Spaces:
Sleeping
Sleeping
File size: 13,169 Bytes
9f48dd6 afbaef9 9f48dd6 afbaef9 16ae9d0 afbaef9 9f48dd6 16ae9d0 9f48dd6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 | from datetime import datetime, timedelta, timezone
from pathlib import Path
import sys
import pandas as pd
sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
from data.scraper import NewsScraper
from engine.analytics import AnalyticsEngine
def _headline_frame(rows):
return pd.DataFrame(rows)
def test_scraper_init():
scraper = NewsScraper(limit=10)
assert scraper.limit == 10
def test_scraper_query_diversity():
scraper = NewsScraper(limit=600)
queries = scraper._build_queries("TSLA")
assert len(queries) >= 50
def test_summary_does_not_eager_load_models():
engine = AnalyticsEngine()
assert engine.finbert is None
assert engine.distilroberta is None
assert engine.ranker is None
now = datetime.now(timezone.utc)
df = _headline_frame(
[
{
"title": "TSLA beats estimates and raises guidance",
"timestamp": now.isoformat(),
"ensemble_pol": 0.82,
"finbert_pol": 0.9,
"roberta_pol": 0.75,
"finbert_score": 0.95,
"roberta_score": 0.88,
"agreement": 1.0,
"conviction": 0.8,
"significance": 0.9,
}
]
)
summary = engine.get_summary(df)
assert summary["direction_call"] in {"UP", "MIXED"}
assert summary["direction_score"] >= 50
assert summary["event_support"] > 0.5
assert engine.finbert is None
assert engine.distilroberta is None
assert engine.ranker is None
def test_positive_direction_summary_is_bullish():
engine = AnalyticsEngine()
now = datetime.now(timezone.utc)
df = _headline_frame(
[
{
"title": "TSLA beats estimates and raises guidance after record deliveries",
"timestamp": now.isoformat(),
"ensemble_pol": 0.86,
"finbert_pol": 0.91,
"roberta_pol": 0.79,
"finbert_score": 0.97,
"roberta_score": 0.9,
"agreement": 1.0,
"conviction": 0.82,
"significance": 0.95,
},
{
"title": "Analyst upgrades TSLA and raises price target",
"timestamp": (now - timedelta(hours=3)).isoformat(),
"ensemble_pol": 0.72,
"finbert_pol": 0.8,
"roberta_pol": 0.63,
"finbert_score": 0.92,
"roberta_score": 0.84,
"agreement": 1.0,
"conviction": 0.71,
"significance": 0.88,
},
{
"title": "TSLA wins major battery contract in growth push",
"timestamp": (now - timedelta(hours=8)).isoformat(),
"ensemble_pol": 0.61,
"finbert_pol": 0.68,
"roberta_pol": 0.53,
"finbert_score": 0.88,
"roberta_score": 0.8,
"agreement": 1.0,
"conviction": 0.61,
"significance": 0.82,
},
]
)
summary = engine.get_summary(df)
assert summary["direction_call"] == "UP"
assert summary["direction_score"] >= 60
assert "Bullish" in summary["state_title"]
assert summary["bullish_pressure"] > summary["bearish_pressure"]
assert summary["state_explanation"]
assert summary["bullish_drivers"]
def test_negative_direction_summary_is_bearish():
engine = AnalyticsEngine()
now = datetime.now(timezone.utc)
df = _headline_frame(
[
{
"title": "TSLA cuts guidance as revenue falls below estimates",
"timestamp": now.isoformat(),
"ensemble_pol": -0.88,
"finbert_pol": -0.93,
"roberta_pol": -0.81,
"finbert_score": 0.97,
"roberta_score": 0.9,
"agreement": 1.0,
"conviction": 0.86,
"significance": 0.96,
},
{
"title": "SEC investigation and lawsuit deepen pressure on TSLA stock",
"timestamp": (now - timedelta(hours=2)).isoformat(),
"ensemble_pol": -0.78,
"finbert_pol": -0.85,
"roberta_pol": -0.68,
"finbert_score": 0.94,
"roberta_score": 0.86,
"agreement": 1.0,
"conviction": 0.76,
"significance": 0.91,
},
{
"title": "Analyst downgrade sends TSLA lower on demand fears",
"timestamp": (now - timedelta(hours=5)).isoformat(),
"ensemble_pol": -0.67,
"finbert_pol": -0.72,
"roberta_pol": -0.59,
"finbert_score": 0.89,
"roberta_score": 0.82,
"agreement": 1.0,
"conviction": 0.64,
"significance": 0.84,
},
]
)
summary = engine.get_summary(df)
assert summary["direction_call"] == "DOWN"
assert summary["direction_score"] <= 40
assert "Bearish" in summary["state_title"]
assert summary["bearish_pressure"] > summary["bullish_pressure"]
assert summary["bearish_risks"]
def test_mixed_flow_lowers_confidence():
engine = AnalyticsEngine()
now = datetime.now(timezone.utc)
df = _headline_frame(
[
{
"title": "TSLA beats estimates but warns on margin headwinds",
"timestamp": now.isoformat(),
"ensemble_pol": 0.18,
"finbert_pol": 0.24,
"roberta_pol": 0.1,
"finbert_score": 0.81,
"roberta_score": 0.76,
"agreement": 1.0,
"conviction": 0.2,
"significance": 0.72,
},
{
"title": "Analyst downgrade offsets recent TSLA rally",
"timestamp": (now - timedelta(hours=4)).isoformat(),
"ensemble_pol": -0.22,
"finbert_pol": -0.28,
"roberta_pol": -0.14,
"finbert_score": 0.82,
"roberta_score": 0.74,
"agreement": 1.0,
"conviction": 0.22,
"significance": 0.75,
},
{
"title": "Investors await TSLA delivery update as outlook remains uncertain",
"timestamp": (now - timedelta(hours=9)).isoformat(),
"ensemble_pol": 0.02,
"finbert_pol": 0.04,
"roberta_pol": 0.0,
"finbert_score": 0.7,
"roberta_score": 0.66,
"agreement": 1.0,
"conviction": 0.04,
"significance": 0.63,
},
]
)
summary = engine.get_summary(df)
assert summary["direction_confidence"] < 70
assert summary["direction_call"] == "MIXED"
assert 35 <= summary["direction_score"] <= 65
def test_single_generic_headline_is_not_overcalled():
engine = AnalyticsEngine()
now = datetime.now(timezone.utc)
df = _headline_frame(
[
{
"title": "TSLA launches new product for mass market buyers",
"timestamp": now.isoformat(),
"ensemble_pol": 0.35,
"finbert_pol": 0.38,
"roberta_pol": 0.3,
"finbert_score": 0.82,
"roberta_score": 0.79,
"agreement": 1.0,
"conviction": 0.3,
"significance": 0.68,
}
]
)
summary = engine.get_summary(df)
assert summary["direction_call"] == "MIXED"
assert 45 <= summary["direction_score"] <= 58
assert summary["headline_concentration"] >= 0.95
assert summary["effective_articles"] <= 1.1
def test_major_singleton_event_can_escape_midpoint_bias():
engine = AnalyticsEngine()
now = datetime.now(timezone.utc)
df = _headline_frame(
[
{
"title": "AAPL beats estimates and raises guidance for next quarter",
"timestamp": now.isoformat(),
"ensemble_pol": 0.8,
"finbert_pol": 0.86,
"roberta_pol": 0.72,
"finbert_score": 0.95,
"roberta_score": 0.87,
"agreement": 1.0,
"conviction": 0.77,
"significance": 0.93,
}
]
)
summary = engine.get_summary(df)
assert summary["direction_call"] == "UP"
assert summary["direction_score"] >= 57
assert summary["vibe"] >= 7
assert summary["event_support"] >= 0.72
def test_stale_signal_needs_fresh_confirmation():
engine = AnalyticsEngine()
now = datetime.now(timezone.utc)
df = _headline_frame(
[
{
"title": "TSLA beats estimates and raises guidance",
"timestamp": (now - timedelta(days=5)).isoformat(),
"ensemble_pol": 0.82,
"finbert_pol": 0.9,
"roberta_pol": 0.74,
"finbert_score": 0.95,
"roberta_score": 0.87,
"agreement": 1.0,
"conviction": 0.79,
"significance": 0.92,
},
{
"title": "Investors await TSLA update as outlook remains uncertain",
"timestamp": now.isoformat(),
"ensemble_pol": 0.01,
"finbert_pol": 0.03,
"roberta_pol": 0.0,
"finbert_score": 0.71,
"roberta_score": 0.67,
"agreement": 1.0,
"conviction": 0.02,
"significance": 0.6,
},
]
)
summary = engine.get_summary(df)
assert summary["direction_call"] == "MIXED"
assert summary["direction_confidence"] < 55
assert summary["recency_support"] < 0.7
def test_vibe_scale_moves_off_center_for_mild_directional_lean():
engine = AnalyticsEngine()
now = datetime.now(timezone.utc)
bullish = _headline_frame(
[
{
"title": "Oracle partnership expands enterprise demand pipeline",
"timestamp": now.isoformat(),
"ensemble_pol": 0.39,
"finbert_pol": 0.44,
"roberta_pol": 0.31,
"finbert_score": 0.85,
"roberta_score": 0.79,
"agreement": 1.0,
"conviction": 0.35,
"significance": 0.73,
},
{
"title": "Analyst note turns constructive on Oracle cloud growth",
"timestamp": (now - timedelta(hours=5)).isoformat(),
"ensemble_pol": 0.28,
"finbert_pol": 0.33,
"roberta_pol": 0.2,
"finbert_score": 0.81,
"roberta_score": 0.74,
"agreement": 1.0,
"conviction": 0.25,
"significance": 0.7,
},
]
)
bearish = _headline_frame(
[
{
"title": "Intel downgrade reflects weaker PC demand expectations",
"timestamp": now.isoformat(),
"ensemble_pol": -0.41,
"finbert_pol": -0.46,
"roberta_pol": -0.34,
"finbert_score": 0.86,
"roberta_score": 0.8,
"agreement": 1.0,
"conviction": 0.37,
"significance": 0.75,
},
{
"title": "Intel delay raises execution concerns for next launch",
"timestamp": (now - timedelta(hours=4)).isoformat(),
"ensemble_pol": -0.29,
"finbert_pol": -0.34,
"roberta_pol": -0.22,
"finbert_score": 0.82,
"roberta_score": 0.76,
"agreement": 1.0,
"conviction": 0.26,
"significance": 0.71,
},
]
)
bullish_summary = engine.get_summary(bullish)
bearish_summary = engine.get_summary(bearish)
assert bullish_summary["vibe"] >= 6
assert bearish_summary["vibe"] <= 4
def test_estimate_time():
engine = AnalyticsEngine()
eta = engine.estimate_time(600)
assert 20 <= eta <= 200
eta_small = engine.estimate_time(50)
assert eta_small < eta
def test_self_calibration():
engine = AnalyticsEngine()
initial_eta = engine.estimate_time(600)
engine.record_timing("finbert_per_batch", 0.1, 1)
engine.record_timing("roberta_per_batch", 0.05, 1)
engine.record_timing("ranker_per_batch", 0.15, 1)
engine.record_timing("scrape_per_article", 0.005, 1)
calibrated_eta = engine.estimate_time(600)
assert calibrated_eta < initial_eta
def test_cleanup():
with open("test_dummy.csv", "w", encoding="utf-8") as file:
file.write("test")
NewsScraper.cleanup()
import glob
assert len(glob.glob("test_dummy.csv")) == 0
|