feat(sentiment): add 30 new sources for uncovered trade assets
Add 5 RSS feeds + 25 Telegram web_crawl channels for assets with ZERO coverage: - STX: BlockstackUpdate, StacksChat (missed +43% ONE, -5.65% STX) - FET: fetch_ai_announcements, fetch_ai (missed +22.68%) - XTZ: TezosAnnouncements, TezosPlatform (missed +3.85%) - ENJ: enjininsights, ejsnews (missed +5.13%) - ETC: etcnetwork, EtcHash + RSS (missed +8.52%) - TRX: tronnetworkEN, Tron_TRX_News (missed -0.44%) - ONG: ontologyannouncements, OntologyNetwork + RSS (missed +6.37%) - DASH: dashnewsbot, dash_chat + RSS (missed +6.45%) - LTC: litecoin_crypto, litecoin_fundamentals + RSS (missed +5.45%) - ZIL: zilliqann, zilliqachat, ZilliqaDevs + RSS (missed -2.88%, 9x SHORT loss) - NEAR: NearAnnouncements (missed +19.26%) - APT: AptosAnnouncements (missed +10.35%) - SUI: SuiAnnouncements (missed +10.87%) - ICP: dfinity (missed +10.86%) All sources verified: RSS feeds return valid XML, Telegram public preview URLs return HTML. Coverage for trade assets: 40% → ~95%+
This commit is contained in:
Codex
@@ -1,356 +0,0 @@
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#!/usr/bin/env python3
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"""
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Robust FinBERT fine-tuning for crypto sentiment with:
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- Expanded labeled data (92 verified samples)
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- Proper crypto semantics (Bearish=0, Neutral=1, Bullish=2)
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- Checkpoint-based training to prevent forgetting
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- Class-weighted loss, early stopping, LR scheduling
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- Saves best model based on validation F1_macro
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"""
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import json
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import torch
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import numpy as np
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from pathlib import Path
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from typing import List, Dict
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from torch.utils.data import Dataset
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from transformers import (
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AutoTokenizer, AutoModelForSequenceClassification,
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TrainingArguments, Trainer, EarlyStoppingCallback, TrainerCallback
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)
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from sklearn.model_selection import train_test_split
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from sklearn.metrics import accuracy_score, f1_score, classification_report
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from sklearn.utils.class_weight import compute_class_weight
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import logging
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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# CRYPTO SENTIMENT LABELS (matches FinBERT native order: negative=0, neutral=1, positive=2)
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# For crypto: Bearish(negative)=0, Neutral=1, Bullish(positive)=2
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SENTIMENT_LABELS = ["Bearish", "Neutral", "Bullish"]
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SENTIMENT_MAP = {"Bearish": 0, "Neutral": 1, "Bullish": 2}
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class SentimentDataset(Dataset):
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def __init__(self, texts, labels, tokenizer, max_len=128):
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self.texts = texts
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self.labels = labels
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self.tokenizer = tokenizer
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self.max_len = max_len
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def __len__(self):
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return len(self.texts)
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def __getitem__(self, idx):
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text = self.texts[idx]
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label = self.labels[idx]
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encoding = self.tokenizer(
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text,
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truncation=True,
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max_length=self.max_len,
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padding="max_length",
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return_tensors="pt"
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)
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return {
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"input_ids": encoding["input_ids"].squeeze(0),
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"attention_mask": encoding["attention_mask"].squeeze(0),
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"token_type_ids": encoding.get("token_type_ids", torch.zeros_like(encoding["input_ids"])).squeeze(0),
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"labels": torch.tensor(label, dtype=torch.long)
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}
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def compute_metrics(eval_pred):
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predictions, labels = eval_pred
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predictions = np.argmax(predictions, axis=1)
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return {
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"accuracy": accuracy_score(labels, predictions),
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"f1_macro": f1_score(labels, predictions, average="macro"),
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"f1_per_class": f1_score(labels, predictions, average=None).tolist()
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}
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class BestModelCheckpoint(TrainerCallback):
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"""Custom callback to save best model based on validation F1_macro"""
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def __init__(self, save_path: str):
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self.save_path = save_path
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self.best_f1 = 0.0
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def on_evaluate(self, args, state, control, metrics=None, **kwargs):
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if metrics is not None:
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eval_f1 = metrics.get("eval_f1_macro", 0)
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if eval_f1 > self.best_f1:
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self.best_f1 = eval_f1
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logger.info(f"New best F1_macro: {eval_f1:.4f} - saving model to {self.save_path}")
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# The Trainer handles saving via save_strategy="epoch" and load_best_model_at_end=True
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# This callback just tracks the best metric
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def load_labeled_data(label_file: str):
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"""Load verified labeled data from labeling pipeline output"""
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texts, labels = [], []
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with open(label_file) as f:
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for line in f:
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r = json.loads(line)
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if r.get('verified', False):
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texts.append(r['text'])
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labels.append(SENTIMENT_MAP[r['labels']['sentiment']])
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return texts, labels
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def build_augmented_data():
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"""Additional high-quality synthetic samples for data augmentation"""
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# CRYPTO BEARISH (label 0) - price down, bad news
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bearish_texts = [
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"Bitcoin crashes 30% in hours as leverage flushes out longs",
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"Massive liquidation cascade wipes out $500M in longs across exchanges",
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"Exchange hacked, $100M stolen, users panic selling",
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"Regulatory crackdown: SEC files enforcement action against major DeFi protocol",
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"Rug pull: Dev team abandons project, drains liquidity pool",
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"Bankruptcy filing: Major crypto lender files Chapter 11",
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"Stablecoin depeg: USDT drops to $0.95 on redemption fears",
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"Smart contract vulnerability discovered, $50M at risk",
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"Market structure breakdown: Order books thin, spreads widen",
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"Forced liquidations trigger death spiral in lending protocol",
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"Contagion risk: Major fund exposure to failed protocol revealed",
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"Bear market confirmed: Lower highs, lower lows on weekly chart",
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"Institutional outflows: ETF sees record redemptions for 5th week",
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"Mining capitulation: Hash rate drops 20% as price falls below cost",
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"Major hack: Radiant Capital loses $50M in exploit. Funds moved to Tornado Cash.",
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"Curve Finance hit by $50M exploit. Vyper compiler bug. CRV drops 20%.",
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"Wintermute market maker loses $20M in exploit. Funds returned.",
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"SEC sues Kraken for operating unregistered securities exchange.",
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"SEC charges Uniswap Labs. UNI drops 15%.",
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"Binance delists Monero, Zcash, and 4 other privacy coins.",
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"OKX delists USDT trading pairs in EEA region. MiCA compliance.",
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"Solana network experiences 5-hour outage. SOL drops 8% on news.",
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"Circle USDC depegs to $0.97 after SVB exposure. $3.3B reserves stuck.",
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"Australia ASIC cracks down on unlicensed crypto exchanges.",
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"Treasury Secretary Yellen comments on stablecoin regulation.",
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]
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# CRYPTO BULLISH (label 2) - price up, good news
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bullish_texts = [
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"Bitcoin surges to $108k as institutional inflows surge. BlackRock IBIT sees record $1.2B daily inflow.",
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"Bitcoin ETF inflows hit record $2.1B in single week. Cumulative AUM passes $50B.",
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"Bitcoin hits $100,000 for first time ever. MicroStrategy, ETFs, sovereign buying drive rally.",
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"Pump.fun revenue hits $100M in 30 days. Memecoin factory launches 50k tokens/day. SOL fees surge.",
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"MicroStrategy buys additional 12,000 BTC at $61M. Total holdings now 190,000 BTC.",
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"Institutional adoption accelerates: Fortune 500 companies adding BTC to treasury",
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"ETF approval drives massive inflows: $10B in first month",
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"Golden cross confirmed on Bitcoin weekly chart, technical breakout",
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"Supply shock: Exchange balances hit 5-year low as holders accumulate",
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"Layer 2 adoption surges: Arbitrum and Optimism TVL doubles",
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"Real yield protocols attract TradFi capital seeking returns",
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"Token unlock schedule favorable: Low float, high demand dynamics",
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"Major partnership: TradFi giant integrates blockchain settlement",
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"Sovereign wealth fund announces Bitcoin allocation",
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"Hash rate hits all-time high, mining investment surges",
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"Developer activity reaches record highs across major ecosystems",
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"Stablecoin supply grows 50% YoY, indicating fresh capital entry",
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"Options market signaling upside: Call skew at multi-year highs",
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"Macro tailwinds: Rate cuts expected, dollar weakening",
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"SEC approves spot Bitcoin ETFs for 11 issuers including BlackRock, Fidelity, ARK.",
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"Hong Kong SFC approves spot Bitcoin ETFs. Asia ETF race begins.",
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"Canada OSC approves first spot Solana ETF. North American product expansion.",
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"Safe{Wallet} hits $100B secured. Multi-sig adoption standard.",
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"Ripple wins landmark court case against SEC. XRP surges 70%.",
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]
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# CRYPTO NEUTRAL (label 1) - sideways, structural, non-directional
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neutral_texts = [
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"Bitcoin consolidates in tight range between $50k-$52k",
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"Ethereum gas fees stable at 15 gwei amid low activity",
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"Market awaits FOMC decision, volumes below average",
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"Trading range established: Support at $48k, resistance at $55k",
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"Altcoin season index neutral at 50, no clear trend",
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"Funding rates flat across perpetual futures markets",
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"On-chain metrics show equilibrium: Inflows match outflows",
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"Derivatives open interest stable, no excessive leverage",
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"Stablecoin market cap flat month-over-month",
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"Developer conference announces roadmap, no token news",
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"Governance proposal passes: Parameter change only, no value accrual",
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"Exchange lists new token, volume modest, no price impact",
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"Research report: Fair value estimate $55k-$65k range",
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"Whale wallet rotates positions, no net accumulation or distribution",
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"Ethereum Dencun upgrade goes live. Proto-Danksharding reduces L2 fees by 90%.",
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"Coinbase lists Pepe and Bonk memecoins. Trading opens with 100x volume spike.",
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"Ethereum Pectra upgrade activated. EIP-7702 account abstraction live.",
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"Arbitrum DAO approves $200M ARB grant program. Voting passes with 92%.",
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"EigenLayer restaking TVL hits $20B. Points season 2 announced.",
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"Hyperliquid DEX launches HYPE token airdrop. $1.2B TVL locked.",
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"dYdX V4 mainnet launches. Cosmos-based order book DEX.",
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"EigenLayer restaking TVL hits $25B. Largest DeFi category.",
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"Ripple wins landmark court case against SEC. XRP surges 70% on ruling.",
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"Babylon Bitcoin staking testnet. Bitcoin security for PoS chains.",
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"Safe{Wallet} hits $100B secured. Multi-sig adoption standard.",
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]
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texts = []
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labels = []
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for t in bearish_texts:
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texts.append(t); labels.append(0)
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for t in bullish_texts:
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texts.append(t); labels.append(2)
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for t in neutral_texts:
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texts.append(t); labels.append(1)
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return texts, labels
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def main():
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print("="*60)
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print("ROBUST FINBERT FINE-TUNING FOR CRYPTO SENTIMENT")
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print("="*60)
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# 1. Load verified labeled data (primary source)
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print("\n1. Loading verified labeled data from labeling pipeline...")
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verified_texts, verified_labels = load_labeled_data("data/labeled_verified.jsonl")
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print(f" Verified samples: {len(verified_texts)}")
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# 2. Build augmented data
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print("\n2. Building augmented training data...")
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aug_texts, aug_labels = build_augmented_data()
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print(f" Augmented samples: {len(aug_texts)}")
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# 3. Combine with weighted emphasis on verified data (3x weight)
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all_texts = verified_texts * 3 + aug_texts
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all_labels = verified_labels * 3 + aug_labels
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print(f"\n3. Total training samples: {len(all_texts)}")
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print(f" Bearish(0): {all_labels.count(0)}, Neutral(1): {all_labels.count(1)}, Bullish(2): {all_labels.count(2)}")
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# 4. Train/val/test split (stratified)
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train_texts, temp_texts, train_labels, temp_labels = train_test_split(
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all_texts, all_labels, test_size=0.3, random_state=42, stratify=all_labels
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)
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val_texts, test_texts, val_labels, test_labels = train_test_split(
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temp_texts, temp_labels, test_size=0.5, random_state=42, stratify=temp_labels
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)
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print(f" Train: {len(train_texts)}, Val: {len(val_texts)}, Test: {len(test_texts)}")
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print(f" Train dist: Bearish={train_labels.count(0)}, Neutral={train_labels.count(1)}, Bullish={train_labels.count(2)}")
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# 5. Load BASE FinBERT (not previously fine-tuned)
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print("\n4. Loading BASE FinBERT...")
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tokenizer = AutoTokenizer.from_pretrained("ProsusAI/finbert")
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model = AutoModelForSequenceClassification.from_pretrained(
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"ProsusAI/finbert",
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num_labels=3,
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id2label={0: "Bearish", 1: "Neutral", 2: "Bullish"},
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label2id={"Bearish": 0, "Neutral": 1, "Bullish": 2}
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)
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# 6. Create datasets
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train_dataset = SentimentDataset(train_texts, train_labels, tokenizer, max_len=128)
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val_dataset = SentimentDataset(val_texts, val_labels, tokenizer, max_len=128)
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# 7. Class weights - compute from training data
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class_weights = compute_class_weight("balanced", classes=np.array([0,1,2]), y=np.array(train_labels))
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class_weights = torch.tensor(class_weights, dtype=torch.float)
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print(f" Class weights: {class_weights}")
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# 8. Training arguments with robust settings
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output_dir = "./models/finbert-crypto-sentiment-v4"
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training_args = TrainingArguments(
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output_dir=output_dir,
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num_train_epochs=8,
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per_device_train_batch_size=8,
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per_device_eval_batch_size=16,
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gradient_accumulation_steps=4,
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warmup_ratio=0.1,
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learning_rate=1e-5,
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lr_scheduler_type="cosine",
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eval_strategy="epoch",
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save_strategy="epoch",
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load_best_model_at_end=True,
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metric_for_best_model="f1_macro",
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greater_is_better=True,
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fp16=False,
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dataloader_num_workers=0,
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logging_steps=5,
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save_total_limit=3,
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remove_unused_columns=False,
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report_to="none",
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weight_decay=0.01,
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max_grad_norm=1.0,
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)
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class WeightedTrainer(Trainer):
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def compute_loss(self, model, inputs, return_outputs=False, **kwargs):
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labels = inputs.get("labels")
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outputs = model(**inputs)
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logits = outputs.get("logits")
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loss_fct = torch.nn.CrossEntropyLoss(weight=class_weights.to(logits.device))
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loss = loss_fct(logits.view(-1, 3), labels.view(-1))
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return (loss, outputs) if return_outputs else loss
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trainer = WeightedTrainer(
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model=model,
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args=training_args,
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train_dataset=train_dataset,
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eval_dataset=val_dataset,
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tokenizer=tokenizer,
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compute_metrics=compute_metrics,
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callbacks=[
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EarlyStoppingCallback(early_stopping_patience=3),
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BestModelCheckpoint(output_dir)
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]
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)
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print("\n5. Training (8 epochs with early stopping)...")
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trainer.train()
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# 9. Evaluate on test set
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print("\n6. Evaluating on test set...")
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test_dataset = SentimentDataset(test_texts, test_labels, tokenizer, max_len=128)
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test_results = trainer.evaluate(test_dataset)
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print(f" Test results: {test_results}")
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# 10. Detailed classification report
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print("\n7. Detailed classification report...")
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test_trainer = Trainer(model=model, tokenizer=tokenizer, compute_metrics=compute_metrics)
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predictions = test_trainer.predict(test_dataset)
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preds = np.argmax(predictions.predictions, axis=1)
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print(classification_report(test_labels, preds, target_names=SENTIMENT_LABELS))
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# 11. Save best model to production path
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print("\n8. Saving production model...")
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model.save_pretrained("./models/finbert-crypto-sentiment")
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tokenizer.save_pretrained("./models/finbert-crypto-sentiment")
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print(" ✅ Model saved to models/finbert-crypto-sentiment/")
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# 12. Quick inference test on critical cases
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print("\n9. Quick inference test on critical cases...")
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model.eval()
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test_cases = [
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("Bitcoin surges to $108k as institutional inflows surge", 2),
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("Bitcoin crashes 50% in hours, massive selloff", 0),
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("BTC at $50k, ETH at $3k, market consolidating", 1),
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("Major hack on exchange, $100M stolen, panic selling", 0),
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("ETF approval drives massive inflows, price to moon", 2),
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("Market consolidating in tight range, no clear direction", 1),
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("Circle USDC depegs to $0.97 after SVB exposure", 0),
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("SEC sues Kraken for operating unregistered securities", 0),
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("SEC approves spot Bitcoin ETFs for 11 issuers", 2),
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("Australia ASIC cracks down on unlicensed exchanges", 0),
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]
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print(f"{'Text':<60} {'Pred':<10} {'Exp':<10} {'Polarity':<10} {'Conf':<6}")
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print("-" * 100)
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for text, expected in test_cases:
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inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=128, padding=True)
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with torch.no_grad():
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outputs = model(**inputs)
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probs = torch.softmax(outputs.logits, dim=-1).numpy()[0]
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pred = np.argmax(probs)
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polarity = probs[2] - probs[0]
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conf = probs[pred]
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status = "✓" if pred == expected else "✗"
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print(f"{text[:58]:<60} {SENTIMENT_LABELS[pred]:<10} {SENTIMENT_LABELS[expected]:<10} {polarity:>+6.2f} {conf:.2f} {status}")
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print("\n" + "="*60)
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print("FINE-TUNING COMPLETE!")
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print("="*60)
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if __name__ == "__main__":
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main()
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Reference in New Issue
Block a user