Financial Market Trend Analysis - USA

Utilizing data from major U.S. financial indices, including the S&P 500, Dow Jones, NASDAQ, and more, this AI embedding provides in-depth analysis and potential predictions for financial market trends.

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Rating: 5/5

Data Amount

2 TB

Data Freshness

Updated July 2023

Use Cases




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Example output

Can you analyze the market trends for the technology sector in the US for the last 5 years?

Yes, based on the data I've analyzed, here are some key trends in the US technology sector over the last 5 years...

1. 2019: "Strong growth observed, with major players like Apple, Microsoft, and Google reaching new heights. The tech sector outperformed the broader market."
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2. 2020: "Despite the pandemic, the tech sector showed remarkable resilience. The shift to remote work boosted demand for cloud computing, software solutions, and other digital services."
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3. 2021: "Tech stocks had a volatile year due to changing market conditions. However, the sector ended the year positively overall."
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4. 2022: "The sector experienced some correction from the previous years' highs, but growth trends remain strong."
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5. 2023: "Despite market uncertainties, the technology sector continued its overall growth trajectory, led by advancements in AI, cloud computing, and cybersecurity."
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Financial Market Trend Analysis - USA

How is the financial data indexed and embeddings created?

How do we keep the data updated?

Our system is integrated with major financial indices data streams. Every day, a process runs to check for new data or updates to existing data. New and updated data is preprocessed, indexed, and embeddings are generated before it’s stored in our vector database. This ensures our AI embeddings always have the most recent financial market data.

How to use

Python Code

You can access the embedding using EmbedElite python package or curl request.

# this returns a list of embeddings
from embedelite import load_embedding

embeddings = load_embedding("financial-market-trends-usa")

# this returns an object which can be directly inserted into Qdrant
result = load_embedding("financial-market-trends-usa", embed_for="qdrant")
# result is {"embeddings": [], "documents": [] "ids": []}



curl -X POST -H "Content-Type: application/json" -d '{
"doc_id": "financial-market-trends-usa"


    "mappings": {
        "properties": {
        "doc_source": {"type": "keyword"},
        "sentence": { "type": "text" },
        "embeddings": { "type": "dense_vector", "dims": 1536, "index": False },
        "doc_source": {"type": "keyword"}