Daily Sentiment Forecasts: Crypto, Stocks, Gold
Budget: $25 – $50 USD
I’m looking for a data-driven partner who can turn the daily noise surrounding crypto-currencies, major stock indexes, and spot gold into clear, quantitative sentiment forecasts I can trade on.
Scope
• The only lens I care about is sentiment. Fundamental ratios and classical chart patterns are out of scope; instead, I want to know how the crowd feels and, more importantly, how that emotion is shifting day to day.
• Coverage must span the full crypto market (top-cap coins at minimum), key equity benchmarks (S&P 500, Nasdaq, Dow, etc.), and gold.
• Frequency is daily. By 08:00 UTC each morning I need a fresh read that includes yesterday’s data and a forward-looking signal for the next 24 hours.
Preferred Workflow & Tools
I’m comfortable if you pull data from Twitter, Reddit, news feeds, on-chain analytics, or alternative datasets such as Google Trends, then process it with Python, R, or any modern NLP stack (Hugging Face, NLTK, spaCy). Machine-learning classifiers, transformer models, or simple sentiment lexicons—use whatever combination produces the most stable, out-of-sample accuracy.
Deliverables
1. A daily file (CSV or JSON) containing:
• aggregate sentiment score per asset,
• confidence interval or probability of a positive/negative move,
• short textual commentary (two-three sentences) that explains notable drivers.
2. A lightweight dashboard or notebook so I can reproduce the pipeline and audit the calculations.
3. A brief method note outlining data sources, preprocessing steps, model logic, and key hyper-parameters.
Acceptance Criteria
• The pipeline runs end-to-end in under one hour on a standard cloud instance.
• Back-tests on the past 6 months show statistically significant correlation (p < 0.05) between the sentiment signal and 1-day forward price change for at least two of the three asset classes.
• All code is clean, commented, and handed over under an open licence suitable for commercial use.
If you’re confident you can surface actionable sentiment every day and back it up with transparent, reproducible code, I’d like to see a short proposal and a sample signal.
Scope
• The only lens I care about is sentiment. Fundamental ratios and classical chart patterns are out of scope; instead, I want to know how the crowd feels and, more importantly, how that emotion is shifting day to day.
• Coverage must span the full crypto market (top-cap coins at minimum), key equity benchmarks (S&P 500, Nasdaq, Dow, etc.), and gold.
• Frequency is daily. By 08:00 UTC each morning I need a fresh read that includes yesterday’s data and a forward-looking signal for the next 24 hours.
Preferred Workflow & Tools
I’m comfortable if you pull data from Twitter, Reddit, news feeds, on-chain analytics, or alternative datasets such as Google Trends, then process it with Python, R, or any modern NLP stack (Hugging Face, NLTK, spaCy). Machine-learning classifiers, transformer models, or simple sentiment lexicons—use whatever combination produces the most stable, out-of-sample accuracy.
Deliverables
1. A daily file (CSV or JSON) containing:
• aggregate sentiment score per asset,
• confidence interval or probability of a positive/negative move,
• short textual commentary (two-three sentences) that explains notable drivers.
2. A lightweight dashboard or notebook so I can reproduce the pipeline and audit the calculations.
3. A brief method note outlining data sources, preprocessing steps, model logic, and key hyper-parameters.
Acceptance Criteria
• The pipeline runs end-to-end in under one hour on a standard cloud instance.
• Back-tests on the past 6 months show statistically significant correlation (p < 0.05) between the sentiment signal and 1-day forward price change for at least two of the three asset classes.
• All code is clean, commented, and handed over under an open licence suitable for commercial use.
If you’re confident you can surface actionable sentiment every day and back it up with transparent, reproducible code, I’d like to see a short proposal and a sample signal.
Related categories:
Python
Excel
Statistics
Statistical Analysis
Data Science
Data Analysis
Trading
Natural Language Processing