🧠 Reading Wall Street’s Mood With AI (and Trading on It)

#ai #blockchain #cryptocurrency #web3

Markets move on sentiment as much as they do on fundamentals. Analysts spend hours digging into reports, filings, or newsfeeds just to figure out whether Wall Street is feeling bullish or bearish. I wanted a faster, developer-friendly way — so I built an AI agent that reveals market sentiment in seconds.

How It Works ⚙️

Ingestion: financial headlines, SEC filings, curated news APIs.
NLP: a finance-tuned model (e.g., FinBERT) for accurate tone on earnings/legal jargon.
Scoring: outputs weighted bullish/bearish/neutral + momentum (is sentiment accelerating?).
Action: wire to exchange APIs (e.g., WhiteBIT) so a signal can turn into an order instantly.

Quick Start (Python) 🧩

# pip install transformers torch --quiet
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch, math

MODEL = "ProsusAI/finbert"
labels = ["bearish", "neutral", "bullish"]

tok = AutoTokenizer.from_pretrained(MODEL)
mdl = AutoModelForSequenceClassification.from_pretrained(MODEL)

def score(text):
    t = tok(text, return_tensors="pt", truncation=True, max_length=512)
    with torch.no_grad():
        logits = mdl(**t).logits.softmax(dim=-1).squeeze().tolist()
    return dict(zip(labels, [round(p*100, 1) for p in logits]))

headlines = [
    "REX-Osprey files DOGE ETF; SEC review pending",
    "MegaCap beats on EPS but cuts guidance",
    "Crypto markets steady as inflows slow"
]

for h in headlines:
    print(h, "→", score(h))

Output (example):

REX-Osprey files DOGE ETF; SEC review pending → {'bearish': 12.3, 'neutral': 56.7, 'bullish': 31.0}

From here, I aggregate intraday scores, compute EWMA for momentum, and trigger alerts when sentiment flips and volume confirms.

Why It Matters for Crypto 💡

Crypto is narrative-first. Pairing AI sentiment with execution speed (e.g., via Coinbase or WhiteBIT API) turns minutes of reading into seconds of action.