Building the industry's first open-source datasets for investment research. Unlike traditional vendors who limit trials to institutions, we believe in open access. Free access to all datasets with a brief delay; subscribers receive real-time data.
👋 About Me
I'm Derek Snow, founder and researcher at sov.ai. My focus lies in AI & ML in Quantitative Finance, where I develop and curate datasets to advance research and applications in this field.
🌐 Connect with Me
LinkedIn | GitHub | Hugging Face
📚 Datasets
The price is for commercial usage of lagged data. If you are an academic ignore it, simply download your data from Huggingface!
| Emoji | Dataset | Description | Documentation | Price p/m |
|---|---|---|---|---|
| 📰 | sovai/news_sentiment | Two types of news datasets have been developed, one is ticker-matched, and the next is theme-matched. | Documentation | $200 |
| 📈 | sovai/price_breakout | A dataset with daily updated predictions of price breaking upwards for US Equities. | Documentation | $220 |
| 🔍 | sovai/insider_flow_prediction | More than 60+ insider trading features helpful for machine learning, including a flow prediction value. | Documentation | $465 |
| 💼 | sovai/institutional_trading | The dataset provides a comprehensive analysis of institutional investment behaviors, strategies, and portfolio dynamics. | Documentation | $580 |
| 📢 | sovai/lobbying_data | A ticker-matched lobbying data to see fine-grained corporate lobbying behavior. | Documentation | $645 |
| 🔽 | sovai/short_selling | This section covers the usage of various short-selling datasets for risk analysis. | Documentation | $780 |
| 📖 | sovai/wikipedia_views | A look at some of the largest firms and their daily Wikipedia page views and trends. | Documentation | $200 |
| 💊 | sovai/pharma_clinical_trials | This section covers a very unique dataset that tags clinical trials with their predicted outcome success. | Documentation | $850 |
| 📊 | sovai/factor_signals | This dataset includes traditional accounting factors, alternative financial metrics, and advanced statistical analyses, enabling sophisticated financial modeling. | Documentation | $270 |
| 📉 | sovai/financial_ratios | More than 80+ financial ratios calculated from financial statement and market data. | Documentation | $270 |
| 📜 | sovai/government_contracts | Government contracts data from publicly traded companies. | Documentation | $580 |
| sovai/corp_risks | Chapter 7 and Chapter 11 bankruptcy predictions made easy for over 13,000 US publicly traded stocks. | Documentation | $270 | |
| 🛡️ | sovai/risks | We offer daily updates on global risk perceptions, using leading indicators and advanced models to forecast various types of risk. | Documentation | $270 |
| 💬 | sovai/cfpb_complaints | This section covers the usage of the Consumer Financial Complaint ticker-mapped dataset. | Documentation | $480 |
| 🧮 | sovai/risk_indicators | We construct a comprehensive corporate risk score for US stocks by analyzing company events. | Documentation | $270 |
| 🚦 | sovai/traffic_agencies | Data on government website agency traffic. | Documentation | $250 |
| 👥 | sovai/earnings_surprise | Earnings announcements are obtained from external sources as well as estimate information leading up to the actual announcement. | Documentation | $680 |
| ❗ | sovai/bankruptcy | Chapter 7 and Chapter 11 bankruptcy predictions made easy for over 5,000 US publicly traded stocks. | Documentation | $270 |
Cost Tip: For commercial access to all 30 real-time datasets on docs.sov.ai, I recommend you subscribe to the $285 p/m package, you can save as much as 90% of the costs.
All our datasets are in beta, be part of our development process. Submit suggestions or error reports through the issues portal.
🧪 Example Use Cases
Below are example code snippets demonstrating how to load each dataset using the Hugging Face datasets library.
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from datasets import load_dataset df_news_sentiment = load_dataset("sovai/news_sentiment", split="train").to_pandas()
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from datasets import load_dataset df_price_breakout = load_dataset("sovai/price_breakout", split="train").to_pandas()
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🔍 sovai/insider_flow_prediction
from datasets import load_dataset df_insider_flow = load_dataset("sovai/insider_flow_prediction", split="train").to_pandas()
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from datasets import load_dataset df_institutional_trading = load_dataset("sovai/institutional_trading", split="train").to_pandas()
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from datasets import load_dataset df_lobbying_data = load_dataset("sovai/lobbying_data", split="train").to_pandas()
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from datasets import load_dataset df_short_selling = load_dataset("sovai/short_selling", split="train").to_pandas()
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from datasets import load_dataset df_wikipedia_views = load_dataset("sovai/wikipedia_views", split="train").to_pandas()
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💊 sovai/pharma_clinical_trials
from datasets import load_dataset df_pharma_trials = load_dataset("sovai/pharma_clinical_trials", split="train").to_pandas()
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from datasets import load_dataset df_factor_signals = load_dataset("sovai/factor_signals", split="train").to_pandas()
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from datasets import load_dataset df_financial_ratios = load_dataset("sovai/financial_ratios", split="train").to_pandas()
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from datasets import load_dataset df_government_contracts = load_dataset("sovai/government_contracts", split="train").to_pandas()
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from datasets import load_dataset df_corp_risks = load_dataset("sovai/corp_risks", split="train").to_pandas()
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🛡️ sovai/risks
from datasets import load_dataset df_risks = load_dataset("sovai/risks", split="train").to_pandas()
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from datasets import load_dataset df_cfpb_complaints = load_dataset("sovai/cfpb_complaints", split="train").to_pandas()
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from datasets import load_dataset df_risk_indicators = load_dataset("sovai/risk_indicators", split="train").to_pandas()
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from datasets import load_dataset df_traffic_agencies = load_dataset("sovai/traffic_agencies", split="train").to_pandas()
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from datasets import load_dataset df_earnings_surprise = load_dataset("sovai/earnings_surprise", split="train").to_pandas()
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from datasets import load_dataset df_bankruptcy = load_dataset("sovai/bankruptcy", split="train").to_pandas()