Trading Strategy Python API Docs | dltHub

Trading Strategy is a market-data platform and API that provides historical and real-time DeFi/DEX market data, streaming JSONL candle feeds, dataset downloads, and programmatic access for backtesting and live trading. The REST API base URL is https://tradingstrategy.ai/api and Some endpoints (backtesting and large dataset downloads) require an API key; real‑time endpoints are public..

dlt is an open-source Python library that handles authentication, pagination, and schema evolution automatically. dlthub provides AI context files that enable code assistants to generate production-ready pipelines. Install with uv pip install "dlt[workspace]" and start loading Trading Strategy data in under 10 minutes.

---

What data can I load from Trading Strategy?

Here are some of the endpoints you can load from Trading Strategy:

---

How do I authenticate with the Trading Strategy API?

Real‑time endpoints are publicly accessible without authentication. Backtesting and large dataset downloads require an API key supplied as a query parameter or header as described in the provider dashboard.

1. Get your credentials

1. Sign up or log in at tradingstrategy.ai.
2. Open the API or developer settings in your dashboard.
3. Create a new API key or view an existing one.
4. Copy the key and use it with the authenticated endpoints as documented.

2. Add them to .dlt/secrets.toml

Copy
[sources.trading_strategy_source]
api_key = "your_api_key_here"

dlt reads this automatically at runtime — never hardcode tokens in your pipeline script. For production environments, see setting up credentials with dlt for environment variable and vault-based options.

---

How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

Copy
uv venv && source .venv/bin/activate
uv pip install "dlt[workspace]"

1. Install the dlt AI Workbench:

Copy
dlt ai init --agent <your-agent> # <agent>: claude | cursor | codex

This installs project rules, a secrets management skill, appropriate ignore files, and configures the dlt MCP server for your agent. Learn more →

2. Install the rest-api-pipeline toolkit:

Copy
dlt ai toolkit rest-api-pipeline install

This loads the skills and context about dlt the agent uses to build the pipeline iteratively, efficiently, and safely. The agent uses MCP tools to inspect credentials — it never needs to read your secrets.toml directly. Learn more →

3. Start LLM-assisted coding:

Use /find-source to load data from the Trading Strategy API into DuckDB.

The rest-api-pipeline toolkit takes over from here — it reads relevant API documentation, presents you with options for which endpoints to load, and follows a structured workflow to scaffold, debug, and validate the pipeline step by step.

4. Run the pipeline:

Copy
python trading_strategy_pipeline.py

If everything is configured correctly, you'll see output like this:

Copy
Pipeline trading_strategy_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset trading_strategy_data
The duckdb destination used duckdb:/trading_strategy.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

Inspect your pipeline and data:

Copy
dlt pipeline trading_strategy_pipeline show

This opens the Pipeline Dashboard where you can verify pipeline state, load metrics, schema (tables, columns, types), and query the loaded data directly.

---

Python pipeline example

This example loads candles_jsonl and pair_details from the Trading Strategy API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

Copy
import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def trading_strategy_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://tradingstrategy.ai/api",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "candles_jsonl", "endpoint": {"path": "candles-jsonl"}},
            {"name": "pair_details", "endpoint": {"path": "pair-details", "data_selector": "summary"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="trading_strategy_pipeline",
        destination="duckdb",
        dataset_name="trading_strategy_data",
    )
    load_info = pipeline.run(trading_strategy_source())
    print(load_info)

To add more endpoints, append entries from the resource table to the "resources" list using the same name, path, and data_selector pattern.

---

How do I query the loaded data?

Once the pipeline runs, dlt creates one table per resource. You can query with Python or SQL.

Python (pandas DataFrame):

Copy
import dlt

data = dlt.pipeline("trading_strategy_pipeline").dataset()
sessions_df = data.pair_details.df()
print(sessions_df.head())

SQL (DuckDB example):

Copy
SELECT * FROM trading_strategy_data.pair_details LIMIT 10;

In a marimo or Jupyter notebook:

Copy
import dlt

data = dlt.pipeline("trading_strategy_pipeline").dataset()
data.pair_details.df().head()

See how to explore your data in marimo Notebooks and how to query your data in Python with dataset.

---

What destinations can I load Trading Strategy data to?

dlt supports loading into any of these destinations — only the destination parameter changes:

Change the destination in dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. See the full destinations list.

---

Troubleshooting

Authentication failures

• Protected endpoints return 401 Unauthorized when the API key is missing or invalid. Ensure the key is passed correctly as a query parameter or header.

Rate limits

• The candles‑jsonl endpoint is limited to ~200 requests per minute and a maximum response size of 100 MB. Exceeding these limits returns 429 Too Many Requests.

Streaming / JSONL parsing

• The candles-jsonl endpoint streams each record as a separate line. Use a line‑by‑line parser rather than response.json().

Large file downloads

• Parquet endpoints (candles-all, pair-universe, liquidity-all) return binary files and require authentication. Handle the response as a binary stream and save the file before processing.

Ensure that the API key is valid to avoid 401 Unauthorized errors. Also, verify endpoint paths and parameters to avoid 404 Not Found errors.

---

Next steps

Continue your data engineering journey with the other toolkits of the dltHub AI Workbench:

• data-exploration — Build custom notebooks, charts, and dashboards for deeper analysis with marimo notebooks.
• dlthub-runtime — Deploy, schedule, and monitor your pipeline in production.

Copy
dlt ai toolkit data-exploration install
dlt ai toolkit dlthub-runtime install

• How to deploy a pipeline
• How to explore your data in marimo Notebooks
• How to query your data in Python with dataset
• Join the dlt community on Slack