ad45cb429c
REST API wrapping OpenBB SDK for stock data, sentiment analysis, technical indicators, macro data, and rule-based portfolio analysis. - Stock data via yfinance (quote, profile, metrics, financials, historical, news) - News sentiment via Alpha Vantage (per-article, per-ticker scores) - Analyst data via Finnhub (recommendations, insider trades, upgrades) - Macro data via FRED (Fed rate, CPI, GDP, unemployment, treasury yields) - Technical indicators via openbb-technical (RSI, MACD, SMA, EMA, Bollinger) - Rule-based portfolio analysis engine (BUY_MORE/HOLD/SELL) - Stock discovery (gainers, losers, active, undervalued, growth) - 102 tests, all passing
133 lines
3.6 KiB
Python
133 lines
3.6 KiB
Python
from models import ActionEnum, ConfidenceEnum
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from analysis_service import (
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compute_analysis,
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_score_pnl,
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_score_pe,
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_score_revenue_growth,
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_score_target_price,
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)
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class TestScorePnl:
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def test_large_loss_suggests_buy(self):
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score, reason = _score_pnl(-0.25)
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assert score == 1
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assert "averaging down" in reason
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def test_large_profit_suggests_sell(self):
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score, reason = _score_pnl(0.60)
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assert score == -1
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assert "taking profit" in reason
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def test_moderate_suggests_hold(self):
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score, reason = _score_pnl(0.10)
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assert score == 0
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class TestScorePe:
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def test_low_pe(self):
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score, _ = _score_pe(10.0)
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assert score == 1
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def test_high_pe(self):
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score, _ = _score_pe(50.0)
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assert score == -1
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def test_normal_pe(self):
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score, _ = _score_pe(20.0)
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assert score == 0
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def test_negative_pe(self):
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score, _ = _score_pe(-5.0)
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assert score == -1
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def test_none_pe(self):
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score, reason = _score_pe(None)
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assert score == 0
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assert reason is None
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class TestScoreRevenueGrowth:
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def test_strong_growth(self):
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score, _ = _score_revenue_growth(0.20)
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assert score == 1
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def test_negative_growth(self):
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score, _ = _score_revenue_growth(-0.05)
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assert score == -1
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def test_moderate_growth(self):
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score, _ = _score_revenue_growth(0.05)
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assert score == 0
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def test_none(self):
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score, reason = _score_revenue_growth(None)
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assert score == 0
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class TestScoreTargetPrice:
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def test_big_upside(self):
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score, _ = _score_target_price(100.0, 120.0)
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assert score == 1
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def test_big_downside(self):
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score, _ = _score_target_price(100.0, 85.0)
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assert score == -1
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def test_near_target(self):
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score, _ = _score_target_price(100.0, 105.0)
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assert score == 0
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def test_none_price(self):
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score, _ = _score_target_price(None, 120.0)
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assert score == 0
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class TestComputeAnalysis:
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def test_strong_buy_signals(self):
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result = compute_analysis(
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current_price=100.0,
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buy_in_price=130.0, # loss > 20%
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target_price=120.0, # upside > 15%
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metrics={"pe_ratio": 10.0, "revenue_growth": 0.20},
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)
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assert result.action == ActionEnum.BUY_MORE
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assert result.confidence == ConfidenceEnum.HIGH
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def test_strong_sell_signals(self):
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result = compute_analysis(
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current_price=200.0,
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buy_in_price=100.0, # profit > 50%
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target_price=170.0, # downside
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metrics={"pe_ratio": 50.0, "revenue_growth": -0.10},
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)
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assert result.action == ActionEnum.SELL
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def test_mixed_signals_hold(self):
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result = compute_analysis(
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current_price=100.0,
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buy_in_price=95.0,
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target_price=105.0,
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metrics={"pe_ratio": 20.0, "revenue_growth": 0.05},
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)
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assert result.action == ActionEnum.HOLD
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def test_no_data(self):
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result = compute_analysis(
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current_price=None,
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buy_in_price=100.0,
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target_price=None,
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metrics={},
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)
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assert result.action == ActionEnum.HOLD
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assert result.confidence == ConfidenceEnum.LOW
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def test_reasons_populated(self):
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result = compute_analysis(
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current_price=100.0,
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buy_in_price=90.0,
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target_price=110.0,
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metrics={"pe_ratio": 25.0},
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)
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assert len(result.reasons) > 0
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