remove @ from github handles in acknowledgements

Shutdown Subproc Env on signal

.github/workflows/ci.yml

@@ -24,7 +24,7 @@ jobs:
     strategy:
       matrix:
         os: [ ubuntu-18.04, ubuntu-20.04, ubuntu-22.04 ]
-        python-version: ["3.8", "3.9", "3.10"]
+        python-version: ["3.8", "3.9", "3.10.6"]
 
     steps:
     - uses: actions/checkout@v3
@@ -74,7 +74,7 @@ jobs:
       if: matrix.python-version == '3.9' && matrix.os == 'ubuntu-22.04'
 
     - name: Coveralls
-      if: (runner.os == 'Linux' && matrix.python-version == '3.10' && matrix.os == 'ubuntu-22.04')
+      if: (runner.os == 'Linux' && matrix.python-version == '3.9')
       env:
         # Coveralls token. Not used as secret due to github not providing secrets to forked repositories
         COVERALLS_REPO_TOKEN: 6D1m0xupS3FgutfuGao8keFf9Hc0FpIXu
@@ -121,7 +121,7 @@ jobs:
     strategy:
       matrix:
         os: [ macos-latest ]
-        python-version: ["3.8", "3.9", "3.10"]
+        python-version: ["3.8", "3.9", "3.10.6"]
 
     steps:
     - uses: actions/checkout@v3
@@ -205,7 +205,7 @@ jobs:
     strategy:
       matrix:
         os: [ windows-latest ]
-        python-version: ["3.8", "3.9", "3.10"]
+        python-version: ["3.8", "3.9", "3.10.6"]
 
     steps:
     - uses: actions/checkout@v3

.github/workflows/draft-pdf.yml

@@ -0,0 +1,23 @@
+on: [push]
+
+jobs:
+  paper:
+    runs-on: ubuntu-latest
+    name: Paper Draft
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v2
+      - name: Build draft PDF
+        uses: openjournals/openjournals-draft-action@master
+        with:
+          journal: joss
+          # This should be the path to the paper within your repo.
+          paper-path: docs/JOSS_paper/paper.md
+      - name: Upload
+        uses: actions/upload-artifact@v1
+        with:
+          name: paper
+          # This is the output path where Pandoc will write the compiled
+          # PDF. Note, this should be the same directory as the input
+          # paper.md
+          path: docs/JOSS_paper/paper.pdf

.gitignore

@@ -113,3 +113,4 @@ target/
 !config_examples/config_full.example.json
 !config_examples/config_kraken.example.json
 !config_examples/config_freqai.example.json
+!config_examples/config_freqai-rl.example.json

.pre-commit-config.yaml

@@ -15,9 +15,9 @@ repos:
         additional_dependencies:
           - types-cachetools==5.2.1
           - types-filelock==3.2.7
-          - types-requests==2.28.11.2
-          - types-tabulate==0.9.0.0
-          - types-python-dateutil==2.8.19.2
+          - types-requests==2.28.11
+          - types-tabulate==0.8.11
+          - types-python-dateutil==2.8.19
         # stages: [push]
 
   - repo: https://github.com/pycqa/isort

config_examples/config_binance.example.json

@@ -53,7 +53,7 @@
             "XTZ/BTC"
         ],
         "pair_blacklist": [
-            "BNB/.*"
+            "BNB/BTC"
         ]
     },
     "pairlists": [

config_examples/config_freqai.example.json

@@ -18,8 +18,13 @@
         "name": "binance",
         "key": "",
         "secret": "",
-        "ccxt_config": {},
-        "ccxt_async_config": {},
+        "ccxt_config": {
+            "enableRateLimit": true
+        },
+        "ccxt_async_config": {
+            "enableRateLimit": true,
+            "rateLimit": 200
+        },
         "pair_whitelist": [
             "1INCH/USDT",
             "ALGO/USDT"

docker/Dockerfile.armhf

@@ -11,7 +11,7 @@ ENV FT_APP_ENV="docker"
 # Prepare environment
 RUN mkdir /freqtrade \
   && apt-get update \
-  && apt-get -y install sudo libatlas3-base curl sqlite3 libhdf5-dev libutf8proc-dev libsnappy-dev \
+  && apt-get -y install sudo libatlas3-base curl sqlite3 libhdf5-dev \
   && apt-get clean \
   && useradd -u 1000 -G sudo -U -m ftuser \
   && chown ftuser:ftuser /freqtrade \
@@ -37,7 +37,6 @@ ENV LD_LIBRARY_PATH /usr/local/lib
 COPY --chown=ftuser:ftuser requirements.txt /freqtrade/
 USER ftuser
 RUN  pip install --user --no-cache-dir numpy \
-  && pip install --user /tmp/pyarrow-*.whl \
   && pip install --user --no-cache-dir -r requirements.txt
 
 # Copy dependencies to runtime-image

docs/JOSS_paper/note_to_editors.txt

@@ -0,0 +1,15 @@
+Dear Editors,
+We present a paper for ``FreqAI`` a machine learning sandbox for researchers and citizen scientists alike.
+There are a large number of authors, however all have contributed in a significant way to this paper.
+For clarity the contribution of each author is outlined:
+
+  - Robert Caulk            : Conception and software development
+  - Elin Tornquist          : Theoretical brainstorming, data analysis, tool dev
+  - Matthias Voppichler     : Software architecture and code review
+  - Andrew R. Lawless       : Extensive testing, feature brainstorming
+  - Ryan McMullan           : Extensive testing, feature brainstorming
+  - Wagner Costa Santos     : Major backtesting developments, extensive testing
+  - Pascal Schmidt          : Extensive testing, feature brainstorming
+  - Timothy C. Pogue        : Webhooks forecast sharing
+  - Stefan P. Gehring       : Extensive testing, feature brainstorming
+  - Johan van der Vlugt     : Extensive testing, feature brainstorming

docs/JOSS_paper/paper.bib

@@ -0,0 +1,207 @@
+@article{scikit-learn,
+ title={Scikit-learn: Machine Learning in {P}ython},
+ author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
+         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
+         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
+         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
+ journal={Journal of Machine Learning Research},
+ volume={12},
+ pages={2825--2830},
+ year={2011}
+}
+
+@inproceedings{catboost,
+author = {Prokhorenkova, Liudmila and Gusev, Gleb and Vorobev, Aleksandr and Dorogush, Anna Veronika and Gulin, Andrey},
+title = {CatBoost: Unbiased Boosting with Categorical Features},
+year = {2018},
+publisher = {Curran Associates Inc.},
+address = {Red Hook, NY, USA},
+abstract = {This paper presents the key algorithmic techniques behind CatBoost, a new gradient boosting toolkit. Their combination leads to CatBoost outperforming other publicly available boosting implementations in terms of quality on a variety of datasets. Two critical algorithmic advances introduced in CatBoost are the implementation of ordered boosting, a permutation-driven alternative to the classic algorithm, and an innovative algorithm for processing categorical features. Both techniques were created to fight a prediction shift caused by a special kind of target leakage present in all currently existing implementations of gradient boosting algorithms. In this paper, we provide a detailed analysis of this problem and demonstrate that proposed algorithms solve it effectively, leading to excellent empirical results.},
+booktitle = {Proceedings of the 32nd International Conference on Neural Information Processing Systems},
+pages = {6639–6649},
+numpages = {11},
+location = {Montr\'{e}al, Canada},
+series = {NIPS'18}
+}
+
+
+@article{lightgbm,
+  title={Lightgbm: A highly efficient gradient boosting decision tree},
+  author={Ke, Guolin and Meng, Qi and Finley, Thomas and Wang, Taifeng and Chen, Wei and Ma, Weidong and Ye, Qiwei and Liu, Tie-Yan},
+  journal={Advances in neural information processing systems},
+  volume={30},
+  pages={3146--3154},
+  year={2017}
+} 
+
+@inproceedings{xgboost,
+ author = {Chen, Tianqi and Guestrin, Carlos},
+ title = {{XGBoost}: A Scalable Tree Boosting System},
+ booktitle = {Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
+ series = {KDD '16},
+ year = {2016},
+ isbn = {978-1-4503-4232-2},
+ location = {San Francisco, California, USA},
+ pages = {785--794},
+ numpages = {10},
+ url = {http://doi.acm.org/10.1145/2939672.2939785},
+ doi = {10.1145/2939672.2939785},
+ acmid = {2939785},
+ publisher = {ACM},
+ address = {New York, NY, USA},
+ keywords = {large-scale machine learning},
+}
+
+@article{stable-baselines3,
+  author  = {Antonin Raffin and Ashley Hill and Adam Gleave and Anssi Kanervisto and Maximilian Ernestus and Noah Dormann},
+  title   = {Stable-Baselines3: Reliable Reinforcement Learning Implementations},
+  journal = {Journal of Machine Learning Research},
+  year    = {2021},
+  volume  = {22},
+  number  = {268},
+  pages   = {1-8},
+  url     = {http://jmlr.org/papers/v22/20-1364.html}
+}
+
+@misc{openai,
+      title={OpenAI Gym}, 
+      author={Greg Brockman and Vicki Cheung and Ludwig Pettersson and Jonas Schneider and John Schulman and Jie Tang and Wojciech Zaremba},
+      year={2016},
+      eprint={1606.01540},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG}
+}
+
+@misc{tensorflow,
+title={ {TensorFlow}: Large-Scale Machine Learning on Heterogeneous Systems},
+url={https://www.tensorflow.org/},
+note={Software available from tensorflow.org},
+author={
+    Mart\'{i}n~Abadi and
+    Ashish~Agarwal and
+    Paul~Barham and
+    Eugene~Brevdo and
+    Zhifeng~Chen and
+    Craig~Citro and
+    Greg~S.~Corrado and
+    Andy~Davis and
+    Jeffrey~Dean and
+    Matthieu~Devin and
+    Sanjay~Ghemawat and
+    Ian~Goodfellow and
+    Andrew~Harp and
+    Geoffrey~Irving and
+    Michael~Isard and
+    Yangqing Jia and
+    Rafal~Jozefowicz and
+    Lukasz~Kaiser and
+    Manjunath~Kudlur and
+    Josh~Levenberg and
+    Dandelion~Man\'{e} and
+    Rajat~Monga and
+    Sherry~Moore and
+    Derek~Murray and
+    Chris~Olah and
+    Mike~Schuster and
+    Jonathon~Shlens and
+    Benoit~Steiner and
+    Ilya~Sutskever and
+    Kunal~Talwar and
+    Paul~Tucker and
+    Vincent~Vanhoucke and
+    Vijay~Vasudevan and
+    Fernanda~Vi\'{e}gas and
+    Oriol~Vinyals and
+    Pete~Warden and
+    Martin~Wattenberg and
+    Martin~Wicke and
+    Yuan~Yu and
+    Xiaoqiang~Zheng},
+  year={2015},
+}
+
+@incollection{pytorch,
+title = {PyTorch: An Imperative Style, High-Performance Deep Learning Library},
+author = {Paszke, Adam and Gross, Sam and Massa, Francisco and Lerer, Adam and Bradbury, James and Chanan, Gregory and Killeen, Trevor and Lin, Zeming and Gimelshein, Natalia and Antiga, Luca and Desmaison, Alban and Kopf, Andreas and Yang, Edward and DeVito, Zachary and Raison, Martin and Tejani, Alykhan and Chilamkurthy, Sasank and Steiner, Benoit and Fang, Lu and Bai, Junjie and Chintala, Soumith},
+booktitle = {Advances in Neural Information Processing Systems 32},
+editor = {H. Wallach and H. Larochelle and A. Beygelzimer and F. d\textquotesingle Alch\'{e}-Buc and E. Fox and R. Garnett},
+pages = {8024--8035},
+year = {2019},
+publisher = {Curran Associates, Inc.},
+url = {http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf}
+}
+
+@ARTICLE{scipy,
+  author  = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and
+            Haberland, Matt and Reddy, Tyler and Cournapeau, David and
+            Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and
+            Bright, Jonathan and {van der Walt}, St{\'e}fan J. and
+            Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and
+            Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and
+            Kern, Robert and Larson, Eric and Carey, C J and
+            Polat, {\.I}lhan and Feng, Yu and Moore, Eric W. and
+            {VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and
+            Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and
+            Harris, Charles R. and Archibald, Anne M. and
+            Ribeiro, Ant{\^o}nio H. and Pedregosa, Fabian and
+            {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},
+  title   = {{{SciPy} 1.0: Fundamental Algorithms for Scientific
+            Computing in Python}},
+  journal = {Nature Methods},
+  year    = {2020},
+  volume  = {17},
+  pages   = {261--272},
+  adsurl  = {https://rdcu.be/b08Wh},
+  doi     = {10.1038/s41592-019-0686-2},
+}
+
+@Article{numpy,
+ title         = {Array programming with {NumPy}},
+ author        = {Charles R. Harris and K. Jarrod Millman and St{\'{e}}fan J.
+                 van der Walt and Ralf Gommers and Pauli Virtanen and David
+                 Cournapeau and Eric Wieser and Julian Taylor and Sebastian
+                 Berg and Nathaniel J. Smith and Robert Kern and Matti Picus
+                 and Stephan Hoyer and Marten H. van Kerkwijk and Matthew
+                 Brett and Allan Haldane and Jaime Fern{\'{a}}ndez del
+                 R{\'{i}}o and Mark Wiebe and Pearu Peterson and Pierre
+                 G{\'{e}}rard-Marchant and Kevin Sheppard and Tyler Reddy and
+                 Warren Weckesser and Hameer Abbasi and Christoph Gohlke and
+                 Travis E. Oliphant},
+ year          = {2020},
+ month         = sep,
+ journal       = {Nature},
+ volume        = {585},
+ number        = {7825},
+ pages         = {357--362},
+ doi           = {10.1038/s41586-020-2649-2},
+ publisher     = {Springer Science and Business Media {LLC}},
+ url           = {https://doi.org/10.1038/s41586-020-2649-2}
+}
+
+ @inproceedings{pandas,
+  title={Data structures for statistical computing in python},
+  author={McKinney, Wes and others},
+  booktitle={Proceedings of the 9th Python in Science Conference},
+  volume={445},
+  pages={51--56},
+  year={2010},
+  organization={Austin, TX},
+  doi={10.25080/Majora-92bf1922-00a}
+}
+
+
+
+@online{finrl,
+  title = {AI4Finance-Foundation},
+  year = 2022,
+  url = {https://github.com/AI4Finance-Foundation/FinRL},
+  urldate = {2022-09-30}
+}
+
+
+@online{tensortrade,
+  title = {tensortrade},
+  year = 2022,
+  url = {https://tensortradex.readthedocs.io/en/latest/L},
+  urldate = {2022-09-30}
+}

docs/JOSS_paper/paper.jats

@@ -0,0 +1,941 @@
+<?xml version="1.0" encoding="utf-8" ?>
+<!DOCTYPE article PUBLIC "-//NLM//DTD JATS (Z39.96) Journal Publishing DTD v1.2 20190208//EN"
+                  "JATS-publishing1.dtd">
+<article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" dtd-version="1.2" article-type="other">
+<front>
+<journal-meta>
+<journal-id></journal-id>
+<journal-title-group>
+<journal-title>Journal of Open Source Software</journal-title>
+<abbrev-journal-title>JOSS</abbrev-journal-title>
+</journal-title-group>
+<issn publication-format="electronic">2475-9066</issn>
+<publisher>
+<publisher-name>Open Journals</publisher-name>
+</publisher>
+</journal-meta>
+<article-meta>
+<article-id pub-id-type="publisher-id">0</article-id>
+<article-id pub-id-type="doi">N/A</article-id>
+<title-group>
+<article-title><monospace>FreqAI</monospace>: generalizing adaptive
+modeling for chaotic time-series market forecasts</article-title>
+</title-group>
+<contrib-group>
+<contrib contrib-type="author">
+<contrib-id contrib-id-type="orcid">0000-0001-5618-8629</contrib-id>
+<name>
+<surname>Ph.D</surname>
+<given-names>Robert A. Caulk</given-names>
+</name>
+<xref ref-type="aff" rid="aff-1"/>
+<xref ref-type="aff" rid="aff-2"/>
+</contrib>
+<contrib contrib-type="author">
+<contrib-id contrib-id-type="orcid">0000-0003-3289-8604</contrib-id>
+<name>
+<surname>Ph.D</surname>
+<given-names>Elin Törnquist</given-names>
+</name>
+<xref ref-type="aff" rid="aff-1"/>
+<xref ref-type="aff" rid="aff-2"/>
+</contrib>
+<contrib contrib-type="author">
+<name>
+<surname>Voppichler</surname>
+<given-names>Matthias</given-names>
+</name>
+<xref ref-type="aff" rid="aff-2"/>
+</contrib>
+<contrib contrib-type="author">
+<name>
+<surname>Lawless</surname>
+<given-names>Andrew R.</given-names>
+</name>
+<xref ref-type="aff" rid="aff-2"/>
+</contrib>
+<contrib contrib-type="author">
+<name>
+<surname>McMullan</surname>
+<given-names>Ryan</given-names>
+</name>
+<xref ref-type="aff" rid="aff-2"/>
+</contrib>
+<contrib contrib-type="author">
+<name>
+<surname>Santos</surname>
+<given-names>Wagner Costa</given-names>
+</name>
+<xref ref-type="aff" rid="aff-1"/>
+<xref ref-type="aff" rid="aff-2"/>
+</contrib>
+<contrib contrib-type="author">
+<name>
+<surname>Pogue</surname>
+<given-names>Timothy C.</given-names>
+</name>
+<xref ref-type="aff" rid="aff-1"/>
+<xref ref-type="aff" rid="aff-2"/>
+</contrib>
+<contrib contrib-type="author">
+<name>
+<surname>van der Vlugt</surname>
+<given-names>Johan</given-names>
+</name>
+<xref ref-type="aff" rid="aff-2"/>
+</contrib>
+<contrib contrib-type="author">
+<name>
+<surname>Gehring</surname>
+<given-names>Stefan P.</given-names>
+</name>
+<xref ref-type="aff" rid="aff-2"/>
+</contrib>
+<contrib contrib-type="author">
+<name>
+<surname>Schmidt</surname>
+<given-names>Pascal</given-names>
+</name>
+<xref ref-type="aff" rid="aff-2"/>
+</contrib>
+<aff id="aff-1">
+<institution-wrap>
+<institution>Emergent Methods LLC, Arvada Colorado, 80005,
+USA</institution>
+</institution-wrap>
+</aff>
+<aff id="aff-2">
+<institution-wrap>
+<institution>Freqtrade open source project</institution>
+</institution-wrap>
+</aff>
+</contrib-group>
+<volume>¿VOL?</volume>
+<issue>¿ISSUE?</issue>
+<fpage>¿PAGE?</fpage>
+<permissions>
+<copyright-statement>Authors of papers retain copyright and release the
+work under a Creative Commons Attribution 4.0 International License (CC
+BY 4.0)</copyright-statement>
+<copyright-year>2022</copyright-year>
+<copyright-holder>The article authors</copyright-holder>
+<license license-type="open-access" xlink:href="https://creativecommons.org/licenses/by/4.0/">
+<license-p>Authors of papers retain copyright and release the work under
+a Creative Commons Attribution 4.0 International License (CC BY
+4.0)</license-p>
+</license>
+</permissions>
+<kwd-group kwd-group-type="author">
+<kwd>Python</kwd>
+<kwd>Machine Learning</kwd>
+<kwd>adaptive modeling</kwd>
+<kwd>chaotic systems</kwd>
+<kwd>time-series forecasting</kwd>
+</kwd-group>
+</article-meta>
+</front>
+<body>
+<sec id="statement-of-need">
+  <title>Statement of need</title>
+  <p>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. <monospace>scikit-learn</monospace>), 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.
+  <ext-link ext-link-type="uri" xlink:href="https://www.freqtrade.io/en/latest/freqai/"><monospace>FreqAI</monospace></ext-link>
+  aims to provide a generalized and extensible open-sourced framework
+  geared toward live deployments of adaptive modeling for market
+  forecasting. The <monospace>FreqAI</monospace> framework is
+  effectively a sandbox for the rich world of open-source machine
+  learning libraries. Inside the <monospace>FreqAI</monospace> 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.</p>
+</sec>
+<sec id="summary">
+  <title>Summary</title>
+  <p><ext-link ext-link-type="uri" xlink:href="https://www.freqtrade.io/en/latest/freqai/"><monospace>FreqAI</monospace></ext-link>
+  evolved from a desire to test and compare a range of adaptive
+  time-series forecasting methods on chaotic data. Cryptocurrency
+  markets provide a unique data source since they are operational 24/7
+  and the data is freely available. Luckily, an existing open-source
+  software,
+  <ext-link ext-link-type="uri" xlink:href="https://www.freqtrade.io/en/stable/"><monospace>Freqtrade</monospace></ext-link>,
+  had already matured under a range of talented developers to support
+  robust data collection/storage, as well as robust live environmental
+  interactions for standard algorithmic trading.
+  <monospace>Freqtrade</monospace> also provides a set of data
+  analysis/visualization tools for the evaluation of historical
+  performance as well as live environmental feedback.
+  <monospace>FreqAI</monospace> builds on top of
+  <monospace>Freqtrade</monospace> to include a user-friendly well
+  tested interface for integrating external machine learning libraries
+  for adaptive time-series forecasting. Beyond enabling the integration
+  of existing libraries, <monospace>FreqAI</monospace> hosts a range of
+  custom algorithms and methodologies aimed at improving computational
+  and predictive performances. Thus, <monospace>FreqAI</monospace>
+  contains a range of unique features which can be easily tested in
+  combination with all the existing Python-accessible machine learning
+  libraries to generate novel research on live and historical data.</p>
+  <p>The high-level overview of the software is depicted in Figure
+  1.</p>
+  <p><named-content content-type="image">freqai-algo</named-content>
+  <italic>Abstracted overview of FreqAI algorithm</italic></p>
+  <sec id="connecting-machine-learning-libraries">
+    <title>Connecting machine learning libraries</title>
+    <p>Although the <monospace>FreqAI</monospace> framework is designed
+    to accommodate any Python library in the “Model training” and
+    “Feature set engineering” portions of the software (Figure 1), it
+    already boasts a wide range of well documented examples based on
+    various combinations of:</p>
+    <list list-type="bullet">
+      <list-item>
+        <p>scikit-learn
+        (<xref alt="Pedregosa et al., 2011" rid="ref-scikit-learn" ref-type="bibr">Pedregosa
+        et al., 2011</xref>), Catboost
+        (<xref alt="Prokhorenkova et al., 2018" rid="ref-catboost" ref-type="bibr">Prokhorenkova
+        et al., 2018</xref>), LightGBM
+        (<xref alt="Ke et al., 2017" rid="ref-lightgbm" ref-type="bibr">Ke
+        et al., 2017</xref>), XGBoost
+        (<xref alt="Chen &amp; Guestrin, 2016" rid="ref-xgboost" ref-type="bibr">Chen
+        &amp; Guestrin, 2016</xref>), stable_baselines3
+        (<xref alt="Raffin et al., 2021" rid="ref-stable-baselines3" ref-type="bibr">Raffin
+        et al., 2021</xref>), openai gym
+        (<xref alt="Brockman et al., 2016" rid="ref-openai" ref-type="bibr">Brockman
+        et al., 2016</xref>), tensorflow
+        (<xref alt="Abadi et al., 2015" rid="ref-tensorflow" ref-type="bibr">Abadi
+        et al., 2015</xref>), pytorch
+        (<xref alt="Paszke et al., 2019" rid="ref-pytorch" ref-type="bibr">Paszke
+        et al., 2019</xref>), Scipy
+        (<xref alt="Virtanen et al., 2020" rid="ref-scipy" ref-type="bibr">Virtanen
+        et al., 2020</xref>), Numpy
+        (<xref alt="Harris et al., 2020" rid="ref-numpy" ref-type="bibr">Harris
+        et al., 2020</xref>), and pandas
+        (<xref alt="McKinney &amp; others, 2010" rid="ref-pandas" ref-type="bibr">McKinney
+        &amp; others, 2010</xref>).</p>
+      </list-item>
+    </list>
+    <p>These mature projects contain a wide range of peer-reviewed and
+    industry standard methods, including:</p>
+    <list list-type="bullet">
+      <list-item>
+        <p>Regression, Classification, Neural Networks, Reinforcement
+        Learning, Support Vector Machines, Principal Component Analysis,
+        point clustering, and much more.</p>
+      </list-item>
+    </list>
+    <p>which are all leveraged in <monospace>FreqAI</monospace> for
+    users to use as templates or extend with their own methods.</p>
+  </sec>
+  <sec id="furnishing-novel-methods-and-features">
+    <title>Furnishing novel methods and features</title>
+    <p>Beyond the industry standard methods available through external
+    libraries - <monospace>FreqAI</monospace> includes novel methods
+    which are not available anywhere else in the open-source (or
+    scientific) world. For example, <monospace>FreqAI</monospace>
+    provides :</p>
+    <list list-type="bullet">
+      <list-item>
+        <p>a custom algorithm/methodology for adaptive modeling</p>
+      </list-item>
+      <list-item>
+        <p>rapid and self-monitored feature engineering tools</p>
+      </list-item>
+      <list-item>
+        <p>unique model features/indicators</p>
+      </list-item>
+      <list-item>
+        <p>optimized data collection algorithms</p>
+      </list-item>
+      <list-item>
+        <p>safely integrated outlier detection methods</p>
+      </list-item>
+      <list-item>
+        <p>websocket communicated forecasts</p>
+      </list-item>
+    </list>
+    <p>Of particular interest for researchers,
+    <monospace>FreqAI</monospace> provides the option of large scale
+    experimentation via an optimized websocket communications
+    interface.</p>
+  </sec>
+  <sec id="optimizing-the-back-end">
+    <title>Optimizing the back-end</title>
+    <p><monospace>FreqAI</monospace> aims to make it simple for users to
+    combine all the above tools to run studies based in two distinct
+    modules:</p>
+    <list list-type="bullet">
+      <list-item>
+        <p>backtesting studies</p>
+      </list-item>
+      <list-item>
+        <p>live-deployments</p>
+      </list-item>
+    </list>
+    <p>Both of these modules and their respective data management
+    systems are built on top of
+    <ext-link ext-link-type="uri" xlink:href="https://www.freqtrade.io/en/latest/"><monospace>Freqtrade</monospace></ext-link>,
+    a mature and actively developed cryptocurrency trading software.
+    This means that <monospace>FreqAI</monospace> benefits from a wide
+    range of tangential/disparate feature developments such as:</p>
+    <list list-type="bullet">
+      <list-item>
+        <p>FreqUI, a graphical interface for backtesting and live
+        monitoring</p>
+      </list-item>
+      <list-item>
+        <p>telegram control</p>
+      </list-item>
+      <list-item>
+        <p>robust database handling</p>
+      </list-item>
+      <list-item>
+        <p>futures/leverage trading</p>
+      </list-item>
+      <list-item>
+        <p>dollar cost averaging</p>
+      </list-item>
+      <list-item>
+        <p>trading strategy handling</p>
+      </list-item>
+      <list-item>
+        <p>a variety of free data sources via CCXT (FTX, Binance, Kucoin
+        etc.)</p>
+      </list-item>
+    </list>
+    <p>These features derive from a strong external developer community
+    that shares in the benefit and stability of a communal CI
+    (Continuous Integration) system. Beyond the developer community,
+    <monospace>FreqAI</monospace> benefits strongly from the userbase of
+    <monospace>Freqtrade</monospace>, where most
+    <monospace>FreqAI</monospace> beta-testers/developers originated.
+    This symbiotic relationship between <monospace>Freqtrade</monospace>
+    and <monospace>FreqAI</monospace> ignited a thoroughly tested
+    <ext-link ext-link-type="uri" xlink:href="https://github.com/freqtrade/freqtrade/pull/6832"><monospace>beta</monospace></ext-link>,
+    which demanded a four month beta and
+    <ext-link ext-link-type="uri" xlink:href="https://www.freqtrade.io/en/latest/freqai/">comprehensive
+    documentation</ext-link> containing:</p>
+    <list list-type="bullet">
+      <list-item>
+        <p>numerous example scripts</p>
+      </list-item>
+      <list-item>
+        <p>a full parameter table</p>
+      </list-item>
+      <list-item>
+        <p>methodological descriptions</p>
+      </list-item>
+      <list-item>
+        <p>high-resolution diagrams/figures</p>
+      </list-item>
+      <list-item>
+        <p>detailed parameter setting recommendations</p>
+      </list-item>
+    </list>
+  </sec>
+  <sec id="providing-a-reproducible-foundation-for-researchers">
+    <title>Providing a reproducible foundation for researchers</title>
+    <p><monospace>FreqAI</monospace> provides an extensible, robust,
+    framework for researchers and citizen data scientists. The
+    <monospace>FreqAI</monospace> sandbox enables rapid conception and
+    testing of exotic hypotheses. From a research perspective,
+    <monospace>FreqAI</monospace> handles the multitude of logistics
+    associated with live deployments, historical backtesting, and
+    feature engineering. With <monospace>FreqAI</monospace>, researchers
+    can focus on their primary interests of feature engineering and
+    hypothesis testing rather than figuring out how to collect and
+    handle data. Further - the well maintained and easily installed
+    open-source framework of <monospace>FreqAI</monospace> enables
+    reproducible scientific studies. This reproducibility component is
+    essential to general scientific advancement in time-series
+    forecasting for chaotic systems.</p>
+  </sec>
+</sec>
+<sec id="technical-details">
+  <title>Technical details</title>
+  <p>Typical users configure <monospace>FreqAI</monospace> via two
+  files:</p>
+  <list list-type="order">
+    <list-item>
+      <p>A <monospace>configuration</monospace> file
+      (<monospace>--config</monospace>) which provides access to the
+      full parameter list available
+      <ext-link ext-link-type="uri" xlink:href="https://www.freqtrade.io/en/latest/freqai/">here</ext-link>:</p>
+    </list-item>
+  </list>
+  <list list-type="bullet">
+    <list-item>
+      <p>control high-level feature engineering</p>
+    </list-item>
+    <list-item>
+      <p>customize adaptive modeling techniques</p>
+    </list-item>
+    <list-item>
+      <p>set any model training parameters available in third-party
+      libraries</p>
+    </list-item>
+    <list-item>
+      <p>manage adaptive modeling parameters (retrain frequency,
+      training window size, continual learning, etc.)</p>
+    </list-item>
+  </list>
+  <list list-type="order">
+    <list-item>
+      <label>2.</label>
+      <p>A strategy file (<monospace>--strategy</monospace>) where
+      users:</p>
+    </list-item>
+  </list>
+  <list list-type="bullet">
+    <list-item>
+      <p>list of the base training features</p>
+    </list-item>
+    <list-item>
+      <p>set standard technical-analysis strategies</p>
+    </list-item>
+    <list-item>
+      <p>control trade entry/exit criteria</p>
+    </list-item>
+  </list>
+  <p>With these two files, most users can exploit a wide range of
+  pre-existing integrations in <monospace>Catboost</monospace> and 7
+  other libraries with a simple command:</p>
+  <preformat>freqtrade trade --config config_freqai.example.json --strategy FreqaiExampleStrategy --freqaimodel CatboostRegressor</preformat>
+  <p>Advanced users will edit one of the existing
+  <monospace>--freqaimodel</monospace> files, which are simply an
+  children of the <monospace>IFreqaiModel</monospace> (details below).
+  Within these files, advanced users can customize training procedures,
+  prediction procedures, outlier detection methods, data preparation,
+  data saving methods, etc. This is all configured in a way where they
+  can customize as little or as much as they want. This flexible
+  customization is owed to the foundational architecture in
+  <monospace>FreqAI</monospace>, which is comprised of three distinct
+  Python objects:</p>
+  <list list-type="bullet">
+    <list-item>
+      <p><monospace>IFreqaiModel</monospace></p>
+      <list list-type="bullet">
+        <list-item>
+          <p>A singular long-lived object containing all the necessary
+          logic to collect data, store data, process data, engineer
+          features, run training, and inference models.</p>
+        </list-item>
+      </list>
+    </list-item>
+    <list-item>
+      <p><monospace>FreqaiDataKitchen</monospace></p>
+      <list list-type="bullet">
+        <list-item>
+          <p>A short-lived object which is uniquely created for each
+          asset/model. Beyond metadata, it also contains a variety of
+          data processing tools.</p>
+        </list-item>
+      </list>
+    </list-item>
+    <list-item>
+      <p><monospace>FreqaiDataDrawer</monospace></p>
+      <list list-type="bullet">
+        <list-item>
+          <p>Singular long-lived object containing all the historical
+          predictions, models, and save/load methods.</p>
+        </list-item>
+      </list>
+    </list-item>
+  </list>
+  <p>These objects interact with one another with one goal in mind - to
+  provide a clean data set to machine learning experts/enthusiasts at
+  the user endpoint. These power-users interact with an inherited
+  <monospace>IFreqaiModel</monospace> that allows them to dig as deep or
+  as shallow as they wish into the inheritence tree. Typical power-users
+  focus their efforts on customizing training procedures and testing
+  exotic functionalities available in third-party libraries. Thus,
+  power-users are freed from the algorithmic weight associated with data
+  management, and can instead focus their energy on testing creative
+  hypotheses. Meanwhile, some users choose to override deeper
+  functionalities within <monospace>IFreqaiModel</monospace> to help
+  them craft unique data structures and training procedures.</p>
+  <p>The class structure and algorithmic details are depicted in the
+  following diagram:</p>
+  <p><named-content content-type="image">image</named-content>
+  <italic>Class diagram summarizing object interactions in
+  FreqAI</italic></p>
+</sec>
+<sec id="online-documentation">
+  <title>Online documentation</title>
+  <p>The documentation for
+  <ext-link ext-link-type="uri" xlink:href="https://www.freqtrade.io/en/latest/freqai/"><monospace>FreqAI</monospace></ext-link>
+  is available online at
+  <ext-link ext-link-type="uri" xlink:href="https://www.freqtrade.io/en/latest/freqai/">https://www.freqtrade.io/en/latest/freqai/</ext-link>
+  and covers a wide range of materials:</p>
+  <list list-type="bullet">
+    <list-item>
+      <p>Quick-start with a single command and example files -
+      (beginners)</p>
+    </list-item>
+    <list-item>
+      <p>Introduction to the feature engineering interface and basic
+      configurations - (intermediate users)</p>
+    </list-item>
+    <list-item>
+      <p>Parameter table with indepth descriptions and default parameter
+      setting recommendations - (intermediate users)</p>
+    </list-item>
+    <list-item>
+      <p>Data analysis and post-processing - (advanced users)</p>
+    </list-item>
+    <list-item>
+      <p>Methodological considerations complemented by high resolution
+      figures - (advanced users)</p>
+    </list-item>
+    <list-item>
+      <p>Instructions for integrating third party machine learning
+      libraries into custom prediction models - (advanced users)</p>
+    </list-item>
+    <list-item>
+      <p>Software architectural description with class diagram -
+      (developers)</p>
+    </list-item>
+    <list-item>
+      <p>File structure descriptions - (developers)</p>
+    </list-item>
+  </list>
+  <p>The docs direct users to a variety of pre-made examples which
+  integrate <monospace>Catboost</monospace>,
+  <monospace>LightGBM</monospace>, <monospace>XGBoost</monospace>,
+  <monospace>Sklearn</monospace>,
+  <monospace>stable_baselines3</monospace>,
+  <monospace>torch</monospace>, <monospace>tensorflow</monospace>.
+  Meanwhile, developers will also find thorough docstrings and type
+  hinting throughout the source code to aid in code readability and
+  customization.</p>
+  <p><monospace>FreqAI</monospace> also benefits from a strong support
+  network of users and developers on the
+  <ext-link ext-link-type="uri" xlink:href="https://discord.gg/w6nDM6cM4y"><monospace>Freqtrade</monospace>
+  discord</ext-link> as well as on the
+  <ext-link ext-link-type="uri" xlink:href="https://discord.gg/xE4RMg4QYw"><monospace>FreqAI</monospace>
+  discord</ext-link>. Within the <monospace>FreqAI</monospace> discord,
+  users will find a deep and easily searched knowledge base containing
+  common errors. But more importantly, users in the
+  <monospace>FreqAI</monospace> discord share anectdotal and
+  quantitative observations which compare performance between various
+  third-party libraries and methods.</p>
+</sec>
+<sec id="state-of-the-field">
+  <title>State of the field</title>
+  <p>There are two other open-source tools which are geared toward
+  helping users build models for time-series forecasts on market based
+  data. However, each of these tools suffer from a non-generalized
+  frameworks that do not permit comparison of methods and libraries.
+  Additionally, they do not permit easy live-deployments or
+  adaptive-modeling methods. For example, two open-sourced projects
+  called
+  <ext-link ext-link-type="uri" xlink:href="https://tensortradex.readthedocs.io/en/latest/"><monospace>tensortrade</monospace></ext-link>
+  (<xref alt="Tensortrade, 2022" rid="ref-tensortrade" ref-type="bibr"><italic>Tensortrade</italic>,
+  2022</xref>) and
+  <ext-link ext-link-type="uri" xlink:href="https://github.com/AI4Finance-Foundation/FinRL"><monospace>FinRL</monospace></ext-link>
+  (<xref alt="AI4Finance-Foundation, 2022" rid="ref-finrl" ref-type="bibr"><italic>AI4Finance-Foundation</italic>,
+  2022</xref>) limit users to the exploration of reinforcement learning
+  on historical data. These softwares also do not provide robust live
+  deployments, they do not furnish novel feature engineering algorithms,
+  and they do not provide custom data analysis tools.
+  <monospace>FreqAI</monospace> fills the gap.</p>
+</sec>
+<sec id="on-going-research">
+  <title>On-going research</title>
+  <p>Emergent Methods, based in Arvada CO, is actively using
+  <monospace>FreqAI</monospace> to perform large scale experiments aimed
+  at comparing machine learning libraries in live and historical
+  environments. Past projects include backtesting parametric sweeps,
+  while active projects include a 3 week live deployment comparison
+  between <monospace>CatboosRegressor</monospace>,
+  <monospace>LightGBMRegressor</monospace>, and
+  <monospace>XGBoostRegressor</monospace>. Results from these studies
+  are on track for publication in scientific journals as well as more
+  general data science blogs (e.g. Medium).</p>
+</sec>
+<sec id="installing-and-running-freqai">
+  <title>Installing and running <monospace>FreqAI</monospace></title>
+  <p><monospace>FreqAI</monospace> is automatically installed with
+  <monospace>Freqtrade</monospace> using the following commands on linux
+  systems:</p>
+  <preformat>git clone git@github.com:freqtrade/freqtrade.git
+cd freqtrade
+./setup.sh -i</preformat>
+  <p>However, <monospace>FreqAI</monospace> also benefits from
+  <monospace>Freqtrade</monospace> docker distributions, and can be run
+  with docker by pulling the stable or develop images from
+  <monospace>Freqtrade</monospace> distributions.</p>
+</sec>
+<sec id="funding-sources">
+  <title>Funding sources</title>
+  <p><ext-link ext-link-type="uri" xlink:href="https://www.freqtrade.io/en/latest/freqai/"><monospace>FreqAI</monospace></ext-link>
+  has had no official sponsors, and is entirely grass roots. All
+  donations into the project (e.g. the GitHub sponsor system) are kept
+  inside the project to help support development of open-sourced and
+  communally beneficial features.</p>
+</sec>
+<sec id="acknowledgements">
+  <title>Acknowledgements</title>
+  <p>We would like to acknowledge various beta testers of
+  <monospace>FreqAI</monospace>:</p>
+  <list list-type="bullet">
+    <list-item>
+      <p>Richárd Józsa</p>
+    </list-item>
+    <list-item>
+      <p>Juha Nykänen</p>
+    </list-item>
+    <list-item>
+      <p>Salah Lamkadem</p>
+    </list-item>
+  </list>
+  <p>As well as various <monospace>Freqtrade</monospace>
+  <ext-link ext-link-type="uri" xlink:href="https://github.com/freqtrade/freqtrade/graphs/contributors">developers</ext-link>
+  maintaining tangential, yet essential, modules.</p>
+</sec>
+</body>
+<back>
+<ref-list>
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docs/JOSS_paper/paper.md

@@ -0,0 +1,212 @@
+---
+title: '`FreqAI`: generalizing adaptive modeling for chaotic time-series market forecasts'
+tags:
+  - Python
+  - Machine Learning
+  - adaptive modeling
+  - chaotic systems
+  - time-series forecasting
+authors:
+  - name: Robert A. Caulk Ph.D
+    orcid: 0000-0001-5618-8629
+    affiliation: 1, 2 
+  - name: Elin Törnquist Ph.D
+    orcid: 0000-0003-3289-8604
+    affiliation: 1, 2
+  - name: Matthias Voppichler 
+    orcid: 
+    affiliation: 2
+  - name: Andrew R. Lawless
+    orcid: 
+    affiliation: 2
+  - name: Ryan McMullan
+    orcid: 
+    affiliation: 2
+  - name: Wagner Costa Santos
+    orcid: 
+    affiliation: 1, 2
+  - name: Timothy C. Pogue
+    orcid: 
+    affiliation: 1, 2
+  - name: Johan van der Vlugt
+    orcid: 
+    affiliation: 2
+  - name: Stefan P. Gehring
+    orcid: 
+    affiliation: 2
+  - name: Pascal Schmidt
+    orcid: 0000-0001-9328-4345
+    affiliation: 2
+
+<!-- affiliation: "1, 2" # (Multiple affiliations must be quoted) -->
+affiliations:
+ - name: Emergent Methods LLC, Arvada Colorado, 80005, USA
+   index: 1
+ - name: Freqtrade open source project
+   index: 2
+date: October 2022
+bibliography: paper.bib
+
+
+---
+
+
+# Statement of need
+
+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 "citizen 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`](https://www.freqtrade.io/en/latest/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. 
+
+
+# Summary
+
+[`FreqAI`](https://www.freqtrade.io/en/latest/freqai/) evolved from a desire to test and compare a range of adaptive time-series forecasting methods on chaotic data. Cryptocurrency markets provide a unique data source since they are operational 24/7 and the data is freely available via a variety of open-sourced [exchange APIs](https://docs.ccxt.com/en/latest/manual.html#exchange-structure). Luckily, an existing open-source software, [`Freqtrade`](https://www.freqtrade.io/en/stable/), had already matured under a range of talented developers to support robust data collection/storage, as well as robust live environmental interactions for standard algorithmic trading. `Freqtrade` also provides a set of data analysis/visualization tools for the evaluation of historical performance as well as live environmental feedback. `FreqAI` builds on top of `Freqtrade` to include a user-friendly well tested interface for integrating external machine learning libraries for adaptive time-series forecasting. Beyond enabling the integration of existing libraries, `FreqAI` hosts a range of custom algorithms and methodologies aimed at improving computational and predictive performances. Thus, `FreqAI` contains a range of unique features which can be easily tested in combination with all the existing Python-accessible machine learning libraries to generate novel research on live and historical data. 
+
+The high-level overview of the software is depicted in Figure 1.
+
+![freqai-algo](assets/freqai_algo.jpg)
+*Abstracted overview of FreqAI algorithm*
+
+## Connecting machine learning libraries
+
+Although the `FreqAI`  framework is designed to accommodate any Python library in the "Model training" and "Feature set engineering" portions of the software (Figure 1), it already boasts a wide range of well documented examples based on various combinations of:
+
+* scikit-learn [@scikit-learn], Catboost [@catboost], LightGBM [@lightgbm], XGBoost [@xgboost], stable_baselines3 [@stable-baselines3], openai gym [@openai], tensorflow [@tensorflow], pytorch [@pytorch], Scipy [@scipy], Numpy [@numpy], and pandas [@pandas]. 
+
+These mature projects contain a wide range of peer-reviewed and industry standard methods, including:
+
+* Regression, Classification, Neural Networks, Reinforcement Learning, Support Vector Machines, Principal Component Analysis, point clustering, and much more. 
+
+which are all leveraged in `FreqAI` for users to use as templates or extend with their own methods.
+  
+## Furnishing novel methods and features
+
+Beyond the industry standard methods available through external libraries - `FreqAI` includes novel methods which are not available anywhere else in the open-source (or scientific) world. For example, `FreqAI` provides :
+
+* a custom algorithm/methodology for adaptive modeling details [here](https://www.freqtrade.io/en/stable/freqai/#general-approach) and [here](https://www.freqtrade.io/en/stable/freqai-developers/#project-architecture)
+* rapid and self-monitored feature engineering tools, details [here](https://www.freqtrade.io/en/stable/freqai-feature-engineering/#feature-engineering)
+* unique model features/indicators, such as the [inlier metric](https://www.freqtrade.io/en/stable/freqai-feature-engineering/#inlier-metric)
+* optimized data collection/storage algorithms, all code shown [here](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/freqai/data_drawer.py)
+* safely integrated outlier detection methods, details [here](https://www.freqtrade.io/en/stable/freqai-feature-engineering/#outlier-detection)
+* websocket communicated forecasts, details [here](https://www.freqtrade.io/en/stable/producer-consumer/)
+
+Of particular interest for researchers, `FreqAI` provides the option of large scale experimentation via an optimized [websocket communications interface](https://www.freqtrade.io/en/stable/producer-consumer/).
+
+## Optimizing the back-end
+
+`FreqAI` aims to make it simple for users to combine all the above tools to run studies based in two distinct modules:
+
+* backtesting studies
+* live-deployments 
+
+Both of these modules and their respective data management systems are built on top of [`Freqtrade`](https://www.freqtrade.io/en/latest/), a mature and actively developed cryptocurrency trading software. This means that `FreqAI` benefits from a wide range of tangential/disparate feature developments such as:
+
+* FreqUI, a graphical interface for backtesting and live monitoring
+* telegram control
+* robust database handling
+* futures/leverage trading
+* dollar cost averaging
+* trading strategy handling
+* a variety of free data sources via [CCXT](https://docs.ccxt.com/en/latest/manual.html#exchange-structure) (FTX, Binance, Kucoin etc.)
+
+These features derive from a strong external developer community that shares in the benefit and stability of a communal CI (Continuous Integration) system. Beyond the developer community, `FreqAI` benefits strongly from the userbase of `Freqtrade`, where most `FreqAI` beta-testers/developers originated. This symbiotic relationship between `Freqtrade` and `FreqAI` ignited a thoroughly tested [`beta`](https://github.com/freqtrade/freqtrade/pull/6832), which demanded a four month beta and [comprehensive documentation](https://www.freqtrade.io/en/latest/freqai/) containing:
+
+* numerous example scripts
+* a full parameter table
+* methodological descriptions
+* high-resolution diagrams/figures
+* detailed parameter setting recommendations
+
+## Providing a reproducible foundation for researchers
+
+`FreqAI` provides an extensible, robust, framework for researchers and citizen data scientists. The `FreqAI` sandbox enables rapid conception and testing of exotic hypotheses. From a research perspective, `FreqAI` handles the multitude of logistics associated with live deployments, historical backtesting, and feature engineering. With `FreqAI`, researchers can focus on their primary interests of feature engineering and hypothesis testing rather than figuring out how to collect and handle data. Further - the well maintained and easily installed open-source framework of `FreqAI` enables reproducible scientific studies. This reproducibility component is essential to general scientific advancement in time-series forecasting for chaotic systems. 
+
+# Technical details
+
+Typical users configure `FreqAI` via two files:
+
+1. A `configuration` file (`--config`) which provides access to the full parameter list available [here](https://www.freqtrade.io/en/latest/freqai/):
+  * control high-level feature engineering
+  * customize adaptive modeling techniques
+  * set any model training parameters available in third-party libraries
+  * manage adaptive modeling parameters (retrain frequency, training window size, continual learning, etc.)
+
+2. A strategy file (`--strategy`) where users:
+  * list of the base training features
+  * set standard technical-analysis strategies
+  * control trade entry/exit criteria
+
+With these two files, most users can exploit a wide range of pre-existing integrations in `Catboost` and 7 other libraries with a simple command:
+
+```
+freqtrade trade --config config_freqai.example.json --strategy FreqaiExampleStrategy --freqaimodel CatboostRegressor
+```
+
+Advanced users will edit one of the existing `--freqaimodel` files, which are simply an children of the `IFreqaiModel` (details below). Within these files, advanced users can customize training procedures, prediction procedures, outlier detection methods, data preparation, data saving methods, etc. This is all configured in a way where they can customize as little or as much as they want. This flexible customization is owed to the foundational architecture in `FreqAI`, which is comprised of three distinct Python objects:
+
+* `IFreqaiModel`
+  * A singular long-lived object containing all the necessary logic to collect data, store data, process data, engineer features, run training, and inference models. 
+* `FreqaiDataKitchen`
+  * A short-lived object which is uniquely created for each asset/model. Beyond metadata, it also contains a variety of data processing tools. 
+* `FreqaiDataDrawer`
+  * Singular long-lived object containing all the historical predictions, models, and save/load methods. 
+
+These objects interact with one another with one goal in mind - to provide a clean data set to machine learning experts/enthusiasts at the user endpoint. These power-users interact with an inherited `IFreqaiModel` that allows them to dig as deep or as shallow as they wish into the inheritence tree. Typical power-users focus their efforts on customizing training procedures and testing exotic functionalities available in third-party libraries. Thus, power-users are freed from the algorithmic weight associated with data management, and can instead focus their energy on testing creative hypotheses. Meanwhile, some users choose to override deeper functionalities within `IFreqaiModel` to help them craft unique data structures and training procedures.
+
+The class structure and algorithmic details are depicted in the following diagram: 
+
+![image](assets/freqai_algorithm-diagram.jpg)
+*Class diagram summarizing object interactions in FreqAI*
+
+# Online documentation
+
+The documentation for [`FreqAI`](https://www.freqtrade.io/en/latest/freqai/) is available online at [https://www.freqtrade.io/en/latest/freqai/](https://www.freqtrade.io/en/latest/freqai/) and covers a wide range of materials:
+
+* Quick-start with a single command and example files - (beginners)
+* Introduction to the feature engineering interface and basic configurations - (intermediate users)
+* Parameter table with indepth descriptions and default parameter setting recommendations - (intermediate users)
+* Data analysis and post-processing - (advanced users)
+* Methodological considerations complemented by high resolution figures - (advanced users)
+* Instructions for integrating third party machine learning libraries into custom prediction models - (advanced users)
+* Software architectural description with class diagram - (developers) 
+* File structure descriptions - (developers)
+
+The docs direct users to a variety of pre-made examples which integrate `Catboost`, `LightGBM`, `XGBoost`, `Sklearn`, `stable_baselines3`, `torch`, `tensorflow`. Meanwhile, developers will also find thorough docstrings and type hinting throughout the source code to aid in code readability and customization. 
+
+`FreqAI` also benefits from a strong support network of users and developers on the [`Freqtrade` discord](https://discord.gg/w6nDM6cM4y) as well as on the [`FreqAI` discord](https://discord.gg/xE4RMg4QYw). Within the `FreqAI` discord, users will find a deep and easily searched knowledge base containing common errors. But more importantly, users in the `FreqAI` discord share anectdotal and quantitative observations which compare performance between various third-party libraries and methods.
+
+# State of the field
+
+There are two other open-source tools which are geared toward helping users build models for time-series forecasts on market based data. However, each of these tools suffer from a non-generalized frameworks that do not permit comparison of methods and libraries. Additionally, they do not permit easy live-deployments or adaptive-modeling methods. For example, two open-sourced projects called [`tensortrade`](https://tensortradex.readthedocs.io/en/latest/) [@tensortrade] and [`FinRL`](https://github.com/AI4Finance-Foundation/FinRL) [@finrl] limit users to the exploration of reinforcement learning on historical data. These softwares also do not provide robust live deployments, they do not furnish novel feature engineering algorithms, and they do not provide custom data analysis tools. `FreqAI` fills the gap. 
+
+# On-going research
+
+Emergent Methods, based in Arvada CO, is actively using `FreqAI` to perform large scale experiments aimed at comparing machine learning libraries in live and historical environments. Past projects include backtesting parametric sweeps, while active projects include a 3 week live deployment comparison between `CatboostRegressor`, `LightGBMRegressor`, and `XGBoostRegressor`. Results from these studies are planned for submission to scientific journals as well as more general data science blogs (e.g. Medium).
+
+# Installing and running `FreqAI`
+
+`FreqAI` is automatically installed with `Freqtrade` using the following commands on linux systems:
+
+```
+git clone git@github.com:freqtrade/freqtrade.git
+cd freqtrade
+./setup.sh -i
+```
+
+However, `FreqAI` also benefits from `Freqtrade` docker distributions, and can be run with docker by pulling the stable or develop images from `Freqtrade` distributions.
+
+# Funding sources
+
+[`FreqAI`](https://www.freqtrade.io/en/latest/freqai/) has had no official sponsors, and is entirely grass roots. All donations into the project (e.g. the GitHub sponsor system) are kept inside the project to help support development of open-sourced and communally beneficial features. 
+
+# Acknowledgements
+
+We would like to acknowledge various beta testers of `FreqAI`:
+
+- Longlong Yu (lolongcovas)
+- Richárd Józsa (richardjozsa)
+- Juha Nykänen (suikula)
+- Emre Suzen (aemr3)
+- Salah Lamkadem (ikonx)
+
+As well as various `Freqtrade` [developers](https://github.com/freqtrade/freqtrade/graphs/contributors) maintaining tangential, yet essential, modules.
+
+# References

docs/advanced-hyperopt.md

@@ -78,8 +78,6 @@ This function needs to return a floating point number (`float`). Smaller numbers
 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:
 
 ```python
-from freqtrade.optimize.space import Categorical, Dimension, Integer, SKDecimal
-
 class MyAwesomeStrategy(IStrategy):
     class HyperOpt:
         # Define a custom stoploss space.
@@ -96,33 +94,6 @@ class MyAwesomeStrategy(IStrategy):
                 SKDecimal(0.01, 0.07, decimals=3, name='roi_p2'),
                 SKDecimal(0.01, 0.20, decimals=3, name='roi_p3'),
             ]
-
-        def generate_roi_table(params: Dict) -> Dict[int, float]:
-
-            roi_table = {}
-            roi_table[0] = params['roi_p1'] + params['roi_p2'] + params['roi_p3']
-            roi_table[params['roi_t3']] = params['roi_p1'] + params['roi_p2']
-            roi_table[params['roi_t3'] + params['roi_t2']] = params['roi_p1']
-            roi_table[params['roi_t3'] + params['roi_t2'] + params['roi_t1']] = 0
-
-            return roi_table
-
-        def trailing_space() -> List[Dimension]:
-            # All parameters here are mandatory, you can only modify their type or the range.
-            return [
-                # Fixed to true, if optimizing trailing_stop we assume to use trailing stop at all times.
-                Categorical([True], name='trailing_stop'),
-
-                SKDecimal(0.01, 0.35, decimals=3, name='trailing_stop_positive'),
-                # 'trailing_stop_positive_offset' should be greater than 'trailing_stop_positive',
-                # so this intermediate parameter is used as the value of the difference between
-                # them. The value of the 'trailing_stop_positive_offset' is constructed in the
-                # generate_trailing_params() method.
-                # This is similar to the hyperspace dimensions used for constructing the ROI tables.
-                SKDecimal(0.001, 0.1, decimals=3, name='trailing_stop_positive_offset_p1'),
-
-                Categorical([True, False], name='trailing_only_offset_is_reached'),
-        ]
 ```
 
 !!! Note

docs/backtesting.md

@@ -522,13 +522,13 @@ Since backtesting lacks some detailed information about what happens within a ca
 - ROI
   - exits are compared to high - but the ROI value is used (e.g. ROI = 2%, high=5% - so the exit will be at 2%)
   - exits are never "below the candle", so a ROI of 2% may result in a exit at 2.4% if low was at 2.4% profit
-  - Force-exits caused by `<N>=-1` ROI entries use low as exit value, unless N falls on the candle open (e.g. `120: -1` for 1h candles)
+  - Forceexits caused by `<N>=-1` ROI entries use low as exit value, unless N falls on the candle open (e.g. `120: -1` for 1h candles)
 - Stoploss exits happen exactly at stoploss price, even if low was lower, but the loss will be `2 * fees` higher than the stoploss price
 - Stoploss is evaluated before ROI within one candle. So you can often see more trades with the `stoploss` exit reason comparing to the results obtained with the same strategy in the Dry Run/Live Trade modes
 - Low happens before high for stoploss, protecting capital first
 - Trailing stoploss
   - Trailing Stoploss is only adjusted if it's below the candle's low (otherwise it would be triggered)
-  - On trade entry candles that trigger trailing stoploss, the "minimum offset" (`stop_positive_offset`) is assumed (instead of high) - and the stop is calculated from this point. This rule is NOT applicable to custom-stoploss scenarios, since there's no information about the stoploss logic available.
+  - On trade entry candles that trigger trailing stoploss, the "minimum offset" (`stop_positive_offset`) is assumed (instead of high) - and the stop is calculated from this point
   - High happens first - adjusting stoploss
   - Low uses the adjusted stoploss (so exits with large high-low difference are backtested correctly)
   - ROI applies before trailing-stop, ensuring profits are "top-capped" at ROI if both ROI and trailing stop applies

docs/configuration.md

@@ -215,18 +215,16 @@ Mandatory parameters are marked as **Required**, which means that they are requi
 | `telegram.balance_dust_level` | Dust-level (in stake currency) - currencies with a balance below this will not be shown by `/balance`. <br> **Datatype:** float
 | `telegram.reload` | Allow "reload" buttons on telegram messages. <br>*Defaults to `True`.<br> **Datatype:** boolean
 | `telegram.notification_settings.*` | Detailed notification settings. Refer to the [telegram documentation](telegram-usage.md) for details.<br> **Datatype:** dictionary
-| `telegram.allow_custom_messages` | Enable the sending of Telegram messages from strategies via the dataprovider.send_msg() function. <br> **Datatype:** Boolean
 | | **Webhook**
 | `webhook.enabled` | Enable usage of Webhook notifications <br> **Datatype:** Boolean
 | `webhook.url` | URL for the webhook. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
-| `webhook.entry` | Payload to send on entry. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
-| `webhook.entry_cancel` | Payload to send on entry order cancel. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
-| `webhook.entry_fill` | Payload to send on entry order filled. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
-| `webhook.exit` | Payload to send on exit. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
-| `webhook.exit_cancel` | Payload to send on exit order cancel. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
-| `webhook.exit_fill` | Payload to send on exit order filled. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
-| `webhook.status` | Payload to send on status calls. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
-| `webhook.allow_custom_messages` | Enable the sending of Webhook messages from strategies via the dataprovider.send_msg() function. <br> **Datatype:** Boolean
+| `webhook.webhookentry` | Payload to send on entry. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
+| `webhook.webhookentrycancel` | Payload to send on entry order cancel. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
+| `webhook.webhookentryfill` | Payload to send on entry order filled. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
+| `webhook.webhookexit` | Payload to send on exit. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
+| `webhook.webhookexitcancel` | Payload to send on exit order cancel. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
+| `webhook.webhookexitfill` | Payload to send on exit order filled. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
+| `webhook.webhookstatus` | Payload to send on status calls. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details. <br> **Datatype:** String
 | | **Rest API / FreqUI / Producer-Consumer**
 | `api_server.enabled` | Enable usage of API Server. See the [API Server documentation](rest-api.md) for more details. <br> **Datatype:** Boolean
 | `api_server.listen_ip_address` | Bind IP address. See the [API Server documentation](rest-api.md) for more details. <br> **Datatype:** IPv4

docs/deprecated.md

@@ -66,11 +66,11 @@ We will keep a compatibility layer for 1-2 versions (so both `buy_tag` and `ente
 
 #### Naming changes
 
-Webhook terminology changed from "sell" to "exit", and from "buy" to "entry", removing "webhook" in the process.
+Webhook terminology changed from "sell" to "exit", and from "buy" to "entry".
 
-* `webhookbuy`, `webhookentry` -> `entry`
-* `webhookbuyfill`, `webhookentryfill` -> `entry_fill`
-* `webhookbuycancel`, `webhookentrycancel` -> `entry_cancel`
-* `webhooksell`, `webhookexit` -> `exit`
-* `webhooksellfill`, `webhookexitfill` -> `exit_fill`
-* `webhooksellcancel`, `webhookexitcancel` -> `exit_cancel`
+* `webhookbuy` -> `webhookentry`
+* `webhookbuyfill` -> `webhookentryfill`
+* `webhookbuycancel` -> `webhookentrycancel`
+* `webhooksell` -> `webhookexit`
+* `webhooksellfill` -> `webhookexitfill`
+* `webhooksellcancel` -> `webhookexitcancel`

docs/faq.md

@@ -102,12 +102,6 @@ If this happens for all pairs in the pairlist, this might indicate a recent exch
 
 Irrespectively of the reason, Freqtrade will fill up these candles with "empty" candles, where open, high, low and close are set to the previous candle close - and volume is empty. In a chart, this will look like a `_` - and is aligned with how exchanges usually represent 0 volume candles.
 
-### I'm getting "Price jump between 2 candles detected"
-
-This message is a warning that the candles had a price jump of > 30%.
-This might be a sign that the pair stopped trading, and some token exchange took place (e.g. COCOS in 2021 - where price jumped from 0.0000154 to 0.01621).
-This message is often accompanied by ["Missing data fillup"](#im-getting-missing-data-fillup-messages-in-the-log) - as trading on such pairs is often stopped for some time.
-
 ### I'm getting "Outdated history for pair xxx" in the log
 
 The bot is trying to tell you that it got an outdated last candle (not the last complete candle).

docs/freqai-configuration.md

@@ -192,11 +192,11 @@ dataframe["target_roi"] = dataframe["&-s_close_mean"] + dataframe["&-s_close_std
 dataframe["sell_roi"] = dataframe["&-s_close_mean"] - dataframe["&-s_close_std"] * 1.25
 ```
 
-To consider the population of *historical predictions* for creating the dynamic target instead of information from the training as discussed above, you would set `fit_live_predictions_candles` in the config to the number of historical prediction candles you wish to use to generate target statistics.
+To consider the population of *historical predictions* for creating the dynamic target instead of information from the training as discussed above, you would set `fit_live_prediction_candles` in the config to the number of historical prediction candles you wish to use to generate target statistics.
 
 ```json
     "freqai": {
-        "fit_live_predictions_candles": 300,
+        "fit_live_prediction_candles": 300,
     }
 ```
 
@@ -204,44 +204,14 @@ If this value is set, FreqAI will initially use the predictions from the trainin
 
 ## Using different prediction models
 
-FreqAI has multiple example prediction model libraries that are ready to be used as is via the flag `--freqaimodel`. These libraries include `CatBoost`, `LightGBM`, and `XGBoost` regression, classification, and multi-target models, and can be found in `freqai/prediction_models/`.
+FreqAI has multiple example prediction model libraries that are ready to be used as is via the flag `--freqaimodel`. These libraries include `Catboost`, `LightGBM`, and `XGBoost` regression, classification, and multi-target models, and can be found in `freqai/prediction_models/`. However, it is possible to customize and create your own prediction models using the `IFreqaiModel` class. You are encouraged to inherit `fit()`, `train()`, and `predict()` to let these customize various aspects of the training procedures.
 
-Regression and classification models differ in what targets they predict - a regression model will predict a target of continuous values, for example what price BTC will be at tomorrow, whilst a classifier will predict a target of discrete values, for example if the price of BTC will go up tomorrow or not. This means that you have to specify your targets differently depending on which model type you are using (see details [below](#setting-model-targets)).
+### Setting classifier targets
 
-All of the aforementioned model libraries implement gradient boosted decision tree algorithms. They all work on the principle of ensemble learning, where predictions from multiple simple learners are combined to get a final prediction that is more stable and generalized. The simple learners in this case are decision trees. Gradient boosting refers to the method of learning, where each simple learner is built in sequence - the subsequent learner is used to improve on the error from the previous learner. If you want to learn more about the different model libraries you can find the information in their respective docs:
-
-* CatBoost: https://catboost.ai/en/docs/
-* 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.
-
-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.
-
-### Setting model targets
-
-#### Regressors
-
-If you are using a regressor, you need to specify a target that has continuous values. FreqAI includes a variety of regressors, such as the `CatboostRegressor`via the flag `--freqaimodel CatboostRegressor`. An example of how you could set a regression target for predicting the price 100 candles into the future would be
-
-```python
-df['&s-close_price'] = df['close'].shift(-100)
-```
-
-If you want to predict multiple targets, you need to define multiple labels using the same syntax as shown above.
-
-#### Classifiers
-
-If you are using a classifier, you need to specify a target that has discrete values. FreqAI includes a variety of classifiers, such as the `CatboostClassifier` via the flag `--freqaimodel CatboostClassifier`. If you elects to use a classifier, the classes need to be set using strings. For example, if you want to predict if the price 100 candles into the future goes up or down you would set 
+FreqAI includes a variety of classifiers, such as the `CatboostClassifier` via the flag `--freqaimodel CatboostClassifier`. If you elects to use a classifier, the classes need to be set using strings. For example:
 
 ```python
 df['&s-up_or_down'] = np.where( df["close"].shift(-100) > df["close"], 'up', 'down')
 ```
 
-If you want to predict multiple targets you must specify all labels in the same label column. You could, for example, add the label `same` to define where the price was unchanged by setting
-
-```python
-df['&s-up_or_down'] = np.where( df["close"].shift(-100) > df["close"], 'up', 'down')
-df['&s-up_or_down'] = np.where( df["close"].shift(-100) == df["close"], 'same', df['&s-up_or_down'])
-```
+Additionally, the example classifier models do not accommodate multiple labels, but they do allow multi-class classification within a single label column.

docs/freqai-parameter-table.md

@@ -37,16 +37,25 @@ 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).
-| `write_metrics_to_disk` | Collect train timings, inference timings and cpu usage in json file. <br> **Datatype:** Boolean. <br> Default: `False`
 |  |  **Data split parameters**
 | `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`.
 |  |  **Model training parameters**
-| `model_training_parameters` | A flexible dictionary that includes all parameters available by the selected model library. For example, if you use `LightGBMRegressor`, this dictionary can contain any parameter available by the `LightGBMRegressor` [here](https://lightgbm.readthedocs.io/en/latest/pythonapi/lightgbm.LGBMRegressor.html) (external website). If you select a different model, this dictionary can contain any parameter from that model. A list of the currently available models can be found [here](freqai-configuration.md#using-different-prediction-models).  <br> **Datatype:** Dictionary.
+| `model_training_parameters` | A flexible dictionary that includes all parameters available by the selected model library. For example, if you use `LightGBMRegressor`, this dictionary can contain any parameter available by the `LightGBMRegressor` [here](https://lightgbm.readthedocs.io/en/latest/pythonapi/lightgbm.LGBMRegressor.html) (external website). If you select a different model, this dictionary can contain any parameter from that model.  <br> **Datatype:** Dictionary.
 | `n_estimators` | The number of boosted trees to fit in the training of the model. <br> **Datatype:** Integer.
 | `learning_rate` | Boosting learning rate during training of the model. <br> **Datatype:** Float.
 | `n_jobs`, `thread_count`, `task_type` | Set the number of threads for parallel processing and the `task_type` (`gpu` or `cpu`). Different model libraries use different parameter names. <br> **Datatype:** Float.
+|  |  *Reinforcement Learning Parameters**
+| `rl_config` | A dictionary containing the control parameters for a Reinforcement Learning model. <br> **Datatype:** Dictionary.
+| `train_cycles` | Training time steps will be set based on the `train_cycles * number of training data points. <br> **Datatype:** Integer.
+| `cpu_count` | Number of processors to dedicate to the Reinforcement Learning training process. <br> **Datatype:** int.
+| `max_trade_duration_candles`| Guides the agent training to keep trades below desired length. Example usage shown in `prediction_models/ReinforcementLearner.py` within the user customizable `calculate_reward()` <br> **Datatype:** int.
+| `model_type` | Model string from stable_baselines3 or SBcontrib. Available strings include: `'TRPO', 'ARS', 'RecurrentPPO', 'MaskablePPO', 'PPO', 'A2C', 'DQN'`. User should ensure that `model_training_parameters` match those available to the corresponding stable_baselines3 model by visiting their documentaiton. [PPO doc](https://stable-baselines3.readthedocs.io/en/master/modules/ppo.html) (external website) <br> **Datatype:** string.
+| `policy_type` | One of the available policy types from stable_baselines3 <br> **Datatype:** string.
+| `max_training_drawdown_pct` | The maximum drawdown that the agent is allowed to experience during training. <br> **Datatype:** float. <br> Default: 0.8
+| `cpu_count` | Number of threads/cpus to dedicate to the Reinforcement Learning training process (depending on if `ReinforcementLearning_multiproc` is selected or not). <br> **Datatype:** int. 
+| `model_reward_parameters` | Parameters used inside the user customizable `calculate_reward()` function in `ReinforcementLearner.py` <br> **Datatype:** int.
 |  |  **Extraneous parameters**
 | `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`.
 | `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`.

docs/freqai-reinforcement-learning.md

@@ -0,0 +1,202 @@
+# Reinforcement Learning
+
+!!! Note
+    Reinforcement learning dependencies include large packages such as `torch`, which should be explicitly requested during `./setup.sh -i` by answering "y" to the question "Do you also want dependencies for freqai-rl (~700mb additional space required) [y/N]?" Users who prefer docker should ensure they use the docker image appended with `_freqaiRL`. 
+
+Setting up and running a Reinforcement Learning model is the same as running a Regressor or Classifier. The same two flags, `--freqaimodel` and `--strategy`, must be defined on the command line:
+
+```bash
+freqtrade trade --freqaimodel ReinforcementLearner --strategy MyRLStrategy --config config.json
+```
+
+where `ReinforcementLearner` will use the templated `ReinforcementLearner` from `freqai/prediction_models/ReinforcementLearner`. The strategy, on the other hand, follows the same base [feature engineering](freqai-feature-engineering.md) with `populate_any_indicators` as a typical Regressor:
+
+```python
+    def populate_any_indicators(
+        self, pair, df, tf, informative=None, set_generalized_indicators=False
+    ):
+
+        coin = pair.split('/')[0]
+
+        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"%-{coin}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t)
+            informative[f"%-{coin}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
+            informative[f"%-{coin}adx-period_{t}"] = ta.ADX(informative, window=t)
+
+        # The following features are necessary for RL models
+        informative[f"%-{coin}raw_close"] = informative["close"]
+        informative[f"%-{coin}raw_open"] = informative["open"]
+        informative[f"%-{coin}raw_high"] = informative["high"]
+        informative[f"%-{coin}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
+```
+
+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 environent:
+
+```python
+        # The following features are necessary for RL models
+        informative[f"%-{coin}raw_close"] = informative["close"]
+        informative[f"%-{coin}raw_open"] = informative["open"]
+        informative[f"%-{coin}raw_high"] = informative["high"]
+        informative[f"%-{coin}raw_low"] = informative["low"]
+```
+
+Finally, there is no explicit "label" to make - instead the you need to assign the `&-action` column which will contain the agent's actions when accessed in `populate_entry/exit_trends()`. In the present example, the user set the neutral action to 0. This value should align with the environment used. FreqAI provides two environments, both use 0 as the neutral action.
+
+After users realize there are no labels to set, they will soon understand that the agent is making its "own" entry and exit decisions. This makes strategy construction rather simple. The entry and exit signals come from the agent in the form of an integer - which are used directly to decide entries and exits in the strategy:
+
+```python
+    def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
+
+        enter_long_conditions = [df["do_predict"] == 1, df["&-action"] == 1]
+
+        if enter_long_conditions:
+            df.loc[
+                reduce(lambda x, y: x & y, enter_long_conditions), ["enter_long", "enter_tag"]
+            ] = (1, "long")
+
+        enter_short_conditions = [df["do_predict"] == 1, df["&-action"] == 3]
+
+        if enter_short_conditions:
+            df.loc[
+                reduce(lambda x, y: x & y, enter_short_conditions), ["enter_short", "enter_tag"]
+            ] = (1, "short")
+
+        return df
+
+    def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
+        exit_long_conditions = [df["do_predict"] == 1, df["&-action"] == 2]
+        if exit_long_conditions:
+            df.loc[reduce(lambda x, y: x & y, exit_long_conditions), "exit_long"] = 1
+
+        exit_short_conditions = [df["do_predict"] == 1, df["&-action"] == 4]
+        if exit_short_conditions:
+            df.loc[reduce(lambda x, y: x & y, exit_short_conditions), "exit_short"] = 1
+
+        return df
+```
+
+It is important to consider that `&-action` depends on which environment they choose to use. The example above shows 5 actions, where 0 is neutral, 1 is enter long, 2 is exit long, 3 is enter short and 4 is exit short. 
+
+## Configuring the Reinforcement Learner
+
+In order to configure the `Reinforcement Learner` the following dictionary to their `freqai` config:
+
+```json
+        "rl_config": {
+            "train_cycles": 25,
+            "max_trade_duration_candles": 300,
+            "max_training_drawdown_pct": 0.02,
+            "cpu_count": 8,
+            "model_type": "PPO",
+            "policy_type": "MlpPolicy",
+            "model_reward_parameters": {
+                "rr": 1,
+                "profit_aim": 0.025
+            }
+        }
+```
+
+Parameter details can be found [here](freqai-parameter-table.md), but in general the `train_cycles` decides how many times the agent should cycle through the candle data in its artificial environemtn to train weights in the model. `model_type` is a string which selects one of the available models in [stable_baselines](https://stable-baselines3.readthedocs.io/en/master/)(external link). 
+
+## Creating the reward
+
+As users begin to modify the strategy and the prediction model, they will quickly realize some important differences between the Reinforcement Learner and the Regressors/Classifiers. Firstly, the strategy does not set a target value (no labels!). Instead, the user sets a `calculate_reward()` function inside their custom `ReinforcementLearner.py` file. A default `calculate_reward()` is provided inside `prediction_models/ReinforcementLearner.py` to give users the necessary building blocks to start their own models. It is inside the `calculate_reward()` where users express their creative theories about the market. For example, the user wants to reward their agent when it makes a winning trade, and penalize the agent when it makes a losing trade. Or perhaps, the user wishes to reward the agnet for entering trades, and penalize the agent for sitting in trades too long. Below we show examples of how these rewards are all calculated:
+
+```python
+    class MyRLEnv(Base5ActionRLEnv):
+        """
+        User made custom environment. This class inherits from BaseEnvironment and gym.env.
+        Users can override any functions from those parent classes. Here is an example
+        of a user customized `calculate_reward()` function.
+        """
+        def calculate_reward(self, action):
+            # first, penalize if the action is not valid
+            if not self._is_valid(action):
+                return -2
+            pnl = self.get_unrealized_profit()
+
+            factor = 100
+            # reward agent for entering trades
+            if action in (Actions.Long_enter.value, Actions.Short_enter.value) \
+                    and self._position == Positions.Neutral:
+                return 25
+            # discourage agent from not entering trades
+            if action == Actions.Neutral.value and self._position == Positions.Neutral:
+                return -1
+            max_trade_duration = self.rl_config.get('max_trade_duration_candles', 300)
+            trade_duration = self._current_tick - self._last_trade_tick
+            if trade_duration <= max_trade_duration:
+                factor *= 1.5
+            elif trade_duration > max_trade_duration:
+                factor *= 0.5
+            # discourage sitting in position
+            if self._position in (Positions.Short, Positions.Long) and \
+               action == Actions.Neutral.value:
+                return -1 * trade_duration / max_trade_duration
+            # close long
+            if action == Actions.Long_exit.value and self._position == Positions.Long:
+                if pnl > self.profit_aim * self.rr:
+                    factor *= self.rl_config['model_reward_parameters'].get('win_reward_factor', 2)
+                return float(pnl * factor)
+            # close short
+            if action == Actions.Short_exit.value and self._position == Positions.Short:
+                if pnl > self.profit_aim * self.rr:
+                    factor *= self.rl_config['model_reward_parameters'].get('win_reward_factor', 2)
+                return float(pnl * factor)
+            return 0.
+```
+
+### Creating a custom agent
+
+Users can inherit from `stable_baselines3` and customize anything they wish about their agent. Doing this is for advanced users only, an example is presented in `freqai/RL/ReinforcementLearnerCustomAgent.py`
+
+### Using Tensorboard
+
+Reinforcement Learning models benefit from tracking training metrics. FreqAI has integrated Tensorboard to allow users to track training and evaluation performance across all coins and across all retrainings. To start, the user should ensure Tensorboard is installed on their computer:
+
+```bash
+pip3 install tensorboard
+```
+
+Next, the user can activate Tensorboard with the following command:
+
+```bash
+cd freqtrade
+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 the user wishes to view the output in their browser at 127.0.0.1:6060 (6060 is the default port used by Tensorboard).
+
+![tensorboard](assets/tensorboard.png)

docs/freqai-running.md

@@ -142,32 +142,15 @@ dataframe['outlier'] = np.where(dataframe['DI_values'] > self.di_max.value/10, 1
 
 This specific hyperopt would help you understand the appropriate `DI_values` for your particular parameter space.
 
-## Using Tensorboard
-
-CatBoost models benefit from tracking training metrics via Tensorboard. You can take advantage of the FreqAI integration to track training and evaluation performance across all coins and across all retrainings. Tensorboard is activated via the following command:
-
-```bash
-cd freqtrade
-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 
-        },
+        "identifier": "example"
     }
 ```
 
-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. 
+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.

docs/requirements-docs.txt

@@ -1,6 +1,6 @@
 markdown==3.3.7
-mkdocs==1.4.1
-mkdocs-material==8.5.7
+mkdocs==1.4.0
+mkdocs-material==8.5.6
 mdx_truly_sane_lists==1.3
-pymdown-extensions==9.7
+pymdown-extensions==9.6
 jinja2==3.1.2

docs/stoploss.md

@@ -87,7 +87,7 @@ At this stage the bot contains the following stoploss support modes:
 2. Trailing stop loss.
 3. Trailing stop loss, custom positive loss.
 4. Trailing stop loss only once the trade has reached a certain offset.
-5. [Custom stoploss function](strategy-callbacks.md#custom-stoploss)
+5. [Custom stoploss function](strategy-advanced.md#custom-stoploss)
 
 ### Static Stop Loss
 

docs/strategy-callbacks.md

@@ -159,7 +159,6 @@ The stoploss price can only ever move upwards - if the stoploss value returned f
 
 The method must return a stoploss value (float / number) as a percentage of the current price.
 E.g. If the `current_rate` is 200 USD, then returning `0.02` will set the stoploss price 2% lower, at 196 USD.
-During backtesting, `current_rate` (and `current_profit`) are provided against the candle's high (or low for short trades) - while the resulting stoploss is evaluated against the candle's low (or high for short trades).
 
 The absolute value of the return value is used (the sign is ignored), so returning `0.05` or `-0.05` have the same result, a stoploss 5% below the current price.
 

docs/strategy-customization.md

@@ -659,9 +659,9 @@ informative = self.dp.get_pair_dataframe(pair=inf_pair,
 ```
 
 !!! Warning "Warning about backtesting"
-    In backtesting, `dp.get_pair_dataframe()` behavior differs depending on where it's called.
-    Within `populate_*()` methods, `dp.get_pair_dataframe()` returns the full timerange. Please make sure to not "look into the future" to avoid surprises when running in dry/live mode.
-    Within [callbacks](strategy-callbacks.md), you'll get the full timerange up to the current (simulated) candle.
+    Be careful when using dataprovider in backtesting. `historic_ohlcv()` (and `get_pair_dataframe()`
+    for the backtesting runmode) provides the full time-range in one go,
+    so please be aware of it and make sure to not "look into the future" to avoid surprises when running in dry/live mode.
 
 ### *get_analyzed_dataframe(pair, timeframe)*
 
@@ -670,13 +670,13 @@ It can also be used in specific callbacks to get the signal that caused the acti
 
 ``` python
 # fetch current dataframe
+if self.dp.runmode.value in ('live', 'dry_run'):
     dataframe, last_updated = self.dp.get_analyzed_dataframe(pair=metadata['pair'],
                                                                 timeframe=self.timeframe)
 ```
 
 !!! Note "No data available"
     Returns an empty dataframe if the requested pair was not cached.
-    You can check for this with `if dataframe.empty:` and handle this case accordingly.
     This should not happen when using whitelisted pairs.
 
 ### *orderbook(pair, maximum)*

docs/strategy_migration.md

@@ -43,25 +43,19 @@ Note : `forcesell`, `forcebuy`, `emergencysell` are changed to `force_exit`, `fo
   * `order_time_in_force` buy -> entry, sell -> exit.
   * `order_types` buy -> entry, sell -> exit.
   * `unfilledtimeout` buy -> entry, sell -> exit.
-  * `ignore_buying_expired_candle_after` -> moved to root level instead of "ask_strategy/exit_pricing"
 * Terminology changes
   * Sell reasons changed to reflect the new naming of "exit" instead of sells. Be careful in your strategy if you're using `exit_reason` checks and eventually update your strategy.
     * `sell_signal` -> `exit_signal`
     * `custom_sell` -> `custom_exit`
     * `force_sell` -> `force_exit`
     * `emergency_sell` -> `emergency_exit`
-  * Order pricing
-    * `bid_strategy` -> `entry_pricing`
-    * `ask_strategy` -> `exit_pricing`
-    * `ask_last_balance` -> `price_last_balance`
-    * `bid_last_balance` -> `price_last_balance`
   * Webhook terminology changed from "sell" to "exit", and from "buy" to entry
-    * `webhookbuy` -> `entry`
-    * `webhookbuyfill` -> `entry_fill`
-    * `webhookbuycancel` -> `entry_cancel`
-    * `webhooksell` -> `exit`
-    * `webhooksellfill` -> `exit_fill`
-    * `webhooksellcancel` -> `exit_cancel`
+    * `webhookbuy` -> `webhookentry`
+    * `webhookbuyfill` -> `webhookentryfill`
+    * `webhookbuycancel` -> `webhookentrycancel`
+    * `webhooksell` -> `webhookexit`
+    * `webhooksellfill` -> `webhookexitfill`
+    * `webhooksellcancel` -> `webhookexitcancel`
   * Telegram notification settings
     * `buy` -> `entry`
     * `buy_fill` -> `entry_fill`
@@ -449,7 +443,6 @@ Please refer to the [pricing documentation](configuration.md#prices-used-for-ord
         "use_order_book": true,
         "order_book_top": 1,
         "bid_last_balance": 0.0
-        "ignore_buying_expired_candle_after": 120
     }
 }
 ```
@@ -473,7 +466,6 @@ after:
         "use_order_book": true,
         "order_book_top": 1,
         "price_last_balance": 0.0
-    },
-    "ignore_buying_expired_candle_after": 120
+    }
 }
 ```

docs/telegram-usage.md

@@ -77,7 +77,6 @@ Example configuration showing the different settings:
     "enabled": true,
     "token": "your_telegram_token",
     "chat_id": "your_telegram_chat_id",
-    "allow_custom_messages": true,
     "notification_settings": {
         "status": "silent",
         "warning": "on",
@@ -116,7 +115,6 @@ Example configuration showing the different settings:
 `show_candle` - show candle values as part of entry/exit messages. Only possible values are `"ohlc"` or `"off"`.
 
 `balance_dust_level` will define what the `/balance` command takes as "dust" - Currencies with a balance below this will be shown.
-`allow_custom_messages` completely disable strategy messages.
 `reload` allows you to disable reload-buttons on selected messages.
 
 ## Create a custom keyboard (command shortcut buttons)

docs/utils.md

@@ -169,43 +169,6 @@ Example: Search dedicated strategy path.
 freqtrade list-strategies --strategy-path ~/.freqtrade/strategies/
 ```
 
-## List freqAI models
-
-Use the `list-freqaimodels` subcommand to see all freqAI models available.
-
-This subcommand is useful for finding problems in your environment with loading freqAI models: modules with models that contain errors and failed to load are printed in red (LOAD FAILED), while models with duplicate names are printed in yellow (DUPLICATE NAME).
-
-```
-usage: freqtrade list-freqaimodels [-h] [-v] [--logfile FILE] [-V] [-c PATH]
-                                   [-d PATH] [--userdir PATH]
-                                   [--freqaimodel-path PATH] [-1] [--no-color]
-
-optional arguments:
-  -h, --help            show this help message and exit
-  --freqaimodel-path PATH
-                        Specify additional lookup path for freqaimodels.
-  -1, --one-column      Print output in one column.
-  --no-color            Disable colorization of hyperopt results. May be
-                        useful if you are redirecting output to a file.
-
-Common arguments:
-  -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).
-  --logfile FILE        Log to the file specified. Special values are:
-                        'syslog', 'journald'. See the documentation for more
-                        details.
-  -V, --version         show program's version number and exit
-  -c PATH, --config PATH
-                        Specify configuration file (default:
-                        `userdir/config.json` or `config.json` whichever
-                        exists). Multiple --config options may be used. Can be
-                        set to `-` to read config from stdin.
-  -d PATH, --datadir PATH, --data-dir PATH
-                        Path to directory with historical backtesting data.
-  --userdir PATH, --user-data-dir PATH
-                        Path to userdata directory.
-
-```
-
 ## List Exchanges
 
 Use the `list-exchanges` subcommand to see the exchanges available for the bot.

docs/webhook-config.md

@@ -10,37 +10,37 @@ Sample configuration (tested using IFTTT).
   "webhook": {
         "enabled": true,
         "url": "https://maker.ifttt.com/trigger/<YOUREVENT>/with/key/<YOURKEY>/",
-        "entry": {
+        "webhookentry": {
             "value1": "Buying {pair}",
             "value2": "limit {limit:8f}",
             "value3": "{stake_amount:8f} {stake_currency}"
         },
-        "entry_cancel": {
+        "webhookentrycancel": {
             "value1": "Cancelling Open Buy Order for {pair}",
             "value2": "limit {limit:8f}",
             "value3": "{stake_amount:8f} {stake_currency}"
         },
-         "entry_fill": {
+         "webhookentryfill": {
             "value1": "Buy Order for {pair} filled",
             "value2": "at {open_rate:8f}",
             "value3": ""
         },
-        "exit": {
+        "webhookexit": {
             "value1": "Exiting {pair}",
             "value2": "limit {limit:8f}",
             "value3": "profit: {profit_amount:8f} {stake_currency} ({profit_ratio})"
         },
-        "exit_cancel": {
+        "webhookexitcancel": {
             "value1": "Cancelling Open Exit Order for {pair}",
             "value2": "limit {limit:8f}",
             "value3": "profit: {profit_amount:8f} {stake_currency} ({profit_ratio})"
         },
-        "exit_fill": {
+        "webhookexitfill": {
             "value1": "Exit Order for {pair} filled",
             "value2": "at {close_rate:8f}.",
             "value3": ""
         },
-        "status": {
+        "webhookstatus": {
             "value1": "Status: {status}",
             "value2": "",
             "value3": ""
@@ -57,7 +57,7 @@ You can set the POST body format to Form-Encoded (default), JSON-Encoded, or raw
         "enabled": true,
         "url": "https://<YOURSUBDOMAIN>.cloud.mattermost.com/hooks/<YOURHOOK>",
         "format": "json",
-        "status": {
+        "webhookstatus": {
             "text": "Status: {status}"
         }
     },
@@ -88,30 +88,17 @@ Optional parameters are available to enable automatic retries for webhook messag
         "url": "https://<YOURHOOKURL>",
         "retries": 3,
         "retry_delay": 0.2,
-        "status": {
+        "webhookstatus": {
             "status": "Status: {status}"
         }
     },
 ```
 
-Custom messages can be sent to Webhook endpoints via the `self.dp.send_msg()` function from within the strategy. To enable this, set the `allow_custom_messages` option to `true`:
-
-```json
-  "webhook": {
-        "enabled": true,
-        "url": "https://<YOURHOOKURL>",
-        "allow_custom_messages": true,
-        "strategy_msg": {
-            "status": "StrategyMessage: {msg}"
-        }
-    },
-```
-
 Different payloads can be configured for different events. Not all fields are necessary, but you should configure at least one of the dicts, otherwise the webhook will never be called.
 
-### Entry
+### Webhookentry
 
-The fields in `webhook.entry` are filled when the bot executes a long/short. Parameters are filled using string.format.
+The fields in `webhook.webhookentry` are filled when the bot executes a long/short. Parameters are filled using string.format.
 Possible parameters are:
 
 * `trade_id`
@@ -131,9 +118,9 @@ Possible parameters are:
 * `current_rate`
 * `enter_tag`
 
-### Entry cancel
+### Webhookentrycancel
 
-The fields in `webhook.entry_cancel` are filled when the bot cancels a long/short order. Parameters are filled using string.format.
+The fields in `webhook.webhookentrycancel` are filled when the bot cancels a long/short order. Parameters are filled using string.format.
 Possible parameters are:
 
 * `trade_id`
@@ -152,9 +139,9 @@ Possible parameters are:
 * `current_rate`
 * `enter_tag`
 
-### Entry fill
+### Webhookentryfill
 
-The fields in `webhook.entry_fill` are filled when the bot filled a long/short order. Parameters are filled using string.format.
+The fields in `webhook.webhookentryfill` are filled when the bot filled a long/short order. Parameters are filled using string.format.
 Possible parameters are:
 
 * `trade_id`
@@ -173,9 +160,9 @@ Possible parameters are:
 * `current_rate`
 * `enter_tag`
 
-### Exit
+### Webhookexit
 
-The fields in `webhook.exit` are filled when the bot exits a trade. Parameters are filled using string.format.
+The fields in `webhook.webhookexit` are filled when the bot exits a trade. Parameters are filled using string.format.
 Possible parameters are:
 
 * `trade_id`
@@ -197,9 +184,9 @@ Possible parameters are:
 * `open_date`
 * `close_date`
 
-### Exit fill
+### Webhookexitfill
 
-The fields in `webhook.exit_fill` are filled when the bot fills a exit order (closes a Trade). Parameters are filled using string.format.
+The fields in `webhook.webhookexitfill` are filled when the bot fills a exit order (closes a Trade). Parameters are filled using string.format.
 Possible parameters are:
 
 * `trade_id`
@@ -222,9 +209,9 @@ Possible parameters are:
 * `open_date`
 * `close_date`
 
-### Exit cancel
+### Webhookexitcancel
 
-The fields in `webhook.exit_cancel` are filled when the bot cancels a exit order. Parameters are filled using string.format.
+The fields in `webhook.webhookexitcancel` are filled when the bot cancels a exit order. Parameters are filled using string.format.
 Possible parameters are:
 
 * `trade_id`
@@ -247,9 +234,9 @@ Possible parameters are:
 * `open_date`
 * `close_date`
 
-### Status
+### Webhookstatus
 
-The fields in `webhook.status` are used for regular status messages (Started / Stopped / ...). Parameters are filled using string.format.
+The fields in `webhook.webhookstatus` are used for regular status messages (Started / Stopped / ...). Parameters are filled using string.format.
 
 The only possible value here is `{status}`.
 
@@ -293,6 +280,7 @@ You can configure this as follows:
 }
 ```
 
+
 The above represents the default (`exit_fill` and `entry_fill` are optional and will default to the above configuration) - modifications are obviously possible.
 
 Available fields correspond to the fields for webhooks and are documented in the corresponding webhook sections.
@@ -300,13 +288,3 @@ Available fields correspond to the fields for webhooks and are documented in the
 The notifications will look as follows by default.
 
 ![discord-notification](assets/discord_notification.png)
-
-Custom messages can be sent from a strategy to Discord endpoints via the dataprovider.send_msg() function. To enable this, set the `allow_custom_messages` option to `true`:
-
-```json
-  "discord": {
-        "enabled": true,
-        "webhook_url": "https://discord.com/api/webhooks/<Your webhook URL ...>",
-        "allow_custom_messages": true,
-    },
-```

freqtrade/__init__.py

@@ -1,5 +1,5 @@
 """ Freqtrade bot """
-__version__ = '2022.10'
+__version__ = '2022.10.dev'
 
 if 'dev' in __version__:
     try:
@@ -16,6 +16,6 @@ if 'dev' in __version__:
             from pathlib import Path
             versionfile = Path('./freqtrade_commit')
             if versionfile.is_file():
-                __version__ = f"docker-{__version__}-{versionfile.read_text()[:8]}"
+                __version__ = f"docker-{versionfile.read_text()[:8]}"
         except Exception:
             pass

freqtrade/commands/__init__.py

@@ -15,9 +15,9 @@ from freqtrade.commands.db_commands import start_convert_db
 from freqtrade.commands.deploy_commands import (start_create_userdir, start_install_ui,
                                                 start_new_strategy)
 from freqtrade.commands.hyperopt_commands import start_hyperopt_list, start_hyperopt_show
-from freqtrade.commands.list_commands import (start_list_exchanges, start_list_freqAI_models,
-                                              start_list_markets, start_list_strategies,
-                                              start_list_timeframes, start_show_trades)
+from freqtrade.commands.list_commands import (start_list_exchanges, start_list_markets,
+                                              start_list_strategies, start_list_timeframes,
+                                              start_show_trades)
 from freqtrade.commands.optimize_commands import (start_backtesting, start_backtesting_show,
                                                   start_edge, start_hyperopt)
 from freqtrade.commands.pairlist_commands import start_test_pairlist

freqtrade/commands/arguments.py

@@ -41,8 +41,6 @@ ARGS_EDGE = ARGS_COMMON_OPTIMIZE + ["stoploss_range"]
 ARGS_LIST_STRATEGIES = ["strategy_path", "print_one_column", "print_colorized",
                         "recursive_strategy_search"]
 
-ARGS_LIST_FREQAIMODELS = ["freqaimodel_path", "print_one_column", "print_colorized"]
-
 ARGS_LIST_HYPEROPTS = ["hyperopt_path", "print_one_column", "print_colorized"]
 
 ARGS_BACKTEST_SHOW = ["exportfilename", "backtest_show_pair_list"]
@@ -108,8 +106,8 @@ ARGS_ANALYZE_ENTRIES_EXITS = ["exportfilename", "analysis_groups", "enter_reason
                               "exit_reason_list", "indicator_list"]
 
 NO_CONF_REQURIED = ["convert-data", "convert-trade-data", "download-data", "list-timeframes",
-                    "list-markets", "list-pairs", "list-strategies", "list-freqaimodels",
-                    "list-data", "hyperopt-list", "hyperopt-show", "backtest-filter",
+                    "list-markets", "list-pairs", "list-strategies", "list-data",
+                    "hyperopt-list", "hyperopt-show", "backtest-filter",
                     "plot-dataframe", "plot-profit", "show-trades", "trades-to-ohlcv"]
 
 NO_CONF_ALLOWED = ["create-userdir", "list-exchanges", "new-strategy"]
@@ -194,11 +192,10 @@ class Arguments:
                                         start_create_userdir, start_download_data, start_edge,
                                         start_hyperopt, start_hyperopt_list, start_hyperopt_show,
                                         start_install_ui, start_list_data, start_list_exchanges,
-                                        start_list_freqAI_models, start_list_markets,
-                                        start_list_strategies, start_list_timeframes,
-                                        start_new_config, start_new_strategy, start_plot_dataframe,
-                                        start_plot_profit, start_show_trades, start_test_pairlist,
-                                        start_trading, start_webserver)
+                                        start_list_markets, start_list_strategies,
+                                        start_list_timeframes, start_new_config, start_new_strategy,
+                                        start_plot_dataframe, start_plot_profit, start_show_trades,
+                                        start_test_pairlist, start_trading, start_webserver)
 
         subparsers = self.parser.add_subparsers(dest='command',
                                                 # Use custom message when no subhandler is added
@@ -365,15 +362,6 @@ class Arguments:
         list_strategies_cmd.set_defaults(func=start_list_strategies)
         self._build_args(optionlist=ARGS_LIST_STRATEGIES, parser=list_strategies_cmd)
 
-        # Add list-freqAI Models subcommand
-        list_freqaimodels_cmd = subparsers.add_parser(
-            'list-freqaimodels',
-            help='Print available freqAI models.',
-            parents=[_common_parser],
-        )
-        list_freqaimodels_cmd.set_defaults(func=start_list_freqAI_models)
-        self._build_args(optionlist=ARGS_LIST_FREQAIMODELS, parser=list_freqaimodels_cmd)
-
         # Add list-timeframes subcommand
         list_timeframes_cmd = subparsers.add_parser(
             'list-timeframes',

freqtrade/commands/list_commands.py

@@ -1,6 +1,7 @@
 import csv
 import logging
 import sys
+from pathlib import Path
 from typing import Any, Dict, List
 
 import rapidjson
@@ -9,6 +10,7 @@ from colorama import init as colorama_init
 from tabulate import tabulate
 
 from freqtrade.configuration import setup_utils_configuration
+from freqtrade.constants import USERPATH_STRATEGIES
 from freqtrade.enums import RunMode
 from freqtrade.exceptions import OperationalException
 from freqtrade.exchange import market_is_active, validate_exchanges
@@ -39,7 +41,7 @@ def start_list_exchanges(args: Dict[str, Any]) -> None:
         print(tabulate(exchanges, headers=['Exchange name', 'Valid', 'reason']))
 
 
-def _print_objs_tabular(objs: List, print_colorized: bool) -> None:
+def _print_objs_tabular(objs: List, print_colorized: bool, base_dir: Path) -> None:
     if print_colorized:
         colorama_init(autoreset=True)
         red = Fore.RED
@@ -53,7 +55,7 @@ def _print_objs_tabular(objs: List, print_colorized: bool) -> None:
     names = [s['name'] for s in objs]
     objs_to_print = [{
         'name': s['name'] if s['name'] else "--",
-        'location': s['location_rel'],
+        'location': s['location'].relative_to(base_dir),
         'status': (red + "LOAD FAILED" + reset if s['class'] is None
                    else "OK" if names.count(s['name']) == 1
                    else yellow + "DUPLICATE NAME" + reset)
@@ -74,8 +76,9 @@ def start_list_strategies(args: Dict[str, Any]) -> None:
     """
     config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
 
+    directory = Path(config.get('strategy_path', config['user_data_dir'] / USERPATH_STRATEGIES))
     strategy_objs = StrategyResolver.search_all_objects(
-        config, not args['print_one_column'], config.get('recursive_strategy_search', False))
+        directory, not args['print_one_column'], config.get('recursive_strategy_search', False))
     # Sort alphabetically
     strategy_objs = sorted(strategy_objs, key=lambda x: x['name'])
     for obj in strategy_objs:
@@ -87,22 +90,7 @@ def start_list_strategies(args: Dict[str, Any]) -> None:
     if args['print_one_column']:
         print('\n'.join([s['name'] for s in strategy_objs]))
     else:
-        _print_objs_tabular(strategy_objs, config.get('print_colorized', False))
-
-
-def start_list_freqAI_models(args: Dict[str, Any]) -> None:
-    """
-    Print files with FreqAI models custom classes available in the directory
-    """
-    config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
-    from freqtrade.resolvers.freqaimodel_resolver import FreqaiModelResolver
-    model_objs = FreqaiModelResolver.search_all_objects(config, not args['print_one_column'])
-    # Sort alphabetically
-    model_objs = sorted(model_objs, key=lambda x: x['name'])
-    if args['print_one_column']:
-        print('\n'.join([s['name'] for s in model_objs]))
-    else:
-        _print_objs_tabular(model_objs, config.get('print_colorized', False))
+        _print_objs_tabular(strategy_objs, config.get('print_colorized', False), directory)
 
 
 def start_list_timeframes(args: Dict[str, Any]) -> None:

freqtrade/configuration/config_validation.py

@@ -86,7 +86,6 @@ def validate_config_consistency(conf: Dict[str, Any], preliminary: bool = False)
     _validate_unlimited_amount(conf)
     _validate_ask_orderbook(conf)
     _validate_freqai_hyperopt(conf)
-    _validate_freqai_include_timeframes(conf)
     _validate_consumers(conf)
     validate_migrated_strategy_settings(conf)
 
@@ -335,26 +334,6 @@ def _validate_freqai_hyperopt(conf: Dict[str, Any]) -> None:
             'Using analyze-per-epoch parameter is not supported with a FreqAI strategy.')
 
 
-def _validate_freqai_include_timeframes(conf: Dict[str, Any]) -> None:
-    freqai_enabled = conf.get('freqai', {}).get('enabled', False)
-    if freqai_enabled:
-        main_tf = conf.get('timeframe', '5m')
-        freqai_include_timeframes = conf.get('freqai', {}).get('feature_parameters', {}
-                                                               ).get('include_timeframes', [])
-
-        from freqtrade.exchange import timeframe_to_seconds
-        main_tf_s = timeframe_to_seconds(main_tf)
-        offending_lines = []
-        for tf in freqai_include_timeframes:
-            tf_s = timeframe_to_seconds(tf)
-            if tf_s < main_tf_s:
-                offending_lines.append(tf)
-        if offending_lines:
-            raise OperationalException(
-                f"Main timeframe of {main_tf} must be smaller or equal to FreqAI "
-                f"`include_timeframes`.Offending include-timeframes: {', '.join(offending_lines)}")
-
-
 def _validate_consumers(conf: Dict[str, Any]) -> None:
     emc_conf = conf.get('external_message_consumer', {})
     if emc_conf.get('enabled', False):

freqtrade/configuration/directory_operations.py

@@ -3,8 +3,7 @@ import shutil
 from pathlib import Path
 from typing import Optional
 
-from freqtrade.constants import (USER_DATA_FILES, USERPATH_FREQAIMODELS, USERPATH_HYPEROPTS,
-                                 USERPATH_NOTEBOOKS, USERPATH_STRATEGIES, Config)
+from freqtrade.constants import USER_DATA_FILES, Config
 from freqtrade.exceptions import OperationalException
 
 
@@ -50,8 +49,8 @@ def create_userdata_dir(directory: str, create_dir: bool = False) -> Path:
     :param create_dir: Create directory if it does not exist.
     :return: Path object containing the directory
     """
-    sub_dirs = ["backtest_results", "data", USERPATH_HYPEROPTS, "hyperopt_results", "logs",
-                USERPATH_NOTEBOOKS, "plot", USERPATH_STRATEGIES, USERPATH_FREQAIMODELS]
+    sub_dirs = ["backtest_results", "data", "hyperopts", "hyperopt_results", "logs",
+                "notebooks", "plot", "strategies", ]
     folder = Path(directory)
     chown_user_directory(folder)
     if not folder.is_dir():

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
 
 
 DEFAULT_CONFIG = 'config.json'
@@ -282,7 +282,6 @@ CONF_SCHEMA = {
                 'enabled': {'type': 'boolean'},
                 'token': {'type': 'string'},
                 'chat_id': {'type': 'string'},
-                'allow_custom_messages': {'type': 'boolean', 'default': True},
                 'balance_dust_level': {'type': 'number', 'minimum': 0.0},
                 'notification_settings': {
                     'type': 'object',
@@ -345,8 +344,6 @@ CONF_SCHEMA = {
                 'format': {'type': 'string', 'enum': WEBHOOK_FORMAT_OPTIONS, 'default': 'form'},
                 'retries': {'type': 'integer', 'minimum': 0},
                 'retry_delay': {'type': 'number', 'minimum': 0},
-                **dict([(x, {'type': 'object'}) for x in RPCMessageType]),
-                # Below -> Deprecated
                 'webhookentry': {'type': 'object'},
                 'webhookentrycancel': {'type': 'object'},
                 'webhookentryfill': {'type': 'object'},
@@ -540,8 +537,6 @@ CONF_SCHEMA = {
             "properties": {
                 "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},
                 "conv_width": {"type": "integer", "default": 2},
                 "train_period_days": {"type": "integer", "default": 0},
                 "backtest_period_days": {"type": "number", "default": 7},
@@ -576,10 +571,7 @@ CONF_SCHEMA = {
                     },
                 },
                 "model_training_parameters": {
-                    "type": "object",
-                    "properties": {
-                        "n_estimators": {"type": "integer", "default": 1000}
-                    },
+                    "type": "object"
                 },
             },
             "required": [
@@ -658,6 +650,5 @@ LongShort = Literal['long', 'short']
 EntryExit = Literal['entry', 'exit']
 BuySell = Literal['buy', 'sell']
 MakerTaker = Literal['maker', 'taker']
-BidAsk = Literal['bid', 'ask']
 
 Config = Dict[str, Any]

freqtrade/data/history/idatahandler.py

@@ -303,7 +303,7 @@ 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']
@@ -323,9 +323,8 @@ class IDataHandler(ABC):
             self._check_empty_df(pairdf, pair, timeframe, candle_type, warn_no_data)
             return pairdf
 
-    def _check_empty_df(
-            self, pairdf: DataFrame, pair: str, timeframe: str, candle_type: CandleType,
-            warn_no_data: bool, warn_price: bool = False) -> bool:
+    def _check_empty_df(self, pairdf: DataFrame, pair: str, timeframe: str,
+                        candle_type: CandleType, warn_no_data: bool):
         """
         Warn on empty dataframe
         """
@@ -336,20 +335,6 @@ class IDataHandler(ABC):
                     "Use `freqtrade download-data` to download the data"
                 )
             return True
-        elif warn_price:
-            candle_price_gap = 0
-            if (candle_type in (CandleType.SPOT, CandleType.FUTURES) and
-                    not pairdf.empty
-                    and 'close' in pairdf.columns and 'open' in pairdf.columns):
-                # Detect gaps between prior close and open
-                gaps = ((pairdf['open'] - pairdf['close'].shift(1)) / pairdf['close'].shift(1))
-                gaps = gaps.dropna()
-                if len(gaps):
-                    candle_price_gap = max(abs(gaps))
-            if candle_price_gap > 0.1:
-                logger.info(f"Price jump in {pair}, {timeframe}, {candle_type} between two candles "
-                            f"of {candle_price_gap:.2%} detected.")
-
         return False
 
     def _validate_pairdata(self, pair, pairdata: DataFrame, timeframe: str,

freqtrade/exchange/binance.py

@@ -11,7 +11,6 @@ from freqtrade.enums import CandleType, MarginMode, 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.misc import deep_merge_dicts, json_load
 
 
@@ -60,7 +59,7 @@ class Binance(Exchange):
                 )
             ))
 
-    def get_tickers(self, symbols: Optional[List[str]] = None, cached: bool = False) -> Tickers:
+    def get_tickers(self, symbols: Optional[List[str]] = None, cached: bool = False) -> Dict:
         tickers = super().get_tickers(symbols=symbols, cached=cached)
         if self.trading_mode == TradingMode.FUTURES:
             # Binance's future result has no bid/ask values.

freqtrade/exchange/exchange.py

@@ -18,12 +18,12 @@ import ccxt.async_support as ccxt_async
 from cachetools import TTLCache
 from ccxt import ROUND_DOWN, ROUND_UP, TICK_SIZE, TRUNCATE, decimal_to_precision
 from dateutil import parser
-from pandas import DataFrame, concat
+from pandas import DataFrame
 
-from freqtrade.constants import (DEFAULT_AMOUNT_RESERVE_PERCENT, NON_OPEN_EXCHANGE_STATES, BidAsk,
-                                 BuySell, Config, EntryExit, ListPairsWithTimeframes, MakerTaker,
+from freqtrade.constants import (DEFAULT_AMOUNT_RESERVE_PERCENT, NON_OPEN_EXCHANGE_STATES, BuySell,
+                                 Config, EntryExit, ListPairsWithTimeframes, MakerTaker,
                                  PairWithTimeframe)
-from freqtrade.data.converter import clean_ohlcv_dataframe, ohlcv_to_dataframe, trades_dict_to_list
+from freqtrade.data.converter import ohlcv_to_dataframe, trades_dict_to_list
 from freqtrade.enums import OPTIMIZE_MODES, CandleType, MarginMode, TradingMode
 from freqtrade.exceptions import (DDosProtection, ExchangeError, InsufficientFundsError,
                                   InvalidOrderException, OperationalException, PricingError,
@@ -31,7 +31,6 @@ from freqtrade.exceptions import (DDosProtection, ExchangeError, InsufficientFun
 from freqtrade.exchange.common import (API_FETCH_ORDER_RETRY_COUNT, BAD_EXCHANGES,
                                        EXCHANGE_HAS_OPTIONAL, EXCHANGE_HAS_REQUIRED,
                                        remove_credentials, retrier, retrier_async)
-from freqtrade.exchange.types import 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
@@ -180,14 +179,13 @@ class Exchange:
             exchange_config, ccxt_async, ccxt_kwargs=ccxt_async_config)
 
         logger.info(f'Using Exchange "{self.name}"')
-        self.required_candle_call_count = 1
+
         if validate:
             # Initial markets load
             self._load_markets()
             self.validate_config(config)
-            self._startup_candle_count: int = config.get('startup_candle_count', 0)
             self.required_candle_call_count = self.validate_required_startup_candles(
-                self._startup_candle_count, config.get('timeframe', ''))
+                config.get('startup_candle_count', 0), config.get('timeframe', ''))
 
         # Converts the interval provided in minutes in config to seconds
         self.markets_refresh_interval: int = exchange_config.get(
@@ -410,13 +408,11 @@ class Exchange:
         else:
             return DataFrame()
 
-    def get_contract_size(self, pair: str) -> Optional[float]:
+    def get_contract_size(self, pair: str) -> float:
         if self.trading_mode == TradingMode.FUTURES:
-            market = self.markets.get(pair, {})
+            market = self.markets[pair]
             contract_size: float = 1.0
-            if not market:
-                return None
-            if market.get('contractSize') is not None:
+            if market['contractSize'] is not None:
                 # ccxt has contractSize in markets as string
                 contract_size = float(market['contractSize'])
             return contract_size
@@ -1423,17 +1419,14 @@ class Exchange:
             raise OperationalException(e) from e
 
     @retrier
-    def get_tickers(self, symbols: Optional[List[str]] = None, cached: bool = False) -> Tickers:
+    def get_tickers(self, symbols: Optional[List[str]] = None, cached: bool = False) -> Dict:
         """
         :param cached: Allow cached result
         :return: fetch_tickers result
         """
-        tickers: Tickers
-        if not self.exchange_has('fetchTickers'):
-            return {}
         if cached:
             with self._cache_lock:
-                tickers = self._fetch_tickers_cache.get('fetch_tickers')  # type: ignore
+                tickers = self._fetch_tickers_cache.get('fetch_tickers')
             if tickers:
                 return tickers
         try:
@@ -1456,12 +1449,12 @@ class Exchange:
     # Pricing info
 
     @retrier
-    def fetch_ticker(self, pair: str) -> Ticker:
+    def fetch_ticker(self, pair: str) -> dict:
         try:
             if (pair not in self.markets or
                     self.markets[pair].get('active', False) is False):
                 raise ExchangeError(f"Pair {pair} not available")
-            data: Ticker = self._api.fetch_ticker(pair)
+            data = self._api.fetch_ticker(pair)
             return data
         except ccxt.DDoSProtection as e:
             raise DDosProtection(e) from e
@@ -1512,7 +1505,7 @@ class Exchange:
         except ccxt.BaseError as e:
             raise OperationalException(e) from e
 
-    def _get_price_side(self, side: str, is_short: bool, conf_strategy: Dict) -> BidAsk:
+    def _get_price_side(self, side: str, is_short: bool, conf_strategy: Dict) -> str:
         price_side = conf_strategy['price_side']
 
         if price_side in ('same', 'other'):
@@ -1531,7 +1524,7 @@ 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[dict] = None, ticker: Optional[dict] = None) -> float:
         """
         Calculates bid/ask target
         bid rate - between current ask price and last price
@@ -1857,22 +1850,10 @@ class Exchange:
         return pair, timeframe, candle_type, data
 
     def _build_coroutine(self, pair: str, timeframe: str, candle_type: CandleType,
-                         since_ms: Optional[int], cache: bool) -> Coroutine:
-        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)
-            min_date = date_minus_candles(timeframe, candle_limit - 5).timestamp()
-            # Check if 1 call can get us updated candles without hole in the data.
-            if min_date < self._pairs_last_refresh_time.get((pair, timeframe, candle_type), 0):
-                # Cache can be used - do one-off call.
-                not_all_data = False
-            else:
-                # Time jump detected, evict cache
-                logger.info(
-                    f"Time jump detected. Evicting cache for {pair}, {timeframe}, {candle_type}")
-                del self._klines[(pair, timeframe, candle_type)]
+                         since_ms: Optional[int]) -> Coroutine:
 
-        if (not since_ms and (self._ft_has["ohlcv_require_since"] or not_all_data)):
+        if (not since_ms
+                and (self._ft_has["ohlcv_require_since"] or self.required_candle_call_count > 1)):
             # Multiple calls for one pair - to get more history
             one_call = timeframe_to_msecs(timeframe) * self.ohlcv_candle_limit(
                 timeframe, candle_type, since_ms)
@@ -1897,8 +1878,10 @@ class Exchange:
         input_coroutines = []
         cached_pairs = []
         for pair, timeframe, candle_type in set(pair_list):
-            if (timeframe not in self.timeframes
-                    and candle_type in (CandleType.SPOT, CandleType.FUTURES)):
+            if (
+                timeframe not in self.timeframes
+                and candle_type in (CandleType.SPOT, CandleType.FUTURES)
+            ):
                 logger.warning(
                     f"Cannot download ({pair}, {timeframe}) combination as this timeframe is "
                     f"not available on {self.name}. Available timeframes are "
@@ -1907,9 +1890,8 @@ class Exchange:
 
             if ((pair, timeframe, candle_type) not in self._klines or not cache
                     or self._now_is_time_to_refresh(pair, timeframe, candle_type)):
-
-                input_coroutines.append(
-                    self._build_coroutine(pair, timeframe, candle_type, since_ms, cache))
+                input_coroutines.append(self._build_coroutine(
+                    pair, timeframe, candle_type=candle_type, since_ms=since_ms))
 
             else:
                 logger.debug(
@@ -1919,29 +1901,6 @@ class Exchange:
 
         return input_coroutines, cached_pairs
 
-    def _process_ohlcv_df(self, pair: str, timeframe: str, c_type: CandleType, ticks: List[List],
-                          cache: bool, drop_incomplete: bool) -> DataFrame:
-        # keeping last candle time as last refreshed time of the pair
-        if ticks and cache:
-            self._pairs_last_refresh_time[(pair, timeframe, c_type)] = ticks[-1][0] // 1000
-        # keeping parsed dataframe in cache
-        ohlcv_df = ohlcv_to_dataframe(ticks, timeframe, pair=pair, fill_missing=True,
-                                      drop_incomplete=drop_incomplete)
-        if cache:
-            if (pair, timeframe, c_type) in self._klines:
-                old = self._klines[(pair, timeframe, c_type)]
-                # Reassign so we return the updated, combined df
-                ohlcv_df = clean_ohlcv_dataframe(concat([old, ohlcv_df], axis=0), timeframe, pair,
-                                                 fill_missing=True, drop_incomplete=False)
-                candle_limit = self.ohlcv_candle_limit(timeframe, self._config['candle_type_def'])
-                # Age out old candles
-                ohlcv_df = ohlcv_df.tail(candle_limit + self._startup_candle_count)
-                ohlcv_df = ohlcv_df.reset_index(drop=True)
-                self._klines[(pair, timeframe, c_type)] = ohlcv_df
-            else:
-                self._klines[(pair, timeframe, c_type)] = ohlcv_df
-        return ohlcv_df
-
     def refresh_latest_ohlcv(self, pair_list: ListPairsWithTimeframes, *,
                              since_ms: Optional[int] = None, cache: bool = True,
                              drop_incomplete: Optional[bool] = None
@@ -1978,11 +1937,16 @@ class Exchange:
                     continue
                 # Deconstruct tuple (has 4 elements)
                 pair, timeframe, c_type, ticks = res
-                ohlcv_df = self._process_ohlcv_df(
-                    pair, timeframe, c_type, ticks, cache, drop_incomplete)
-
+                # keeping last candle time as last refreshed time of the pair
+                if ticks:
+                    self._pairs_last_refresh_time[(pair, timeframe, c_type)] = ticks[-1][0] // 1000
+                # keeping parsed dataframe in cache
+                ohlcv_df = ohlcv_to_dataframe(
+                    ticks, timeframe, pair=pair, fill_missing=True,
+                    drop_incomplete=drop_incomplete)
                 results_df[(pair, timeframe, c_type)] = ohlcv_df
-
+                if cache:
+                    self._klines[(pair, timeframe, c_type)] = ohlcv_df
         # Return cached klines
         for pair, timeframe, c_type in cached_pairs:
             results_df[(pair, timeframe, c_type)] = self.klines(
@@ -1996,9 +1960,9 @@ class Exchange:
         # Timeframe in seconds
         interval_in_sec = timeframe_to_seconds(timeframe)
 
-        return (
+        return not (
             (self._pairs_last_refresh_time.get((pair, timeframe, candle_type), 0)
-             + interval_in_sec) < arrow.utcnow().int_timestamp
+             + interval_in_sec) >= arrow.utcnow().int_timestamp
         )
 
     @retrier_async
@@ -2025,8 +1989,8 @@ class Exchange:
             candle_limit = self.ohlcv_candle_limit(
                 timeframe, candle_type=candle_type, since_ms=since_ms)
 
-            if candle_type and candle_type != CandleType.SPOT:
-                params.update({'price': candle_type.value})
+            if candle_type != CandleType.SPOT:
+                params.update({'price': candle_type})
             if candle_type != CandleType.FUNDING_RATE:
                 data = await self._api_async.fetch_ohlcv(
                     pair, timeframe=timeframe, since=since_ms,

freqtrade/exchange/kraken.py

@@ -12,7 +12,6 @@ from freqtrade.exceptions import (DDosProtection, InsufficientFundsError, Invali
                                   OperationalException, TemporaryError)
 from freqtrade.exchange import Exchange
 from freqtrade.exchange.common import retrier
-from freqtrade.exchange.types import Tickers
 
 
 logger = logging.getLogger(__name__)
@@ -46,7 +45,7 @@ class Kraken(Exchange):
         return (parent_check and
                 market.get('darkpool', False) is False)
 
-    def get_tickers(self, symbols: Optional[List[str]] = None, cached: bool = False) -> Tickers:
+    def get_tickers(self, symbols: Optional[List[str]] = None, cached: bool = False) -> Dict:
         # Only fetch tickers for current stake currency
         # Otherwise the request for kraken becomes too large.
         symbols = list(self.get_markets(quote_currencies=[self._config['stake_currency']]))

freqtrade/exchange/types.py

@@ -1,16 +0,0 @@
-from typing import Dict, Optional, TypedDict
-
-
-class Ticker(TypedDict):
-    symbol: str
-    ask: Optional[float]
-    askVolume: Optional[float]
-    bid: Optional[float]
-    bidVolume: Optional[float]
-    last: Optional[float]
-    quoteVolume: Optional[float]
-    baseVolume: Optional[float]
-    # Several more - only listing required.
-
-
-Tickers = Dict[str, Ticker]

freqtrade/freqai/RL/Base4ActionRLEnv.py

@@ -0,0 +1,134 @@
+import logging
+from enum import Enum
+
+from gym import spaces
+
+from freqtrade.freqai.RL.BaseEnvironment import BaseEnvironment, Positions
+
+
+logger = logging.getLogger(__name__)
+
+
+class Actions(Enum):
+    Neutral = 0
+    Exit = 1
+    Long_enter = 2
+    Short_enter = 3
+
+
+class Base4ActionRLEnv(BaseEnvironment):
+    """
+    Base class for a 4 action environment
+    """
+
+    def set_action_space(self):
+        self.action_space = spaces.Discrete(len(Actions))
+
+    def step(self, action: int):
+        """
+        Logic for a single step (incrementing one candle in time)
+        by the agent
+        :param: action: int = the action type that the agent plans
+            to take for the current step.
+        :returns:
+            observation = current state of environment
+            step_reward = the reward from `calculate_reward()`
+            _done = if the agent "died" or if the candles finished
+            info = dict passed back to openai gym lib
+        """
+        self._done = False
+        self._current_tick += 1
+
+        if self._current_tick == self._end_tick:
+            self._done = True
+
+        self._update_unrealized_total_profit()
+
+        step_reward = self.calculate_reward(action)
+        self.total_reward += step_reward
+
+        trade_type = None
+        if self.is_tradesignal(action):
+            """
+            Action: Neutral, position: Long ->  Close Long
+            Action: Neutral, position: Short -> Close Short
+
+            Action: Long, position: Neutral -> Open Long
+            Action: Long, position: Short -> Close Short and Open Long
+
+            Action: Short, position: Neutral -> Open Short
+            Action: Short, position: Long -> Close Long and Open Short
+            """
+
+            if action == Actions.Neutral.value:
+                self._position = Positions.Neutral
+                trade_type = "neutral"
+                self._last_trade_tick = None
+            elif action == Actions.Long_enter.value:
+                self._position = Positions.Long
+                trade_type = "long"
+                self._last_trade_tick = self._current_tick
+            elif action == Actions.Short_enter.value:
+                self._position = Positions.Short
+                trade_type = "short"
+                self._last_trade_tick = self._current_tick
+            elif action == Actions.Exit.value:
+                self._position = Positions.Neutral
+                trade_type = "neutral"
+                self._last_trade_tick = None
+            else:
+                print("case not defined")
+
+            if trade_type is not None:
+                self.trade_history.append(
+                    {'price': self.current_price(), 'index': self._current_tick,
+                     'type': trade_type})
+
+        if self._total_profit < 1 - self.rl_config.get('max_training_drawdown_pct', 0.8):
+            self._done = True
+
+        self._position_history.append(self._position)
+
+        info = dict(
+            tick=self._current_tick,
+            total_reward=self.total_reward,
+            total_profit=self._total_profit,
+            position=self._position.value
+        )
+
+        observation = self._get_observation()
+
+        self._update_history(info)
+
+        return observation, step_reward, self._done, info
+
+    def is_tradesignal(self, action: int):
+        """
+        Determine if the signal is a trade signal
+        e.g.: agent wants a Actions.Long_exit while it is in a Positions.short
+        """
+        return not ((action == Actions.Neutral.value and self._position == Positions.Neutral) or
+                    (action == Actions.Neutral.value and self._position == Positions.Short) or
+                    (action == Actions.Neutral.value and self._position == Positions.Long) or
+                    (action == Actions.Short_enter.value and self._position == Positions.Short) or
+                    (action == Actions.Short_enter.value and self._position == Positions.Long) or
+                    (action == Actions.Exit.value and self._position == Positions.Neutral) or
+                    (action == Actions.Long_enter.value and self._position == Positions.Long) or
+                    (action == Actions.Long_enter.value and self._position == Positions.Short))
+
+    def _is_valid(self, action: int):
+        """
+        Determine if the signal is valid.
+        e.g.: agent wants a Actions.Long_exit while it is in a Positions.short
+        """
+        # Agent should only try to exit if it is in position
+        if action == Actions.Exit.value:
+            if self._position not in (Positions.Short, Positions.Long):
+                return False
+
+        # Agent should only try to enter if it is not in position
+        if action in (Actions.Short_enter.value, Actions.Long_enter.value):
+            if self._position != Positions.Neutral:
+                return False
+
+        return True

freqtrade/freqai/RL/Base5ActionRLEnv.py

@@ -0,0 +1,201 @@
+import logging
+from enum import Enum
+
+import numpy as np
+import pandas as pd
+from gym import spaces
+from pandas import DataFrame
+
+from freqtrade.freqai.RL.BaseEnvironment import BaseEnvironment, Positions
+
+
+logger = logging.getLogger(__name__)
+
+
+class Actions(Enum):
+    Neutral = 0
+    Long_enter = 1
+    Long_exit = 2
+    Short_enter = 3
+    Short_exit = 4
+
+
+def mean_over_std(x):
+    std = np.std(x, ddof=1)
+    mean = np.mean(x)
+    return mean / std if std > 0 else 0
+
+
+class Base5ActionRLEnv(BaseEnvironment):
+    """
+    Base class for a 5 action environment
+    """
+
+    def set_action_space(self):
+        self.action_space = spaces.Discrete(len(Actions))
+
+    def reset(self):
+
+        self._done = False
+
+        if self.starting_point is True:
+            self._position_history = (self._start_tick * [None]) + [self._position]
+        else:
+            self._position_history = (self.window_size * [None]) + [self._position]
+
+        self._current_tick = self._start_tick
+        self._last_trade_tick = None
+        self._position = Positions.Neutral
+
+        self.total_reward = 0.
+        self._total_profit = 1.  # unit
+        self.history = {}
+        self.trade_history = []
+        self.portfolio_log_returns = np.zeros(len(self.prices))
+
+        self._profits = [(self._start_tick, 1)]
+        self.close_trade_profit = []
+        self._total_unrealized_profit = 1
+
+        return self._get_observation()
+
+    def step(self, action: int):
+        """
+        Logic for a single step (incrementing one candle in time)
+        by the agent
+        :param: action: int = the action type that the agent plans
+            to take for the current step.
+        :returns:
+            observation = current state of environment
+            step_reward = the reward from `calculate_reward()`
+            _done = if the agent "died" or if the candles finished
+            info = dict passed back to openai gym lib
+        """
+        self._done = False
+        self._current_tick += 1
+
+        if self._current_tick == self._end_tick:
+            self._done = True
+
+        self.update_portfolio_log_returns(action)
+
+        self._update_unrealized_total_profit()
+        step_reward = self.calculate_reward(action)
+        self.total_reward += step_reward
+
+        trade_type = None
+        if self.is_tradesignal(action):
+            """
+            Action: Neutral, position: Long ->  Close Long
+            Action: Neutral, position: Short -> Close Short
+
+            Action: Long, position: Neutral -> Open Long
+            Action: Long, position: Short -> Close Short and Open Long
+
+            Action: Short, position: Neutral -> Open Short
+            Action: Short, position: Long -> Close Long and Open Short
+            """
+
+            if action == Actions.Neutral.value:
+                self._position = Positions.Neutral
+                trade_type = "neutral"
+                self._last_trade_tick = None
+            elif action == Actions.Long_enter.value:
+                self._position = Positions.Long
+                trade_type = "long"
+                self._last_trade_tick = self._current_tick
+            elif action == Actions.Short_enter.value:
+                self._position = Positions.Short
+                trade_type = "short"
+                self._last_trade_tick = self._current_tick
+            elif action == Actions.Long_exit.value:
+                self._update_total_profit()
+                self._position = Positions.Neutral
+                trade_type = "neutral"
+                self._last_trade_tick = None
+            elif action == Actions.Short_exit.value:
+                self._update_total_profit()
+                self._position = Positions.Neutral
+                trade_type = "neutral"
+                self._last_trade_tick = None
+            else:
+                print("case not defined")
+
+            if trade_type is not None:
+                self.trade_history.append(
+                    {'price': self.current_price(), 'index': self._current_tick,
+                     'type': trade_type})
+
+        if (self._total_profit < self.max_drawdown or
+                self._total_unrealized_profit < self.max_drawdown):
+            self._done = True
+
+        self._position_history.append(self._position)
+
+        info = dict(
+            tick=self._current_tick,
+            total_reward=self.total_reward,
+            total_profit=self._total_profit,
+            position=self._position.value
+        )
+
+        observation = self._get_observation()
+
+        self._update_history(info)
+
+        return observation, step_reward, self._done, info
+
+    def _get_observation(self):
+        features_window = self.signal_features[(
+            self._current_tick - self.window_size):self._current_tick]
+        features_and_state = DataFrame(np.zeros((len(features_window), 3)),
+                                       columns=['current_profit_pct', 'position', 'trade_duration'],
+                                       index=features_window.index)
+
+        features_and_state['current_profit_pct'] = self.get_unrealized_profit()
+        features_and_state['position'] = self._position.value
+        features_and_state['trade_duration'] = self.get_trade_duration()
+        features_and_state = pd.concat([features_window, features_and_state], axis=1)
+        return features_and_state
+
+    def get_trade_duration(self):
+        if self._last_trade_tick is None:
+            return 0
+        else:
+            return self._current_tick - self._last_trade_tick
+
+    def is_tradesignal(self, action: int):
+        # trade signal
+        """
+        Determine if the signal is a trade signal
+        e.g.: agent wants a Actions.Long_exit while it is in a Positions.short
+        """
+        return not ((action == Actions.Neutral.value and self._position == Positions.Neutral) or
+                    (action == Actions.Neutral.value and self._position == Positions.Short) or
+                    (action == Actions.Neutral.value and self._position == Positions.Long) or
+                    (action == Actions.Short_enter.value and self._position == Positions.Short) or
+                    (action == Actions.Short_enter.value and self._position == Positions.Long) or
+                    (action == Actions.Short_exit.value and self._position == Positions.Long) or
+                    (action == Actions.Short_exit.value and self._position == Positions.Neutral) or
+                    (action == Actions.Long_enter.value and self._position == Positions.Long) or
+                    (action == Actions.Long_enter.value and self._position == Positions.Short) or
+                    (action == Actions.Long_exit.value and self._position == Positions.Short) or
+                    (action == Actions.Long_exit.value and self._position == Positions.Neutral))
+
+    def _is_valid(self, action: int):
+        # trade signal
+        """
+        Determine if the signal is valid.
+        e.g.: agent wants a Actions.Long_exit while it is in a Positions.short
+        """
+        # Agent should only try to exit if it is in position
+        if action in (Actions.Short_exit.value, Actions.Long_exit.value):
+            if self._position not in (Positions.Short, Positions.Long):
+                return False
+
+        # Agent should only try to enter if it is not in position
+        if action in (Actions.Short_enter.value, Actions.Long_enter.value):
+            if self._position != Positions.Neutral:
+                return False
+
+        return True

freqtrade/freqai/RL/BaseEnvironment.py

@@ -0,0 +1,267 @@
+import logging
+from abc import abstractmethod
+from enum import Enum
+from typing import Optional
+
+import gym
+import numpy as np
+import pandas as pd
+from gym import spaces
+from gym.utils import seeding
+from pandas import DataFrame
+
+
+logger = logging.getLogger(__name__)
+
+
+class Positions(Enum):
+    Short = 0
+    Long = 1
+    Neutral = 0.5
+
+    def opposite(self):
+        return Positions.Short if self == Positions.Long else Positions.Long
+
+
+class BaseEnvironment(gym.Env):
+    """
+    Base class for environments. This class is agnostic to action count.
+    Inherited classes customize this to include varying action counts/types,
+    See RL/Base5ActionRLEnv.py and RL/Base4ActionRLEnv.py
+    """
+
+    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 = {}):
+
+        self.rl_config = config['freqai']['rl_config']
+        self.id = id
+        self.seed(seed)
+        self.reset_env(df, prices, window_size, reward_kwargs, starting_point)
+        self.max_drawdown = 1 - self.rl_config.get('max_training_drawdown_pct', 0.8)
+        self.compound_trades = config['stake_amount'] == 'unlimited'
+
+    def reset_env(self, df: DataFrame, prices: DataFrame, window_size: int,
+                  reward_kwargs: dict, starting_point=True):
+        """
+        Resets the environment when the agent fails (in our case, if the drawdown
+        exceeds the user set max_training_drawdown_pct)
+        """
+        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.fee = 0.0015
+
+        # # spaces
+        self.shape = (window_size, self.signal_features.shape[1] + 3)
+        self.set_action_space()
+        self.observation_space = spaces.Box(
+            low=-1, high=1, shape=self.shape, dtype=np.float32)
+
+        # episode
+        self._start_tick: int = self.window_size
+        self._end_tick: int = len(self.prices) - 1
+        self._done: bool = False
+        self._current_tick: int = self._start_tick
+        self._last_trade_tick: Optional[int] = None
+        self._position = Positions.Neutral
+        self._position_history: list = [None]
+        self.total_reward: float = 0
+        self._total_profit: float = 1
+        self._total_unrealized_profit: float = 1
+        self.history: dict = {}
+        self.trade_history: list = []
+
+    @abstractmethod
+    def set_action_space(self):
+        """
+        Unique to the environment action count. Must be inherited.
+        """
+
+    def seed(self, seed: int = 1):
+        self.np_random, seed = seeding.np_random(seed)
+        return [seed]
+
+    def reset(self):
+
+        self._done = False
+
+        if self.starting_point is True:
+            self._position_history = (self._start_tick * [None]) + [self._position]
+        else:
+            self._position_history = (self.window_size * [None]) + [self._position]
+
+        self._current_tick = self._start_tick
+        self._last_trade_tick = None
+        self._position = Positions.Neutral
+
+        self.total_reward = 0.
+        self._total_profit = 1.  # unit
+        self.history = {}
+        self.trade_history = []
+        self.portfolio_log_returns = np.zeros(len(self.prices))
+
+        self._profits = [(self._start_tick, 1)]
+        self.close_trade_profit = []
+        self._total_unrealized_profit = 1
+
+        return self._get_observation()
+
+    @abstractmethod
+    def step(self, action: int):
+        """
+        Step depeneds on action types, this must be inherited.
+        """
+        return
+
+    def _get_observation(self):
+        """
+        This may or may not be independent of action types, user can inherit
+        this in their custom "MyRLEnv"
+        """
+        features_window = self.signal_features[(
+            self._current_tick - self.window_size):self._current_tick]
+        features_and_state = DataFrame(np.zeros((len(features_window), 3)),
+                                       columns=['current_profit_pct', 'position', 'trade_duration'],
+                                       index=features_window.index)
+
+        features_and_state['current_profit_pct'] = self.get_unrealized_profit()
+        features_and_state['position'] = self._position.value
+        features_and_state['trade_duration'] = self.get_trade_duration()
+        features_and_state = pd.concat([features_window, features_and_state], axis=1)
+        return features_and_state
+
+    def get_trade_duration(self):
+        """
+        Get the trade duration if the agent is in a trade
+        """
+        if self._last_trade_tick is None:
+            return 0
+        else:
+            return self._current_tick - self._last_trade_tick
+
+    def get_unrealized_profit(self):
+        """
+        Get the unrealized profit if the agent is in a trade
+        """
+        if self._last_trade_tick is None:
+            return 0.
+
+        if self._position == Positions.Neutral:
+            return 0.
+        elif self._position == Positions.Short:
+            current_price = self.add_entry_fee(self.prices.iloc[self._current_tick].open)
+            last_trade_price = self.add_exit_fee(self.prices.iloc[self._last_trade_tick].open)
+            return (last_trade_price - current_price) / last_trade_price
+        elif self._position == Positions.Long:
+            current_price = self.add_exit_fee(self.prices.iloc[self._current_tick].open)
+            last_trade_price = self.add_entry_fee(self.prices.iloc[self._last_trade_tick].open)
+            return (current_price - last_trade_price) / last_trade_price
+        else:
+            return 0.
+
+    @abstractmethod
+    def is_tradesignal(self, action: int):
+        """
+        Determine if the signal is a trade signal. This is
+        unique to the actions in the environment, and therefore must be
+        inherited.
+        """
+        return
+
+    def _is_valid(self, action: int):
+        """
+        Determine if the signal is valid.This is
+        unique to the actions in the environment, and therefore must be
+        inherited.
+        """
+        return
+
+    def add_entry_fee(self, price):
+        return price * (1 + self.fee)
+
+    def add_exit_fee(self, price):
+        return price / (1 + self.fee)
+
+    def _update_history(self, info):
+        if not self.history:
+            self.history = {key: [] for key in info.keys()}
+
+        for key, value in info.items():
+            self.history[key].append(value)
+
+    @abstractmethod
+    def calculate_reward(self, action):
+        """
+        An example reward function. This is the one function that users will likely
+        wish to inject their own creativity into.
+        :params:
+        action: int = The action made by the agent for the current candle.
+        :returns:
+        float = the reward to give to the agent for current step (used for optimization
+            of weights in NN)
+        """
+
+    def _update_unrealized_total_profit(self):
+        """
+        Update the unrealized total profit incase of episode end.
+        """
+        if self._position in (Positions.Long, Positions.Short):
+            pnl = self.get_unrealized_profit()
+            if self.compound_trades:
+                # assumes unit stake and compounding
+                unrl_profit = self._total_profit * (1 + pnl)
+            else:
+                # assumes unit stake and no compounding
+                unrl_profit = self._total_profit + pnl
+            self._total_unrealized_profit = unrl_profit
+
+    def _update_total_profit(self):
+        pnl = self.get_unrealized_profit()
+        if self.compound_trades:
+            # assumes unite stake and compounding
+            self._total_profit = self._total_profit * (1 + pnl)
+        else:
+            # assumes unit stake and no compounding
+            self._total_profit += pnl
+
+    def most_recent_return(self, action: int):
+        """
+        Calculate the tick to tick return if in a trade.
+        Return is generated from rising prices in Long
+        and falling prices in Short positions.
+        The actions Sell/Buy or Hold during a Long position trigger the sell/buy-fee.
+        """
+        # Long positions
+        if self._position == Positions.Long:
+            current_price = self.prices.iloc[self._current_tick].open
+            previous_price = self.prices.iloc[self._current_tick - 1].open
+
+            if (self._position_history[self._current_tick - 1] == Positions.Short
+                    or self._position_history[self._current_tick - 1] == Positions.Neutral):
+                previous_price = self.add_entry_fee(previous_price)
+
+            return np.log(current_price) - np.log(previous_price)
+
+        # Short positions
+        if self._position == Positions.Short:
+            current_price = self.prices.iloc[self._current_tick].open
+            previous_price = self.prices.iloc[self._current_tick - 1].open
+            if (self._position_history[self._current_tick - 1] == Positions.Long
+                    or self._position_history[self._current_tick - 1] == Positions.Neutral):
+                previous_price = self.add_exit_fee(previous_price)
+
+            return np.log(previous_price) - np.log(current_price)
+
+        return 0
+
+    def update_portfolio_log_returns(self, action):
+        self.portfolio_log_returns[self._current_tick] = self.most_recent_return(action)
+
+    def current_price(self) -> float:
+        return self.prices.iloc[self._current_tick].open

freqtrade/freqai/RL/BaseReinforcementLearningModel.py

@@ -0,0 +1,376 @@
+import logging
+from abc import abstractmethod
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Any, Callable, Dict, Tuple, Type, Union
+
+import gym
+import numpy as np
+import numpy.typing as npt
+import pandas as pd
+import torch as th
+import torch.multiprocessing
+from pandas import DataFrame
+from stable_baselines3.common.callbacks import EvalCallback
+from stable_baselines3.common.monitor import Monitor
+from stable_baselines3.common.utils import set_random_seed
+from stable_baselines3.common.vec_env import SubprocVecEnv
+
+from freqtrade.exceptions import OperationalException
+from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
+from freqtrade.freqai.freqai_interface import IFreqaiModel
+from freqtrade.freqai.RL.Base5ActionRLEnv import Actions, Base5ActionRLEnv
+from freqtrade.freqai.RL.BaseEnvironment import Positions
+from freqtrade.persistence import Trade
+
+
+logger = logging.getLogger(__name__)
+
+torch.multiprocessing.set_sharing_strategy('file_system')
+
+SB3_MODELS = ['PPO', 'A2C', 'DQN']
+SB3_CONTRIB_MODELS = ['TRPO', 'ARS', 'RecurrentPPO', 'MaskablePPO']
+
+
+class BaseReinforcementLearningModel(IFreqaiModel):
+    """
+    User created Reinforcement Learning Model prediction class
+    """
+
+    def __init__(self, **kwargs):
+        super().__init__(config=kwargs['config'])
+        self.max_threads = min(self.freqai_info['rl_config'].get(
+            'cpu_count', 1), max(int(self.max_system_threads / 2), 1))
+        th.set_num_threads(self.max_threads)
+        self.reward_params = self.freqai_info['rl_config']['model_reward_parameters']
+        self.train_env: Union[SubprocVecEnv, gym.Env] = None
+        self.eval_env: Union[SubprocVecEnv, gym.Env] = None
+        self.eval_callback: EvalCallback = None
+        self.model_type = self.freqai_info['rl_config']['model_type']
+        self.rl_config = self.freqai_info['rl_config']
+        self.continual_learning = self.freqai_info.get('continual_learning', False)
+        if self.model_type in SB3_MODELS:
+            import_str = 'stable_baselines3'
+        elif self.model_type in SB3_CONTRIB_MODELS:
+            import_str = 'sb3_contrib'
+        else:
+            raise OperationalException(f'{self.model_type} not available in stable_baselines3 or '
+                                       f'sb3_contrib. please choose one of {SB3_MODELS} or '
+                                       f'{SB3_CONTRIB_MODELS}')
+
+        mod = __import__(import_str, fromlist=[
+                         self.model_type])
+        self.MODELCLASS = getattr(mod, self.model_type)
+        self.policy_type = self.freqai_info['rl_config']['policy_type']
+        self.unset_outlier_removal()
+
+    def unset_outlier_removal(self):
+        """
+        If user has activated any function that may remove training points, this
+        function will set them to false and warn them
+        """
+        if self.ft_params.get('use_SVM_to_remove_outliers', False):
+            self.ft_params.update({'use_SVM_to_remove_outliers': False})
+            logger.warning('User tried to use SVM with RL. Deactivating SVM.')
+        if self.ft_params.get('use_DBSCAN_to_remove_outliers', False):
+            self.ft_params.update({'use_SVM_to_remove_outliers': False})
+            logger.warning('User tried to use DBSCAN with RL. Deactivating DBSCAN.')
+        if self.freqai_info['data_split_parameters'].get('shuffle', False):
+            self.freqai_info['data_split_parameters'].update('shuffle', False)
+            logger.warning('User tried to shuffle training data. Setting shuffle to False')
+
+    def train(
+        self, unfiltered_df: DataFrame, pair: str, dk: FreqaiDataKitchen, **kwargs
+    ) -> Any:
+        """
+        Filter the training data and train a model to it. Train makes heavy use of the datakitchen
+        for storing, saving, loading, and analyzing the data.
+        :param unfiltered_df: Full dataframe for the current training period
+        :param metadata: pair metadata from strategy.
+        :returns:
+        :model: Trained model which can be used to inference (self.predict)
+        """
+
+        logger.info("--------------------Starting training " f"{pair} --------------------")
+
+        features_filtered, labels_filtered = dk.filter_features(
+            unfiltered_df,
+            dk.training_features_list,
+            dk.label_list,
+            training_filter=True,
+        )
+
+        data_dictionary: Dict[str, Any] = dk.make_train_test_datasets(
+            features_filtered, labels_filtered)
+        dk.fit_labels()  # FIXME useless for now, but just satiating append methods
+
+        # normalize all data based on train_dataset only
+        prices_train, prices_test = self.build_ohlc_price_dataframes(dk.data_dictionary, pair, dk)
+        data_dictionary = dk.normalize_data(data_dictionary)
+
+        # data cleaning/analysis
+        self.data_cleaning_train(dk)
+
+        logger.info(
+            f'Training model on {len(dk.data_dictionary["train_features"].columns)}'
+            f' features and {len(data_dictionary["train_features"])} data points'
+        )
+
+        self.set_train_and_eval_environments(data_dictionary, prices_train, prices_test, dk)
+
+        model = self.fit(data_dictionary, dk)
+
+        logger.info(f"--------------------done training {pair}--------------------")
+
+        return model
+
+    def set_train_and_eval_environments(self, data_dictionary: Dict[str, DataFrame],
+                                        prices_train: DataFrame, prices_test: DataFrame,
+                                        dk: FreqaiDataKitchen):
+        """
+        User can override this if they are using a custom MyRLEnv
+        :params:
+        data_dictionary: dict = common data dictionary containing train and test
+            features/labels/weights.
+        prices_train/test: DataFrame = dataframe comprised of the prices to be used in the
+            environment during training
+        or testing
+        dk: FreqaiDataKitchen = the datakitchen for the current pair
+        """
+        train_df = data_dictionary["train_features"]
+        test_df = data_dictionary["test_features"]
+
+        self.train_env = self.MyRLEnv(df=train_df, prices=prices_train, window_size=self.CONV_WIDTH,
+                                      reward_kwargs=self.reward_params, config=self.config)
+        self.eval_env = Monitor(self.MyRLEnv(df=test_df, prices=prices_test,
+                                window_size=self.CONV_WIDTH,
+                                reward_kwargs=self.reward_params, config=self.config))
+        self.eval_callback = EvalCallback(self.eval_env, deterministic=True,
+                                          render=False, eval_freq=len(train_df),
+                                          best_model_save_path=str(dk.data_path))
+
+    @abstractmethod
+    def fit(self, data_dictionary: Dict[str, Any], dk: FreqaiDataKitchen, **kwargs):
+        """
+        Agent customizations and abstract Reinforcement Learning customizations
+        go in here. Abstract method, so this function must be overridden by
+        user class.
+        """
+        return
+
+    def get_state_info(self, pair: str) -> Tuple[float, float, int]:
+        """
+        State info during dry/live/backtesting which is fed back
+        into the model.
+        :param:
+        pair: str = COIN/STAKE to get the environment information for
+        :returns:
+        market_side: float = representing short, long, or neutral for
+            pair
+        trade_duration: int = the number of candles that the trade has
+            been open for
+        """
+        open_trades = Trade.get_trades_proxy(is_open=True)
+        market_side = 0.5
+        current_profit: float = 0
+        trade_duration = 0
+        for trade in open_trades:
+            if trade.pair == pair:
+                if self.strategy.dp._exchange is None:  # type: ignore
+                    logger.error('No exchange available.')
+                else:
+                    current_value = self.strategy.dp._exchange.get_rate(  # type: ignore
+                                pair, refresh=False, side="exit", is_short=trade.is_short)
+                openrate = trade.open_rate
+                now = datetime.now(timezone.utc).timestamp()
+                trade_duration = int((now - trade.open_date.timestamp()) / self.base_tf_seconds)
+                if 'long' in str(trade.enter_tag):
+                    market_side = 1
+                    current_profit = (current_value - openrate) / openrate
+                else:
+                    market_side = 0
+                    current_profit = (openrate - current_value) / openrate
+
+        return market_side, current_profit, int(trade_duration)
+
+    def predict(
+        self, unfiltered_df: DataFrame, dk: FreqaiDataKitchen, **kwargs
+    ) -> Tuple[DataFrame, npt.NDArray[np.int_]]:
+        """
+        Filter the prediction features data and predict with it.
+        :param: unfiltered_dataframe: Full dataframe for the current backtest period.
+        :return:
+        :pred_df: dataframe containing the predictions
+        :do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove
+        data (NaNs) or felt uncertain about data (PCA and DI index)
+        """
+
+        dk.find_features(unfiltered_df)
+        filtered_dataframe, _ = dk.filter_features(
+            unfiltered_df, dk.training_features_list, training_filter=False
+        )
+        filtered_dataframe = dk.normalize_data_from_metadata(filtered_dataframe)
+        dk.data_dictionary["prediction_features"] = filtered_dataframe
+
+        # optional additional data cleaning/analysis
+        self.data_cleaning_predict(dk)
+
+        pred_df = self.rl_model_predict(
+            dk.data_dictionary["prediction_features"], dk, self.model)
+        pred_df.fillna(0, inplace=True)
+
+        return (pred_df, dk.do_predict)
+
+    def rl_model_predict(self, dataframe: DataFrame,
+                         dk: FreqaiDataKitchen, model: Any) -> DataFrame:
+        """
+        A helper function to make predictions in the Reinforcement learning module.
+        :params:
+        dataframe: DataFrame = the dataframe of features to make the predictions on
+        dk: FreqaiDatakitchen = data kitchen for the current pair
+        model: Any = the trained model used to inference the features.
+        """
+        output = pd.DataFrame(np.zeros(len(dataframe)), columns=dk.label_list)
+
+        def _predict(window):
+            market_side, current_profit, trade_duration = self.get_state_info(dk.pair)
+            observations = dataframe.iloc[window.index]
+            observations['current_profit_pct'] = current_profit
+            observations['position'] = market_side
+            observations['trade_duration'] = trade_duration
+            res, _ = model.predict(observations, deterministic=True)
+            return res
+
+        output = output.rolling(window=self.CONV_WIDTH).apply(_predict)
+
+        return output
+
+    def build_ohlc_price_dataframes(self, data_dictionary: dict,
+                                    pair: str, dk: FreqaiDataKitchen) -> Tuple[DataFrame,
+                                                                               DataFrame]:
+        """
+        Builds the train prices and test prices for the environment.
+        """
+
+        coin = pair.split('/')[0]
+        train_df = data_dictionary["train_features"]
+        test_df = data_dictionary["test_features"]
+
+        # price data for model training and evaluation
+        tf = self.config['timeframe']
+        ohlc_list = [f'%-{coin}raw_open_{tf}', f'%-{coin}raw_low_{tf}',
+                     f'%-{coin}raw_high_{tf}', f'%-{coin}raw_close_{tf}']
+        rename_dict = {f'%-{coin}raw_open_{tf}': 'open', f'%-{coin}raw_low_{tf}': 'low',
+                       f'%-{coin}raw_high_{tf}': ' high', f'%-{coin}raw_close_{tf}': 'close'}
+
+        prices_train = train_df.filter(ohlc_list, axis=1)
+        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.rename(columns=rename_dict, inplace=True)
+        prices_test.reset_index(drop=True)
+
+        return prices_train, prices_test
+
+    def load_model_from_disk(self, dk: FreqaiDataKitchen) -> Any:
+        """
+        Can be used by user if they are trying to limit_ram_usage *and*
+        perform continual learning.
+        For now, this is unused.
+        """
+        exists = Path(dk.data_path / f"{dk.model_filename}_model").is_file()
+        if exists:
+            model = self.MODELCLASS.load(dk.data_path / f"{dk.model_filename}_model")
+        else:
+            logger.info('No model file on disk to continue learning from.')
+
+        return model
+
+    # Nested class which can be overridden by user to customize further
+    class MyRLEnv(Base5ActionRLEnv):
+        """
+        User can override any function in BaseRLEnv and gym.Env. Here the user
+        sets a custom reward based on profit and trade duration.
+        """
+
+        def calculate_reward(self, action: int) -> float:
+            """
+            An example reward function. This is the one function that users will likely
+            wish to inject their own creativity into.
+            :params:
+            action: int = The action made by the agent for the current candle.
+            :returns:
+            float = the reward to give to the agent for current step (used for optimization
+                of weights in NN)
+            """
+            # first, penalize if the action is not valid
+            if not self._is_valid(action):
+                return -2
+
+            pnl = self.get_unrealized_profit()
+            rew = np.sign(pnl) * (pnl + 1)
+            factor = 100.
+
+            # reward agent for entering trades
+            if (action in (Actions.Long_enter.value, Actions.Short_enter.value)
+                    and self._position == Positions.Neutral):
+                return 25
+            # discourage agent from not entering trades
+            if action == Actions.Neutral.value and self._position == Positions.Neutral:
+                return -1
+
+            max_trade_duration = self.rl_config.get('max_trade_duration_candles', 300)
+            if self._last_trade_tick:
+                trade_duration = self._current_tick - self._last_trade_tick
+            else:
+                trade_duration = 0
+
+            if trade_duration <= max_trade_duration:
+                factor *= 1.5
+            elif trade_duration > max_trade_duration:
+                factor *= 0.5
+
+            # discourage sitting in position
+            if (self._position in (Positions.Short, Positions.Long) and
+               action == Actions.Neutral.value):
+                return -1 * trade_duration / max_trade_duration
+
+            # close long
+            if action == Actions.Long_exit.value and self._position == Positions.Long:
+                if pnl > self.profit_aim * self.rr:
+                    factor *= self.rl_config['model_reward_parameters'].get('win_reward_factor', 2)
+                return float(rew * factor)
+
+            # close short
+            if action == Actions.Short_exit.value and self._position == Positions.Short:
+                if pnl > self.profit_aim * self.rr:
+                    factor *= self.rl_config['model_reward_parameters'].get('win_reward_factor', 2)
+                return float(rew * factor)
+
+            return 0.
+
+
+def make_env(MyRLEnv: Type[gym.Env], env_id: str, rank: int,
+             seed: int, train_df: DataFrame, price: DataFrame,
+             reward_params: Dict[str, int], window_size: int, monitor: bool = False,
+             config: Dict[str, Any] = {}) -> Callable:
+    """
+    Utility function for multiprocessed env.
+
+    :param env_id: (str) the environment ID
+    :param num_env: (int) the number of environment you wish to have in subprocesses
+    :param seed: (int) the inital seed for RNG
+    :param rank: (int) index of the subprocess
+    :return: (Callable)
+    """
+
+    def _init() -> gym.Env:
+
+        env = MyRLEnv(df=train_df, prices=price, window_size=window_size,
+                      reward_kwargs=reward_params, id=env_id, seed=seed + rank, config=config)
+        if monitor:
+            env = Monitor(env)
+        return env
+    set_random_seed(seed)
+    return _init

freqtrade/freqai/base_models/BaseClassifierModel.py

@@ -51,7 +51,7 @@ class BaseClassifierModel(IFreqaiModel):
                     f"{end_date} --------------------")
         # split data into train/test data.
         data_dictionary = dk.make_train_test_datasets(features_filtered, labels_filtered)
-        if not self.freqai_info.get("fit_live_predictions_candles", 0) or not self.live:
+        if not self.freqai_info.get("fit_live_predictions", 0) or not self.live:
             dk.fit_labels()
         # normalize all data based on train_dataset only
         data_dictionary = dk.normalize_data(data_dictionary)
@@ -78,7 +78,7 @@ class BaseClassifierModel(IFreqaiModel):
     ) -> Tuple[DataFrame, npt.NDArray[np.int_]]:
         """
         Filter the prediction features data and predict with it.
-        :param unfiltered_df: Full dataframe for the current backtest period.
+        :param: unfiltered_df: Full dataframe for the current backtest period.
         :return:
         :pred_df: dataframe containing the predictions
         :do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove

freqtrade/freqai/base_models/BaseRegressionModel.py

@@ -50,7 +50,7 @@ class BaseRegressionModel(IFreqaiModel):
                     f"{end_date} --------------------")
         # split data into train/test data.
         data_dictionary = dk.make_train_test_datasets(features_filtered, labels_filtered)
-        if not self.freqai_info.get("fit_live_predictions_candles", 0) or not self.live:
+        if not self.freqai_info.get("fit_live_predictions", 0) or not self.live:
             dk.fit_labels()
         # normalize all data based on train_dataset only
         data_dictionary = dk.normalize_data(data_dictionary)
@@ -77,7 +77,7 @@ class BaseRegressionModel(IFreqaiModel):
     ) -> Tuple[DataFrame, npt.NDArray[np.int_]]:
         """
         Filter the prediction features data and predict with it.
-        :param unfiltered_df: Full dataframe for the current backtest period.
+        :param: unfiltered_df: Full dataframe for the current backtest period.
         :return:
         :pred_df: dataframe containing the predictions
         :do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove

freqtrade/freqai/base_models/BaseTensorFlowModel.py

@@ -3,10 +3,10 @@ from time import time
 from typing import Any
 
 from pandas import DataFrame
-
+import numpy as np
 from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
 from freqtrade.freqai.freqai_interface import IFreqaiModel
-
+import tensorflow as tf
 
 logger = logging.getLogger(__name__)
 
@@ -17,6 +17,13 @@ class BaseTensorFlowModel(IFreqaiModel):
     User *must* inherit from this class and set fit() and predict().
     """
 
+    def __init__(self, **kwargs):
+        super().__init__(config=kwargs['config'])
+        self.keras = True
+        if self.ft_params.get("DI_threshold", 0):
+            self.ft_params["DI_threshold"] = 0
+            logger.warning("DI threshold is not configured for Keras models yet. Deactivating.")
+
     def train(
         self, unfiltered_df: DataFrame, pair: str, dk: FreqaiDataKitchen, **kwargs
     ) -> Any:
@@ -47,7 +54,7 @@ class BaseTensorFlowModel(IFreqaiModel):
                     f"{end_date} --------------------")
         # split data into train/test data.
         data_dictionary = dk.make_train_test_datasets(features_filtered, labels_filtered)
-        if not self.freqai_info.get("fit_live_predictions_candles", 0) or not self.live:
+        if not self.freqai_info.get("fit_live_predictions", 0) or not self.live:
             dk.fit_labels()
         # normalize all data based on train_dataset only
         data_dictionary = dk.normalize_data(data_dictionary)
@@ -68,3 +75,76 @@ class BaseTensorFlowModel(IFreqaiModel):
                     f"({end_time - start_time:.2f} secs) --------------------")
 
         return model
+
+
+class WindowGenerator:
+    def __init__(
+        self,
+        input_width,
+        label_width,
+        shift,
+        train_df=None,
+        val_df=None,
+        test_df=None,
+        train_labels=None,
+        val_labels=None,
+        test_labels=None,
+        batch_size=None,
+    ):
+        # Store the raw data.
+        self.train_df = train_df
+        self.val_df = val_df
+        self.test_df = test_df
+        self.train_labels = train_labels
+        self.val_labels = val_labels
+        self.test_labels = test_labels
+        self.batch_size = batch_size
+        self.input_width = input_width
+        self.label_width = label_width
+        self.shift = shift
+        self.total_window_size = input_width + shift
+        self.input_slice = slice(0, input_width)
+        self.input_indices = np.arange(self.total_window_size)[self.input_slice]
+
+    def make_dataset(self, data, labels=None):
+        data = np.array(data, dtype=np.float32)
+        if labels is not None:
+            labels = np.array(labels, dtype=np.float32)
+        ds = tf.keras.preprocessing.timeseries_dataset_from_array(
+            data=data,
+            targets=labels,
+            sequence_length=self.total_window_size,
+            sequence_stride=1,
+            sampling_rate=1,
+            shuffle=False,
+            batch_size=self.batch_size,
+        )
+
+        return ds
+
+    @property
+    def train(self):
+        return self.make_dataset(self.train_df, self.train_labels)
+
+    @property
+    def val(self):
+        return self.make_dataset(self.val_df, self.val_labels)
+
+    @property
+    def test(self):
+        return self.make_dataset(self.test_df, self.test_labels)
+
+    @property
+    def inference(self):
+        return self.make_dataset(self.test_df)
+
+    @property
+    def example(self):
+        """Get and cache an example batch of `inputs, labels` for plotting."""
+        result = getattr(self, "_example", None)
+        if result is None:
+            # No example batch was found, so get one from the `.train` dataset
+            result = next(iter(self.train))
+            # And cache it for next time
+            self._example = result
+        return result

freqtrade/freqai/data_drawer.py

@@ -1,15 +1,14 @@
 import collections
+import json
 import logging
 import re
 import shutil
 import threading
-from datetime import datetime, timezone
 from pathlib import Path
 from typing import Any, Dict, Tuple, TypedDict
 
 import numpy as np
 import pandas as pd
-import psutil
 import rapidjson
 from joblib import dump, load
 from joblib.externals import cloudpickle
@@ -66,8 +65,6 @@ class FreqaiDataDrawer:
         self.pair_dict: Dict[str, pair_info] = {}
         # dictionary holding all actively inferenced models in memory given a model filename
         self.model_dictionary: Dict[str, Any] = {}
-        # all additional metadata that we want to keep in ram
-        self.meta_data_dictionary: Dict[str, Dict[str, Any]] = {}
         self.model_return_values: Dict[str, DataFrame] = {}
         self.historic_data: Dict[str, Dict[str, DataFrame]] = {}
         self.historic_predictions: Dict[str, DataFrame] = {}
@@ -81,60 +78,37 @@ class FreqaiDataDrawer:
         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.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.load_metric_tracker_from_disk()
         self.training_queue: Dict[str, int] = {}
         self.history_lock = threading.Lock()
         self.save_lock = threading.Lock()
         self.pair_dict_lock = threading.Lock()
-        self.metric_tracker_lock = threading.Lock()
         self.old_DBSCAN_eps: Dict[str, float] = {}
         self.empty_pair_dict: pair_info = {
                 "model_filename": "", "trained_timestamp": 0,
                 "data_path": "", "extras": {}}
-        self.metric_tracker: Dict[str, Dict[str, Dict[str, list]]] = {}
-
-    def update_metric_tracker(self, metric: str, value: float, pair: str) -> None:
-        """
-        General utility for adding and updating custom metrics. Typically used
-        for adding training performance, train timings, inferenc timings, cpu loads etc.
-        """
-        with self.metric_tracker_lock:
-            if pair not in self.metric_tracker:
-                self.metric_tracker[pair] = {}
-            if metric not in self.metric_tracker[pair]:
-                self.metric_tracker[pair][metric] = {'timestamp': [], 'value': []}
-
-            timestamp = int(datetime.now(timezone.utc).timestamp())
-            self.metric_tracker[pair][metric]['value'].append(value)
-            self.metric_tracker[pair][metric]['timestamp'].append(timestamp)
-
-    def collect_metrics(self, time_spent: float, pair: str):
-        """
-        Add metrics to the metric tracker dictionary
-        """
-        load1, load5, load15 = psutil.getloadavg()
-        cpus = psutil.cpu_count()
-        self.update_metric_tracker('train_time', time_spent, pair)
-        self.update_metric_tracker('cpu_load1min', load1 / cpus, pair)
-        self.update_metric_tracker('cpu_load5min', load5 / cpus, pair)
-        self.update_metric_tracker('cpu_load15min', load15 / cpus, pair)
+        self.limit_ram_use = self.freqai_info.get('limit_ram_usage', False)
+        if 'rl_config' in self.freqai_info:
+            self.model_type = 'stable_baselines'
+            logger.warning('User indicated rl_config, FreqAI will now use stable_baselines3'
+                           ' to save models.')
+        else:
+            self.model_type = self.freqai_info.get('model_save_type', 'joblib')
 
     def load_drawer_from_disk(self):
         """
         Locate and load a previously saved data drawer full of all pair model metadata in
         present model folder.
-        Load any existing metric tracker that may be present.
+        :return: bool - whether or not the drawer was located
         """
         exists = self.pair_dictionary_path.is_file()
         if exists:
             with open(self.pair_dictionary_path, "r") as fp:
-                self.pair_dict = rapidjson.load(fp, number_mode=rapidjson.NM_NATIVE)
+                self.pair_dict = json.load(fp)
         elif not self.follow_mode:
             logger.info("Could not find existing datadrawer, starting from scratch")
         else:
@@ -143,18 +117,7 @@ class FreqaiDataDrawer:
                 "sending null values back to strategy"
             )
 
-    def load_metric_tracker_from_disk(self):
-        """
-        Tries to load an existing metrics dictionary if the user
-        wants to collect metrics.
-        """
-        if self.freqai_info.get('write_metrics_to_disk', False):
-            exists = self.metric_tracker_path.is_file()
-            if exists:
-                with open(self.metric_tracker_path, "r") as fp:
-                    self.metric_tracker = rapidjson.load(fp, number_mode=rapidjson.NM_NATIVE)
-            else:
-                logger.info("Could not find existing metric tracker, starting from scratch")
+        return exists
 
     def load_historic_predictions_from_disk(self):
         """
@@ -190,7 +153,7 @@ class FreqaiDataDrawer:
 
     def save_historic_predictions_to_disk(self):
         """
-        Save historic predictions pickle to disk
+        Save data drawer full of all pair model metadata in present model folder.
         """
         with open(self.historic_predictions_path, "wb") as fp:
             cloudpickle.dump(self.historic_predictions, fp, protocol=cloudpickle.DEFAULT_PROTOCOL)
@@ -198,15 +161,6 @@ class FreqaiDataDrawer:
         # create a backup
         shutil.copy(self.historic_predictions_path, self.historic_predictions_bkp_path)
 
-    def save_metric_tracker_to_disk(self):
-        """
-        Save metric tracker of all pair metrics collected.
-        """
-        with self.save_lock:
-            with open(self.metric_tracker_path, 'w') as fp:
-                rapidjson.dump(self.metric_tracker, fp, default=self.np_encoder,
-                               number_mode=rapidjson.NM_NATIVE)
-
     def save_drawer_to_disk(self):
         """
         Save data drawer full of all pair model metadata in present model folder.
@@ -465,7 +419,8 @@ class FreqaiDataDrawer:
     def save_data(self, model: Any, coin: str, dk: FreqaiDataKitchen) -> None:
         """
         Saves all data associated with a model for a single sub-train time range
-        :param model: User trained model which can be reused for inferencing to generate
+        :params:
+        :model: User trained model which can be reused for inferencing to generate
         predictions
         """
 
@@ -475,10 +430,12 @@ class FreqaiDataDrawer:
         save_path = Path(dk.data_path)
 
         # Save the trained model
-        if not dk.keras:
+        if self.model_type == 'joblib':
             dump(model, save_path / f"{dk.model_filename}_model.joblib")
-        else:
+        elif self.model_type == 'keras':
             model.save(save_path / f"{dk.model_filename}_model.h5")
+        elif 'stable_baselines' in self.model_type:
+            model.save(save_path / f"{dk.model_filename}_model.zip")
 
         if dk.svm_model is not None:
             dump(dk.svm_model, save_path / f"{dk.model_filename}_svm_model.joblib")
@@ -505,15 +462,10 @@ class FreqaiDataDrawer:
                 dk.pca, open(dk.data_path / f"{dk.model_filename}_pca_object.pkl", "wb")
             )
 
-        # if self.live:
-        # store as much in ram as possible to increase performance
+        if not self.limit_ram_use:
             self.model_dictionary[coin] = model
         self.pair_dict[coin]["model_filename"] = dk.model_filename
         self.pair_dict[coin]["data_path"] = str(dk.data_path)
-        if coin not in self.meta_data_dictionary:
-            self.meta_data_dictionary[coin] = {}
-        self.meta_data_dictionary[coin]["train_df"] = dk.data_dictionary["train_features"]
-        self.meta_data_dictionary[coin]["meta_data"] = dk.data
         self.save_drawer_to_disk()
 
         return
@@ -524,7 +476,7 @@ class FreqaiDataDrawer:
         presaved backtesting (prediction file loading).
         """
         with open(dk.data_path / f"{dk.model_filename}_metadata.json", "r") as fp:
-            dk.data = rapidjson.load(fp, number_mode=rapidjson.NM_NATIVE)
+            dk.data = json.load(fp)
             dk.training_features_list = dk.data["training_features_list"]
             dk.label_list = dk.data["label_list"]
 
@@ -550,29 +502,28 @@ class FreqaiDataDrawer:
                     / dk.data_path.parts[-1]
                 )
 
-        if coin in self.meta_data_dictionary:
-            dk.data = self.meta_data_dictionary[coin]["meta_data"]
-            dk.data_dictionary["train_features"] = self.meta_data_dictionary[coin]["train_df"]
-        else:
         with open(dk.data_path / f"{dk.model_filename}_metadata.json", "r") as fp:
-                dk.data = rapidjson.load(fp, number_mode=rapidjson.NM_NATIVE)
+            dk.data = json.load(fp)
+            dk.training_features_list = dk.data["training_features_list"]
+            dk.label_list = dk.data["label_list"]
 
         dk.data_dictionary["train_features"] = pd.read_pickle(
             dk.data_path / f"{dk.model_filename}_trained_df.pkl"
         )
 
-        dk.training_features_list = dk.data["training_features_list"]
-        dk.label_list = dk.data["label_list"]
-
         # try to access model in memory instead of loading object from disk to save time
-        if dk.live and coin in self.model_dictionary:
+        if dk.live and coin in self.model_dictionary and not self.limit_ram_use:
             model = self.model_dictionary[coin]
-        elif not dk.keras:
+        elif self.model_type == 'joblib':
             model = load(dk.data_path / f"{dk.model_filename}_model.joblib")
-        else:
+        elif self.model_type == 'keras':
             from tensorflow import keras
-
             model = keras.models.load_model(dk.data_path / f"{dk.model_filename}_model.h5")
+        elif self.model_type == 'stable_baselines':
+            mod = __import__('stable_baselines3', fromlist=[
+                             self.freqai_info['rl_config']['model_type']])
+            MODELCLASS = getattr(mod, self.freqai_info['rl_config']['model_type'])
+            model = MODELCLASS.load(dk.data_path / f"{dk.model_filename}_model")
 
         if Path(dk.data_path / f"{dk.model_filename}_svm_model.joblib").is_file():
             dk.svm_model = load(dk.data_path / f"{dk.model_filename}_svm_model.joblib")
@@ -582,6 +533,10 @@ class FreqaiDataDrawer:
                 f"Unable to load model, ensure model exists at " f"{dk.data_path} "
             )
 
+        # load it into ram if it was loaded from disk
+        if coin not in self.model_dictionary and not self.limit_ram_use:
+            self.model_dictionary[coin] = model
+
         if self.config["freqai"]["feature_parameters"]["principal_component_analysis"]:
             dk.pca = cloudpickle.load(
                 open(dk.data_path / f"{dk.model_filename}_pca_object.pkl", "rb")
@@ -594,7 +549,8 @@ class FreqaiDataDrawer:
         Append new candles to our stores historic data (in memory) so that
         we do not need to load candle history from disk and we dont need to
         pinging exchange multiple times for the same candle.
-        :param dataframe: DataFrame = strategy provided dataframe
+        :params:
+        dataframe: DataFrame = strategy provided dataframe
         """
         feat_params = self.freqai_info["feature_parameters"]
         with self.history_lock:
@@ -640,7 +596,8 @@ class FreqaiDataDrawer:
         """
         Load pair histories for all whitelist and corr_pairlist pairs.
         Only called once upon startup of bot.
-        :param timerange: TimeRange = full timerange required to populate all indicators
+        :params:
+        timerange: TimeRange = full timerange required to populate all indicators
         for training according to user defined train_period_days
         """
         history_data = self.historic_data
@@ -664,9 +621,10 @@ class FreqaiDataDrawer:
         """
         Searches through our historic_data in memory and returns the dataframes relevant
         to the present pair.
-        :param timerange: TimeRange = full timerange required to populate all indicators
+        :params:
+        timerange: TimeRange = full timerange required to populate all indicators
         for training according to user defined train_period_days
-        :param metadata: dict = strategy furnished pair metadata
+        metadata: dict = strategy furnished pair metadata
         """
         with self.history_lock:
             corr_dataframes: Dict[Any, Any] = {}
@@ -677,8 +635,7 @@ class FreqaiDataDrawer:
             )
 
             for tf in self.freqai_info["feature_parameters"].get("include_timeframes"):
-                base_dataframes[tf] = dk.slice_dataframe(
-                    timerange, historic_data[pair][tf]).reset_index(drop=True)
+                base_dataframes[tf] = dk.slice_dataframe(timerange, historic_data[pair][tf])
                 if pairs:
                     for p in pairs:
                         if pair in p:
@@ -687,6 +644,6 @@ class FreqaiDataDrawer:
                             corr_dataframes[p] = {}
                         corr_dataframes[p][tf] = dk.slice_dataframe(
                             timerange, historic_data[p][tf]
-                        ).reset_index(drop=True)
+                        )
 
         return corr_dataframes, base_dataframes

freqtrade/freqai/data_kitchen.py

@@ -9,6 +9,7 @@ from typing import Any, Dict, List, Tuple
 import numpy as np
 import numpy.typing as npt
 import pandas as pd
+import psutil
 from pandas import DataFrame
 from scipy import stats
 from sklearn import linear_model
@@ -76,9 +77,10 @@ class FreqaiDataKitchen:
         self.backtest_predictions_folder: str = "backtesting_predictions"
         self.live = live
         self.pair = pair
+        self.model_save_type = self.freqai_config.get('model_save_type', 'joblib')
 
         self.svm_model: linear_model.SGDOneClassSVM = None
-        self.keras: bool = self.freqai_config.get("keras", False)
+        # self.model_save_type: bool = self.freqai_config.get("keras", False)
         self.set_all_pairs()
         if not self.live:
             if not self.config["timerange"]:
@@ -95,7 +97,10 @@ class FreqaiDataKitchen:
             )
 
         self.data['extra_returns_per_train'] = self.freqai_config.get('extra_returns_per_train', {})
-        self.thread_count = self.freqai_config.get("data_kitchen_thread_count", -1)
+        if not self.freqai_config.get("data_kitchen_thread_count", 0):
+            self.thread_count = max(int(psutil.cpu_count() * 2 - 2), 1)
+        else:
+            self.thread_count = self.freqai_config["data_kitchen_thread_count"]
         self.train_dates: DataFrame = pd.DataFrame()
         self.unique_classes: Dict[str, list] = {}
         self.unique_class_list: list = []
@@ -107,8 +112,9 @@ class FreqaiDataKitchen:
     ) -> None:
         """
         Set the paths to the data for the present coin/botloop
-        :param metadata: dict = strategy furnished pair metadata
-        :param trained_timestamp: int = timestamp of most recent training
+        :params:
+        metadata: dict = strategy furnished pair metadata
+        trained_timestamp: int = timestamp of most recent training
         """
         self.full_path = Path(
             self.config["user_data_dir"] / "models" / str(self.freqai_config.get("identifier"))
@@ -128,8 +134,8 @@ class FreqaiDataKitchen:
         Given the dataframe for the full history for training, split the data into
         training and test data according to user specified parameters in configuration
         file.
-        :param filtered_dataframe: cleaned dataframe ready to be split.
-        :param labels: cleaned labels ready to be split.
+        :filtered_dataframe: cleaned dataframe ready to be split.
+        :labels: cleaned labels ready to be split.
         """
         feat_dict = self.freqai_config["feature_parameters"]
 
@@ -188,13 +194,12 @@ class FreqaiDataKitchen:
         remove all NaNs. Any row with a NaN is removed from training dataset or replaced with
         0s in the prediction dataset. However, prediction dataset do_predict will reflect any
         row that had a NaN and will shield user from that prediction.
-
-        :param unfiltered_df: the full dataframe for the present training period
-        :param training_feature_list: list, the training feature list constructed by
-                                      self.build_feature_list() according to user specified
-                                      parameters in the configuration file.
-        :param labels: the labels for the dataset
-        :param training_filter: boolean which lets the function know if it is training data or
+        :params:
+        :unfiltered_df: the full dataframe for the present training period
+        :training_feature_list: list, the training feature list constructed by
+        self.build_feature_list() according to user specified parameters in the configuration file.
+        :labels: the labels for the dataset
+        :training_filter: boolean which lets the function know if it is training data or
         prediction data to be filtered.
         :returns:
         :filtered_df: dataframe cleaned of NaNs and only containing the user
@@ -210,10 +215,7 @@ class FreqaiDataKitchen:
             const_cols = list((filtered_df.nunique() == 1).loc[lambda x: x].index)
             if const_cols:
                 filtered_df = filtered_df.filter(filtered_df.columns.difference(const_cols))
-                self.data['constant_features_list'] = const_cols
                 logger.warning(f"Removed features {const_cols} with constant values.")
-            else:
-                self.data['constant_features_list'] = []
             # we don't care about total row number (total no. datapoints) in training, we only care
             # about removing any row with NaNs
             # if labels has multiple columns (user wants to train multiple modelEs), we detect here
@@ -244,8 +246,6 @@ class FreqaiDataKitchen:
             self.data["filter_drop_index_training"] = drop_index
 
         else:
-            if len(self.data['constant_features_list']):
-                filtered_df = self.check_pred_labels(filtered_df)
             # we are backtesting so we need to preserve row number to send back to strategy,
             # so now we use do_predict to avoid any prediction based on a NaN
             drop_index = pd.isnull(filtered_df).any(axis=1)
@@ -290,8 +290,8 @@ class FreqaiDataKitchen:
     def normalize_data(self, data_dictionary: Dict) -> Dict[Any, Any]:
         """
         Normalize all data in the data_dictionary according to the training dataset
-        :param data_dictionary: dictionary containing the cleaned and
-                                split training/test data/labels
+        :params:
+        :data_dictionary: dictionary containing the cleaned and split training/test data/labels
         :returns:
         :data_dictionary: updated dictionary with standardized values.
         """
@@ -465,22 +465,6 @@ class FreqaiDataKitchen:
 
         return df
 
-    def check_pred_labels(self, df_predictions: DataFrame) -> DataFrame:
-        """
-        Check that prediction feature labels match training feature labels.
-        :param df_predictions: incoming predictions
-        """
-        constant_labels = self.data['constant_features_list']
-        df_predictions = df_predictions.filter(
-            df_predictions.columns.difference(constant_labels)
-        )
-        logger.warning(
-            f"Removed {len(constant_labels)} features from prediction features, "
-            f"these were considered constant values during most recent training."
-        )
-
-        return df_predictions
-
     def principal_component_analysis(self) -> None:
         """
         Performs Principal Component Analysis on the data for dimensionality reduction
@@ -537,7 +521,8 @@ class FreqaiDataKitchen:
     def pca_transform(self, filtered_dataframe: DataFrame) -> None:
         """
         Use an existing pca transform to transform data into components
-        :param filtered_dataframe: DataFrame = the cleaned dataframe
+        :params:
+        filtered_dataframe: DataFrame = the cleaned dataframe
         """
         pca_components = self.pca.transform(filtered_dataframe)
         self.data_dictionary["prediction_features"] = pd.DataFrame(
@@ -581,10 +566,11 @@ class FreqaiDataKitchen:
         """
         Build/inference a Support Vector Machine to detect outliers
         in training data and prediction
-        :param predict: bool = If true, inference an existing SVM model, else construct one
+        :params:
+        predict: bool = If true, inference an existing SVM model, else construct one
         """
 
-        if self.keras:
+        if self.model_save_type == 'keras':
             logger.warning(
                 "SVM outlier removal not currently supported for Keras based models. "
                 "Skipping user requested function."
@@ -666,10 +652,10 @@ class FreqaiDataKitchen:
         Use DBSCAN to cluster training data and remove "noisy" data (read outliers).
         User controls this via the config param `DBSCAN_outlier_pct` which indicates the
         pct of training data that they want to be considered outliers.
-        :param predict: bool = If False (training), iterate to find the best hyper parameters
-                        to match user requested outlier percent target.
-                        If True (prediction), use the parameters determined from
-                        the previous training to estimate if the current prediction point
+        :params:
+        predict: bool = If False (training), iterate to find the best hyper parameters to match
+        user requested outlier percent target. If True (prediction), use the parameters
+        determined from the previous training to estimate if the current prediction point
         is an outlier.
         """
 
@@ -959,9 +945,6 @@ class FreqaiDataKitchen:
             append_df[f"{label}_mean"] = self.data["labels_mean"][label]
             append_df[f"{label}_std"] = self.data["labels_std"][label]
 
-        for extra_col in self.data["extra_returns_per_train"]:
-            append_df["{extra_col}"] = self.data["extra_returns_per_train"][extra_col]
-
         append_df["do_predict"] = do_predict
         if self.freqai_config["feature_parameters"].get("DI_threshold", 0) > 0:
             append_df["DI_values"] = self.DI_values
@@ -1140,13 +1123,15 @@ class FreqaiDataKitchen:
         prediction_dataframe: DataFrame = pd.DataFrame(),
     ) -> 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 informative pair dataframes
+        Use the user defined strategy for populating indicators during
+        retrain
+        :params:
+        strategy: IStrategy = user defined strategy object
+        corr_dataframes: dict = dict containing the informative pair dataframes
         (for user defined timeframes)
-        :param base_dataframes: dict = dict containing the current pair dataframes
+        base_dataframes: dict = dict containing the current pair dataframes
         (for user defined timeframes)
-        :param metadata: dict = strategy furnished pair metadata
+        metadata: dict = strategy furnished pair metadata
         :returns:
         dataframe: DataFrame = dataframe containing populated indicators
         """

freqtrade/freqai/freqai_interface.py

@@ -7,10 +7,11 @@ from collections import deque
 from datetime import datetime, timezone
 from pathlib import Path
 from threading import Lock
-from typing import Any, Dict, List, Literal, Tuple
+from typing import Any, Dict, List, Optional, Tuple
 
 import numpy as np
 import pandas as pd
+import psutil
 from numpy.typing import NDArray
 from pandas import DataFrame
 
@@ -72,10 +73,10 @@ class IFreqaiModel(ABC):
         self.identifier: str = self.freqai_info.get("identifier", "no_id_provided")
         self.scanning = False
         self.ft_params = self.freqai_info["feature_parameters"]
-        self.keras: bool = self.freqai_info.get("keras", False)
-        if self.keras and self.ft_params.get("DI_threshold", 0):
-            self.ft_params["DI_threshold"] = 0
-            logger.warning("DI threshold is not configured for Keras models yet. Deactivating.")
+        # self.keras: bool = self.freqai_info.get("keras", False)
+        # if self.keras and self.ft_params.get("DI_threshold", 0):
+        #     self.ft_params["DI_threshold"] = 0
+        #     logger.warning("DI threshold is not configured for Keras models yet. Deactivating.")
         self.CONV_WIDTH = self.freqai_info.get("conv_width", 2)
         if self.ft_params.get("inlier_metric_window", 0):
             self.CONV_WIDTH = self.ft_params.get("inlier_metric_window", 0) * 2
@@ -96,12 +97,15 @@ class IFreqaiModel(ABC):
 
         self._threads: List[threading.Thread] = []
         self._stop_event = threading.Event()
+        self.strategy: Optional[IStrategy] = None
+        self.max_system_threads = max(int(psutil.cpu_count() * 2 - 2), 1)
 
     def __getstate__(self):
         """
         Return an empty state to be pickled in hyperopt
         """
         return ({})
+        self.strategy: Optional[IStrategy] = None
 
     def assert_config(self, config: Config) -> None:
 
@@ -122,6 +126,7 @@ class IFreqaiModel(ABC):
 
         self.live = strategy.dp.runmode in (RunMode.DRY_RUN, RunMode.LIVE)
         self.dd.set_pair_dict_info(metadata)
+        self.strategy = strategy
 
         if self.live:
             self.inference_timer('start')
@@ -144,7 +149,7 @@ class IFreqaiModel(ABC):
         dataframe = dk.remove_features_from_df(dk.return_dataframe)
         self.clean_up()
         if self.live:
-            self.inference_timer('stop', metadata["pair"])
+            self.inference_timer('stop')
         return dataframe
 
     def clean_up(self):
@@ -156,6 +161,13 @@ class IFreqaiModel(ABC):
         self.model = None
         self.dk = None
 
+    def _on_stop(self):
+        """
+        Callback for Subclasses to override to include logic for shutting down resources
+        when SIGINT is sent.
+        """
+        return
+
     def shutdown(self):
         """
         Cleans up threads on Shutdown, set stop event. Join threads to wait
@@ -164,6 +176,8 @@ class IFreqaiModel(ABC):
         logger.info("Stopping FreqAI")
         self._stop_event.set()
 
+        self._on_stop()
+
         logger.info("Waiting on Training iteration")
         for _thread in self._threads:
             _thread.join()
@@ -196,31 +210,29 @@ class IFreqaiModel(ABC):
             (_, trained_timestamp, _) = self.dd.get_pair_dict_info(pair)
 
             dk = FreqaiDataKitchen(self.config, self.live, pair)
+            dk.set_paths(pair, trained_timestamp)
             (
                 retrain,
                 new_trained_timerange,
                 data_load_timerange,
             ) = dk.check_if_new_training_required(trained_timestamp)
+            dk.set_paths(pair, new_trained_timerange.stopts)
 
             if retrain:
                 self.train_timer('start')
-                dk.set_paths(pair, new_trained_timerange.stopts)
                 try:
                     self.extract_data_and_train_model(
                         new_trained_timerange, pair, strategy, dk, data_load_timerange
                     )
                 except Exception as msg:
-                    logger.warning(f"Training {pair} raised exception {msg.__class__.__name__}. "
-                                   f"Message: {msg}, skipping.")
+                    logger.warning(f'Training {pair} raised exception {msg}, skipping.')
 
-                self.train_timer('stop', pair)
+                self.train_timer('stop')
 
                 # only rotate the queue after the first has been trained.
                 self.train_queue.rotate(-1)
 
                 self.dd.save_historic_predictions_to_disk()
-                if self.freqai_info.get('write_metrics_to_disk', False):
-                    self.dd.save_metric_tracker_to_disk()
 
     def start_backtesting(
         self, dataframe: DataFrame, metadata: dict, dk: FreqaiDataKitchen
@@ -269,7 +281,9 @@ class IFreqaiModel(ABC):
             )
 
             trained_timestamp_int = int(trained_timestamp.stopts)
-            dk.set_paths(pair, trained_timestamp_int)
+            dk.data_path = Path(
+                dk.full_path / f"sub-train-{pair.split('/')[0]}_{trained_timestamp_int}"
+                )
 
             dk.set_new_model_names(pair, trained_timestamp)
 
@@ -602,11 +616,11 @@ class IFreqaiModel(ABC):
         If the user reuses an identifier on a subsequent instance,
         this function will not be called. In that case, "real" predictions
         will be appended to the loaded set of historic predictions.
-        :param df: DataFrame = the dataframe containing the training feature data
-        :param model: Any = A model which was `fit` using a common library such as
+        :param: df: DataFrame = the dataframe containing the training feature data
+        :param: model: Any = A model which was `fit` using a common library such as
         catboost or lightgbm
-        :param dk: FreqaiDataKitchen = object containing methods for data analysis
-        :param pair: str = current pair
+        :param: dk: FreqaiDataKitchen = object containing methods for data analysis
+        :param: pair: str = current pair
         """
 
         self.dd.historic_predictions[pair] = pred_df
@@ -631,7 +645,8 @@ class IFreqaiModel(ABC):
 
         # # for keras type models, the conv_window needs to be prepended so
         # # viewing is correct in frequi
-        if self.freqai_info.get('keras', False) or self.ft_params.get('inlier_metric_window', 0):
+        if (not self.freqai_info.get('model_save_type', 'joblib') or
+                self.ft_params.get('inlier_metric_window', 0)):
             n_lost_points = self.freqai_info.get('conv_width', 2)
             zeros_df = DataFrame(np.zeros((n_lost_points, len(hist_preds_df.columns))),
                                  columns=hist_preds_df.columns)
@@ -657,7 +672,7 @@ class IFreqaiModel(ABC):
 
         return
 
-    def inference_timer(self, do: Literal['start', 'stop'] = 'start', pair: str = ''):
+    def inference_timer(self, do='start'):
         """
         Timer designed to track the cumulative time spent in FreqAI for one pass through
         the whitelist. This will check if the time spent is more than 1/4 the time
@@ -668,10 +683,7 @@ class IFreqaiModel(ABC):
             self.begin_time = time.time()
         elif do == 'stop':
             end = time.time()
-            time_spent = (end - self.begin_time)
-            if self.freqai_info.get('write_metrics_to_disk', False):
-                self.dd.update_metric_tracker('inference_time', time_spent, pair)
-            self.inference_time += time_spent
+            self.inference_time += (end - self.begin_time)
             if self.pair_it == self.total_pairs:
                 logger.info(
                     f'Total time spent inferencing pairlist {self.inference_time:.2f} seconds')
@@ -682,7 +694,7 @@ class IFreqaiModel(ABC):
                 self.inference_time = 0
         return
 
-    def train_timer(self, do: Literal['start', 'stop'] = 'start', pair: str = ''):
+    def train_timer(self, do='start'):
         """
         Timer designed to track the cumulative time spent training the full pairlist in
         FreqAI.
@@ -692,11 +704,7 @@ class IFreqaiModel(ABC):
             self.begin_time_train = time.time()
         elif do == 'stop':
             end = time.time()
-            time_spent = (end - self.begin_time_train)
-            if self.freqai_info.get('write_metrics_to_disk', False):
-                self.dd.collect_metrics(time_spent, pair)
-
-            self.train_time += time_spent
+            self.train_time += (end - self.begin_time_train)
             if self.pair_it_train == self.total_pairs:
                 logger.info(
                     f'Total time spent training pairlist {self.train_time:.2f} seconds')

freqtrade/freqai/prediction_models/CNNPredictionModel.py

@@ -0,0 +1,144 @@
+import logging
+from typing import Any, Dict, Tuple
+
+from pandas import DataFrame
+from freqtrade.exceptions import OperationalException
+from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
+import tensorflow as tf
+from freqtrade.freqai.base_models.BaseTensorFlowModel import BaseTensorFlowModel, WindowGenerator
+from tensorflow.keras.layers import Input, Conv1D, Dense
+from tensorflow.keras.models import Model
+import numpy as np
+
+logger = logging.getLogger(__name__)
+
+# tf.config.run_functions_eagerly(True)
+# tf.data.experimental.enable_debug_mode()
+
+MAX_EPOCHS = 10
+
+
+class CNNPredictionModel(BaseTensorFlowModel):
+    """
+    User created prediction model. The class needs to override three necessary
+    functions, predict(), fit().
+    """
+
+    def fit(self, data_dictionary: Dict[str, Any], dk: FreqaiDataKitchen) -> Any:
+        """
+        User sets up the training and test data to fit their desired model here
+        :params:
+        :data_dictionary: the dictionary constructed by DataHandler to hold
+        all the training and test data/labels.
+        """
+        train_df = data_dictionary["train_features"]
+        train_labels = data_dictionary["train_labels"]
+        test_df = data_dictionary["test_features"]
+        test_labels = data_dictionary["test_labels"]
+        n_labels = len(train_labels.columns)
+        if n_labels > 1:
+            raise OperationalException(
+                "Neural Net not yet configured for multi-targets. Please "
+                " reduce number of targets to 1 in strategy."
+            )
+
+        n_features = len(data_dictionary["train_features"].columns)
+        BATCH_SIZE = self.freqai_info.get("batch_size", 64)
+        input_dims = [BATCH_SIZE, self.CONV_WIDTH, n_features]
+
+        w1 = WindowGenerator(
+            input_width=self.CONV_WIDTH,
+            label_width=1,
+            shift=1,
+            train_df=train_df,
+            val_df=test_df,
+            train_labels=train_labels,
+            val_labels=test_labels,
+            batch_size=BATCH_SIZE,
+        )
+
+        model = self.create_model(input_dims, n_labels)
+
+        steps_per_epoch = np.ceil(len(test_df) / BATCH_SIZE)
+        lr_schedule = tf.keras.optimizers.schedules.InverseTimeDecay(
+            0.001, decay_steps=steps_per_epoch * 1000, decay_rate=1, staircase=False
+        )
+
+        early_stopping = tf.keras.callbacks.EarlyStopping(
+            monitor="loss", patience=3, mode="min", min_delta=0.0001
+        )
+
+        model.compile(
+            loss=tf.losses.MeanSquaredError(),
+            optimizer=tf.optimizers.Adam(lr_schedule),
+            metrics=[tf.metrics.MeanAbsoluteError()],
+        )
+
+        model.fit(
+            w1.train,
+            epochs=MAX_EPOCHS,
+            shuffle=False,
+            validation_data=w1.val,
+            callbacks=[early_stopping],
+            verbose=1,
+        )
+
+        return model
+
+    def predict(
+        self, unfiltered_dataframe: DataFrame, dk: FreqaiDataKitchen, first=True
+    ) -> Tuple[DataFrame, DataFrame]:
+        """
+        Filter the prediction features data and predict with it.
+        :param: unfiltered_dataframe: Full dataframe for the current backtest period.
+        :return:
+        :predictions: np.array of predictions
+        :do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove
+        data (NaNs) or felt uncertain about data (PCA and DI index)
+        """
+
+        dk.find_features(unfiltered_dataframe)
+        filtered_dataframe, _ = dk.filter_features(
+            unfiltered_dataframe, dk.training_features_list, training_filter=False
+        )
+        filtered_dataframe = dk.normalize_data_from_metadata(filtered_dataframe)
+        dk.data_dictionary["prediction_features"] = filtered_dataframe
+
+        # optional additional data cleaning/analysis
+        self.data_cleaning_predict(dk, filtered_dataframe)
+
+        if first:
+            full_df = dk.data_dictionary["prediction_features"]
+
+            w1 = WindowGenerator(
+                input_width=self.CONV_WIDTH,
+                label_width=1,
+                shift=1,
+                test_df=full_df,
+                batch_size=len(full_df),
+            )
+
+            predictions = self.model.predict(w1.inference)
+            len_diff = len(dk.do_predict) - len(predictions)
+            if len_diff > 0:
+                dk.do_predict = dk.do_predict[len_diff:]
+
+        else:
+            data = dk.data_dictionary["prediction_features"]
+            data = tf.expand_dims(data, axis=0)
+            predictions = self.model(data, training=False)
+
+        predictions = predictions[:, 0, 0]
+        pred_df = DataFrame(predictions, columns=dk.label_list)
+
+        pred_df = dk.denormalize_labels_from_metadata(pred_df)
+
+        return (pred_df, np.ones(len(pred_df)))
+
+    def create_model(self, input_dims, n_labels) -> Any:
+
+        input_layer = Input(shape=(input_dims[1], input_dims[2]))
+        Layer_1 = Conv1D(filters=32, kernel_size=(self.CONV_WIDTH,), activation="relu")(input_layer)
+        Layer_3 = Dense(units=32, activation="relu")(Layer_1)
+        output_layer = Dense(units=n_labels)(Layer_3)
+        return Model(inputs=input_layer, outputs=output_layer)

freqtrade/freqai/prediction_models/CatboostClassifier.py

@@ -1,6 +1,4 @@
 import logging
-import sys
-from pathlib import Path
 from typing import Any, Dict
 
 from catboost import CatBoostClassifier, Pool
@@ -22,7 +20,8 @@ class CatboostClassifier(BaseClassifierModel):
     def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
         """
         User sets up the training and test data to fit their desired model here
-        :param data_dictionary: the dictionary constructed by DataHandler to hold
+        :params:
+        :data_dictionary: the dictionary constructed by DataHandler to hold
         all the training and test data/labels.
         """
 
@@ -31,25 +30,15 @@ class CatboostClassifier(BaseClassifierModel):
             label=data_dictionary["train_labels"],
             weight=data_dictionary["train_weights"],
         )
-        if self.freqai_info.get("data_split_parameters", {}).get("test_size", 0.1) == 0:
-            test_data = None
-        else:
-            test_data = Pool(
-                data=data_dictionary["test_features"],
-                label=data_dictionary["test_labels"],
-                weight=data_dictionary["test_weights"],
-            )
 
         cbr = CatBoostClassifier(
-            allow_writing_files=True,
+            allow_writing_files=False,
             loss_function='MultiClass',
-            train_dir=Path(dk.data_path),
             **self.model_training_parameters,
         )
 
         init_model = self.get_init_model(dk.pair)
 
-        cbr.fit(X=train_data, eval_set=test_data, init_model=init_model,
-                log_cout=sys.stdout, log_cerr=sys.stderr)
+        cbr.fit(train_data, init_model=init_model)
 
         return cbr

freqtrade/freqai/prediction_models/CatboostRegressor.py

@@ -1,6 +1,4 @@
 import logging
-import sys
-from pathlib import Path
 from typing import Any, Dict
 
 from catboost import CatBoostRegressor, Pool
@@ -43,12 +41,10 @@ class CatboostRegressor(BaseRegressionModel):
         init_model = self.get_init_model(dk.pair)
 
         model = CatBoostRegressor(
-            allow_writing_files=True,
-            train_dir=Path(dk.data_path),
+            allow_writing_files=False,
             **self.model_training_parameters,
         )
 
-        model.fit(X=train_data, eval_set=test_data, init_model=init_model,
-                  log_cout=sys.stdout, log_cerr=sys.stderr)
+        model.fit(X=train_data, eval_set=test_data, init_model=init_model)
 
         return model

freqtrade/freqai/prediction_models/CatboostRegressorMultiTarget.py

@@ -1,6 +1,4 @@
 import logging
-import sys
-from pathlib import Path
 from typing import Any, Dict
 
 from catboost import CatBoostRegressor, Pool
@@ -28,8 +26,7 @@ class CatboostRegressorMultiTarget(BaseRegressionModel):
         """
 
         cbr = CatBoostRegressor(
-            allow_writing_files=True,
-            train_dir=Path(dk.data_path),
+            allow_writing_files=False,
             **self.model_training_parameters,
         )
 
@@ -59,10 +56,8 @@ class CatboostRegressorMultiTarget(BaseRegressionModel):
 
         fit_params = []
         for i in range(len(eval_sets)):
-            fit_params.append({
-                    'eval_set': eval_sets[i],  'init_model': init_models[i],
-                    'log_cout': sys.stdout, 'log_cerr': sys.stderr,
-                 })
+            fit_params.append(
+                {'eval_set': eval_sets[i],  'init_model': init_models[i]})
 
         model = FreqaiMultiOutputRegressor(estimator=cbr)
         thread_training = self.freqai_info.get('multitarget_parallel_training', False)

freqtrade/freqai/prediction_models/LightGBMClassifier.py

@@ -20,7 +20,8 @@ class LightGBMClassifier(BaseClassifierModel):
     def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
         """
         User sets up the training and test data to fit their desired model here
-        :param data_dictionary: the dictionary constructed by DataHandler to hold
+        :params:
+        :data_dictionary: the dictionary constructed by DataHandler to hold
         all the training and test data/labels.
         """
 

freqtrade/freqai/prediction_models/ReinforcementLearner.py

@@ -0,0 +1,118 @@
+import logging
+from pathlib import Path
+from typing import Any, Dict
+
+import torch as th
+
+from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
+from freqtrade.freqai.RL.Base5ActionRLEnv import Actions, Base5ActionRLEnv, Positions
+from freqtrade.freqai.RL.BaseReinforcementLearningModel import BaseReinforcementLearningModel
+
+
+logger = logging.getLogger(__name__)
+
+
+class ReinforcementLearner(BaseReinforcementLearningModel):
+    """
+    User created Reinforcement Learning Model prediction model.
+    """
+
+    def fit(self, data_dictionary: Dict[str, Any], dk: FreqaiDataKitchen, **kwargs):
+        """
+        User customizable fit method
+        :params:
+        data_dictionary: dict = common data dictionary containing all train/test
+            features/labels/weights.
+        dk: FreqaiDatakitchen = data kitchen for current pair.
+        :returns:
+        model: Any = trained model to be used for inference in dry/live/backtesting
+        """
+        train_df = data_dictionary["train_features"]
+        total_timesteps = self.freqai_info["rl_config"]["train_cycles"] * len(train_df)
+
+        policy_kwargs = dict(activation_fn=th.nn.ReLU,
+                             net_arch=[128, 128])
+
+        if dk.pair not in self.dd.model_dictionary or not self.continual_learning:
+            model = self.MODELCLASS(self.policy_type, self.train_env, policy_kwargs=policy_kwargs,
+                                    tensorboard_log=Path(
+                                        dk.full_path / "tensorboard" / dk.pair.split('/')[0]),
+                                    **self.freqai_info['model_training_parameters']
+                                    )
+        else:
+            logger.info('Continual training activated - starting training from previously '
+                        'trained agent.')
+            model = self.dd.model_dictionary[dk.pair]
+            model.set_env(self.train_env)
+
+        model.learn(
+            total_timesteps=int(total_timesteps),
+            callback=self.eval_callback
+        )
+
+        if Path(dk.data_path / "best_model.zip").is_file():
+            logger.info('Callback found a best model.')
+            best_model = self.MODELCLASS.load(dk.data_path / "best_model")
+            return best_model
+
+        logger.info('Couldnt find best model, using final model instead.')
+
+        return model
+
+    class MyRLEnv(Base5ActionRLEnv):
+        """
+        User can override any function in BaseRLEnv and gym.Env. Here the user
+        sets a custom reward based on profit and trade duration.
+        """
+
+        def calculate_reward(self, action):
+            """
+            An example reward function. This is the one function that users will likely
+            wish to inject their own creativity into.
+            :params:
+            action: int = The action made by the agent for the current candle.
+            :returns:
+            float = the reward to give to the agent for current step (used for optimization
+                of weights in NN)
+            """
+            # first, penalize if the action is not valid
+            if not self._is_valid(action):
+                return -2
+
+            pnl = self.get_unrealized_profit()
+            factor = 100
+
+            # reward agent for entering trades
+            if (action in (Actions.Long_enter.value, Actions.Short_enter.value)
+                    and self._position == Positions.Neutral):
+                return 25
+            # discourage agent from not entering trades
+            if action == Actions.Neutral.value and self._position == Positions.Neutral:
+                return -1
+
+            max_trade_duration = self.rl_config.get('max_trade_duration_candles', 300)
+            trade_duration = self._current_tick - self._last_trade_tick
+
+            if trade_duration <= max_trade_duration:
+                factor *= 1.5
+            elif trade_duration > max_trade_duration:
+                factor *= 0.5
+
+            # discourage sitting in position
+            if (self._position in (Positions.Short, Positions.Long) and
+                    action == Actions.Neutral.value):
+                return -1 * trade_duration / max_trade_duration
+
+            # close long
+            if action == Actions.Long_exit.value and self._position == Positions.Long:
+                if pnl > self.profit_aim * self.rr:
+                    factor *= self.rl_config['model_reward_parameters'].get('win_reward_factor', 2)
+                return float(pnl * factor)
+
+            # close short
+            if action == Actions.Short_exit.value and self._position == Positions.Short:
+                if pnl > self.profit_aim * self.rr:
+                    factor *= self.rl_config['model_reward_parameters'].get('win_reward_factor', 2)
+                return float(pnl * factor)
+
+            return 0.

freqtrade/freqai/prediction_models/ReinforcementLearner_multiproc.py

@@ -0,0 +1,100 @@
+import logging
+from pathlib import Path
+from typing import Any, Dict  # , Tuple
+
+# import numpy.typing as npt
+import torch as th
+from pandas import DataFrame
+from stable_baselines3.common.callbacks import EvalCallback
+from stable_baselines3.common.vec_env import SubprocVecEnv
+
+from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
+from freqtrade.freqai.RL.BaseReinforcementLearningModel import (BaseReinforcementLearningModel,
+                                                                make_env)
+
+
+logger = logging.getLogger(__name__)
+
+
+class ReinforcementLearner_multiproc(BaseReinforcementLearningModel):
+    """
+    User created Reinforcement Learning Model prediction model.
+    """
+
+    def fit(self, data_dictionary: Dict[str, Any], dk: FreqaiDataKitchen, **kwargs):
+
+        train_df = data_dictionary["train_features"]
+        total_timesteps = self.freqai_info["rl_config"]["train_cycles"] * len(train_df)
+
+        # model arch
+        policy_kwargs = dict(activation_fn=th.nn.ReLU,
+                             net_arch=[128, 128])
+
+        if dk.pair not in self.dd.model_dictionary or not self.continual_learning:
+            model = self.MODELCLASS(self.policy_type, self.train_env, policy_kwargs=policy_kwargs,
+                                    tensorboard_log=Path(
+                                        dk.full_path / "tensorboard" / dk.pair.split('/')[0]),
+                                    **self.freqai_info['model_training_parameters']
+                                    )
+        else:
+            logger.info('Continual learning activated - starting training from previously '
+                        'trained agent.')
+            model = self.dd.model_dictionary[dk.pair]
+            model.set_env(self.train_env)
+
+        model.learn(
+            total_timesteps=int(total_timesteps),
+            callback=self.eval_callback
+        )
+
+        if Path(dk.data_path / "best_model.zip").is_file():
+            logger.info('Callback found a best model.')
+            best_model = self.MODELCLASS.load(dk.data_path / "best_model")
+            return best_model
+
+        logger.info('Couldnt find best model, using final model instead.')
+
+        return model
+
+    def set_train_and_eval_environments(self, data_dictionary: Dict[str, Any],
+                                        prices_train: DataFrame, prices_test: DataFrame,
+                                        dk: FreqaiDataKitchen):
+        """
+        User can override this if they are using a custom MyRLEnv
+        :params:
+        data_dictionary: dict = common data dictionary containing train and test
+            features/labels/weights.
+        prices_train/test: DataFrame = dataframe comprised of the prices to be used in
+            the environment during training
+        or testing
+        dk: FreqaiDataKitchen = the datakitchen for the current pair
+        """
+        train_df = data_dictionary["train_features"]
+        test_df = data_dictionary["test_features"]
+
+        env_id = "train_env"
+        self.train_env = SubprocVecEnv([make_env(self.MyRLEnv, env_id, i, 1, train_df, prices_train,
+                                        self.reward_params, self.CONV_WIDTH, monitor=True,
+                                        config=self.config) for i
+                                        in range(self.max_threads)])
+
+        eval_env_id = 'eval_env'
+        self.eval_env = SubprocVecEnv([make_env(self.MyRLEnv, eval_env_id, i, 1,
+                                                test_df, prices_test,
+                                                self.reward_params, self.CONV_WIDTH, monitor=True,
+                                                config=self.config) for i
+                                       in range(self.max_threads)])
+        self.eval_callback = EvalCallback(self.eval_env, deterministic=True,
+                                          render=False, eval_freq=len(train_df),
+                                          best_model_save_path=str(dk.data_path))
+
+    def _on_stop(self):
+        """
+        Hook called on bot shutdown. Close SubprocVecEnv subprocesses for clean shutdown.
+        """
+
+        if self.train_env:
+            self.train_env.close()
+
+        if self.eval_env:
+            self.eval_env.close()

freqtrade/freqai/prediction_models/XGBoostClassifier.py

@@ -26,7 +26,8 @@ class XGBoostClassifier(BaseClassifierModel):
     def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
         """
         User sets up the training and test data to fit their desired model here
-        :param data_dictionary: the dictionary constructed by DataHandler to hold
+        :params:
+        :data_dictionary: the dictionary constructed by DataHandler to hold
         all the training and test data/labels.
         """
 
@@ -64,7 +65,7 @@ class XGBoostClassifier(BaseClassifierModel):
     ) -> Tuple[DataFrame, npt.NDArray[np.int_]]:
         """
         Filter the prediction features data and predict with it.
-        :param unfiltered_df: Full dataframe for the current backtest period.
+        :param: unfiltered_df: Full dataframe for the current backtest period.
         :return:
         :pred_df: dataframe containing the predictions
         :do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove

freqtrade/freqai/prediction_models/XGBoostRFClassifier.py

@@ -1,84 +0,0 @@
-import logging
-from typing import Any, Dict, Tuple
-
-import numpy as np
-import numpy.typing as npt
-import pandas as pd
-from pandas import DataFrame
-from pandas.api.types import is_integer_dtype
-from sklearn.preprocessing import LabelEncoder
-from xgboost import XGBRFClassifier
-
-from freqtrade.freqai.base_models.BaseClassifierModel import BaseClassifierModel
-from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
-
-
-logger = logging.getLogger(__name__)
-
-
-class XGBoostRFClassifier(BaseClassifierModel):
-    """
-    User created prediction model. The class needs to override three necessary
-    functions, predict(), train(), fit(). The class inherits ModelHandler which
-    has its own DataHandler where data is held, saved, loaded, and managed.
-    """
-
-    def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
-        """
-        User sets up the training and test data to fit their desired model here
-        :param data_dictionary: the dictionary constructed by DataHandler to hold
-            all the training and test data/labels.
-        """
-
-        X = data_dictionary["train_features"].to_numpy()
-        y = data_dictionary["train_labels"].to_numpy()[:, 0]
-
-        le = LabelEncoder()
-        if not is_integer_dtype(y):
-            y = pd.Series(le.fit_transform(y), dtype="int64")
-
-        if self.freqai_info.get('data_split_parameters', {}).get('test_size', 0.1) == 0:
-            eval_set = None
-        else:
-            test_features = data_dictionary["test_features"].to_numpy()
-            test_labels = data_dictionary["test_labels"].to_numpy()[:, 0]
-
-            if not is_integer_dtype(test_labels):
-                test_labels = pd.Series(le.transform(test_labels), dtype="int64")
-
-            eval_set = [(test_features, test_labels)]
-
-        train_weights = data_dictionary["train_weights"]
-
-        init_model = self.get_init_model(dk.pair)
-
-        model = XGBRFClassifier(**self.model_training_parameters)
-
-        model.fit(X=X, y=y, eval_set=eval_set, sample_weight=train_weights,
-                  xgb_model=init_model)
-
-        return model
-
-    def predict(
-        self, unfiltered_df: DataFrame, dk: FreqaiDataKitchen, **kwargs
-    ) -> Tuple[DataFrame, npt.NDArray[np.int_]]:
-        """
-        Filter the prediction features data and predict with it.
-        :param  unfiltered_df: Full dataframe for the current backtest period.
-        :return:
-        :pred_df: dataframe containing the predictions
-        :do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove
-        data (NaNs) or felt uncertain about data (PCA and DI index)
-        """
-
-        (pred_df, dk.do_predict) = super().predict(unfiltered_df, dk, **kwargs)
-
-        le = LabelEncoder()
-        label = dk.label_list[0]
-        labels_before = list(dk.data['labels_std'].keys())
-        labels_after = le.fit_transform(labels_before).tolist()
-        pred_df[label] = le.inverse_transform(pred_df[label])
-        pred_df = pred_df.rename(
-            columns={labels_after[i]: labels_before[i] for i in range(len(labels_before))})
-
-        return (pred_df, dk.do_predict)

freqtrade/freqai/prediction_models/XGBoostRFRegressor.py

@@ -1,46 +0,0 @@
-import logging
-from typing import Any, Dict
-
-from xgboost import XGBRFRegressor
-
-from freqtrade.freqai.base_models.BaseRegressionModel import BaseRegressionModel
-from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
-
-
-logger = logging.getLogger(__name__)
-
-
-class XGBoostRFRegressor(BaseRegressionModel):
-    """
-    User created prediction model. The class needs to override three necessary
-    functions, predict(), train(), fit(). The class inherits ModelHandler which
-    has its own DataHandler where data is held, saved, loaded, and managed.
-    """
-
-    def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
-        """
-        User sets up the training and test data to fit their desired model here
-        :param data_dictionary: the dictionary constructed by DataHandler to hold
-                                all the training and test data/labels.
-        """
-
-        X = data_dictionary["train_features"]
-        y = data_dictionary["train_labels"]
-
-        if self.freqai_info.get("data_split_parameters", {}).get("test_size", 0.1) == 0:
-            eval_set = None
-            eval_weights = None
-        else:
-            eval_set = [(data_dictionary["test_features"], data_dictionary["test_labels"])]
-            eval_weights = [data_dictionary['test_weights']]
-
-        sample_weight = data_dictionary["train_weights"]
-
-        xgb_model = self.get_init_model(dk.pair)
-
-        model = XGBRFRegressor(**self.model_training_parameters)
-
-        model.fit(X=X, y=y, sample_weight=sample_weight, eval_set=eval_set,
-                  sample_weight_eval_set=eval_weights, xgb_model=xgb_model)
-
-        return model

freqtrade/freqai/prediction_models/XGBoostRegressor.py

@@ -29,7 +29,6 @@ class XGBoostRegressor(BaseRegressionModel):
 
         if self.freqai_info.get("data_split_parameters", {}).get("test_size", 0.1) == 0:
             eval_set = None
-            eval_weights = None
         else:
             eval_set = [(data_dictionary["test_features"], data_dictionary["test_labels"])]
             eval_weights = [data_dictionary['test_weights']]

freqtrade/freqtradebot.py

@@ -1471,13 +1471,12 @@ class FreqtradeBot(LoggingMixin):
         )
         return cancelled
 
-    def _safe_exit_amount(self, trade: Trade, pair: str, amount: float) -> float:
+    def _safe_exit_amount(self, pair: str, amount: float) -> float:
         """
         Get sellable amount.
         Should be trade.amount - but will fall back to the available amount if necessary.
         This should cover cases where get_real_amount() was not able to update the amount
         for whatever reason.
-        :param trade: Trade we're working with
         :param pair: Pair we're trying to sell
         :param amount: amount we expect to be available
         :return: amount to sell
@@ -1496,7 +1495,6 @@ class FreqtradeBot(LoggingMixin):
             return amount
         elif wallet_amount > amount * 0.98:
             logger.info(f"{pair} - Falling back to wallet-amount {wallet_amount} -> {amount}.")
-            trade.amount = wallet_amount
             return wallet_amount
         else:
             raise DependencyException(
@@ -1555,7 +1553,7 @@ class FreqtradeBot(LoggingMixin):
             # Emergency sells (default to market!)
             order_type = self.strategy.order_types.get("emergency_exit", "market")
 
-        amount = self._safe_exit_amount(trade, trade.pair, sub_trade_amt or trade.amount)
+        amount = self._safe_exit_amount(trade.pair, sub_trade_amt or trade.amount)
         time_in_force = self.strategy.order_time_in_force['exit']
 
         if (exit_check.exit_type != ExitType.LIQUIDATION
@@ -1830,7 +1828,7 @@ class FreqtradeBot(LoggingMixin):
         never in base currency.
         """
         self.wallets.update()
-        amount_ = trade.amount
+        amount_ = amount
         if order_obj.ft_order_side == trade.exit_side or order_obj.ft_order_side == 'stoploss':
             # check against remaining amount!
             amount_ = trade.amount - amount

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, Iterator, List
 from typing.io import IO
 from urllib.parse import urlparse
 
@@ -186,10 +186,7 @@ def safe_value_fallback(obj: dict, key1: str, key2: str, default_value=None):
     return default_value
 
 
-dictMap = Union[Dict[str, Any], Mapping[str, Any]]
-
-
-def safe_value_fallback2(dict1: dictMap, dict2: dictMap, key1: str, key2: str, default_value=None):
+def safe_value_fallback2(dict1: dict, dict2: dict, key1: str, key2: str, default_value=None):
     """
     Search a value in dict1, return this if it's not None.
     Fall back to dict2 - return key2 from dict2 if it's not None.

freqtrade/optimize/backtesting.py

@@ -151,8 +151,6 @@ class Backtesting:
         self.trading_mode: TradingMode = config.get('trading_mode', TradingMode.SPOT)
         # strategies which define "can_short=True" will fail to load in Spot mode.
         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)
 
         self.init_backtest()
 
@@ -619,16 +617,13 @@ class Backtesting:
                     exit_reason = row[EXIT_TAG_IDX]
                 # Custom exit pricing only for exit-signals
                 if order_type == 'limit':
-                    rate = strategy_safe_wrapper(self.strategy.custom_exit_price,
+                    close_rate = strategy_safe_wrapper(self.strategy.custom_exit_price,
                                                        default_retval=close_rate)(
                         pair=trade.pair,
                         trade=trade,  # type: ignore[arg-type]
                         current_time=exit_candle_time,
                         proposed_rate=close_rate, current_profit=current_profit,
                         exit_tag=exit_reason)
-                    if rate != close_rate:
-                        close_rate = price_to_precision(rate, trade.price_precision,
-                                                        self.precision_mode)
                     # We can't place orders lower than current low.
                     # freqtrade does not support this in live, and the order would fill immediately
                     if trade.is_short:
@@ -665,6 +660,7 @@ class Backtesting:
         # amount = amount or trade.amount
         amount = amount_to_contract_precision(amount or trade.amount, trade.amount_precision,
                                               self.precision_mode, trade.contract_size)
+        rate = price_to_precision(close_rate, trade.price_precision, self.precision_mode)
         order = Order(
             id=self.order_id_counter,
             ft_trade_id=trade.id,
@@ -678,12 +674,12 @@ class Backtesting:
             side=trade.exit_side,
             order_type=order_type,
             status="open",
-            price=close_rate,
-            average=close_rate,
+            price=rate,
+            average=rate,
             amount=amount,
             filled=0,
             remaining=amount,
-            cost=amount * close_rate,
+            cost=amount * rate,
         )
         trade.orders.append(order)
         return trade
@@ -730,11 +726,11 @@ class Backtesting:
     def get_valid_price_and_stake(
         self, pair: str, row: Tuple, propose_rate: float, stake_amount: float,
         direction: LongShort, current_time: datetime, entry_tag: Optional[str],
-        trade: Optional[LocalTrade], order_type: str, price_precision: Optional[float]
+        trade: Optional[LocalTrade], order_type: str
     ) -> Tuple[float, float, float, float]:
 
         if order_type == 'limit':
-            new_rate = strategy_safe_wrapper(self.strategy.custom_entry_price,
+            propose_rate = strategy_safe_wrapper(self.strategy.custom_entry_price,
                                                  default_retval=propose_rate)(
                 pair=pair, current_time=current_time,
                 proposed_rate=propose_rate, entry_tag=entry_tag,
@@ -742,9 +738,6 @@ class Backtesting:
             )  # default value is the open rate
             # We can't place orders higher than current high (otherwise it'd be a stop limit entry)
             # which freqtrade does not support in live.
-            if new_rate != propose_rate:
-                propose_rate = price_to_precision(new_rate, price_precision,
-                                                  self.precision_mode)
             if direction == "short":
                 propose_rate = max(propose_rate, row[LOW_IDX])
             else:
@@ -806,11 +799,9 @@ class Backtesting:
         pos_adjust = trade is not None and requested_rate is None
 
         stake_amount_ = stake_amount or (trade.stake_amount if trade else 0.0)
-        precision_price = self.exchange.get_precision_price(pair)
-
         propose_rate, stake_amount, leverage, min_stake_amount = self.get_valid_price_and_stake(
             pair, row, row[OPEN_IDX], stake_amount_, direction, current_time, entry_tag, trade,
-            order_type, precision_price,
+            order_type
         )
 
         # replace proposed rate if another rate was requested
@@ -826,6 +817,8 @@ class Backtesting:
         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)
+            precision_price = self.exchange.get_precision_price(pair)
+            propose_rate = price_to_precision(propose_rate, precision_price, self.precision_mode)
             amount_p = (stake_amount / propose_rate) * leverage
 
             contract_size = self.exchange.get_contract_size(pair)
@@ -921,12 +914,14 @@ class Backtesting:
         return trade
 
     def handle_left_open(self, open_trades: Dict[str, List[LocalTrade]],
-                         data: Dict[str, List[Tuple]]) -> None:
+                         data: Dict[str, List[Tuple]]) -> List[LocalTrade]:
         """
         Handling of left open trades at the end of backtesting
         """
+        trades = []
         for pair in open_trades.keys():
-            for trade in list(open_trades[pair]):
+            if len(open_trades[pair]) > 0:
+                for trade in open_trades[pair]:
                     if trade.open_order_id and trade.nr_of_successful_entries == 0:
                         # Ignore trade if entry-order did not fill yet
                         continue
@@ -938,6 +933,11 @@ class Backtesting:
                     trade.exit_reason = ExitType.FORCE_EXIT.value
                     trade.close(exit_row[OPEN_IDX], show_msg=False)
                     LocalTrade.close_bt_trade(trade)
+                    # Deepcopy object to have wallets update correctly
+                    trade1 = deepcopy(trade)
+                    trade1.is_open = True
+                    trades.append(trade1)
+        return trades
 
     def trade_slot_available(self, max_open_trades: int, open_trade_count: int) -> bool:
         # Always allow trades when max_open_trades is enabled.
@@ -961,8 +961,9 @@ class Backtesting:
             return 'short'
         return None
 
-    def run_protections(self, pair: str, current_time: datetime, side: LongShort):
-        if self.enable_protections:
+    def run_protections(
+            self, enable_protections, pair: str, current_time: datetime, side: LongShort):
+        if enable_protections:
             self.protections.stop_per_pair(pair, current_time, side)
             self.protections.global_stop(current_time, side)
 
@@ -1068,20 +1069,65 @@ class Backtesting:
             return None
         return row
 
-    def backtest_loop(
-            self, row: Tuple, pair: str, current_time: datetime, end_date: datetime,
-            max_open_trades: int, open_trade_count_start: int) -> int:
+    def backtest(self, processed: Dict,  # noqa: max-complexity: 13
+                 start_date: datetime, end_date: datetime,
+                 max_open_trades: int = 0, position_stacking: bool = False,
+                 enable_protections: bool = False) -> Dict[str, Any]:
         """
-        NOTE: This method is used by Hyperopt at each iteration. Please keep it optimized.
+        Implement backtesting functionality
 
-        Backtesting processing for one candle/pair.
+        NOTE: This method is used by Hyperopt at each iteration. Please keep it optimized.
+        Of course try to not have ugly code. By some accessor are sometime slower than functions.
+        Avoid extensive logging in this method and functions it calls.
+
+        :param processed: a processed dictionary with format {pair, data}, which gets cleared to
+        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
+        :param position_stacking: do we allow position stacking?
+        :param enable_protections: Should protections be enabled?
+        :return: DataFrame with trades (results of backtesting)
         """
-        for t in list(LocalTrade.bt_trades_open_pp[pair]):
+        trades: List[LocalTrade] = []
+        self.prepare_backtest(enable_protections)
+        # Ensure wallets are uptodate (important for --strategy-list)
+        self.wallets.update()
+        # Use dict of lists with data for performance
+        # (looping lists is a lot faster than pandas DataFrames)
+        data: Dict = self._get_ohlcv_as_lists(processed)
+
+        # Indexes per pair, so some pairs are allowed to have a missing start.
+        indexes: Dict = defaultdict(int)
+        current_time = start_date + timedelta(minutes=self.timeframe_min)
+
+        open_trades: Dict[str, List[LocalTrade]] = defaultdict(list)
+        open_trade_count = 0
+
+        self.progress.init_step(BacktestState.BACKTEST, int(
+            (end_date - start_date) / timedelta(minutes=self.timeframe_min)))
+
+        # Loop timerange and get candle for each pair at that point in time
+        while current_time <= end_date:
+            open_trade_count_start = open_trade_count
+            self.check_abort()
+            for i, pair in enumerate(data):
+                row_index = indexes[pair]
+                row = self.validate_row(data, pair, row_index, current_time)
+                if not row:
+                    continue
+
+                row_index += 1
+                indexes[pair] = row_index
+                self.dataprovider._set_dataframe_max_index(row_index)
+
+                for t in list(open_trades[pair]):
                     # 1. Manage currently open orders of active trades
                     if self.manage_open_orders(t, current_time, row):
                         # Close trade
-                open_trade_count_start -= 1
-                LocalTrade.remove_bt_trade(t)
+                        open_trade_count -= 1
+                        open_trades[pair].remove(t)
+                        LocalTrade.trades_open.remove(t)
                         self.wallets.update()
 
                 # 2. Process entries.
@@ -1090,7 +1136,7 @@ class Backtesting:
                 # don't open on the last row
                 trade_dir = self.check_for_trade_entry(row)
                 if (
-            (self._position_stacking or len(LocalTrade.bt_trades_open_pp[pair]) == 0)
+                    (position_stacking or len(open_trades[pair]) == 0)
                     and self.trade_slot_available(max_open_trades, open_trade_count_start)
                     and current_time != end_date
                     and trade_dir is not None
@@ -1102,11 +1148,13 @@ class Backtesting:
                         # This emulates previous behavior - not sure if this is correct
                         # Prevents entering if the trade-slot was freed in this candle
                         open_trade_count_start += 1
+                        open_trade_count += 1
                         # logger.debug(f"{pair} - Emulate creation of new trade: {trade}.")
+                        open_trades[pair].append(trade)
                         LocalTrade.add_bt_trade(trade)
                         self.wallets.update()
 
-        for trade in list(LocalTrade.bt_trades_open_pp[pair]):
+                for trade in list(open_trades[pair]):
                     # 3. Process entry orders.
                     order = trade.select_order(trade.entry_side, is_open=True)
                     if order and self._get_order_filled(order.price, row):
@@ -1132,67 +1180,22 @@ class Backtesting:
                             trade.close(order.price, show_msg=False)
 
                             # logger.debug(f"{pair} - Backtesting exit {trade}")
+                            open_trade_count -= 1
+                            open_trades[pair].remove(trade)
                             LocalTrade.close_bt_trade(trade)
+                            trades.append(trade)
                         self.wallets.update()
-                self.run_protections(pair, current_time, trade.trade_direction)
-        return open_trade_count_start
-
-    def backtest(self, processed: Dict,
-                 start_date: datetime, end_date: datetime,
-                 max_open_trades: int = 0) -> Dict[str, Any]:
-        """
-        Implement backtesting functionality
-
-        NOTE: This method is used by Hyperopt at each iteration. Please keep it optimized.
-        Of course try to not have ugly code. By some accessor are sometime slower than functions.
-        Avoid extensive logging in this method and functions it calls.
-
-        :param processed: a processed dictionary with format {pair, data}, which gets cleared to
-        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)
-        # Ensure wallets are uptodate (important for --strategy-list)
-        self.wallets.update()
-        # Use dict of lists with data for performance
-        # (looping lists is a lot faster than pandas DataFrames)
-        data: Dict = self._get_ohlcv_as_lists(processed)
-
-        # Indexes per pair, so some pairs are allowed to have a missing start.
-        indexes: Dict = defaultdict(int)
-        current_time = start_date + timedelta(minutes=self.timeframe_min)
-
-        self.progress.init_step(BacktestState.BACKTEST, int(
-            (end_date - start_date) / timedelta(minutes=self.timeframe_min)))
-
-        # Loop timerange and get candle for each pair at that point in time
-        while current_time <= end_date:
-            open_trade_count_start = LocalTrade.bt_open_open_trade_count
-            self.check_abort()
-            for i, pair in enumerate(data):
-                row_index = indexes[pair]
-                row = self.validate_row(data, pair, row_index, current_time)
-                if not row:
-                    continue
-
-                row_index += 1
-                indexes[pair] = row_index
-                self.dataprovider._set_dataframe_max_index(row_index)
-
-                open_trade_count_start = self.backtest_loop(
-                    row, pair, current_time, end_date, max_open_trades, open_trade_count_start)
+                        self.run_protections(
+                            enable_protections, pair, current_time, trade.trade_direction)
 
             # Move time one configured time_interval ahead.
             self.progress.increment()
             current_time += timedelta(minutes=self.timeframe_min)
 
-        self.handle_left_open(LocalTrade.bt_trades_open_pp, data=data)
+        trades += self.handle_left_open(open_trades, data=data)
         self.wallets.update()
 
-        results = trade_list_to_dataframe(LocalTrade.trades)
+        results = trade_list_to_dataframe(trades)
         return {
             'results': results,
             'config': self.strategy.config,
@@ -1245,6 +1248,8 @@ class Backtesting:
             start_date=min_date,
             end_date=max_date,
             max_open_trades=max_open_trades,
+            position_stacking=self.config.get('position_stacking', False),
+            enable_protections=self.config.get('enable_protections', False),
         )
         backtest_end_time = datetime.now(timezone.utc)
         results.update({

freqtrade/optimize/hyperopt.py

@@ -122,6 +122,7 @@ class Hyperopt:
         else:
             logger.debug('Ignoring max_open_trades (--disable-max-market-positions was used) ...')
             self.max_open_trades = 0
+        self.position_stacking = self.config.get('position_stacking', False)
 
         if HyperoptTools.has_space(self.config, 'sell'):
             # Make sure use_exit_signal is enabled
@@ -257,7 +258,6 @@ class Hyperopt:
             logger.debug("Hyperopt has 'protection' space")
             # Enable Protections if protection space is selected.
             self.config['enable_protections'] = True
-            self.backtesting.enable_protections = True
             self.protection_space = self.custom_hyperopt.protection_space()
 
         if HyperoptTools.has_space(self.config, 'buy'):
@@ -339,6 +339,8 @@ class Hyperopt:
             start_date=self.min_date,
             end_date=self.max_date,
             max_open_trades=self.max_open_trades,
+            position_stacking=self.position_stacking,
+            enable_protections=self.config.get('enable_protections', False),
         )
         backtest_end_time = datetime.now(timezone.utc)
         bt_results.update({

freqtrade/optimize/hyperopt_tools.py

@@ -12,7 +12,7 @@ import tabulate
 from colorama import Fore, Style
 from pandas import isna, json_normalize
 
-from freqtrade.constants import FTHYPT_FILEVERSION, Config
+from freqtrade.constants import FTHYPT_FILEVERSION, USERPATH_STRATEGIES, Config
 from freqtrade.enums import HyperoptState
 from freqtrade.exceptions import OperationalException
 from freqtrade.misc import deep_merge_dicts, round_coin_value, round_dict, safe_value_fallback2
@@ -50,8 +50,9 @@ class HyperoptTools():
         Get Strategy-location (filename) from strategy_name
         """
         from freqtrade.resolvers.strategy_resolver import StrategyResolver
+        directory = Path(config.get('strategy_path', config['user_data_dir'] / USERPATH_STRATEGIES))
         strategy_objs = StrategyResolver.search_all_objects(
-            config, False, config.get('recursive_strategy_search', False))
+            directory, False, config.get('recursive_strategy_search', False))
         strategies = [s for s in strategy_objs if s['name'] == strategy_name]
         if strategies:
             strategy = strategies[0]

freqtrade/optimize/optimize_reports.py

@@ -408,10 +408,10 @@ def generate_strategy_stats(pairlist: List[str],
 
     exit_reason_stats = generate_exit_reason_stats(max_open_trades=max_open_trades,
                                                    results=results)
-    left_open_results = generate_pair_metrics(
-        pairlist, stake_currency=stake_currency, starting_balance=start_balance,
-        results=results.loc[results['exit_reason'] == 'force_exit'], skip_nan=True)
-
+    left_open_results = generate_pair_metrics(pairlist, stake_currency=stake_currency,
+                                              starting_balance=start_balance,
+                                              results=results.loc[results['is_open']],
+                                              skip_nan=True)
     daily_stats = generate_daily_stats(results)
     trade_stats = generate_trading_stats(results)
     best_pair = max([pair for pair in pair_results if pair['key'] != 'TOTAL'],

freqtrade/persistence/trade_model.py

@@ -2,7 +2,6 @@
 This module contains the class to persist trades into SQLite
 """
 import logging
-from collections import defaultdict
 from datetime import datetime, timedelta, timezone
 from math import isclose
 from typing import Any, Dict, List, Optional
@@ -256,9 +255,6 @@ class LocalTrade():
     # Trades container for backtesting
     trades: List['LocalTrade'] = []
     trades_open: List['LocalTrade'] = []
-    # Copy of trades_open - but indexed by pair
-    bt_trades_open_pp: Dict[str, List['LocalTrade']] = defaultdict(list)
-    bt_open_open_trade_count: int = 0
     total_profit: float = 0
     realized_profit: float = 0
 
@@ -542,8 +538,6 @@ class LocalTrade():
         """
         LocalTrade.trades = []
         LocalTrade.trades_open = []
-        LocalTrade.bt_trades_open_pp = defaultdict(list)
-        LocalTrade.bt_open_open_trade_count = 0
         LocalTrade.total_profit = 0
 
     def adjust_min_max_rates(self, current_price: float, current_price_low: float) -> None:
@@ -1073,8 +1067,6 @@ class LocalTrade():
     @staticmethod
     def close_bt_trade(trade):
         LocalTrade.trades_open.remove(trade)
-        LocalTrade.bt_trades_open_pp[trade.pair].remove(trade)
-        LocalTrade.bt_open_open_trade_count -= 1
         LocalTrade.trades.append(trade)
         LocalTrade.total_profit += trade.close_profit_abs
 
@@ -1082,17 +1074,9 @@ class LocalTrade():
     def add_bt_trade(trade):
         if trade.is_open:
             LocalTrade.trades_open.append(trade)
-            LocalTrade.bt_trades_open_pp[trade.pair].append(trade)
-            LocalTrade.bt_open_open_trade_count += 1
         else:
             LocalTrade.trades.append(trade)
 
-    @staticmethod
-    def remove_bt_trade(trade):
-        LocalTrade.trades_open.remove(trade)
-        LocalTrade.bt_trades_open_pp[trade.pair].remove(trade)
-        LocalTrade.bt_open_open_trade_count -= 1
-
     @staticmethod
     def get_open_trades() -> List[Any]:
         """
@@ -1108,7 +1092,7 @@ class LocalTrade():
         if Trade.use_db:
             return Trade.query.filter(Trade.is_open.is_(True)).count()
         else:
-            return LocalTrade.bt_open_open_trade_count
+            return len(LocalTrade.trades_open)
 
     @staticmethod
     def stoploss_reinitialization(desired_stoploss):
@@ -1520,87 +1504,3 @@ class Trade(_DECL_BASE, LocalTrade):
             Order.status == 'closed'
         ).scalar()
         return trading_volume
-
-    @staticmethod
-    def from_json(json_str: str) -> 'Trade':
-        """
-        Create a Trade instance from a json string.
-
-        Used for debugging purposes - please keep.
-        :param json_str: json string to parse
-        :return: Trade instance
-        """
-        import rapidjson
-        data = rapidjson.loads(json_str)
-        trade = Trade(
-            id=data["trade_id"],
-            pair=data["pair"],
-            base_currency=data["base_currency"],
-            stake_currency=data["quote_currency"],
-            is_open=data["is_open"],
-            exchange=data["exchange"],
-            amount=data["amount"],
-            amount_requested=data["amount_requested"],
-            stake_amount=data["stake_amount"],
-            strategy=data["strategy"],
-            enter_tag=data["enter_tag"],
-            timeframe=data["timeframe"],
-            fee_open=data["fee_open"],
-            fee_open_cost=data["fee_open_cost"],
-            fee_open_currency=data["fee_open_currency"],
-            fee_close=data["fee_close"],
-            fee_close_cost=data["fee_close_cost"],
-            fee_close_currency=data["fee_close_currency"],
-            open_date=datetime.fromtimestamp(data["open_timestamp"] // 1000, tz=timezone.utc),
-            open_rate=data["open_rate"],
-            open_rate_requested=data["open_rate_requested"],
-            open_trade_value=data["open_trade_value"],
-            close_date=(datetime.fromtimestamp(data["close_timestamp"] // 1000, tz=timezone.utc)
-                        if data["close_timestamp"] else None),
-            realized_profit=data["realized_profit"],
-            close_rate=data["close_rate"],
-            close_rate_requested=data["close_rate_requested"],
-            close_profit=data["close_profit"],
-            close_profit_abs=data["close_profit_abs"],
-            exit_reason=data["exit_reason"],
-            exit_order_status=data["exit_order_status"],
-            stop_loss=data["stop_loss_abs"],
-            stop_loss_pct=data["stop_loss_ratio"],
-            stoploss_order_id=data["stoploss_order_id"],
-            stoploss_last_update=(datetime.fromtimestamp(data["stoploss_last_update"] // 1000,
-                                  tz=timezone.utc) if data["stoploss_last_update"] else None),
-            initial_stop_loss=data["initial_stop_loss_abs"],
-            initial_stop_loss_pct=data["initial_stop_loss_ratio"],
-            min_rate=data["min_rate"],
-            max_rate=data["max_rate"],
-            leverage=data["leverage"],
-            interest_rate=data["interest_rate"],
-            liquidation_price=data["liquidation_price"],
-            is_short=data["is_short"],
-            trading_mode=data["trading_mode"],
-            funding_fees=data["funding_fees"],
-            open_order_id=data["open_order_id"],
-        )
-        for order in data["orders"]:
-
-            order_obj = Order(
-                amount=order["amount"],
-                ft_order_side=order["ft_order_side"],
-                ft_pair=order["pair"],
-                ft_is_open=order["is_open"],
-                order_id=order["order_id"],
-                status=order["status"],
-                average=order["average"],
-                cost=order["cost"],
-                filled=order["filled"],
-                order_date=datetime.strptime(order["order_date"], DATETIME_PRINT_FORMAT),
-                order_filled_date=(datetime.fromtimestamp(
-                    order["order_filled_timestamp"] // 1000, tz=timezone.utc)
-                    if order["order_filled_timestamp"] else None),
-                order_type=order["order_type"],
-                price=order["price"],
-                remaining=order["remaining"],
-            )
-            trade.orders.append(order_obj)
-
-        return trade

freqtrade/plugins/pairlist/AgeFilter.py

@@ -10,7 +10,6 @@ from pandas import DataFrame
 
 from freqtrade.constants import Config, ListPairsWithTimeframes
 from freqtrade.exceptions import OperationalException
-from freqtrade.exchange.types import Tickers
 from freqtrade.misc import plural
 from freqtrade.plugins.pairlist.IPairList import IPairList
 from freqtrade.util import PeriodicCache
@@ -68,10 +67,10 @@ class AgeFilter(IPairList):
             f"{self._max_days_listed} {plural(self._max_days_listed, 'day')}"
         ) if self._max_days_listed else '')
 
-    def filter_pairlist(self, pairlist: List[str], tickers: Tickers) -> List[str]:
+    def filter_pairlist(self, pairlist: List[str], tickers: Dict) -> List[str]:
         """
         :param pairlist: pairlist to filter or sort
-        :param tickers: Tickers (from exchange.get_tickers). May be cached.
+        :param tickers: Tickers (from exchange.get_tickers()). May be cached.
         :return: new allowlist
         """
         needed_pairs: ListPairsWithTimeframes = [

freqtrade/plugins/pairlist/IPairList.py

@@ -4,12 +4,11 @@ PairList Handler base class
 import logging
 from abc import ABC, abstractmethod, abstractproperty
 from copy import deepcopy
-from typing import Any, Dict, List, Optional
+from typing import Any, Dict, List
 
 from freqtrade.constants import Config
 from freqtrade.exceptions import OperationalException
 from freqtrade.exchange import Exchange, market_is_active
-from freqtrade.exchange.types import Ticker, Tickers
 from freqtrade.mixins import LoggingMixin
 
 
@@ -62,7 +61,7 @@ class IPairList(LoggingMixin, ABC):
         -> Please overwrite in subclasses
         """
 
-    def _validate_pair(self, pair: str, ticker: Optional[Ticker]) -> bool:
+    def _validate_pair(self, pair: str, ticker: Dict[str, Any]) -> bool:
         """
         Check one pair against Pairlist Handler's specific conditions.
 
@@ -70,12 +69,12 @@ class IPairList(LoggingMixin, ABC):
         filter_pairlist() method.
 
         :param pair: Pair that's currently validated
-        :param ticker: ticker dict as returned from ccxt.fetch_ticker
+        :param ticker: ticker dict as returned from ccxt.fetch_tickers()
         :return: True if the pair can stay, false if it should be removed
         """
         raise NotImplementedError()
 
-    def gen_pairlist(self, tickers: Tickers) -> List[str]:
+    def gen_pairlist(self, tickers: Dict) -> List[str]:
         """
         Generate the pairlist.
 
@@ -86,13 +85,13 @@ class IPairList(LoggingMixin, ABC):
         it will raise the exception if a Pairlist Handler is used at the first
         position in the chain.
 
-        :param tickers: Tickers (from exchange.get_tickers). May be cached.
+        :param tickers: Tickers (from exchange.get_tickers()). May be cached.
         :return: List of pairs
         """
         raise OperationalException("This Pairlist Handler should not be used "
                                    "at the first position in the list of Pairlist Handlers.")
 
-    def filter_pairlist(self, pairlist: List[str], tickers: Tickers) -> List[str]:
+    def filter_pairlist(self, pairlist: List[str], tickers: Dict) -> List[str]:
         """
         Filters and sorts pairlist and returns the whitelist again.
 
@@ -104,14 +103,14 @@ class IPairList(LoggingMixin, ABC):
         own filtration.
 
         :param pairlist: pairlist to filter or sort
-        :param tickers: Tickers (from exchange.get_tickers). May be cached.
+        :param tickers: Tickers (from exchange.get_tickers()). May be cached.
         :return: new whitelist
         """
         if self._enabled:
             # Copy list since we're modifying this list
             for p in deepcopy(pairlist):
                 # Filter out assets
-                if not self._validate_pair(p, tickers[p] if p in tickers else None):
+                if not self._validate_pair(p, tickers[p] if p in tickers else {}):
                     pairlist.remove(p)
 
         return pairlist

freqtrade/plugins/pairlist/OffsetFilter.py

@@ -6,7 +6,6 @@ from typing import Any, Dict, List
 
 from freqtrade.constants import Config
 from freqtrade.exceptions import OperationalException
-from freqtrade.exchange.types import Tickers
 from freqtrade.plugins.pairlist.IPairList import IPairList
 
 
@@ -43,12 +42,12 @@ class OffsetFilter(IPairList):
             return f"{self.name} - Taking {self._number_pairs} Pairs, starting from {self._offset}."
         return f"{self.name} - Offsetting pairs by {self._offset}."
 
-    def filter_pairlist(self, pairlist: List[str], tickers: Tickers) -> List[str]:
+    def filter_pairlist(self, pairlist: List[str], tickers: Dict) -> List[str]:
         """
         Filters and sorts pairlist and returns the whitelist again.
         Called on each bot iteration - please use internal caching if necessary
         :param pairlist: pairlist to filter or sort
-        :param tickers: Tickers (from exchange.get_tickers). May be cached.
+        :param tickers: Tickers (from exchange.get_tickers()). May be cached.
         :return: new whitelist
         """
         if self._offset > len(pairlist):

freqtrade/plugins/pairlist/PerformanceFilter.py

@@ -7,7 +7,6 @@ from typing import Any, Dict, List
 import pandas as pd
 
 from freqtrade.constants import Config
-from freqtrade.exchange.types import Tickers
 from freqtrade.persistence import Trade
 from freqtrade.plugins.pairlist.IPairList import IPairList
 
@@ -40,12 +39,12 @@ class PerformanceFilter(IPairList):
         """
         return f"{self.name} - Sorting pairs by performance."
 
-    def filter_pairlist(self, pairlist: List[str], tickers: Tickers) -> List[str]:
+    def filter_pairlist(self, pairlist: List[str], tickers: Dict) -> List[str]:
         """
         Filters and sorts pairlist and returns the allowlist again.
         Called on each bot iteration - please use internal caching if necessary
         :param pairlist: pairlist to filter or sort
-        :param tickers: Tickers (from exchange.get_tickers). May be cached.
+        :param tickers: Tickers (from exchange.get_tickers()). May be cached.
         :return: new allowlist
         """
         # Get the trading performance for pairs from database

freqtrade/plugins/pairlist/PrecisionFilter.py

@@ -2,11 +2,10 @@
 Precision pair list filter
 """
 import logging
-from typing import Any, Dict, Optional
+from typing import Any, Dict
 
 from freqtrade.constants import Config
 from freqtrade.exceptions import OperationalException
-from freqtrade.exchange.types import Ticker
 from freqtrade.plugins.pairlist.IPairList import IPairList
 
 
@@ -45,15 +44,15 @@ class PrecisionFilter(IPairList):
         """
         return f"{self.name} - Filtering untradable pairs."
 
-    def _validate_pair(self, pair: str, ticker: Optional[Ticker]) -> bool:
+    def _validate_pair(self, pair: str, ticker: Dict[str, Any]) -> bool:
         """
         Check if pair has enough room to add a stoploss to avoid "unsellable" buys of very
         low value pairs.
         :param pair: Pair that's currently validated
-        :param ticker: ticker dict as returned from ccxt.fetch_ticker
+        :param ticker: ticker dict as returned from ccxt.fetch_tickers()
         :return: True if the pair can stay, false if it should be removed
         """
-        if not ticker or ticker.get('last', None) is None:
+        if ticker.get('last', None) is None:
             self.log_once(f"Removed {pair} from whitelist, because "
                           "ticker['last'] is empty (Usually no trade in the last 24h).",
                           logger.info)

freqtrade/plugins/pairlist/PriceFilter.py

@@ -2,11 +2,10 @@
 Price pair list filter
 """
 import logging
-from typing import Any, Dict, Optional
+from typing import Any, Dict
 
 from freqtrade.constants import Config
 from freqtrade.exceptions import OperationalException
-from freqtrade.exchange.types import Ticker
 from freqtrade.plugins.pairlist.IPairList import IPairList
 
 
@@ -65,16 +64,14 @@ class PriceFilter(IPairList):
 
         return f"{self.name} - No price filters configured."
 
-    def _validate_pair(self, pair: str, ticker: Optional[Ticker]) -> bool:
+    def _validate_pair(self, pair: str, ticker: Dict[str, Any]) -> bool:
         """
         Check if if one price-step (pip) is > than a certain barrier.
         :param pair: Pair that's currently validated
-        :param ticker: ticker dict as returned from ccxt.fetch_ticker
+        :param ticker: ticker dict as returned from ccxt.fetch_tickers()
         :return: True if the pair can stay, false if it should be removed
         """
-        if ticker and 'last' in ticker and ticker['last'] is not None and ticker.get('last') != 0:
-            price: float = ticker['last']
-        else:
+        if ticker.get('last', None) is None or ticker.get('last') == 0:
             self.log_once(f"Removed {pair} from whitelist, because "
                           "ticker['last'] is empty (Usually no trade in the last 24h).",
                           logger.info)
@@ -82,8 +79,8 @@ class PriceFilter(IPairList):
 
         # Perform low_price_ratio check.
         if self._low_price_ratio != 0:
-            compare = self._exchange.price_get_one_pip(pair, price)
-            changeperc = compare / price
+            compare = self._exchange.price_get_one_pip(pair, ticker['last'])
+            changeperc = compare / ticker['last']
             if changeperc > self._low_price_ratio:
                 self.log_once(f"Removed {pair} from whitelist, "
                               f"because 1 unit is {changeperc:.3%}", logger.info)
@@ -91,6 +88,7 @@ class PriceFilter(IPairList):
 
         # Perform low_amount check
         if self._max_value != 0:
+            price = ticker['last']
             market = self._exchange.markets[pair]
             limits = market['limits']
             if (limits['amount']['min'] is not None):
@@ -115,14 +113,14 @@ class PriceFilter(IPairList):
 
         # Perform min_price check.
         if self._min_price != 0:
-            if price < self._min_price:
+            if ticker['last'] < self._min_price:
                 self.log_once(f"Removed {pair} from whitelist, "
                               f"because last price < {self._min_price:.8f}", logger.info)
                 return False
 
         # Perform max_price check.
         if self._max_price != 0:
-            if price > self._max_price:
+            if ticker['last'] > self._max_price:
                 self.log_once(f"Removed {pair} from whitelist, "
                               f"because last price > {self._max_price:.8f}", logger.info)
                 return False

freqtrade/plugins/pairlist/ProducerPairList.py

@@ -7,7 +7,6 @@ import logging
 from typing import Any, Dict, List, Optional
 
 from freqtrade.exceptions import OperationalException
-from freqtrade.exchange.types import Tickers
 from freqtrade.plugins.pairlist.IPairList import IPairList
 
 
@@ -69,10 +68,10 @@ class ProducerPairList(IPairList):
 
         return pairs
 
-    def gen_pairlist(self, tickers: Tickers) -> List[str]:
+    def gen_pairlist(self, tickers: Dict) -> List[str]:
         """
         Generate the pairlist
-        :param tickers: Tickers (from exchange.get_tickers). May be cached.
+        :param tickers: Tickers (from exchange.get_tickers()). May be cached.
         :return: List of pairs
         """
         pairs = self._filter_pairlist(None)
@@ -80,12 +79,12 @@ class ProducerPairList(IPairList):
         pairs = self._whitelist_for_active_markets(self.verify_whitelist(pairs, logger.info))
         return pairs
 
-    def filter_pairlist(self, pairlist: List[str], tickers: Tickers) -> List[str]:
+    def filter_pairlist(self, pairlist: List[str], tickers: Dict) -> List[str]:
         """
         Filters and sorts pairlist and returns the whitelist again.
         Called on each bot iteration - please use internal caching if necessary
         :param pairlist: pairlist to filter or sort
-        :param tickers: Tickers (from exchange.get_tickers). May be cached.
+        :param tickers: Tickers (from exchange.get_tickers()). May be cached.
         :return: new whitelist
         """
         return self._filter_pairlist(pairlist)

freqtrade/plugins/pairlist/ShuffleFilter.py

@@ -7,7 +7,6 @@ from typing import Any, Dict, List
 
 from freqtrade.constants import Config
 from freqtrade.enums import RunMode
-from freqtrade.exchange.types import Tickers
 from freqtrade.plugins.pairlist.IPairList import IPairList
 
 
@@ -48,12 +47,12 @@ class ShuffleFilter(IPairList):
         return (f"{self.name} - Shuffling pairs" +
                 (f", seed = {self._seed}." if self._seed is not None else "."))
 
-    def filter_pairlist(self, pairlist: List[str], tickers: Tickers) -> List[str]:
+    def filter_pairlist(self, pairlist: List[str], tickers: Dict) -> List[str]:
         """
         Filters and sorts pairlist and returns the whitelist again.
         Called on each bot iteration - please use internal caching if necessary
         :param pairlist: pairlist to filter or sort
-        :param tickers: Tickers (from exchange.get_tickers). May be cached.
+        :param tickers: Tickers (from exchange.get_tickers()). May be cached.
         :return: new whitelist
         """
         # Shuffle is done inplace

freqtrade/plugins/pairlist/SpreadFilter.py

@@ -2,10 +2,10 @@
 Spread pair list filter
 """
 import logging
-from typing import Any, Dict, Optional
+from typing import Any, Dict
 
 from freqtrade.constants import Config
-from freqtrade.exchange.types import Ticker
+from freqtrade.exceptions import OperationalException
 from freqtrade.plugins.pairlist.IPairList import IPairList
 
 
@@ -22,6 +22,12 @@ class SpreadFilter(IPairList):
         self._max_spread_ratio = pairlistconfig.get('max_spread_ratio', 0.005)
         self._enabled = self._max_spread_ratio != 0
 
+        if not self._exchange.exchange_has('fetchTickers'):
+            raise OperationalException(
+                'Exchange does not support fetchTickers, therefore SpreadFilter cannot be used.'
+                'Please edit your config and restart the bot.'
+            )
+
     @property
     def needstickers(self) -> bool:
         """
@@ -38,14 +44,14 @@ class SpreadFilter(IPairList):
         return (f"{self.name} - Filtering pairs with ask/bid diff above "
                 f"{self._max_spread_ratio:.2%}.")
 
-    def _validate_pair(self, pair: str, ticker: Optional[Ticker]) -> bool:
+    def _validate_pair(self, pair: str, ticker: Dict[str, Any]) -> bool:
         """
         Validate spread for the ticker
         :param pair: Pair that's currently validated
-        :param ticker: ticker dict as returned from ccxt.fetch_ticker
+        :param ticker: ticker dict as returned from ccxt.fetch_tickers()
         :return: True if the pair can stay, false if it should be removed
         """
-        if ticker and 'bid' in ticker and 'ask' in ticker and ticker['ask'] and ticker['bid']:
+        if 'bid' in ticker and 'ask' in ticker and ticker['ask'] and ticker['bid']:
             spread = 1 - ticker['bid'] / ticker['ask']
             if spread > self._max_spread_ratio:
                 self.log_once(f"Removed {pair} from whitelist, because spread "

freqtrade/plugins/pairlist/StaticPairList.py

@@ -8,7 +8,6 @@ from copy import deepcopy
 from typing import Any, Dict, List
 
 from freqtrade.constants import Config
-from freqtrade.exchange.types import Tickers
 from freqtrade.plugins.pairlist.IPairList import IPairList
 
 
@@ -40,10 +39,10 @@ class StaticPairList(IPairList):
         """
         return f"{self.name}"
 
-    def gen_pairlist(self, tickers: Tickers) -> List[str]:
+    def gen_pairlist(self, tickers: Dict) -> List[str]:
         """
         Generate the pairlist
-        :param tickers: Tickers (from exchange.get_tickers). May be cached.
+        :param tickers: Tickers (from exchange.get_tickers()). May be cached.
         :return: List of pairs
         """
         if self._allow_inactive:
@@ -54,12 +53,12 @@ class StaticPairList(IPairList):
             return self._whitelist_for_active_markets(
                 self.verify_whitelist(self._config['exchange']['pair_whitelist'], logger.info))
 
-    def filter_pairlist(self, pairlist: List[str], tickers: Tickers) -> List[str]:
+    def filter_pairlist(self, pairlist: List[str], tickers: Dict) -> List[str]:
         """
         Filters and sorts pairlist and returns the whitelist again.
         Called on each bot iteration - please use internal caching if necessary
         :param pairlist: pairlist to filter or sort
-        :param tickers: Tickers (from exchange.get_tickers). May be cached.
+        :param tickers: Tickers (from exchange.get_tickers()). May be cached.
         :return: new whitelist
         """
         pairlist_ = deepcopy(pairlist)

freqtrade/plugins/pairlist/VolatilityFilter.py

@@ -13,7 +13,6 @@ from pandas import DataFrame
 
 from freqtrade.constants import Config, ListPairsWithTimeframes
 from freqtrade.exceptions import OperationalException
-from freqtrade.exchange.types import Tickers
 from freqtrade.misc import plural
 from freqtrade.plugins.pairlist.IPairList import IPairList
 
@@ -63,11 +62,11 @@ class VolatilityFilter(IPairList):
                 f"{self._min_volatility}-{self._max_volatility} "
                 f" the last {self._days} {plural(self._days, 'day')}.")
 
-    def filter_pairlist(self, pairlist: List[str], tickers: Tickers) -> List[str]:
+    def filter_pairlist(self, pairlist: List[str], tickers: Dict) -> List[str]:
         """
         Validate trading range
         :param pairlist: pairlist to filter or sort
-        :param tickers: Tickers (from exchange.get_tickers). May be cached.
+        :param tickers: Tickers (from exchange.get_tickers()). May be cached.
         :return: new allowlist
         """
         needed_pairs: ListPairsWithTimeframes = [

freqtrade/plugins/pairlist/VolumePairList.py

@@ -5,14 +5,13 @@ Provides dynamic pair list based on trade volumes
 """
 import logging
 from datetime import datetime, timedelta, timezone
-from typing import Any, Dict, List, Literal
+from typing import Any, Dict, List
 
 from cachetools import TTLCache
 
 from freqtrade.constants import Config, ListPairsWithTimeframes
 from freqtrade.exceptions import OperationalException
 from freqtrade.exchange import timeframe_to_minutes, timeframe_to_prev_date
-from freqtrade.exchange.types import Tickers
 from freqtrade.misc import format_ms_time
 from freqtrade.plugins.pairlist.IPairList import IPairList
 
@@ -37,7 +36,7 @@ class VolumePairList(IPairList):
 
         self._stake_currency = config['stake_currency']
         self._number_pairs = self._pairlistconfig['number_assets']
-        self._sort_key: Literal['quoteVolume'] = self._pairlistconfig.get('sort_key', 'quoteVolume')
+        self._sort_key = self._pairlistconfig.get('sort_key', 'quoteVolume')
         self._min_value = self._pairlistconfig.get('min_value', 0)
         self._refresh_period = self._pairlistconfig.get('refresh_period', 1800)
         self._pair_cache: TTLCache = TTLCache(maxsize=1, ttl=self._refresh_period)
@@ -111,10 +110,10 @@ class VolumePairList(IPairList):
         """
         return f"{self.name} - top {self._pairlistconfig['number_assets']} volume pairs."
 
-    def gen_pairlist(self, tickers: Tickers) -> List[str]:
+    def gen_pairlist(self, tickers: Dict) -> List[str]:
         """
         Generate the pairlist
-        :param tickers: Tickers (from exchange.get_tickers). May be cached.
+        :param tickers: Tickers (from exchange.get_tickers()). May be cached.
         :return: List of pairs
         """
         # Generate dynamic whitelist
@@ -151,7 +150,7 @@ class VolumePairList(IPairList):
         Filters and sorts pairlist and returns the whitelist again.
         Called on each bot iteration - please use internal caching if necessary
         :param pairlist: pairlist to filter or sort
-        :param tickers: Tickers (from exchange.get_tickers). May be cached.
+        :param tickers: Tickers (from exchange.get_tickers()). May be cached.
         :return: new whitelist
         """
         if self._use_range:

freqtrade/plugins/pairlist/pairlist_helpers.py

@@ -12,7 +12,7 @@ def expand_pairlist(wildcardpl: List[str], available_pairs: List[str],
     :param wildcardpl: List of Pairlists, which may contain regex
     :param available_pairs: List of all available pairs (`exchange.get_markets().keys()`)
     :param keep_invalid: If sets to True, drops invalid pairs silently while expanding regexes
-    :return: expanded pairlist, with Regexes from wildcardpl applied to match all available pairs.
+    :return expanded pairlist, with Regexes from wildcardpl applied to match all available pairs.
     :raises: ValueError if a wildcard is invalid (like '*/BTC' - which should be `.*/BTC`)
     """
     result = []

freqtrade/plugins/pairlist/rangestabilityfilter.py

@@ -11,7 +11,6 @@ from pandas import DataFrame
 
 from freqtrade.constants import Config, ListPairsWithTimeframes
 from freqtrade.exceptions import OperationalException
-from freqtrade.exchange.types import Tickers
 from freqtrade.misc import plural
 from freqtrade.plugins.pairlist.IPairList import IPairList
 
@@ -61,11 +60,11 @@ class RangeStabilityFilter(IPairList):
                 f"{self._min_rate_of_change}{max_rate_desc} over the "
                 f"last {plural(self._days, 'day')}.")
 
-    def filter_pairlist(self, pairlist: List[str], tickers: Tickers) -> List[str]:
+    def filter_pairlist(self, pairlist: List[str], tickers: Dict) -> List[str]:
         """
         Validate trading range
         :param pairlist: pairlist to filter or sort
-        :param tickers: Tickers (from exchange.get_tickers). May be cached.
+        :param tickers: Tickers (from exchange.get_tickers()). May be cached.
         :return: new allowlist
         """
         needed_pairs: ListPairsWithTimeframes = [

freqtrade/plugins/pairlistmanager.py

@@ -11,7 +11,6 @@ from freqtrade.constants import Config, ListPairsWithTimeframes
 from freqtrade.data.dataprovider import DataProvider
 from freqtrade.enums import CandleType
 from freqtrade.exceptions import OperationalException
-from freqtrade.exchange.types import Tickers
 from freqtrade.mixins import LoggingMixin
 from freqtrade.plugins.pairlist.IPairList import IPairList
 from freqtrade.plugins.pairlist.pairlist_helpers import expand_pairlist
@@ -46,15 +45,6 @@ class PairListManager(LoggingMixin):
         if not self._pairlist_handlers:
             raise OperationalException("No Pairlist Handlers defined")
 
-        if self._tickers_needed and not self._exchange.exchange_has('fetchTickers'):
-            invalid = ". ".join([p.name for p in self._pairlist_handlers if p.needstickers])
-
-            raise OperationalException(
-                "Exchange does not support fetchTickers, therefore the following pairlists "
-                "cannot be used. Please edit your config and restart the bot.\n"
-                f"{invalid}."
-            )
-
         refresh_period = config.get('pairlist_refresh_period', 3600)
         LoggingMixin.__init__(self, logger, refresh_period)
 
@@ -86,7 +76,7 @@ class PairListManager(LoggingMixin):
         return [{p.name: p.short_desc()} for p in self._pairlist_handlers]
 
     @cached(TTLCache(maxsize=1, ttl=1800))
-    def _get_cached_tickers(self) -> Tickers:
+    def _get_cached_tickers(self):
         return self._exchange.get_tickers()
 
     def refresh_pairlist(self) -> None:

freqtrade/resolvers/freqaimodel_resolver.py

@@ -26,7 +26,6 @@ class FreqaiModelResolver(IResolver):
     initial_search_path = (
         Path(__file__).parent.parent.joinpath("freqai/prediction_models").resolve()
     )
-    extra_path = "freqaimodel_path"
 
     @staticmethod
     def load_freqaimodel(config: Config) -> IFreqaiModel:
@@ -51,6 +50,7 @@ class FreqaiModelResolver(IResolver):
             freqaimodel_name,
             config,
             kwargs={"config": config},
+            extra_dir=config.get("freqaimodel_path"),
         )
 
         return freqaimodel

freqtrade/resolvers/iresolver.py

@@ -42,8 +42,6 @@ class IResolver:
     object_type_str: str
     user_subdir: Optional[str] = None
     initial_search_path: Optional[Path]
-    # Optional config setting containing a path (strategy_path, freqaimodel_path)
-    extra_path: Optional[str] = None
 
     @classmethod
     def build_search_paths(cls, config: Config, user_subdir: Optional[str] = None,
@@ -60,9 +58,6 @@ class IResolver:
         for dir in extra_dirs:
             abs_paths.insert(0, Path(dir).resolve())
 
-        if cls.extra_path and (extra := config.get(cls.extra_path)):
-            abs_paths.insert(0, Path(extra).resolve())
-
         return abs_paths
 
     @classmethod
@@ -188,35 +183,9 @@ class IResolver:
         )
 
     @classmethod
-    def search_all_objects(cls, config: Config, enum_failed: bool,
+    def search_all_objects(cls, directory: Path, enum_failed: bool,
                            recursive: bool = False) -> List[Dict[str, Any]]:
         """
-        Searches for valid objects
-        :param config: Config object
-        :param enum_failed: If True, will return None for modules which fail.
-            Otherwise, failing modules are skipped.
-        :param recursive: Recursively walk directory tree searching for strategies
-        :return: List of dicts containing 'name', 'class' and 'location' entries
-        """
-        result = []
-
-        abs_paths = cls.build_search_paths(config, user_subdir=cls.user_subdir)
-        for path in abs_paths:
-            result.extend(cls._search_all_objects(path, enum_failed, recursive))
-        return result
-
-    @classmethod
-    def _build_rel_location(cls, directory: Path, entry: Path) -> str:
-
-        builtin = cls.initial_search_path == directory
-        return f"<builtin>/{entry.relative_to(directory)}" if builtin else str(
-            entry.relative_to(directory))
-
-    @classmethod
-    def _search_all_objects(
-            cls, directory: Path, enum_failed: bool, recursive: bool = False,
-            basedir: Optional[Path] = None) -> List[Dict[str, Any]]:
-        """
         Searches a directory for valid objects
         :param directory: Path to search
         :param enum_failed: If True, will return None for modules which fail.
@@ -235,8 +204,7 @@ class IResolver:
                 and not entry.name.startswith('__')
                 and not entry.name.startswith('.')
             ):
-                objects.extend(cls._search_all_objects(
-                    entry, enum_failed, recursive, basedir or directory))
+                objects.extend(cls.search_all_objects(entry, enum_failed, recursive=recursive))
             # Only consider python files
             if entry.suffix != '.py':
                 logger.debug('Ignoring %s', entry)
@@ -249,6 +217,5 @@ class IResolver:
                     {'name': obj[0].__name__ if obj is not None else '',
                      'class': obj[0] if obj is not None else None,
                      'location': entry,
-                     'location_rel': cls._build_rel_location(basedir or directory, entry),
                      })
         return objects

freqtrade/resolvers/strategy_resolver.py

@@ -30,7 +30,6 @@ class StrategyResolver(IResolver):
     object_type_str = "Strategy"
     user_subdir = USERPATH_STRATEGIES
     initial_search_path = None
-    extra_path = "strategy_path"
 
     @staticmethod
     def load_strategy(config: Config = None) -> IStrategy:

freqtrade/rpc/api_server/api_backtest.py

@@ -89,7 +89,6 @@ async def api_start_backtest(bt_settings: BacktestRequest, background_tasks: Bac
             lastconfig['enable_protections'] = btconfig.get('enable_protections')
             lastconfig['dry_run_wallet'] = btconfig.get('dry_run_wallet')
 
-            ApiServer._bt.enable_protections = btconfig.get('enable_protections', False)
             ApiServer._bt.strategylist = [strat]
             ApiServer._bt.results = {}
             ApiServer._bt.load_prior_backtest()

freqtrade/rpc/api_server/api_v1.py

@@ -1,11 +1,13 @@
 import logging
 from copy import deepcopy
+from pathlib import Path
 from typing import List, Optional
 
 from fastapi import APIRouter, Depends, Query
 from fastapi.exceptions import HTTPException
 
 from freqtrade import __version__
+from freqtrade.constants import USERPATH_STRATEGIES
 from freqtrade.data.history import get_datahandler
 from freqtrade.enums import CandleType, TradingMode
 from freqtrade.exceptions import OperationalException
@@ -251,9 +253,11 @@ def plot_config(rpc: RPC = Depends(get_rpc)):
 
 @router.get('/strategies', response_model=StrategyListResponse, tags=['strategy'])
 def list_strategies(config=Depends(get_config)):
+    directory = Path(config.get(
+        'strategy_path', config['user_data_dir'] / USERPATH_STRATEGIES))
     from freqtrade.resolvers.strategy_resolver import StrategyResolver
     strategies = StrategyResolver.search_all_objects(
-        config, False, config.get('recursive_strategy_search', False))
+        directory, False, config.get('recursive_strategy_search', False))
     strategies = sorted(strategies, key=lambda x: x['name'])
 
     return {'strategies': [x['name'] for x in strategies]}

freqtrade/rpc/api_server/api_ws.py

@@ -4,13 +4,11 @@ from typing import Any, Dict
 from fastapi import APIRouter, Depends, WebSocketDisconnect
 from fastapi.websockets import WebSocket, WebSocketState
 from pydantic import ValidationError
-from websockets.exceptions import WebSocketException
 
 from freqtrade.enums import RPCMessageType, RPCRequestType
 from freqtrade.rpc.api_server.api_auth import validate_ws_token
 from freqtrade.rpc.api_server.deps import get_channel_manager, get_rpc
 from freqtrade.rpc.api_server.ws import WebSocketChannel
-from freqtrade.rpc.api_server.ws.channel import ChannelManager
 from freqtrade.rpc.api_server.ws_schemas import (WSAnalyzedDFMessage, WSMessageSchema,
                                                  WSRequestSchema, WSWhitelistMessage)
 from freqtrade.rpc.rpc import RPC
@@ -37,8 +35,7 @@ async def is_websocket_alive(ws: WebSocket) -> bool:
 async def _process_consumer_request(
     request: Dict[str, Any],
     channel: WebSocketChannel,
-    rpc: RPC,
-    channel_manager: ChannelManager
+    rpc: RPC
 ):
     """
     Validate and handle a request from a websocket consumer
@@ -75,7 +72,7 @@ async def _process_consumer_request(
         # Format response
         response = WSWhitelistMessage(data=whitelist)
         # Send it back
-        await channel_manager.send_direct(channel, response.dict(exclude_none=True))
+        await channel.send(response.dict(exclude_none=True))
 
     elif type == RPCRequestType.ANALYZED_DF:
         limit = None
@@ -90,7 +87,7 @@ async def _process_consumer_request(
         # For every dataframe, send as a separate message
         for _, message in analyzed_df.items():
             response = WSAnalyzedDFMessage(data=message)
-            await channel_manager.send_direct(channel, response.dict(exclude_none=True))
+            await channel.send(response.dict(exclude_none=True))
 
 
 @router.websocket("/message/ws")
@@ -105,6 +102,7 @@ async def message_endpoint(
     """
     try:
         channel = await channel_manager.on_connect(ws)
+
         if await is_websocket_alive(ws):
 
             logger.info(f"Consumer connected - {channel}")
@@ -115,33 +113,28 @@ async def message_endpoint(
                     request = await channel.recv()
 
                     # Process the request here
-                    await _process_consumer_request(request, channel, rpc, channel_manager)
+                    await _process_consumer_request(request, channel, rpc)
 
-            except (WebSocketDisconnect, WebSocketException):
+            except WebSocketDisconnect:
                 # Handle client disconnects
                 logger.info(f"Consumer disconnected - {channel}")
-            except RuntimeError:
+                await channel_manager.on_disconnect(ws)
+            except Exception as e:
+                logger.info(f"Consumer connection failed - {channel}")
+                logger.exception(e)
                 # Handle cases like -
                 # RuntimeError('Cannot call "send" once a closed message has been sent')
-                pass
-            except Exception as e:
-                logger.info(f"Consumer connection failed - {channel}: {e}")
-                logger.debug(e, exc_info=e)
-            finally:
                 await channel_manager.on_disconnect(ws)
 
         else:
-            if channel:
-                await channel_manager.on_disconnect(ws)
             await ws.close()
 
     except RuntimeError:
         # WebSocket was closed
-        # Do nothing
-        pass
+        await channel_manager.on_disconnect(ws)
+
     except Exception as e:
         logger.error(f"Failed to serve - {ws.client}")
         # Log tracebacks to keep track of what errors are happening
         logger.exception(e)
-    finally:
         await channel_manager.on_disconnect(ws)

freqtrade/rpc/api_server/webserver.py

@@ -16,7 +16,6 @@ from freqtrade.constants import Config
 from freqtrade.exceptions import OperationalException
 from freqtrade.rpc.api_server.uvicorn_threaded import UvicornServer
 from freqtrade.rpc.api_server.ws import ChannelManager
-from freqtrade.rpc.api_server.ws_schemas import WSMessageSchemaType
 from freqtrade.rpc.rpc import RPC, RPCException, RPCHandler
 
 
@@ -128,7 +127,7 @@ class ApiServer(RPCHandler):
         cls._has_rpc = False
         cls._rpc = None
 
-    def send_msg(self, msg: Dict[str, Any]) -> None:
+    def send_msg(self, msg: Dict[str, str]) -> None:
         if self._ws_queue:
             sync_q = self._ws_queue.sync_q
             sync_q.put(msg)
@@ -195,10 +194,14 @@ class ApiServer(RPCHandler):
             while True:
                 logger.debug("Getting queue messages...")
                 # Get data from queue
-                message: WSMessageSchemaType = await async_queue.get()
+                message = await async_queue.get()
                 logger.debug(f"Found message of type: {message.get('type')}")
                 # Broadcast it
                 await self._ws_channel_manager.broadcast(message)
+                # Limit messages per sec.
+                # Could cause problems with queue size if too low, and
+                # problems with network traffik if too high.
+                await asyncio.sleep(0.001)
         except asyncio.CancelledError:
             pass
 
@@ -242,7 +245,6 @@ class ApiServer(RPCHandler):
                                   use_colors=False,
                                   log_config=None,
                                   access_log=True if verbosity != 'error' else False,
-                                  ws_ping_interval=None  # We do this explicitly ourselves
                                   )
         try:
             self._server = UvicornServer(uvconfig)

freqtrade/rpc/api_server/ws/channel.py

@@ -1,7 +1,6 @@
-import asyncio
 import logging
 from threading import RLock
-from typing import Any, Dict, List, Optional, Type, Union
+from typing import List, Optional, Type
 from uuid import uuid4
 
 from fastapi import WebSocket as FastAPIWebSocket
@@ -10,7 +9,6 @@ from freqtrade.rpc.api_server.ws.proxy import WebSocketProxy
 from freqtrade.rpc.api_server.ws.serializer import (HybridJSONWebSocketSerializer,
                                                     WebSocketSerializer)
 from freqtrade.rpc.api_server.ws.types import WebSocketType
-from freqtrade.rpc.api_server.ws_schemas import WSMessageSchemaType
 
 
 logger = logging.getLogger(__name__)
@@ -25,8 +23,6 @@ class WebSocketChannel:
         self,
         websocket: WebSocketType,
         channel_id: Optional[str] = None,
-        drain_timeout: int = 3,
-        throttle: float = 0.01,
         serializer_cls: Type[WebSocketSerializer] = HybridJSONWebSocketSerializer
     ):
 
@@ -37,13 +33,7 @@ class WebSocketChannel:
         # The Serializing class for the WebSocket object
         self._serializer_cls = serializer_cls
 
-        self.drain_timeout = drain_timeout
-        self.throttle = throttle
-
         self._subscriptions: List[str] = []
-        # 32 is the size of the receiving queue in websockets package
-        self.queue: asyncio.Queue[Dict[str, Any]] = asyncio.Queue(maxsize=32)
-        self._relay_task = asyncio.create_task(self.relay())
 
         # Internal event to signify a closed websocket
         self._closed = False
@@ -54,34 +44,16 @@ class WebSocketChannel:
     def __repr__(self):
         return f"WebSocketChannel({self.channel_id}, {self.remote_addr})"
 
-    @property
-    def raw_websocket(self):
-        return self._websocket.raw_websocket
-
     @property
     def remote_addr(self):
         return self._websocket.remote_addr
 
-    async def _send(self, data):
+    async def send(self, data):
         """
         Send data on the wrapped websocket
         """
         await self._wrapped_ws.send(data)
 
-    async def send(self, data) -> bool:
-        """
-        Add the data to the queue to be sent.
-        :returns: True if data added to queue, False otherwise
-        """
-        try:
-            await asyncio.wait_for(
-                self.queue.put(data),
-                timeout=self.drain_timeout
-            )
-            return True
-        except asyncio.TimeoutError:
-            return False
-
     async def recv(self):
         """
         Receive data on the wrapped websocket
@@ -100,7 +72,6 @@ class WebSocketChannel:
         """
 
         self._closed = True
-        self._relay_task.cancel()
 
     def is_closed(self) -> bool:
         """
@@ -124,26 +95,6 @@ class WebSocketChannel:
         """
         return message_type in self._subscriptions
 
-    async def relay(self):
-        """
-        Relay messages from the channel's queue and send them out. This is started
-        as a task.
-        """
-        while True:
-            message = await self.queue.get()
-            try:
-                await self._send(message)
-                self.queue.task_done()
-
-                # Limit messages per sec.
-                # Could cause problems with queue size if too low, and
-                # problems with network traffik if too high.
-                # 0.01 = 100/s
-                await asyncio.sleep(self.throttle)
-            except RuntimeError:
-                # The connection was closed, just exit the task
-                return
-
 
 class ChannelManager:
     def __init__(self):
@@ -179,7 +130,6 @@ class ChannelManager:
         with self._lock:
             channel = self.channels.get(websocket)
             if channel:
-                logger.info(f"Disconnecting channel {channel}")
                 if not channel.is_closed():
                     await channel.close()
 
@@ -190,30 +140,36 @@ class ChannelManager:
         Disconnect all Channels
         """
         with self._lock:
-            for websocket in self.channels.copy().keys():
-                await self.on_disconnect(websocket)
+            for websocket, channel in self.channels.copy().items():
+                if not channel.is_closed():
+                    await channel.close()
 
-    async def broadcast(self, message: WSMessageSchemaType):
+            self.channels = dict()
+
+    async def broadcast(self, data):
         """
-        Broadcast a message on all Channels
+        Broadcast data on all Channels
 
-        :param message: The message to send
+        :param data: The data to send
         """
         with self._lock:
-            for channel in self.channels.copy().values():
-                if channel.subscribed_to(message.get('type')):
-                    await self.send_direct(channel, message)
+            message_type = data.get('type')
+            for websocket, channel in self.channels.copy().items():
+                try:
+                    if channel.subscribed_to(message_type):
+                        await channel.send(data)
+                except RuntimeError:
+                    # Handle cannot send after close cases
+                    await self.on_disconnect(websocket)
 
-    async def send_direct(
-            self, channel: WebSocketChannel, message: Union[WSMessageSchemaType, Dict[str, Any]]):
+    async def send_direct(self, channel, data):
         """
-        Send a message directly through direct_channel only
+        Send data directly through direct_channel only
 
-        :param direct_channel: The WebSocketChannel object to send the message through
-        :param message: The message to send
+        :param direct_channel: The WebSocketChannel object to send data through
+        :param data: The data to send
         """
-        if not await channel.send(message):
-            await self.on_disconnect(channel.raw_websocket)
+        await channel.send(data)
 
     def has_channels(self):
         """

freqtrade/rpc/api_server/ws/proxy.py

@@ -15,10 +15,6 @@ class WebSocketProxy:
     def __init__(self, websocket: WebSocketType):
         self._websocket: Union[FastAPIWebSocket, WebSocket] = websocket
 
-    @property
-    def raw_websocket(self):
-        return self._websocket
-
     @property
     def remote_addr(self) -> Tuple[Any, ...]:
         if isinstance(self._websocket, WebSocket):

freqtrade/rpc/api_server/ws_schemas.py

@@ -1,5 +1,5 @@
 from datetime import datetime
-from typing import Any, Dict, List, Optional, TypedDict
+from typing import Any, Dict, List, Optional
 
 from pandas import DataFrame
 from pydantic import BaseModel
@@ -18,12 +18,6 @@ class WSRequestSchema(BaseArbitraryModel):
     data: Optional[Any] = None
 
 
-class WSMessageSchemaType(TypedDict):
-    # Type for typing to avoid doing pydantic typechecks.
-    type: RPCMessageType
-    data: Optional[Dict[str, Any]]
-
-
 class WSMessageSchema(BaseArbitraryModel):
     type: RPCMessageType
     data: Optional[Any] = None

freqtrade/rpc/discord.py

@@ -11,12 +11,13 @@ logger = logging.getLogger(__name__)
 
 class Discord(Webhook):
     def __init__(self, rpc: 'RPC', config: Config):
-        self._config = config
+        # super().__init__(rpc, config)
         self.rpc = rpc
+        self.config = config
         self.strategy = config.get('strategy', '')
         self.timeframe = config.get('timeframe', '')
 
-        self._url = config['discord']['webhook_url']
+        self._url = self.config['discord']['webhook_url']
         self._format = 'json'
         self._retries = 1
         self._retry_delay = 0.1
@@ -30,21 +31,19 @@ class Discord(Webhook):
 
     def send_msg(self, msg) -> None:
 
-        if msg['type'].value in self._config['discord']:
+        if msg['type'].value in self.config['discord']:
             logger.info(f"Sending discord message: {msg}")
 
             msg['strategy'] = self.strategy
             msg['timeframe'] = self.timeframe
-            fields = self._config['discord'].get(msg['type'].value)
+            fields = self.config['discord'].get(msg['type'].value)
             color = 0x0000FF
             if msg['type'] in (RPCMessageType.EXIT, RPCMessageType.EXIT_FILL):
                 profit_ratio = msg.get('profit_ratio')
                 color = (0x00FF00 if profit_ratio > 0 else 0xFF0000)
-            title = msg['type'].value
-            if 'pair' in msg:
-                title = f"Trade: {msg['pair']} {msg['type'].value}"
+
             embeds = [{
-                'title': title,
+                'title': f"Trade: {msg['pair']} {msg['type'].value}",
                 'color': color,
                 'fields': [],
 
@@ -52,7 +51,7 @@ class Discord(Webhook):
             for f in fields:
                 for k, v in f.items():
                     v = v.format(**msg)
-                    embeds[0]['fields'].append(
+                    embeds[0]['fields'].append(  # type: ignore
                         {'name': k, 'value': v, 'inline': True})
 
             # Send the message to discord channel

freqtrade/rpc/external_message_consumer.py

@@ -62,7 +62,7 @@ class ExternalMessageConsumer:
         self.enabled = self._emc_config.get('enabled', False)
         self.producers: List[Producer] = self._emc_config.get('producers', [])
 
-        self.wait_timeout = self._emc_config.get('wait_timeout', 30)  # in seconds
+        self.wait_timeout = self._emc_config.get('wait_timeout', 300)  # in seconds
         self.ping_timeout = self._emc_config.get('ping_timeout', 10)  # in seconds
         self.sleep_time = self._emc_config.get('sleep_time', 10)  # in seconds
 
@@ -174,7 +174,6 @@ class ExternalMessageConsumer:
         :param producer: Dictionary containing producer info
         :param lock: An asyncio Lock
         """
-        channel = None
         while self._running:
             try:
                 host, port = producer['host'], producer['port']
@@ -183,11 +182,7 @@ class ExternalMessageConsumer:
                 ws_url = f"ws://{host}:{port}/api/v1/message/ws?token={token}"
 
                 # This will raise InvalidURI if the url is bad
-                async with websockets.connect(
-                    ws_url,
-                    max_size=self.message_size_limit,
-                    ping_interval=None
-                ) as ws:
+                async with websockets.connect(ws_url, max_size=self.message_size_limit) as ws:
                     channel = WebSocketChannel(ws, channel_id=name)
 
                     logger.info(f"Producer connection success - {channel}")
@@ -229,10 +224,6 @@ class ExternalMessageConsumer:
                 logger.exception(e)
                 continue
 
-            finally:
-                if channel:
-                    await channel.close()
-
     async def _receive_messages(
         self,
         channel: WebSocketChannel,
@@ -270,11 +261,6 @@ class ExternalMessageConsumer:
                     logger.debug(f"Connection to {channel} still alive...")
 
                     continue
-                except (websockets.exceptions.ConnectionClosed):
-                    # Just eat the error and continue reconnecting
-                    logger.warning(f"Disconnection in {channel} - retrying in {self.sleep_time}s")
-                    await asyncio.sleep(self.sleep_time)
-                    break
                 except Exception as e:
                     logger.warning(f"Ping error {channel} - retrying in {self.sleep_time}s")
                     logger.debug(e, exc_info=e)