, timeframe, candle_type)
+        file_new = self._pair_data_filename(self._datadir, new_pair, timeframe, candle_type)
+        # print(file_old, file_new)
+        if file_new.exists():
+            logger.warning(f"{file_new} exists already, can't migrate {pair}.")
+            return
+        file_old.rename(file_new)
+
 
 def get_datahandlerclass(datatype: str) -> Type[IDataHandler]:
     """
@@ -403,8 +418,8 @@ def get_datahandlerclass(datatype: str) -> Type[IDataHandler]:
         raise ValueError(f"No datahandler for datatype {datatype} available.")
 
 
-def get_datahandler(datadir: Path, data_format: str = None,
-                    data_handler: IDataHandler = None) -> IDataHandler:
+def get_datahandler(datadir: Path, data_format: Optional[str] = None,
+                    data_handler: Optional[IDataHandler] = None) -> IDataHandler:
     """
     :param datadir: Folder to save data
     :param data_format: dataformat to use

freqtrade/data/metrics.py

@@ -1,4 +1,6 @@
 import logging
+import math
+from datetime import datetime
 from typing import Dict, Tuple
 
 import numpy as np
@@ -190,3 +192,119 @@ def calculate_cagr(days_passed: int, starting_balance: float, final_balance: flo
     :return: CAGR
     """
     return (final_balance / starting_balance) ** (1 / (days_passed / 365)) - 1
+
+
+def calculate_expectancy(trades: pd.DataFrame) -> float:
+    """
+    Calculate expectancy
+    :param trades: DataFrame containing trades (requires columns close_date and profit_abs)
+    :return: expectancy
+    """
+    if len(trades) == 0:
+        return 0
+
+    expectancy = 1
+
+    profit_sum = trades.loc[trades['profit_abs'] > 0, 'profit_abs'].sum()
+    loss_sum = abs(trades.loc[trades['profit_abs'] < 0, 'profit_abs'].sum())
+    nb_win_trades = len(trades.loc[trades['profit_abs'] > 0])
+    nb_loss_trades = len(trades.loc[trades['profit_abs'] < 0])
+
+    if (nb_win_trades > 0) and (nb_loss_trades > 0):
+        average_win = profit_sum / nb_win_trades
+        average_loss = loss_sum / nb_loss_trades
+        risk_reward_ratio = average_win / average_loss
+        winrate = nb_win_trades / len(trades)
+        expectancy = ((1 + risk_reward_ratio) * winrate) - 1
+    elif nb_win_trades == 0:
+        expectancy = 0
+
+    return expectancy
+
+
+def calculate_sortino(trades: pd.DataFrame, min_date: datetime, max_date: datetime,
+                      starting_balance: float) -> float:
+    """
+    Calculate sortino
+    :param trades: DataFrame containing trades (requires columns profit_abs)
+    :return: sortino
+    """
+    if (len(trades) == 0) or (min_date is None) or (max_date is None) or (min_date == max_date):
+        return 0
+
+    total_profit = trades['profit_abs'] / starting_balance
+    days_period = max(1, (max_date - min_date).days)
+
+    expected_returns_mean = total_profit.sum() / days_period
+
+    down_stdev = np.std(trades.loc[trades['profit_abs'] < 0, 'profit_abs'] / starting_balance)
+
+    if down_stdev != 0 and not np.isnan(down_stdev):
+        sortino_ratio = expected_returns_mean / down_stdev * np.sqrt(365)
+    else:
+        # Define high (negative) sortino ratio to be clear that this is NOT optimal.
+        sortino_ratio = -100
+
+    # print(expected_returns_mean, down_stdev, sortino_ratio)
+    return sortino_ratio
+
+
+def calculate_sharpe(trades: pd.DataFrame, min_date: datetime, max_date: datetime,
+                     starting_balance: float) -> float:
+    """
+    Calculate sharpe
+    :param trades: DataFrame containing trades (requires column profit_abs)
+    :return: sharpe
+    """
+    if (len(trades) == 0) or (min_date is None) or (max_date is None) or (min_date == max_date):
+        return 0
+
+    total_profit = trades['profit_abs'] / starting_balance
+    days_period = max(1, (max_date - min_date).days)
+
+    expected_returns_mean = total_profit.sum() / days_period
+    up_stdev = np.std(total_profit)
+
+    if up_stdev != 0:
+        sharp_ratio = expected_returns_mean / up_stdev * np.sqrt(365)
+    else:
+        # Define high (negative) sharpe ratio to be clear that this is NOT optimal.
+        sharp_ratio = -100
+
+    # print(expected_returns_mean, up_stdev, sharp_ratio)
+    return sharp_ratio
+
+
+def calculate_calmar(trades: pd.DataFrame, min_date: datetime, max_date: datetime,
+                     starting_balance: float) -> float:
+    """
+    Calculate calmar
+    :param trades: DataFrame containing trades (requires columns close_date and profit_abs)
+    :return: calmar
+    """
+    if (len(trades) == 0) or (min_date is None) or (max_date is None) or (min_date == max_date):
+        return 0
+
+    total_profit = trades['profit_abs'].sum() / starting_balance
+    days_period = max(1, (max_date - min_date).days)
+
+    # adding slippage of 0.1% per trade
+    # total_profit = total_profit - 0.0005
+    expected_returns_mean = total_profit / days_period * 100
+
+    # calculate max drawdown
+    try:
+        _, _, _, _, _, max_drawdown = calculate_max_drawdown(
+            trades, value_col="profit_abs", starting_balance=starting_balance
+        )
+    except ValueError:
+        max_drawdown = 0
+
+    if max_drawdown != 0:
+        calmar_ratio = expected_returns_mean / max_drawdown * math.sqrt(365)
+    else:
+        # Define high (negative) calmar ratio to be clear that this is NOT optimal.
+        calmar_ratio = -100
+
+    # print(expected_returns_mean, max_drawdown, calmar_ratio)
+    return calmar_ratio

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

@@ -6,6 +6,7 @@ from freqtrade.enums.exittype import ExitType
 from freqtrade.enums.hyperoptstate import HyperoptState
 from freqtrade.enums.marginmode import MarginMode
 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/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/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

@@ -7,11 +7,11 @@ from typing import Dict, List, Optional, Tuple
 import arrow
 import ccxt
 
-from freqtrade.enums import CandleType, MarginMode, TradingMode
+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
-from freqtrade.exchange.types import Tickers
+from freqtrade.exchange.types import OHLCVResponse, Tickers
 from freqtrade.misc import deep_merge_dicts, json_load
 
 
@@ -28,11 +28,16 @@ class Binance(Exchange):
         "trades_pagination": "id",
         "trades_pagination_arg": "fromId",
         "l2_limit_range": [5, 10, 20, 50, 100, 500, 1000],
-        "ccxt_futures_name": "future"
     }
     _ft_has_futures: Dict = {
         "stoploss_order_types": {"limit": "stop", "market": "stop_market"},
         "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]] = [
@@ -78,33 +83,9 @@ 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
 
@@ -112,7 +93,7 @@ class Binance(Exchange):
                                         since_ms: int, candle_type: CandleType,
                                         is_new_pair: bool = False, raise_: bool = False,
                                         until_ms: Optional[int] = None
-                                        ) -> Tuple[str, str, str, List]:
+                                        ) -> OHLCVResponse:
         """
         Overwrite to introduce "fast new pair" functionality by detecting the pair's listing date
         Does not work for other exchanges, which don't return the earliest data when called with "0"
@@ -150,6 +131,7 @@ 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 +141,12 @@ 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

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__)
@@ -21,17 +28,27 @@ class Bybit(Exchange):
 
     _ft_has: Dict = {
         "ohlcv_candle_limit": 1000,
-        "ccxt_futures_name": "linear",
         "ohlcv_has_history": False,
     }
     _ft_has_futures: Dict = {
+        "ohlcv_candle_limit": 200,
         "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 +64,158 @@ 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):
+        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

@@ -3,11 +3,11 @@
 Cryptocurrency Exchanges support
 """
 import asyncio
-import http
 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
 
@@ -21,9 +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)
@@ -36,7 +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 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
@@ -45,12 +46,6 @@ from freqtrade.plugins.pairlist.pairlist_helpers import expand_pairlist
 logger = logging.getLogger(__name__)
 
 
-# Workaround for adding samesite support to pre 3.8 python
-# Only applies to python3.7, and only on certain exchanges (kraken)
-# Replicates the fix from starlette (which is actually causing this problem)
-http.cookies.Morsel._reserved["samesite"] = "SameSite"  # type: ignore
-
-
 class Exchange:
 
     # Parameters to add directly to buy/sell calls (like agreeing to trading agreement)
@@ -474,7 +469,7 @@ class Exchange:
         try:
             if self._api_async:
                 self.loop.run_until_complete(
-                    self._api_async.load_markets(reload=reload))
+                    self._api_async.load_markets(reload=reload, params={}))
 
         except (asyncio.TimeoutError, ccxt.BaseError) as e:
             logger.warning('Could not load async markets. Reason: %s', e)
@@ -483,7 +478,7 @@ class Exchange:
     def _load_markets(self) -> None:
         """ Initialize markets both sync and async """
         try:
-            self._markets = self._api.load_markets()
+            self._markets = self._api.load_markets(params={})
             self._load_async_markets()
             self._last_markets_refresh = arrow.utcnow().int_timestamp
             if self._ft_has['needs_trading_fees']:
@@ -501,7 +496,7 @@ class Exchange:
             return None
         logger.debug("Performing scheduled market reload..")
         try:
-            self._markets = self._api.load_markets(reload=True)
+            self._markets = self._api.load_markets(reload=True, params={})
             # Also reload async markets to avoid issues with newly listed pairs
             self._load_async_markets(reload=True)
             self._last_markets_refresh = arrow.utcnow().int_timestamp
@@ -606,12 +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'):
@@ -682,7 +692,7 @@ class Exchange:
                 f"Freqtrade does not support {mm_value} {trading_mode.value} on {self.name}"
             )
 
-    def get_option(self, param: str, default: Any = None) -> Any:
+    def get_option(self, param: str, default: Optional[Any] = None) -> Any:
         """
         Get parameter value from _ft_has
         """
@@ -840,7 +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
@@ -850,20 +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
@@ -885,20 +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:
@@ -926,20 +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.
@@ -947,7 +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).
         """
@@ -955,7 +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'],
@@ -1121,8 +1147,8 @@ 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
@@ -1167,6 +1193,10 @@ 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))
 
@@ -1357,7 +1387,7 @@ class Exchange:
             raise OperationalException(e) from e
 
     @retrier
-    def fetch_positions(self, pair: str = None) -> List[Dict]:
+    def fetch_positions(self, pair: Optional[str] = None) -> List[Dict]:
         """
         Fetch positions from the exchange.
         If no pair is given, all positions are returned.
@@ -1497,7 +1527,7 @@ 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).
@@ -1540,7 +1570,7 @@ 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
@@ -1578,7 +1608,8 @@ 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 "
@@ -1705,7 +1736,7 @@ class Exchange:
                 return self._config['fee']
             # validate that markets are loaded before trying to get fee
             if self._api.markets is None or len(self._api.markets) == 0:
-                self._api.load_markets()
+                self._api.load_markets(params={})
 
             return self._api.calculate_fee(symbol=symbol, type=type, side=side, amount=amount,
                                            price=price, takerOrMaker=taker_or_maker)['rate']
@@ -1801,7 +1832,7 @@ class Exchange:
     def get_historic_ohlcv(self, pair: str, timeframe: str,
                            since_ms: int, candle_type: CandleType,
                            is_new_pair: bool = False,
-                           until_ms: int = None) -> List:
+                           until_ms: Optional[int] = None) -> List:
         """
         Get candle history using asyncio and returns the list of candles.
         Handles all async work for this.
@@ -1813,32 +1844,18 @@ class Exchange:
         :param candle_type: '', mark, index, premiumIndex, or funding_rate
         :return: List with candle (OHLCV) data
         """
-        pair, _, _, data = self.loop.run_until_complete(
+        pair, _, _, data, _ = self.loop.run_until_complete(
             self._async_get_historic_ohlcv(pair=pair, timeframe=timeframe,
                                            since_ms=since_ms, until_ms=until_ms,
                                            is_new_pair=is_new_pair, candle_type=candle_type))
         logger.info(f"Downloaded data for {pair} with length {len(data)}.")
         return data
 
-    def get_historic_ohlcv_as_df(self, pair: str, timeframe: str,
-                                 since_ms: int, candle_type: CandleType) -> DataFrame:
-        """
-        Minimal wrapper around get_historic_ohlcv - converting the result into a dataframe
-        :param pair: Pair to download
-        :param timeframe: Timeframe to get data for
-        :param since_ms: Timestamp in milliseconds to get history from
-        :param candle_type: Any of the enum CandleType (must match trading mode!)
-        :return: OHLCV DataFrame
-        """
-        ticks = self.get_historic_ohlcv(pair, timeframe, since_ms=since_ms, candle_type=candle_type)
-        return ohlcv_to_dataframe(ticks, timeframe, pair=pair, fill_missing=True,
-                                  drop_incomplete=self._ohlcv_partial_candle)
-
     async def _async_get_historic_ohlcv(self, pair: str, timeframe: str,
                                         since_ms: int, candle_type: CandleType,
                                         is_new_pair: bool = False, raise_: bool = False,
                                         until_ms: Optional[int] = None
-                                        ) -> Tuple[str, str, str, List]:
+                                        ) -> OHLCVResponse:
         """
         Download historic ohlcv
         :param is_new_pair: used by binance subclass to allow "fast" new pair downloading
@@ -1869,15 +1886,16 @@ class Exchange:
                     continue
                 else:
                     # Deconstruct tuple if it's not an exception
-                    p, _, c, new_data = res
+                    p, _, c, new_data, _ = res
                     if p == pair and c == candle_type:
                         data.extend(new_data)
         # Sort data again after extending the result - above calls return in "async order"
         data = sorted(data, key=lambda x: x[0])
-        return pair, timeframe, candle_type, data
+        return pair, timeframe, candle_type, data, self._ohlcv_partial_candle
 
-    def _build_coroutine(self, pair: str, timeframe: str, candle_type: CandleType,
-                         since_ms: Optional[int], cache: bool) -> Coroutine:
+    def _build_coroutine(
+            self, pair: str, timeframe: str, candle_type: CandleType,
+            since_ms: Optional[int], cache: bool) -> Coroutine[Any, Any, OHLCVResponse]:
         not_all_data = cache and self.required_candle_call_count > 1
         if cache and (pair, timeframe, candle_type) in self._klines:
             candle_limit = self.ohlcv_candle_limit(timeframe, candle_type)
@@ -1914,7 +1932,7 @@ class Exchange:
         """
         Build Coroutines to execute as part of refresh_latest_ohlcv
         """
-        input_coroutines = []
+        input_coroutines: List[Coroutine[Any, Any, OHLCVResponse]] = []
         cached_pairs = []
         for pair, timeframe, candle_type in set(pair_list):
             if (timeframe not in self.timeframes
@@ -1978,7 +1996,6 @@ class Exchange:
         :return: Dict of [{(pair, timeframe): Dataframe}]
         """
         logger.debug("Refreshing candle (OHLCV) data for %d pairs", len(pair_list))
-        drop_incomplete = self._ohlcv_partial_candle if drop_incomplete is None else drop_incomplete
 
         # Gather coroutines to run
         input_coroutines, cached_pairs = self._build_ohlcv_dl_jobs(pair_list, since_ms, cache)
@@ -1996,10 +2013,11 @@ class Exchange:
                 if isinstance(res, Exception):
                     logger.warning(f"Async code raised an exception: {repr(res)}")
                     continue
-                # Deconstruct tuple (has 4 elements)
-                pair, timeframe, c_type, ticks = res
+                # 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
                 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
 
@@ -2025,7 +2043,7 @@ class Exchange:
         timeframe: str,
         candle_type: CandleType,
         since_ms: Optional[int] = None,
-    ) -> Tuple[str, str, str, List]:
+    ) -> OHLCVResponse:
         """
         Asynchronously get candle history data using fetch_ohlcv
         :param candle_type: '', mark, index, premiumIndex, or funding_rate
@@ -2035,8 +2053,8 @@ class Exchange:
             # Fetch OHLCV asynchronously
             s = '(' + arrow.get(since_ms // 1000).isoformat() + ') ' if since_ms is not None else ''
             logger.debug(
-                "Fetching pair %s, interval %s, since %s %s...",
-                pair, timeframe, since_ms, s
+                "Fetching pair %s, %s, interval %s, since %s %s...",
+                pair, candle_type, timeframe, since_ms, s
             )
             params = deepcopy(self._ft_has.get('ohlcv_params', {}))
             candle_limit = self.ohlcv_candle_limit(
@@ -2050,11 +2068,12 @@ class Exchange:
                     limit=candle_limit, params=params)
             else:
                 # Funding rate
-                data = await self._api_async.fetch_funding_rate_history(
-                    pair, since=since_ms,
-                    limit=candle_limit)
-                # Convert funding rate to candle pattern
-                data = [[x['timestamp'], x['fundingRate'], 0, 0, 0, 0] for x in data]
+                data = await self._fetch_funding_rate_history(
+                    pair=pair,
+                    timeframe=timeframe,
+                    limit=candle_limit,
+                    since_ms=since_ms,
+                )
             # Some exchanges sort OHLCV in ASC order and others in DESC.
             # Ex: Bittrex returns the list of OHLCV in ASC order (oldest first, newest last)
             # while GDAX returns the list of OHLCV in DESC order (newest first, oldest last)
@@ -2064,9 +2083,9 @@ class Exchange:
                     data = sorted(data, key=lambda x: x[0])
             except IndexError:
                 logger.exception("Error loading %s. Result was %s.", pair, data)
-                return pair, timeframe, candle_type, []
+                return pair, timeframe, candle_type, [], self._ohlcv_partial_candle
             logger.debug("Done fetching pair %s, interval %s ...", pair, timeframe)
-            return pair, timeframe, candle_type, data
+            return pair, timeframe, candle_type, data, self._ohlcv_partial_candle
 
         except ccxt.NotSupported as e:
             raise OperationalException(
@@ -2082,6 +2101,24 @@ class Exchange:
             raise OperationalException(f'Could not fetch historical candle (OHLCV) data '
                                        f'for pair {pair}. Message: {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 - used to selectively override this by subclasses.
+        """
+        # Funding rate
+        data = await self._api_async.fetch_funding_rate_history(
+            pair, since=since_ms,
+            limit=limit)
+        # Convert funding rate to candle pattern
+        data = [[x['timestamp'], x['fundingRate'], 0, 0, 0, 0] for x in data]
+        return data
+
     # Fetch historic trades
 
     @retrier_async
@@ -2485,7 +2522,8 @@ class Exchange:
         self,
         leverage: float,
         pair: Optional[str] = None,
-        trading_mode: Optional[TradingMode] = None
+        trading_mode: Optional[TradingMode] = None,
+        accept_fail: bool = False,
     ):
         """
         Set's the leverage before making a trade, in order to not
@@ -2494,12 +2532,18 @@ 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 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
@@ -2521,7 +2565,8 @@ 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")
@@ -2535,6 +2580,10 @@ 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
@@ -2668,7 +2717,7 @@ class Exchange:
         :param amount: Trade amount
         :param open_date: Open date of the trade
         :return: funding fee since open_date
-        :raies: ExchangeError if something goes wrong.
+        :raises: ExchangeError if something goes wrong.
         """
         if self.trading_mode == TradingMode.FUTURES:
             if self._config['dry_run']:
@@ -2688,6 +2737,7 @@ 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
@@ -2709,6 +2759,7 @@ class Exchange:
                 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,
@@ -2720,7 +2771,7 @@ class Exchange:
                 pos = positions[0]
                 isolated_liq = pos['liquidationPrice']
 
-        if isolated_liq:
+        if isolated_liq is not None:
             buffer_amount = abs(open_rate - isolated_liq) * self.liquidation_buffer
             isolated_liq = (
                 isolated_liq - buffer_amount
@@ -2738,6 +2789,7 @@ 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
@@ -2745,22 +2797,28 @@ class Exchange:
         """
         Important: Must be fetching data from cached values as this is used by backtesting!
         PERPETUAL:
-         gateio: https://www.gate.io/help/futures/perpetual/22160/calculation-of-liquidation-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:
+            "-" for long, and "+" for short.
+
          okex: https://www.okex.com/support/hc/en-us/articles/
             360053909592-VI-Introduction-to-the-isolated-mode-of-Single-Multi-currency-Portfolio-margin
 
-        :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
             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:
         """
@@ -2789,7 +2847,7 @@ class Exchange:
     def get_maintenance_ratio_and_amt(
         self,
         pair: str,
-        nominal_value: float = 0.0,
+        nominal_value: float,
     ) -> Tuple[float, Optional[float]]:
         """
         Important: Must be fetching data from cached values as this is used by backtesting!

freqtrade/exchange/exchange_utils.py

@@ -15,18 +15,19 @@ from freqtrade.util import FtPrecise
 CcxtModuleType = Any
 
 
-def is_exchange_known_ccxt(exchange_name: str, ccxt_module: CcxtModuleType = None) -> bool:
+def is_exchange_known_ccxt(
+        exchange_name: str, ccxt_module: Optional[CcxtModuleType] = None) -> bool:
     return exchange_name in ccxt_exchanges(ccxt_module)
 
 
-def ccxt_exchanges(ccxt_module: CcxtModuleType = None) -> List[str]:
+def ccxt_exchanges(ccxt_module: Optional[CcxtModuleType] = None) -> List[str]:
     """
     Return the list of all exchanges known to ccxt
     """
     return ccxt_module.exchanges if ccxt_module is not None else ccxt.exchanges
 
 
-def available_exchanges(ccxt_module: CcxtModuleType = None) -> List[str]:
+def available_exchanges(ccxt_module: Optional[CcxtModuleType] = None) -> List[str]:
     """
     Return exchanges available to the bot, i.e. non-bad exchanges in the ccxt list
     """
@@ -86,7 +87,7 @@ def timeframe_to_msecs(timeframe: str) -> int:
     return ccxt.Exchange.parse_timeframe(timeframe) * 1000
 
 
-def timeframe_to_prev_date(timeframe: str, date: datetime = None) -> datetime:
+def timeframe_to_prev_date(timeframe: str, date: Optional[datetime] = None) -> datetime:
     """
     Use Timeframe and determine the candle start date for this date.
     Does not round when given a candle start date.
@@ -102,7 +103,7 @@ def timeframe_to_prev_date(timeframe: str, date: datetime = None) -> datetime:
     return datetime.fromtimestamp(new_timestamp, tz=timezone.utc)
 
 
-def timeframe_to_next_date(timeframe: str, date: datetime = None) -> datetime:
+def timeframe_to_next_date(timeframe: str, date: Optional[datetime] = None) -> datetime:
     """
     Use Timeframe and determine next candle.
     :param timeframe: timeframe in string format (e.g. "5m")

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.
@@ -34,6 +34,12 @@ class Gateio(Exchange):
         "needs_trading_fees": True,
         "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 +55,7 @@ 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,
@@ -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 +158,8 @@ class Kraken(Exchange):
         self,
         leverage: float,
         pair: Optional[str] = None,
-        trading_mode: Optional[TradingMode] = 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 +36,34 @@ 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)
+        res['type'] = ordertype
+        res['status'] = 'open'
+        return res

freqtrade/exchange/okx.py

@@ -5,6 +5,7 @@ import ccxt
 
 from freqtrade.constants import BuySell
 from freqtrade.enums import CandleType, MarginMode, TradingMode
+from freqtrade.enums.pricetype import PriceType
 from freqtrade.exceptions import DDosProtection, OperationalException, TemporaryError
 from freqtrade.exchange import Exchange, date_minus_candles
 from freqtrade.exchange.common import retrier
@@ -27,6 +28,12 @@ class Okx(Exchange):
     _ft_has_futures: Dict = {
         "tickers_have_quoteVolume": False,
         "fee_cost_in_contracts": True,
+        "stop_price_type_field": "tpTriggerPxType",
+        "stop_price_type_value_mapping": {
+            PriceType.LAST: "last",
+            PriceType.MARK: "index",
+            PriceType.INDEX: "mark",
+            },
     }
 
     _supported_trading_mode_margin_pairs: List[Tuple[TradingMode, MarginMode]] = [
@@ -118,13 +125,15 @@ class Okx(Exchange):
         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:

freqtrade/exchange/types.py

@@ -1,4 +1,6 @@
-from typing import Dict, Optional, TypedDict
+from typing import Dict, List, Optional, Tuple, TypedDict
+
+from freqtrade.enums import CandleType
 
 
 class Ticker(TypedDict):
@@ -13,4 +15,16 @@ 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?,
+OHLCVResponse = Tuple[str, str, CandleType, List, bool]

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:

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'
@@ -107,6 +109,7 @@ 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
@@ -143,7 +146,7 @@ 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,
@@ -158,7 +161,7 @@ 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 +169,9 @@ 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
@@ -280,26 +285,36 @@ 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']
-        ohlc_list = [f'%-{pair}raw_open_{tf}', f'%-{pair}raw_low_{tf}',
-                     f'%-{pair}raw_high_{tf}', f'%-{pair}raw_close_{tf}']
-        rename_dict = {f'%-{pair}raw_open_{tf}': 'open', f'%-{pair}raw_low_{tf}': 'low',
-                       f'%-{pair}raw_high_{tf}': ' high', f'%-{pair}raw_close_{tf}': 'close'}
+        rename_dict = {'%-raw_open': 'open', '%-raw_low': 'low',
+                       '%-raw_high': ' high', '%-raw_close': 'close'}
+        rename_dict_old = {f'%-{pair}raw_open_{tf}': 'open', f'%-{pair}raw_low_{tf}': 'low',
+                           f'%-{pair}raw_high_{tf}': ' high', f'%-{pair}raw_close_{tf}': 'close'}
+
+        prices_train = train_df.filter(rename_dict.keys(), axis=1)
+        prices_train_old = train_df.filter(rename_dict_old.keys(), axis=1)
+        if prices_train.empty or not prices_train_old.empty:
+            if not prices_train_old.empty:
+                prices_train = prices_train_old
+                rename_dict = rename_dict_old
+            logger.warning('Reinforcement learning module didnt find the correct raw prices '
+                           'assigned in feature_engineering_standard(). '
+                           'Please assign them with:\n'
+                           'dataframe["%-raw_close"] = dataframe["close"]\n'
+                           'dataframe["%-raw_open"] = dataframe["open"]\n'
+                           'dataframe["%-raw_high"] = dataframe["high"]\n'
+                           'dataframe["%-raw_low"] = dataframe["low"]\n'
+                           'inside `feature_engineering_standard()')
+        elif prices_train.empty:
+            raise OperationalException("No prices found, please follow log warning "
+                                       "instructions to correct the strategy.")
 
-        prices_train = train_df.filter(ohlc_list, axis=1)
-        if prices_train.empty:
-            raise OperationalException('Reinforcement learning module didnt find the raw prices '
-                                       'assigned in populate_any_indicators. Please assign them '
-                                       'with:\n'
-                                       'informative[f"%-{pair}raw_close"] = informative["close"]\n'
-                                       'informative[f"%-{pair}raw_open"] = informative["open"]\n'
-                                       'informative[f"%-{pair}raw_high"] = informative["high"]\n'
-                                       'informative[f"%-{pair}raw_low"] = informative["low"]\n')
         prices_train.rename(columns=rename_dict, inplace=True)
         prices_train.reset_index(drop=True)
 
-        prices_test = test_df.filter(ohlc_list, axis=1)
+        prices_test = test_df.filter(rename_dict.keys(), axis=1)
         prices_test.rename(columns=rename_dict, inplace=True)
         prices_test.reset_index(drop=True)
 
@@ -337,7 +352,7 @@ 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.
@@ -353,10 +368,19 @@ 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/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.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]]] = {}
@@ -149,13 +141,8 @@ class FreqaiDataDrawer:
         if exists:
             with open(self.pair_dictionary_path, "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):
         """
@@ -193,13 +180,8 @@ class FreqaiDataDrawer:
                     self.historic_predictions = cloudpickle.load(fp)
                 logger.warning('FreqAI successfully loaded the backup historical predictions file.')
 
-        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} "
-                "sending null values back to strategy"
-            )
+            logger.info("Could not find existing historic_predictions, starting from scratch")
 
         return exists
 
@@ -231,14 +213,6 @@ class FreqaiDataDrawer:
                 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
@@ -248,28 +222,11 @@ class FreqaiDataDrawer:
                 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 +235,19 @@ 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 +255,6 @@ 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 +366,12 @@ 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 +394,11 @@ 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 +407,6 @@ 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

freqtrade/freqai/data_kitchen.py

@@ -1,10 +1,12 @@
 import copy
+import inspect
 import logging
+import random
 import shutil
 from datetime import datetime, timezone
 from math import cos, sin
 from pathlib import Path
-from typing import Any, Dict, List, Tuple
+from typing import Any, Dict, List, Optional, Tuple
 
 import numpy as np
 import numpy.typing as npt
@@ -23,6 +25,7 @@ from freqtrade.constants import Config
 from freqtrade.data.converter import reduce_dataframe_footprint
 from freqtrade.exceptions import OperationalException
 from freqtrade.exchange import timeframe_to_seconds
+from freqtrade.strategy import merge_informative_pair
 from freqtrade.strategy.interface import IStrategy
 
 
@@ -110,7 +113,7 @@ class FreqaiDataKitchen:
     def set_paths(
         self,
         pair: str,
-        trained_timestamp: int = None,
+        trained_timestamp: Optional[int] = None,
     ) -> None:
         """
         Set the paths to the data for the present coin/botloop
@@ -168,6 +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(
@@ -1145,9 +1161,9 @@ class FreqaiDataKitchen:
 
         for pair in pairs:
             pair = pair.replace(':', '')  # lightgbm doesnt like colons
-            valid_strs = [f"%-{pair}", f"%{pair}", f"%_{pair}"]
-            pair_cols = [col for col in dataframe.columns if
-                         any(substr in col for substr in valid_strs)]
+            pair_cols = [col for col in dataframe.columns if col.startswith("%")
+                         and f"{pair}_" in col]
+
             if pair_cols:
                 pair_cols.insert(0, 'date')
                 corr_dataframes[pair] = dataframe.filter(pair_cols, axis=1)
@@ -1176,6 +1192,105 @@ class FreqaiDataKitchen:
 
         return dataframe
 
+    def get_pair_data_for_features(self,
+                                   pair: str,
+                                   tf: str,
+                                   strategy: IStrategy,
+                                   corr_dataframes: dict = {},
+                                   base_dataframes: dict = {},
+                                   is_corr_pairs: bool = False) -> DataFrame:
+        """
+        Get the data for the pair. If it's not in the dictionary, get it from the data provider
+        :param pair: str = pair to get data for
+        :param tf: str = timeframe to get data for
+        :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 is_corr_pairs: bool = whether the pair is a corr pair or not
+        :return: dataframe = dataframe containing the pair data
+        """
+        if is_corr_pairs:
+            dataframe = corr_dataframes[pair][tf]
+            if not dataframe.empty:
+                return dataframe
+            else:
+                dataframe = strategy.dp.get_pair_dataframe(pair=pair, timeframe=tf)
+                return dataframe
+        else:
+            dataframe = base_dataframes[tf]
+            if not dataframe.empty:
+                return dataframe
+            else:
+                dataframe = strategy.dp.get_pair_dataframe(pair=pair, timeframe=tf)
+                return dataframe
+
+    def merge_features(self, df_main: DataFrame, df_to_merge: DataFrame,
+                       tf: str, timeframe_inf: str, suffix: str) -> DataFrame:
+        """
+        Merge the features of the dataframe and remove HLCV and date added columns
+        :param df_main: DataFrame = main dataframe
+        :param df_to_merge: DataFrame = dataframe to merge
+        :param tf: str = timeframe of the main dataframe
+        :param timeframe_inf: str = timeframe of the dataframe to merge
+        :param suffix: str = suffix to add to the columns of the dataframe to merge
+        :return: dataframe = merged dataframe
+        """
+        dataframe = merge_informative_pair(df_main, df_to_merge, tf, timeframe_inf=timeframe_inf,
+                                           append_timeframe=False, suffix=suffix, ffill=True)
+        skip_columns = [
+            (f"{s}_{suffix}") for s in ["date", "open", "high", "low", "close", "volume"]
+        ]
+        dataframe = dataframe.drop(columns=skip_columns)
+        return dataframe
+
+    def populate_features(self, dataframe: DataFrame, pair: str, strategy: IStrategy,
+                          corr_dataframes: dict, base_dataframes: dict,
+                          is_corr_pairs: bool = False) -> DataFrame:
+        """
+        Use the user defined strategy functions for populating features
+        :param dataframe: DataFrame = dataframe to populate
+        :param pair: str = pair to populate
+        :param strategy: IStrategy = user defined strategy object
+        :param corr_dataframes: dict = dict containing the df pair dataframes
+        :param base_dataframes: dict = dict containing the current pair dataframes
+        :param is_corr_pairs: bool = whether the pair is a corr pair or not
+        :return: dataframe = populated dataframe
+        """
+        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, 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(), metadata=metadata)
+            suffix = "gen"
+
+            informative_df = self.merge_features(informative_df, generic_df, tf, tf, suffix)
+
+            indicators = [col for col in informative_df if col.startswith("%")]
+            for n in range(self.freqai_config["feature_parameters"]["include_shifted_candles"] + 1):
+                if n == 0:
+                    continue
+                df_shift = informative_df[indicators].shift(n)
+                df_shift = df_shift.add_suffix("_shift-" + str(n))
+                informative_df = pd.concat((informative_df, df_shift), axis=1)
+
+            dataframe = self.merge_features(dataframe.copy(), informative_df,
+                                            self.config["timeframe"], tf, f'{pair}_{tf}')
+
+        return dataframe
+
     def use_strategy_to_populate_indicators(
         self,
         strategy: IStrategy,
@@ -1188,7 +1303,87 @@ class FreqaiDataKitchen:
         """
         Use the user defined strategy for populating indicators during retrain
         :param strategy: IStrategy = user defined strategy object
-        :param corr_dataframes: dict = dict containing the informative pair dataframes
+        :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 pair: str = pair to populate
+        :param prediction_dataframe: DataFrame = dataframe containing the pair data
+        used for prediction
+        :param do_corr_pairs: bool = whether to populate corr pairs or not
+        :return:
+        dataframe: DataFrame = dataframe containing populated indicators
+        """
+
+        # this is a hack to check if the user is using the 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", [])
+
+            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)
+            metadata = {"pair": pair}
+            dataframe = strategy.feature_engineering_standard(dataframe.copy(), metadata=metadata)
+            # 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(), metadata=metadata)
+
+            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)
@@ -1367,3 +1562,25 @@ 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

@@ -1,3 +1,4 @@
+import inspect
 import logging
 import threading
 import time
@@ -65,12 +66,11 @@ 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
@@ -106,6 +106,8 @@ class IFreqaiModel(ABC):
         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
+
         record_params(config, self.full_path)
 
     def __getstate__(self):
@@ -136,6 +138,9 @@ 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"])
@@ -147,14 +152,11 @@ 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"])
-            dataframe = self.dk.use_strategy_to_populate_indicators(
-                strategy, prediction_dataframe=dataframe, pair=metadata["pair"]
-            )
             if not self.config.get("freqai_backtest_live_models", False):
                 logger.info(f"Training {len(self.dk.training_timeranges)} timeranges")
-                dk = self.start_backtesting(dataframe, metadata, self.dk)
+                dk = self.start_backtesting(dataframe, metadata, self.dk, strategy)
                 dataframe = dk.remove_features_from_df(dk.return_dataframe)
             else:
                 logger.info(
@@ -225,7 +227,7 @@ 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)
             (
@@ -255,7 +257,7 @@ class IFreqaiModel(ABC):
                     self.dd.save_metric_tracker_to_disk()
 
     def start_backtesting(
-        self, dataframe: DataFrame, metadata: dict, dk: FreqaiDataKitchen
+        self, dataframe: DataFrame, metadata: dict, dk: FreqaiDataKitchen, strategy: IStrategy
     ) -> FreqaiDataKitchen:
         """
         The main broad execution for backtesting. For backtesting, each pair enters and then gets
@@ -267,20 +269,23 @@ class IFreqaiModel(ABC):
         :param dataframe: DataFrame = strategy passed dataframe
         :param metadata: Dict = pair metadata
         :param dk: FreqaiDataKitchen = Data management/analysis tool associated to present pair only
+        :param strategy: Strategy to train on
         :return:
             FreqaiDataKitchen = Data management/analysis tool associated to present pair only
         """
 
         self.pair_it += 1
         train_it = 0
+        pair = metadata["pair"]
+        populate_indicators = True
+        check_features = True
         # Loop enforcing the sliding window training/backtesting paradigm
         # tr_train is the training time range e.g. 1 historical month
         # tr_backtest is the backtesting time range e.g. the week directly
         # 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):
-            pair = metadata["pair"]
-            (_, _, _) = 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
@@ -301,18 +306,46 @@ class IFreqaiModel(ABC):
             dk.set_new_model_names(pair, timestamp_model_id)
 
             if dk.check_if_backtest_prediction_is_valid(len_backtest_df):
-                self.dd.load_metadata(dk)
-                dk.find_features(dataframe)
-                self.check_if_feature_list_matches_strategy(dk)
+                if check_features:
+                    self.dd.load_metadata(dk)
+                    dataframe_dummy_features = self.dk.use_strategy_to_populate_indicators(
+                        strategy, prediction_dataframe=dataframe.tail(1), pair=metadata["pair"]
+                    )
+                    dk.find_features(dataframe_dummy_features)
+                    self.check_if_feature_list_matches_strategy(dk)
+                    check_features = False
                 append_df = dk.get_backtesting_prediction()
                 dk.append_predictions(append_df)
             else:
-                dataframe_train = dk.slice_dataframe(tr_train, dataframe)
-                dataframe_backtest = dk.slice_dataframe(tr_backtest, dataframe)
+                if populate_indicators:
+                    dataframe = self.dk.use_strategy_to_populate_indicators(
+                        strategy, prediction_dataframe=dataframe, pair=metadata["pair"]
+                    )
+                    populate_indicators = False
+
+                dataframe_base_train = dataframe.loc[dataframe["date"] < tr_train.stopdt, :]
+                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, 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)
+
                 if not self.model_exists(dk):
                     dk.find_features(dataframe_train)
                     dk.find_labels(dataframe_train)
-                    self.model = self.train(dataframe_train, pair, dk)
+
+                    try:
+                        self.model = self.train(dataframe_train, pair, dk)
+                    except Exception as msg:
+                        logger.warning(
+                            f"Training {pair} raised exception {msg.__class__.__name__}. "
+                            f"Message: {msg}, skipping.")
+
                     self.dd.pair_dict[pair]["trained_timestamp"] = int(
                         tr_train.stopts)
                     if self.plot_features:
@@ -350,18 +383,8 @@ class IFreqaiModel(ABC):
         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:
@@ -369,27 +392,18 @@ 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)
@@ -551,7 +565,13 @@ 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)
@@ -596,6 +616,8 @@ 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
@@ -611,8 +633,7 @@ 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
@@ -913,9 +934,28 @@ class IFreqaiModel(ABC):
         dk.return_dataframe = dk.return_dataframe.drop(columns=list(columns_to_drop))
         dk.return_dataframe = pd.merge(
             dk.return_dataframe, saved_dataframe, how='left', left_on='date', right_on="date_pred")
-        # dk.return_dataframe = dk.return_dataframe[saved_dataframe.columns].fillna(0)
         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_multiproc.py

@@ -34,7 +34,7 @@ class ReinforcementLearner_multiproc(ReinforcementLearner):
         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)
 
         env_id = "train_env"
         self.train_env = SubprocVecEnv([make_env(self.MyRLEnv, env_id, i, 1,

freqtrade/freqtradebot.py

@@ -33,6 +33,7 @@ from freqtrade.rpc.external_message_consumer import ExternalMessageConsumer
 from freqtrade.strategy.interface import IStrategy
 from freqtrade.strategy.strategy_wrapper import strategy_safe_wrapper
 from freqtrade.util import FtPrecise
+from freqtrade.util.binance_mig import migrate_binance_futures_names
 from freqtrade.wallets import Wallets
 
 
@@ -177,6 +178,8 @@ class FreqtradeBot(LoggingMixin):
         Called on startup and after reloading the bot - triggers notifications and
         performs startup tasks
         """
+        migrate_binance_futures_names(self.config)
+
         self.rpc.startup_messages(self.config, self.pairlists, self.protections)
         # Update older trades with precision and precision mode
         self.startup_backpopulate_precision()
@@ -341,7 +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'))
 
@@ -352,7 +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:
 
@@ -374,7 +385,7 @@ class FreqtradeBot(LoggingMixin):
         for trade in trades:
             if not trade.is_open and not trade.fee_updated(trade.exit_side):
                 # Get sell fee
-                order = trade.select_order(trade.exit_side, False)
+                order = trade.select_order(trade.exit_side, False, only_filled=True)
                 if not order:
                     order = trade.select_order('stoploss', False)
                 if order:
@@ -390,7 +401,7 @@ class FreqtradeBot(LoggingMixin):
         for trade in trades:
             with self._exit_lock:
                 if trade.is_open and not trade.fee_updated(trade.entry_side):
-                    order = trade.select_order(trade.entry_side, False)
+                    order = trade.select_order(trade.entry_side, False, only_filled=True)
                     open_order = trade.select_order(trade.entry_side, True)
                     if order and open_order is None:
                         logger.info(
@@ -720,7 +731,7 @@ class FreqtradeBot(LoggingMixin):
             time_in_force=time_in_force,
             leverage=leverage
         )
-        order_obj = Order.parse_from_ccxt_object(order, pair, side)
+        order_obj = Order.parse_from_ccxt_object(order, pair, side, amount, enter_limit_requested)
         order_id = order['id']
         order_status = order.get('status')
         logger.info(f"Order #{order_id} was created for {pair} and status is {order_status}.")
@@ -747,13 +758,15 @@ 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')
@@ -1065,7 +1078,7 @@ 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:
@@ -1085,7 +1098,7 @@ 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,
@@ -1094,7 +1107,8 @@ class FreqtradeBot(LoggingMixin):
                 leverage=trade.leverage
             )
 
-            order_obj = Order.parse_from_ccxt_object(stoploss_order, trade.pair, 'stoploss')
+            order_obj = Order.parse_from_ccxt_object(stoploss_order, trade.pair, 'stoploss',
+                                                     trade.amount, stop_price)
             trade.orders.append(order_obj)
             trade.stoploss_order_id = str(stoploss_order['id'])
             trade.stoploss_last_update = datetime.now(timezone.utc)
@@ -1156,15 +1170,13 @@ 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.
@@ -1249,11 +1261,11 @@ 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()
@@ -1261,10 +1273,10 @@ 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'])
+                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:
@@ -1518,7 +1530,7 @@ class FreqtradeBot(LoggingMixin):
             *,
             exit_tag: Optional[str] = None,
             ordertype: Optional[str] = None,
-            sub_trade_amt: float = None,
+            sub_trade_amt: Optional[float] = None,
     ) -> bool:
         """
         Executes a trade exit for the given trade and limit
@@ -1595,7 +1607,7 @@ class FreqtradeBot(LoggingMixin):
             self.handle_insufficient_funds(trade)
             return False
 
-        order_obj = Order.parse_from_ccxt_object(order, trade.pair, trade.exit_side)
+        order_obj = Order.parse_from_ccxt_object(order, trade.pair, trade.exit_side, amount, limit)
         trade.orders.append(order_obj)
 
         trade.open_order_id = order['id']
@@ -1612,7 +1624,7 @@ class FreqtradeBot(LoggingMixin):
         return True
 
     def _notify_exit(self, trade: Trade, order_type: str, fill: bool = False,
-                     sub_trade: bool = False, order: Order = None) -> None:
+                     sub_trade: bool = False, order: Optional[Order] = None) -> None:
         """
         Sends rpc notification when a sell occurred.
         """
@@ -1622,7 +1634,7 @@ 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)
@@ -1725,8 +1737,9 @@ class FreqtradeBot(LoggingMixin):
 # Common update trade state methods
 #
 
-    def update_trade_state(self, trade: Trade, order_id: str, action_order: Dict[str, Any] = None,
-                           stoploss_order: bool = False, send_msg: bool = True) -> bool:
+    def update_trade_state(
+            self, trade: Trade, order_id: 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
         Handles closing both buy and sell orders.
@@ -1784,6 +1797,7 @@ 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/main.py

@@ -5,7 +5,7 @@ Read the documentation to know what cli arguments you need.
 """
 import logging
 import sys
-from typing import Any, List
+from typing import Any, List, Optional
 
 from freqtrade.util.gc_setup import gc_set_threshold
 
@@ -23,7 +23,7 @@ from freqtrade.loggers import setup_logging_pre
 logger = logging.getLogger('freqtrade')
 
 
-def main(sysargv: List[str] = None) -> None:
+def main(sysargv: Optional[List[str]] = None) -> None:
     """
     This function will initiate the bot and start the trading loop.
     :return: None

freqtrade/misc.py

@@ -6,7 +6,7 @@ import logging
 import re
 from datetime import datetime
 from pathlib import Path
-from typing import Any, Dict, Iterator, List, Mapping, Union
+from typing import Any, Dict, Iterator, List, Mapping, Optional, Union
 from typing.io import IO
 from urllib.parse import urlparse
 
@@ -205,7 +205,7 @@ def safe_value_fallback2(dict1: dictMap, dict2: dictMap, key1: str, key2: str, d
     return default_value
 
 
-def plural(num: float, singular: str, plural: str = None) -> str:
+def plural(num: float, singular: str, plural: Optional[str] = None) -> str:
     return singular if (num == 1 or num == -1) else plural or singular + 's'
 
 
@@ -269,6 +269,8 @@ def dataframe_to_json(dataframe: pd.DataFrame) -> str:
     def default(z):
         if isinstance(z, pd.Timestamp):
             return z.timestamp() * 1e3
+        if z is pd.NaT:
+            return 'NaT'
         raise TypeError
 
     return str(orjson.dumps(dataframe.to_dict(orient='split'), default=default), 'utf-8')

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/backtesting.py

@@ -15,7 +15,7 @@ from pandas import DataFrame
 
 from freqtrade import constants
 from freqtrade.configuration import TimeRange, validate_config_consistency
-from freqtrade.constants import DATETIME_PRINT_FORMAT, Config, LongShort
+from freqtrade.constants import DATETIME_PRINT_FORMAT, Config, IntOrInf, LongShort
 from freqtrade.data import history
 from freqtrade.data.btanalysis import find_existing_backtest_stats, trade_list_to_dataframe
 from freqtrade.data.converter import trim_dataframe, trim_dataframes
@@ -37,6 +37,7 @@ from freqtrade.plugins.protectionmanager import ProtectionManager
 from freqtrade.resolvers import ExchangeResolver, StrategyResolver
 from freqtrade.strategy.interface import IStrategy
 from freqtrade.strategy.strategy_wrapper import strategy_safe_wrapper
+from freqtrade.util.binance_mig import migrate_binance_futures_data
 from freqtrade.wallets import Wallets
 
 
@@ -157,6 +158,7 @@ class Backtesting:
         self._can_short = self.trading_mode != TradingMode.SPOT
         self._position_stacking: bool = self.config.get('position_stacking', False)
         self.enable_protections: bool = self.config.get('enable_protections', False)
+        migrate_binance_futures_data(config)
 
         self.init_backtest()
 
@@ -573,26 +575,6 @@ class Backtesting:
         """ Rate is within candle, therefore filled"""
         return row[LOW_IDX] <= rate <= row[HIGH_IDX]
 
-    def _get_exit_trade_entry_for_candle(self, trade: LocalTrade,
-                                         row: Tuple) -> Optional[LocalTrade]:
-
-        # Check if we need to adjust our current positions
-        if self.strategy.position_adjustment_enable:
-            trade = self._get_adjust_trade_entry_for_candle(trade, row)
-
-        enter = row[SHORT_IDX] if trade.is_short else row[LONG_IDX]
-        exit_sig = row[ESHORT_IDX] if trade.is_short else row[ELONG_IDX]
-        exits = self.strategy.should_exit(
-            trade, row[OPEN_IDX], row[DATE_IDX].to_pydatetime(),  # type: ignore
-            enter=enter, exit_=exit_sig,
-            low=row[LOW_IDX], high=row[HIGH_IDX]
-        )
-        for exit_ in exits:
-            t = self._get_exit_for_signal(trade, row, exit_)
-            if t:
-                return t
-        return None
-
     def _get_exit_for_signal(
             self, trade: LocalTrade, row: Tuple, exit_: ExitCheckTuple,
             amount: Optional[float] = None) -> Optional[LocalTrade]:
@@ -662,7 +644,7 @@ class Backtesting:
         return None
 
     def _exit_trade(self, trade: LocalTrade, sell_row: Tuple,
-                    close_rate: float, amount: float = None) -> Optional[LocalTrade]:
+                    close_rate: float, amount: Optional[float] = None) -> Optional[LocalTrade]:
         self.order_id_counter += 1
         exit_candle_time = sell_row[DATE_IDX].to_pydatetime()
         order_type = self.strategy.order_types['exit']
@@ -692,11 +674,10 @@ class Backtesting:
         trade.orders.append(order)
         return trade
 
-    def _get_exit_trade_entry(
-            self, trade: LocalTrade, row: Tuple, is_first: bool) -> Optional[LocalTrade]:
+    def _check_trade_exit(self, trade: LocalTrade, row: Tuple) -> Optional[LocalTrade]:
         exit_candle_time: datetime = row[DATE_IDX].to_pydatetime()
 
-        if is_first and self.trading_mode == TradingMode.FUTURES:
+        if self.trading_mode == TradingMode.FUTURES:
             trade.funding_fees = self.exchange.calculate_funding_fees(
                 self.futures_data[trade.pair],
                 amount=trade.amount,
@@ -705,7 +686,22 @@ class Backtesting:
                 close_date=exit_candle_time,
             )
 
-        return self._get_exit_trade_entry_for_candle(trade, row)
+        # Check if we need to adjust our current positions
+        if self.strategy.position_adjustment_enable:
+            trade = self._get_adjust_trade_entry_for_candle(trade, row)
+
+        enter = row[SHORT_IDX] if trade.is_short else row[LONG_IDX]
+        exit_sig = row[ESHORT_IDX] if trade.is_short else row[ELONG_IDX]
+        exits = self.strategy.should_exit(
+            trade, row[OPEN_IDX], row[DATE_IDX].to_pydatetime(),  # type: ignore
+            enter=enter, exit_=exit_sig,
+            low=row[LOW_IDX], high=row[HIGH_IDX]
+        )
+        for exit_ in exits:
+            t = self._get_exit_for_signal(trade, row, exit_)
+            if t:
+                return t
+        return None
 
     def get_valid_price_and_stake(
         self, pair: str, row: Tuple, propose_rate: float, stake_amount: float,
@@ -779,6 +775,11 @@ class Backtesting:
                      trade: Optional[LocalTrade] = None,
                      requested_rate: Optional[float] = None,
                      requested_stake: Optional[float] = None) -> Optional[LocalTrade]:
+        """
+        :param trade: Trade to adjust - initial entry if None
+        :param requested_rate: Adjusted entry rate
+        :param requested_stake: Stake amount for adjusted orders (`adjust_entry_price`).
+        """
 
         current_time = row[DATE_IDX].to_pydatetime()
         entry_tag = row[ENTER_TAG_IDX] if len(row) >= ENTER_TAG_IDX + 1 else None
@@ -804,7 +805,7 @@ class Backtesting:
             return trade
         time_in_force = self.strategy.order_time_in_force['entry']
 
-        if stake_amount and (not min_stake_amount or stake_amount > min_stake_amount):
+        if stake_amount and (not min_stake_amount or stake_amount >= min_stake_amount):
             self.order_id_counter += 1
             base_currency = self.exchange.get_pair_base_currency(pair)
             amount_p = (stake_amount / propose_rate) * leverage
@@ -867,6 +868,7 @@ 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,
             ))
@@ -920,8 +922,9 @@ class Backtesting:
                 trade.close(exit_row[OPEN_IDX], show_msg=False)
                 LocalTrade.close_bt_trade(trade)
 
-    def trade_slot_available(self, max_open_trades: int, open_trade_count: int) -> bool:
+    def trade_slot_available(self, open_trade_count: int) -> bool:
         # Always allow trades when max_open_trades is enabled.
+        max_open_trades: IntOrInf = self.config['max_open_trades']
         if max_open_trades <= 0 or open_trade_count < max_open_trades:
             return True
         # Rejected trade
@@ -1051,7 +1054,8 @@ class Backtesting:
 
     def backtest_loop(
             self, row: Tuple, pair: str, current_time: datetime, end_date: datetime,
-            max_open_trades: int, open_trade_count_start: int, is_first: bool = True) -> int:
+            open_trade_count_start: int, trade_dir: Optional[LongShort],
+            is_first: bool = True) -> int:
         """
         NOTE: This method is used by Hyperopt at each iteration. Please keep it optimized.
 
@@ -1070,11 +1074,10 @@ class Backtesting:
         # max_open_trades must be respected
         # don't open on the last row
         # We only open trades on the main candle, not on detail candles
-        trade_dir = self.check_for_trade_entry(row)
         if (
             (self._position_stacking or len(LocalTrade.bt_trades_open_pp[pair]) == 0)
             and is_first
-            and self.trade_slot_available(max_open_trades, open_trade_count_start)
+            and self.trade_slot_available(open_trade_count_start)
             and current_time != end_date
             and trade_dir is not None
             and not PairLocks.is_pair_locked(pair, row[DATE_IDX], trade_dir)
@@ -1099,7 +1102,7 @@ class Backtesting:
 
                 # 4. Create exit orders (if any)
             if not trade.open_order_id:
-                self._get_exit_trade_entry(trade, row, is_first)  # Place exit order if necessary
+                self._check_trade_exit(trade, row)  # Place exit order if necessary
 
                 # 5. Process exit orders.
             order = trade.select_order(trade.exit_side, is_open=True)
@@ -1121,8 +1124,7 @@ class Backtesting:
         return open_trade_count_start
 
     def backtest(self, processed: Dict,
-                 start_date: datetime, end_date: datetime,
-                 max_open_trades: int = 0) -> Dict[str, Any]:
+                 start_date: datetime, end_date: datetime) -> Dict[str, Any]:
         """
         Implement backtesting functionality
 
@@ -1134,7 +1136,6 @@ class Backtesting:
         optimize memory usage!
         :param start_date: backtesting timerange start datetime
         :param end_date: backtesting timerange end datetime
-        :param max_open_trades: maximum number of concurrent trades, <= 0 means unlimited
         :return: DataFrame with trades (results of backtesting)
         """
         self.prepare_backtest(self.enable_protections)
@@ -1164,7 +1165,15 @@ class Backtesting:
                 indexes[pair] = row_index
                 self.dataprovider._set_dataframe_max_index(row_index)
                 current_detail_time: datetime = row[DATE_IDX].to_pydatetime()
-                if self.timeframe_detail and pair in self.detail_data:
+                trade_dir: Optional[LongShort] = self.check_for_trade_entry(row)
+
+                if (
+                    (trade_dir is not None or len(LocalTrade.bt_trades_open_pp[pair]) > 0)
+                    and self.timeframe_detail and pair in self.detail_data
+                ):
+                    # Spread out into detail timeframe.
+                    # Should only happen when we are either in a trade for this pair
+                    # or when we got the signal for a new trade.
                     exit_candle_end = current_detail_time + timedelta(minutes=self.timeframe_min)
 
                     detail_data = self.detail_data[pair]
@@ -1175,8 +1184,9 @@ class Backtesting:
                     if len(detail_data) == 0:
                         # Fall back to "regular" data if no detail data was found for this candle
                         open_trade_count_start = self.backtest_loop(
-                            row, pair, current_time, end_date, max_open_trades,
-                            open_trade_count_start)
+                            row, pair, current_time, end_date,
+                            open_trade_count_start, trade_dir)
+                        continue
                     detail_data.loc[:, 'enter_long'] = row[LONG_IDX]
                     detail_data.loc[:, 'exit_long'] = row[ELONG_IDX]
                     detail_data.loc[:, 'enter_short'] = row[SHORT_IDX]
@@ -1187,13 +1197,14 @@ class Backtesting:
                     current_time_det = current_time
                     for det_row in detail_data[HEADERS].values.tolist():
                         open_trade_count_start = self.backtest_loop(
-                            det_row, pair, current_time_det, end_date, max_open_trades,
-                            open_trade_count_start, is_first)
+                            det_row, pair, current_time_det, end_date,
+                            open_trade_count_start, trade_dir, is_first)
                         current_time_det += timedelta(minutes=self.timeframe_detail_min)
                         is_first = False
                 else:
                     open_trade_count_start = self.backtest_loop(
-                        row, pair, current_time, end_date, max_open_trades, open_trade_count_start)
+                        row, pair, current_time, end_date,
+                        open_trade_count_start, trade_dir)
 
             # Move time one configured time_interval ahead.
             self.progress.increment()
@@ -1225,13 +1236,11 @@ class Backtesting:
         self._set_strategy(strat)
 
         # Use max_open_trades in backtesting, except --disable-max-market-positions is set
-        if self.config.get('use_max_market_positions', True):
-            # Must come from strategy config, as the strategy may modify this setting.
-            max_open_trades = self.strategy.config['max_open_trades']
-        else:
+        if not self.config.get('use_max_market_positions', True):
             logger.info(
                 'Ignoring max_open_trades (--disable-max-market-positions was used) ...')
-            max_open_trades = 0
+            self.strategy.max_open_trades = float('inf')
+            self.config.update({'max_open_trades': self.strategy.max_open_trades})
 
         # need to reprocess data every time to populate signals
         preprocessed = self.strategy.advise_all_indicators(data)
@@ -1254,7 +1263,6 @@ class Backtesting:
             processed=preprocessed,
             start_date=min_date,
             end_date=max_date,
-            max_open_trades=max_open_trades,
         )
         backtest_end_time = datetime.now(timezone.utc)
         results.update({

freqtrade/optimize/hyperopt.py

@@ -74,6 +74,7 @@ class Hyperopt:
         self.roi_space: List[Dimension] = []
         self.stoploss_space: List[Dimension] = []
         self.trailing_space: List[Dimension] = []
+        self.max_open_trades_space: List[Dimension] = []
         self.dimensions: List[Dimension] = []
 
         self.config = config
@@ -117,11 +118,10 @@ class Hyperopt:
         self.current_best_epoch: Optional[Dict[str, Any]] = None
 
         # Use max_open_trades for hyperopt as well, except --disable-max-market-positions is set
-        if self.config.get('use_max_market_positions', True):
-            self.max_open_trades = self.config['max_open_trades']
-        else:
+        if not self.config.get('use_max_market_positions', True):
             logger.debug('Ignoring max_open_trades (--disable-max-market-positions was used) ...')
-            self.max_open_trades = 0
+            self.backtesting.strategy.max_open_trades = float('inf')
+            config.update({'max_open_trades': self.backtesting.strategy.max_open_trades})
 
         if HyperoptTools.has_space(self.config, 'sell'):
             # Make sure use_exit_signal is enabled
@@ -209,6 +209,10 @@ class Hyperopt:
             result['stoploss'] = {p.name: params.get(p.name) for p in self.stoploss_space}
         if HyperoptTools.has_space(self.config, 'trailing'):
             result['trailing'] = self.custom_hyperopt.generate_trailing_params(params)
+        if HyperoptTools.has_space(self.config, 'trades'):
+            result['max_open_trades'] = {
+                'max_open_trades': self.backtesting.strategy.max_open_trades
+                if self.backtesting.strategy.max_open_trades != float('inf') else -1}
 
         return result
 
@@ -229,6 +233,8 @@ class Hyperopt:
                 'trailing_stop_positive_offset': strategy.trailing_stop_positive_offset,
                 'trailing_only_offset_is_reached': strategy.trailing_only_offset_is_reached,
             }
+        if not HyperoptTools.has_space(self.config, 'trades'):
+            result['max_open_trades'] = {'max_open_trades': strategy.max_open_trades}
         return result
 
     def print_results(self, results) -> None:
@@ -280,8 +286,13 @@ class Hyperopt:
             logger.debug("Hyperopt has 'trailing' space")
             self.trailing_space = self.custom_hyperopt.trailing_space()
 
+        if HyperoptTools.has_space(self.config, 'trades'):
+            logger.debug("Hyperopt has 'trades' space")
+            self.max_open_trades_space = self.custom_hyperopt.max_open_trades_space()
+
         self.dimensions = (self.buy_space + self.sell_space + self.protection_space
-                           + self.roi_space + self.stoploss_space + self.trailing_space)
+                           + self.roi_space + self.stoploss_space + self.trailing_space
+                           + self.max_open_trades_space)
 
     def assign_params(self, params_dict: Dict, category: str) -> None:
         """
@@ -328,6 +339,20 @@ class Hyperopt:
             self.backtesting.strategy.trailing_only_offset_is_reached = \
                 d['trailing_only_offset_is_reached']
 
+        if HyperoptTools.has_space(self.config, 'trades'):
+            if self.config["stake_amount"] == "unlimited" and \
+                    (params_dict['max_open_trades'] == -1 or params_dict['max_open_trades'] == 0):
+                # Ignore unlimited max open trades if stake amount is unlimited
+                params_dict.update({'max_open_trades': self.config['max_open_trades']})
+
+            updated_max_open_trades = int(params_dict['max_open_trades']) \
+                if (params_dict['max_open_trades'] != -1
+                    and params_dict['max_open_trades'] != 0) else float('inf')
+
+            self.config.update({'max_open_trades': updated_max_open_trades})
+
+            self.backtesting.strategy.max_open_trades = updated_max_open_trades
+
         with self.data_pickle_file.open('rb') as f:
             processed = load(f, mmap_mode='r')
             if self.analyze_per_epoch:
@@ -337,8 +362,7 @@ class Hyperopt:
         bt_results = self.backtesting.backtest(
             processed=processed,
             start_date=self.min_date,
-            end_date=self.max_date,
-            max_open_trades=self.max_open_trades,
+            end_date=self.max_date
         )
         backtest_end_time = datetime.now(timezone.utc)
         bt_results.update({

freqtrade/optimize/hyperopt_auto.py

@@ -91,5 +91,8 @@ class HyperOptAuto(IHyperOpt):
     def trailing_space(self) -> List['Dimension']:
         return self._get_func('trailing_space')()
 
+    def max_open_trades_space(self) -> List['Dimension']:
+        return self._get_func('max_open_trades_space')()
+
     def generate_estimator(self, dimensions: List['Dimension'], **kwargs) -> EstimatorType:
         return self._get_func('generate_estimator')(dimensions=dimensions, **kwargs)

freqtrade/optimize/hyperopt_interface.py

@@ -191,6 +191,16 @@ class IHyperOpt(ABC):
             Categorical([True, False], name='trailing_only_offset_is_reached'),
         ]
 
+    def max_open_trades_space(self) -> List[Dimension]:
+        """
+        Create a max open trades space.
+
+        You may override it in your custom Hyperopt class.
+        """
+        return [
+            Integer(-1, 10, name='max_open_trades'),
+        ]
+
     # This is needed for proper unpickling the class attribute timeframe
     # which is set to the actual value by the resolver.
     # Why do I still need such shamanic mantras in modern python?

freqtrade/optimize/hyperopt_loss/hyperopt_loss_calmar.py

@@ -5,13 +5,11 @@ This module defines the alternative HyperOptLoss class which can be used for
 Hyperoptimization.
 """
 from datetime import datetime
-from math import sqrt as msqrt
-from typing import Any, Dict
 
 from pandas import DataFrame
 
 from freqtrade.constants import Config
-from freqtrade.data.metrics import calculate_max_drawdown
+from freqtrade.data.metrics import calculate_calmar
 from freqtrade.optimize.hyperopt import IHyperOptLoss
 
 
@@ -23,42 +21,15 @@ class CalmarHyperOptLoss(IHyperOptLoss):
     """
 
     @staticmethod
-    def hyperopt_loss_function(
-        results: DataFrame,
-        trade_count: int,
-        min_date: datetime,
-        max_date: datetime,
-        config: Config,
-        processed: Dict[str, DataFrame],
-        backtest_stats: Dict[str, Any],
-        *args,
-        **kwargs
-    ) -> float:
+    def hyperopt_loss_function(results: DataFrame, trade_count: int,
+                               min_date: datetime, max_date: datetime,
+                               config: Config, *args, **kwargs) -> float:
         """
         Objective function, returns smaller number for more optimal results.
 
         Uses Calmar Ratio calculation.
         """
-        total_profit = backtest_stats["profit_total"]
-        days_period = (max_date - min_date).days
-
-        # adding slippage of 0.1% per trade
-        total_profit = total_profit - 0.0005
-        expected_returns_mean = total_profit.sum() / days_period * 100
-
-        # calculate max drawdown
-        try:
-            _, _, _, _, _, max_drawdown = calculate_max_drawdown(
-                results, value_col="profit_abs"
-            )
-        except ValueError:
-            max_drawdown = 0
-
-        if max_drawdown != 0:
-            calmar_ratio = expected_returns_mean / max_drawdown * msqrt(365)
-        else:
-            # Define high (negative) calmar ratio to be clear that this is NOT optimal.
-            calmar_ratio = -20.0
-
+        starting_balance = config['dry_run_wallet']
+        calmar_ratio = calculate_calmar(results, min_date, max_date, starting_balance)
         # print(expected_returns_mean, max_drawdown, calmar_ratio)
         return -calmar_ratio

freqtrade/optimize/hyperopt_loss/hyperopt_loss_sharpe.py

@@ -6,9 +6,10 @@ Hyperoptimization.
 """
 from datetime import datetime
 
-import numpy as np
 from pandas import DataFrame
 
+from freqtrade.constants import Config
+from freqtrade.data.metrics import calculate_sharpe
 from freqtrade.optimize.hyperopt import IHyperOptLoss
 
 
@@ -22,25 +23,13 @@ class SharpeHyperOptLoss(IHyperOptLoss):
     @staticmethod
     def hyperopt_loss_function(results: DataFrame, trade_count: int,
                                min_date: datetime, max_date: datetime,
-                               *args, **kwargs) -> float:
+                               config: Config, *args, **kwargs) -> float:
         """
         Objective function, returns smaller number for more optimal results.
 
         Uses Sharpe Ratio calculation.
         """
-        total_profit = results["profit_ratio"]
-        days_period = (max_date - min_date).days
-
-        # adding slippage of 0.1% per trade
-        total_profit = total_profit - 0.0005
-        expected_returns_mean = total_profit.sum() / days_period
-        up_stdev = np.std(total_profit)
-
-        if up_stdev != 0:
-            sharp_ratio = expected_returns_mean / up_stdev * np.sqrt(365)
-        else:
-            # Define high (negative) sharpe ratio to be clear that this is NOT optimal.
-            sharp_ratio = -20.
-
+        starting_balance = config['dry_run_wallet']
+        sharp_ratio = calculate_sharpe(results, min_date, max_date, starting_balance)
         # print(expected_returns_mean, up_stdev, sharp_ratio)
         return -sharp_ratio

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.py

@@ -6,9 +6,10 @@ Hyperoptimization.
 """
 from datetime import datetime
 
-import numpy as np
 from pandas import DataFrame
 
+from freqtrade.constants import Config
+from freqtrade.data.metrics import calculate_sortino
 from freqtrade.optimize.hyperopt import IHyperOptLoss
 
 
@@ -22,28 +23,13 @@ class SortinoHyperOptLoss(IHyperOptLoss):
     @staticmethod
     def hyperopt_loss_function(results: DataFrame, trade_count: int,
                                min_date: datetime, max_date: datetime,
-                               *args, **kwargs) -> float:
+                               config: Config, *args, **kwargs) -> float:
         """
         Objective function, returns smaller number for more optimal results.
 
         Uses Sortino Ratio calculation.
         """
-        total_profit = results["profit_ratio"]
-        days_period = (max_date - min_date).days
-
-        # adding slippage of 0.1% per trade
-        total_profit = total_profit - 0.0005
-        expected_returns_mean = total_profit.sum() / days_period
-
-        results['downside_returns'] = 0
-        results.loc[total_profit < 0, 'downside_returns'] = results['profit_ratio']
-        down_stdev = np.std(results['downside_returns'])
-
-        if down_stdev != 0:
-            sortino_ratio = expected_returns_mean / down_stdev * np.sqrt(365)
-        else:
-            # Define high (negative) sortino ratio to be clear that this is NOT optimal.
-            sortino_ratio = -20.
-
+        starting_balance = config['dry_run_wallet']
+        sortino_ratio = calculate_sortino(results, min_date, max_date, starting_balance)
         # print(expected_returns_mean, down_stdev, sortino_ratio)
         return -sortino_ratio

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

@@ -96,7 +96,7 @@ class HyperoptTools():
         Tell if the space value is contained in the configuration
         """
         # 'trailing' and 'protection spaces are not included in the 'default' set of spaces
-        if space in ('trailing', 'protection'):
+        if space in ('trailing', 'protection', 'trades'):
             return any(s in config['spaces'] for s in [space, 'all'])
         else:
             return any(s in config['spaces'] for s in [space, 'all', 'default'])
@@ -170,7 +170,7 @@ class HyperoptTools():
 
     @staticmethod
     def show_epoch_details(results, total_epochs: int, print_json: bool,
-                           no_header: bool = False, header_str: str = None) -> None:
+                           no_header: bool = False, header_str: Optional[str] = None) -> None:
         """
         Display details of the hyperopt result
         """
@@ -187,7 +187,8 @@ class HyperoptTools():
 
         if print_json:
             result_dict: Dict = {}
-            for s in ['buy', 'sell', 'protection', 'roi', 'stoploss', 'trailing']:
+            for s in ['buy', 'sell', 'protection',
+                      'roi', 'stoploss', 'trailing', 'max_open_trades']:
                 HyperoptTools._params_update_for_json(result_dict, params, non_optimized, s)
             print(rapidjson.dumps(result_dict, default=str, number_mode=rapidjson.NM_NATIVE))
 
@@ -201,6 +202,8 @@ class HyperoptTools():
             HyperoptTools._params_pretty_print(params, 'roi', "ROI table:", non_optimized)
             HyperoptTools._params_pretty_print(params, 'stoploss', "Stoploss:", non_optimized)
             HyperoptTools._params_pretty_print(params, 'trailing', "Trailing stop:", non_optimized)
+            HyperoptTools._params_pretty_print(
+                params, 'max_open_trades', "Max Open Trades:", non_optimized)
 
     @staticmethod
     def _params_update_for_json(result_dict, params, non_optimized, space: str) -> None:
@@ -239,7 +242,9 @@ class HyperoptTools():
             if space == "stoploss":
                 stoploss = safe_value_fallback2(space_params, no_params, space, space)
                 result += (f"stoploss = {stoploss}{appendix}")
-
+            elif space == "max_open_trades":
+                max_open_trades = safe_value_fallback2(space_params, no_params, space, space)
+                result += (f"max_open_trades = {max_open_trades}{appendix}")
             elif space == "roi":
                 result = result[:-1] + f'{appendix}\n'
                 minimal_roi_result = rapidjson.dumps({
@@ -259,7 +264,7 @@ class HyperoptTools():
             print(result)
 
     @staticmethod
-    def _space_params(params, space: str, r: int = None) -> Dict:
+    def _space_params(params, space: str, r: Optional[int] = None) -> Dict:
         d = params.get(space)
         if d:
             # Round floats to `r` digits after the decimal point if requested

freqtrade/optimize/optimize_reports.py

@@ -8,9 +8,10 @@ from pandas import DataFrame, to_datetime
 from tabulate import tabulate
 
 from freqtrade.constants import (DATETIME_PRINT_FORMAT, LAST_BT_RESULT_FN, UNLIMITED_STAKE_AMOUNT,
-                                 Config)
-from freqtrade.data.metrics import (calculate_cagr, calculate_csum, calculate_market_change,
-                                    calculate_max_drawdown)
+                                 Config, IntOrInf)
+from freqtrade.data.metrics import (calculate_cagr, calculate_calmar, calculate_csum,
+                                    calculate_expectancy, calculate_market_change,
+                                    calculate_max_drawdown, calculate_sharpe, calculate_sortino)
 from freqtrade.misc import decimals_per_coin, file_dump_joblib, file_dump_json, round_coin_value
 from freqtrade.optimize.backtest_caching import get_backtest_metadata_filename
 
@@ -190,7 +191,7 @@ def generate_tag_metrics(tag_type: str,
         return []
 
 
-def generate_exit_reason_stats(max_open_trades: int, results: DataFrame) -> List[Dict]:
+def generate_exit_reason_stats(max_open_trades: IntOrInf, results: DataFrame) -> List[Dict]:
     """
     Generate small table outlining Backtest results
     :param max_open_trades: Max_open_trades parameter
@@ -448,6 +449,10 @@ def generate_strategy_stats(pairlist: List[str],
         'profit_total_long_abs': results.loc[~results['is_short'], 'profit_abs'].sum(),
         'profit_total_short_abs': results.loc[results['is_short'], 'profit_abs'].sum(),
         'cagr': calculate_cagr(backtest_days, start_balance, content['final_balance']),
+        'expectancy': calculate_expectancy(results),
+        'sortino': calculate_sortino(results, min_date, max_date, start_balance),
+        'sharpe': calculate_sharpe(results, min_date, max_date, start_balance),
+        'calmar': calculate_calmar(results, min_date, max_date, start_balance),
         'profit_factor': profit_factor,
         'backtest_start': min_date.strftime(DATETIME_PRINT_FORMAT),
         'backtest_start_ts': int(min_date.timestamp() * 1000),
@@ -785,8 +790,13 @@ def text_table_add_metrics(strat_results: Dict) -> str:
                                                   strat_results['stake_currency'])),
             ('Total profit %', f"{strat_results['profit_total']:.2%}"),
             ('CAGR %', f"{strat_results['cagr']:.2%}" if 'cagr' in strat_results else 'N/A'),
+            ('Sortino', f"{strat_results['sortino']:.2f}" if 'sortino' in strat_results else 'N/A'),
+            ('Sharpe', f"{strat_results['sharpe']:.2f}" if 'sharpe' in strat_results else 'N/A'),
+            ('Calmar', f"{strat_results['calmar']:.2f}" if 'calmar' in strat_results else 'N/A'),
             ('Profit factor', f'{strat_results["profit_factor"]:.2f}' if 'profit_factor'
                               in strat_results else 'N/A'),
+            ('Expectancy', f"{strat_results['expectancy']:.2f}" if 'expectancy'
+                           in strat_results else 'N/A'),
             ('Trades per day', strat_results['trades_per_day']),
             ('Avg. daily profit %',
              f"{(strat_results['profit_total'] / strat_results['backtest_days']):.2%}"),