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Intelligence

Source refactorer

  • Use the source refactorer to automatically enhance any Python code, whether it is a full package, Python object, or raw string. If the source is larger than expected, it intelligently splits it into clean AST-based chunks, refactors each using an LLM, and merges them back into polished code. You can choose to return the result, update the source in place, or copy it to your clipboard. For full transparency, you can also preview the diff directly in your browser 📝
Complete a signal function
>>> env["GITHUB_TOKEN"] = "<YOUR_GITHUB_TOKEN>"
>>> env["OPENAI_API_KEY"] = "<YOUR_OPENAI_API_KEY>"

>>> source = """
... @njit
... def signal_func_nb(c, entries, exits):
...     is_entry = vbt.pf_nb.select_nb(c, entries)
...     is_exit = vbt.pf_nb.select_nb(c, exits)
...     
...     # TODO: Enter only if no other asset is active
...
...     return is_entry, is_exit, False, False
... """  # (1)!

>>> new_source = vbt.refactor_source(
...     source, 
...     attach_knowledge=True,  # (2)!
...     model="o3-mini",
...     reasoning_effort="high",
...     system_as_user=True,
...     show_diff=True,  # (3)!
... )
>>> print(new_source)

@njit
def signal_func_nb(c, entries, exits):
    is_entry = vbt.pf_nb.select_nb(c, entries)
    is_exit = vbt.pf_nb.select_nb(c, exits)
    for col in range(c.from_col, c.to_col):
        if col != c.col and c.last_position[col] != 0:
            is_entry = False
            break
    return is_entry, is_exit, False, False
  1. Use TODO or FIXME comments to provide custom instructions.
  2. Attach relevant knowledge from the website and Discord.
  3. Show the produced changes in a browser.

Quick search & chat

  • In addition to embeddings, which require prior generation, VBT now supports BM25 for fast, fully offline lexical search. This is perfect for quickly finding something specific.
Search VBT knowledge for a warning
>>> env["GITHUB_TOKEN"] = "<YOUR_GITHUB_TOKEN>"

>>> vbt.quick_search("UserWarning: Symbols have mismatching index")

ChatVBT

  • Similar to SearchVBT, ChatVBT takes search results and forwards them to an LLM for completion. This allows you to interact seamlessly with the entire VBT knowledge base, receiving detailed and context-aware responses.

Info

The first time you run this command, it may take up to 15 minutes to prepare and embed documents. However, most of the preparation steps are cached and stored, so future searches will be much faster and will not require repeating the process.

Ask VBT a question using ChatGPT
>>> env["GITHUB_TOKEN"] = "<YOUR_GITHUB_TOKEN>"
>>> env["OPENAI_API_KEY"] = "<YOUR_OPENAI_API_KEY>"

>>> vbt.chat("How to rebalance weekly?", formatter="html")
Ask VBT a question using a high-reasoning ChatGPT
>>> env["GITHUB_TOKEN"] = "<YOUR_GITHUB_TOKEN>"
>>> env["OPENAI_API_KEY"] = "<YOUR_OPENAI_API_KEY>"

>>> vbt.settings.set(
...     "knowledge.chat.completions_configs.openai.model", 
...     "o3-mini"  # (1)!
... )
>>> vbt.settings.set(
...     "knowledge.chat.completions_configs.openai.reasoning_effort", 
...     "high"  # (2)!
... )
>>> vbt.settings.set(
...     "knowledge.chat.completions_configs.openai.system_as_user", 
...     True  # (3)!
... )

>>> vbt.chat("How to rebalance weekly?", formatter="html")
  1. Discover more models. Note that the availability of this model depends on your tier. If your tier does not support o3-mini, try another model, such as o1-mini.
  2. Do not forget to update openai to the latest version.
  3. Some models do not support system prompts.
Ask VBT a question using DeepSeek R1
>>> env["GITHUB_TOKEN"] = "<YOUR_GITHUB_TOKEN>"
>>> env["OPENAI_API_KEY"] = "<YOUR_OPENAI_API_KEY>"  # (1)!
>>> env["OPENROUTER_API_KEY"] = "<YOUR_OPENROUTER_API_KEY>"

>>> vbt.settings.set(
...     "knowledge.chat.completions_configs.openai.base_url", 
...     "https://openrouter.ai/api/v1"
... )
>>> vbt.settings.set(
...     "knowledge.chat.completions_configs.openai.api_key", 
...     env["OPENROUTER_API_KEY"]
... )
>>> vbt.settings.set(
...     "knowledge.chat.completions_configs.openai.model", 
...     "deepseek/deepseek-r1"  # (2)!
... )

>>> vbt.chat("How to rebalance weekly?", formatter="html")
  1. Required for embeddings.
  2. Discover more models.
Ask VBT a question using Claude 3.5 Sonnet
>>> env["GITHUB_TOKEN"] = "<YOUR_GITHUB_TOKEN>"
>>> env["OPENAI_API_KEY"] = "<YOUR_OPENAI_API_KEY>"  # (1)!
>>> env["ANTHROPIC_API_KEY"] = "<YOUR_ANTHROPIC_API_KEY>"

>>> vbt.settings.set(
...     "knowledge.chat.completions", 
...     "litellm"
... )
>>> vbt.settings.set(
...     "knowledge.chat.completions_configs.litellm.model", 
...     "anthropic/claude-3-5-sonnet-20241022"  # (2)!
... )

>>> vbt.chat("How to rebalance weekly?", formatter="html")
  1. Required for embeddings.
  2. Discover more models.

Note

Make sure you have the required hardware

The Hugging Face extension for LlamaIndex is required for embeddings and LLMs. Please ensure it is installed.

Ask VBT a question using DeepSeek R1 (locally)
>>> env["GITHUB_TOKEN"] = "<YOUR_GITHUB_TOKEN>"

>>> vbt.settings.set(
...     "knowledge.chat.embeddings", 
...     "llama_index"
... )
>>> vbt.settings.set(
...     "knowledge.chat.embeddings_configs.llama_index.embedding", 
...     "huggingface"
... )
>>> vbt.settings.set(
...     "knowledge.chat.embeddings_configs.llama_index.model_name", 
...     "BAAI/bge-small-en-v1.5"  # (1)!
... )
>>> vbt.settings.set(
...     "knowledge.chat.completions", 
...     "llama_index"
... )
>>> vbt.settings.set(
...     "knowledge.chat.completions_configs.llama_index.llm", 
...     "huggingface"
... )
>>> vbt.settings.set(
...     "knowledge.chat.completions_configs.llama_index.model_name", 
...     "deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B"  # (2)!
... )
>>> vbt.settings.set(
...     "knowledge.chat.completions_configs.llama_index.tokenizer_name", 
...     "deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B"
... )
>>> vbt.settings.set(
...     "knowledge.chat.rank_kwargs.dataset_id", 
...     "local"
... )

>>> vbt.chat("How to rebalance weekly?", formatter="html")
  1. Discover more embedding models.
  2. Discover more DeepSeek models.

SearchVBT

  • Want to find specific information using natural language on the website or Discord? VBT provides a powerful smart search feature called SearchVBT. Enter your query and it will generate an HTML page with well-structured search results. Behind the scenes, SearchVBT uses a RAG pipeline to embed, rank, and retrieve only the most relevant documents from VBT, ensuring precise and efficient search results.

Info

The first time you run this command, it may take up to 15 minutes to prepare and embed documents. However, most preparation steps are cached and stored, so future searches will be significantly faster without needing to repeat the process.

Search VBT knowledge for a warning
>>> env["GITHUB_TOKEN"] = "<YOUR_GITHUB_TOKEN>"
>>> env["OPENAI_API_KEY"] = "<YOUR_API_KEY>"

>>> vbt.search("How to run indicator expressions?")

Self-aware classes

  • Each VBT class offers methods to explore its features, including its API, associated documentation, Discord messages, and code examples. You can even interact with it directly via an LLM!
Ask portfolio optimizer a question
>>> env["GITHUB_TOKEN"] = "<YOUR_GITHUB_TOKEN>"
>>> env["OPENAI_API_KEY"] = "<YOUR_API_KEY>"

>>> vbt.PortfolioOptimizer.find_assets().get("link")
['https://vectorbt.pro/pvt_xxxxxxxx/api/portfolio/pfopt/base/#vectorbtpro.portfolio.pfopt.base',
 'https://vectorbt.pro/pvt_xxxxxxxx/api/generic/analyzable/#vectorbtpro.generic.analyzable.Analyzable',
 'https://vectorbt.pro/pvt_xxxxxxxx/api/base/wrapping/#vectorbtpro.base.wrapping.Wrapping',
 ...
 'https://vectorbt.pro/pvt_xxxxxxxx/features/optimization/#riskfolio-lib',
 'https://vectorbt.pro/pvt_xxxxxxxx/features/optimization/#portfolio-optimization',
 'https://vectorbt.pro/pvt_xxxxxxxx/features/optimization/#pyportfolioopt',
 ...
 'https://discord.com/channels/x/918629995415502888/1064943203071045753',
 'https://discord.com/channels/x/918629995415502888/1067718833646874634',
 'https://discord.com/channels/x/918629995415502888/1067718855734075403',
 ...]

>>> vbt.PortfolioOptimizer.chat("How to rebalance weekly?", formatter="html")

Knowledge assets

  • Each release now includes valuable knowledge assets—JSON files containing private website content and the complete "vectorbt.pro" Discord history. These assets can be used with LLMs and services like Cursor. In addition, VBT offers a palette of classes for working with these assets, providing functions such as converting to Markdown and HTML files, browsing the website offline, performing targeted searches, interacting with LLMs, and much more!
Gather all pages and messages that define signal_func_nb
>>> env["GITHUB_TOKEN"] = "<YOUR_GITHUB_TOKEN>"

>>> pages_asset = vbt.PagesAsset.pull()  # (1)!
>>> messages_asset = vbt.MessagesAsset.pull()
>>> vbt_asset = pages_asset + messages_asset
>>> code = vbt_asset.find_code("def signal_func_nb", return_type="item")
>>> code.print_sample()  # (2)!
  1. The first pull will download the assets, while subsequent pulls will use the cached versions.
  2. Print a random code snippet.
link: https://discord.com/channels/x/918630948248125512/1251081573147742298
block: https://discord.com/channels/x/918630948248125512/1251081573147742298
thread: https://discord.com/channels/x/918630948248125512/1250844139952541837
reference: https://discord.com/channels/x/918630948248125512/1250844139952541837
replies:
- https://discord.com/channels/x/918630948248125512/1251083513336299610
channel: support
timestamp: '2024-06-14 07:51:31'
author: '@polakowo'
content: Something like this
mentions:
- '@fei'
attachments:
- file_name: Screenshot_2024-06-13_at_20.29.45-B4517.png
  content: |-
    Here's the text extracted from the image:

    ```python
    @njit
    def signal_func_nb(c, entries, exits, wait):
        is_entry = vbt.pf_nb.select_nb(c, entries)
        if is_entry:
            return True, False, False, False
        is_exit = vbt.pf_nb.select_nb(c, exits)
        if is_exit:
            if vbt.pf_nb.in_position_nb(c):
                last_order = vbt.pf_nb.get_last_order_nb(c)
                if c.index[c.i] - c.index[last_order["idx"]] >= wait:
                    return False, True, False, False
        return False, False, False, False

    pf = vbt.PF.from_random_signals(
        "BTC-USD",
        n=100,
        signal_func_nb=signal_func_nb,
        signal_args=(
            vbt.Rep("entries"),
            vbt.Rep("exits"),
            vbt.dt.to_ns(vbt.timedelta("1000 days"))
        )
    )
    ```
reactions: 0

And many more...

  • Look forward to more killer features being added every week!