A multi-factor analysis tool that suggests long-term stock & ETF buy recommendations from US, European, Asian, and Superinvestor-consensus markets, computed live from public market data.
- 📈 Multi-factor scoring — composite 0-100 score per asset
- 🏦 Stocks & ETFs — auto-detected and scored with dedicated models
- 🌍 US, European, Asian & Superinvestor markets — S&P 500, DAX, CAC 40, FTSE 100, Nikkei 225, Hang Seng, NSE, and superinvestor consensus picks from DataRoma 13F filings
- 💱 Currency filter — narrow by native currency (
USD,EUR,JPY, …) - ⚡ Concurrent fetching — analyzes ~300 tickers in seconds
- 📊 Rich CLI output — price, 1M/1Y change, score breakdown and rationale
- 🧾 Self-contained HTML export — interactive report with filters and sortable columns
- 🧪 Pure scoring functions — testable without network
- 🧠 Interactive advisor — market pulse, portfolio review, and tailored buy recommendations via the
advisorsubcommand - 🤖 AI-powered advisor — chat with an intelligent investment advisor that analyzes markets, reviews portfolios, and recommends buys — powered by OpenCode agents
- 📊 Backtest validation — historical backtesting with automated before/after comparison script
- 📝 Structured logging — Python standard logging for errors, warnings, and operational events
pip install investdaytippip install --upgrade investdaytipForce reinstall if the version doesn't update (e.g. cached old wheel):
pip install --upgrade --no-cache-dir investdaytipgit clone https://github.com/dfdezdom/investdaytip.git
cd investdaytip
python -m venv .venv
source .venv/bin/activate # Windows: .venv\Scripts\activate
pip install -e ".[dev]"investdaytipThat's it. You'll see the top 5 buys scored across 300+ stocks & ETFs.
investdaytip # Top 5 from full universe (US + EU + Asia, stocks + ETFs)
investdaytip -n 10 # Top 10
investdaytip -a stocks # Only stocks
investdaytip -a etfs # Only ETFs
investdaytip -r us # Only US
investdaytip -r eu # Only Europe
investdaytip -r asia # Only Asia
investdaytip -r superinvestor # Only superinvestor consensus tickers
investdaytip -r us eu # US + Europe
investdaytip -r eu -a stocks # Only European stocks
investdaytip -r asia -a etfs # Only Asian ETFs
investdaytip -c USD # Only USD-denominated assets
investdaytip -c EUR # Only EUR-denominated assets
investdaytip -c USD EUR # USD + EUR assets
investdaytip -c JPY # Only JPY-denominated assets
investdaytip -r eu -c USD # EU region + USD post-filter
investdaytip -r us eu -c USD EUR # US + EU, USD + EUR assets
investdaytip -t AAPL MSFT VOO # Custom ticker list
investdaytip --tickers-file tickers.txt # Custom ticker file (lines, spaces, commas)
investdaytip --tickers-file tickers-files-examples/semiconductors_relevant_tickers.txt
investdaytip --export-html report.html # Export report with interactive filters
investdaytip --export-html # Uses investDayTip-aaaammdd-hhmm.html
# or investDayTip-<tag>-aaaammdd-hhmm.html
# when --tickers-file is set
investdaytip --superinvestor # Fetch DataRoma superinvestor data and display column
investdaytip --include-technical # Include RSI + MACD in scoring (opt-in)
investdaytip --min-market-cap 1B # Raise min market cap to $1B
investdaytip --min-market-cap 0 # Disable market-cap filter
investdaytip --no-cache # Bypass SQLite cache, fetch fresh data
investdaytip --cache-clear # Purge all cached data before running
investdaytip --workers 20 # More parallelism
investdaytip backtest -n 10 -r us # Backtest stock scoring on US market (stocks only)
investdaytip backtest -t AAPL MSFT VOO # Backtest on custom ticker list
investdaytip backtest --export-html # Export backtest results to HTML
investdaytip backtest --no-cache # Bypass cache in backtest
investdaytip backtest --cache-clear # Purge cache before backtest
investdaytip --help
./preview.sh # Serve generated HTML files on localhost:8000| Flag | Description | Default |
|---|---|---|
-n, --top N |
Number of recommendations | 5 |
-t, --tickers ... |
Custom ticker list (overrides universe) | curated universe |
--tickers-file PATH |
Text file with custom tickers (merged with --tickers if both are used) |
disabled |
-a, --asset-class {all,stocks,etfs} |
Asset class filter | all |
-r, --region {all,us,eu,asia,superinvestor} nargs="+" |
Region filter(s) — e.g. -r us eu |
all |
-c, --currency {all,USD,EUR,GBP,…} nargs="+" |
Currency filter(s); narrows universe to matching regions when no -r is given |
all |
-s, --sector TEXT |
Sector/category prefix filter, case-insensitive (e.g. Financial matches Financial Services) |
disabled |
--export-html [PATH] |
Export recommendations to self-contained HTML (investDayTip-aaaammdd-hhmm.html if omitted) |
disabled |
--superinvestor |
Include superinvestor ownership data from DataRoma (adds ~80 HTTP requests, shows column in HTML and CLI) | disabled |
--include-technical |
Include RSI + MACD technical indicators in the Trend pillar scoring (see When to use technical indicators) | disabled |
--min-market-cap VALUE |
Minimum market cap (1B, 500M, 0 to disable; see Market Cap Classification) |
2B |
--no-cache |
Skip SQLite cache, fetch all data live from Yahoo Finance | disabled |
--cache-clear |
Purge the SQLite cache before running | disabled |
--workers N |
Parallel fetch threads | 10 |
-h, --help |
Show the CLI help message and exit | n/a |
InvestDayTip includes an AI-powered investment advisor that lets you chat with an intelligent market analyst directly from your terminal — no memorizing CLI flags.
# Install via the official installer (macOS / Linux)
curl -fsSL https://opencode.ai/install | bash
# Also available via npm, brew, bun, and more — see https://opencode.ai- Market pulse — Full macro analysis (VIX + yield curve + bond volatility + DXY) in ~30 seconds
- Portfolio review — Score your holdings, identify weaknesses, and get actionable signals
- Buy recommendations — Best picks filtered by region, asset class, and risk profile
- Full analysis — All of the above combined into a single comprehensive report
@advisor what's the market pulse?
Expected output:
📊 Market Pulse
Macro Score: 58/100 (Neutral)
- VIX: 14.2 (calm)
- 10Y-2Y Spread: +0.85% (healthy)
- MOVE: 78 (normal)
- DXY: 102.3 (strong)
- Fear & Greed: 67/Greed
Signal: 🟡 HOLD — selective buying| OpenCode AI Agent | investdaytip advisor CLI |
|
|---|---|---|
| Interface | Natural language chat | Interactive menu prompts |
| Flexibility | Ad-hoc questions, follow-ups | Predefined flow |
| Speed | ~30s for market pulse | ~1–2 min for full analysis |
| Output | Clean markdown tables | Rich colored tables (terminal) |
| Best for | Quick questions, exploration | Deep structured analysis |
See .opencode/agents/advisor.md for:
- VIX / macro regime interpretation rules
- Fear & Greed Index signals
- Bubble burst historical indicators (dot-com 2000, railroads 1845)
- Portfolio scoring thresholds and presentation format
- Execution methods (market pulse, portfolio review, full analysis)
Interactive market analysis with multi-indicator macro pulse (VIX, 10Y-2Y yield curve, MOVE bond volatility, DXY dollar strength), portfolio review, and buy recommendations:
investdaytip advisor # Interactive mode (asks for risk, region, etc.)
investdaytip advisor --risk moderate # Non-interactive with risk preset
investdaytip advisor --risk aggressive -r us -a stocks # US stocks, aggressive, non-interactive
investdaytip advisor --risk moderate --portfolio portfolios/portfolio.txt # Custom portfolioThe advisor now displays a composite macro health score (0-100):
- 🟢 ≥70 — Macro healthy
- 🟡 ≥45 — Mixed signals
- 🟠 ≥25 — Macro warning
- 🔴 <25 — Macro danger
See investdaytip advisor --help for all options.
The advisor fetches six live indicators to compute the composite macro health score (0-100):
| Indicator | Ticker | What it measures | Reference ranges | Score impact |
|---|---|---|---|---|
| VIX | ^VIX |
Expected S&P 500 volatility over the next 30 days | ≤15 calm, ≤25 neutral, ≤35 fear, >35 panic | ±20 |
| VXN | ^VXN |
Expected Nasdaq 100 volatility (tech-heavy complement to VIX) | (informational only) | — |
| 10Y-2Y Spread | ^TNX + 2YY=F |
US Treasury yield curve slope — inverted curve signals recession risk | >1% healthy, 0–1% neutral, <0% inverted | −20 / +5 |
| MOVE | ^MOVE |
Bond market volatility (Merrill Lynch Option Volatility Estimate) | <60 calm, 60–100 normal, 100–120 elevated, >120 panic | −15 / +5 |
| DXY | DX-Y.NYB |
US Dollar Index — strength against EUR, JPY, GBP, CAD, SEK, CHF | <95 weak, 95–100 neutral, 100–105 strong, >105 very strong | −10 / +5 |
| Fear & Greed | CNN API | Composite market sentiment from 7 sub-indicators (momentum, breadth, put/call, volatility, junk bonds, safe havens) | 0–100; <25 extreme fear, >75 extreme greed | ±10 (contrarian) |
The score starts at a neutral 50 and each indicator adjusts it up or down based on current readings. The final score determines the macro signal:
- 🟢 ≥70 — Macro healthy → buy
- 🟡 ≥45 — Mixed signals → hold
- 🟠 ≥25 — Macro warning → hold
- 🔴 <25 — Macro danger → sell
All indicators are shown live in the 📈 Market Analysis table when running investdaytip advisor.
Historical validation of the stock scoring model:
investdaytip backtest # Backtest US stocks (default)
investdaytip backtest -n 10 # Top 10 picks per snapshot
investdaytip backtest -r us # US stocks
investdaytip backtest -r eu # European stocks
investdaytip backtest -r asia # Asian stocks
investdaytip backtest -r superinvestor # Superinvestor consensus stocks
investdaytip backtest -t AAPL MSFT # Custom ticker list
investdaytip backtest --benchmark SPY # Custom benchmark
investdaytip backtest --interval-months 6 # Semi-annual snapshots
investdaytip backtest --lag-days 90 # Reporting lag (default: 60)
investdaytip backtest --period 3y # Shorter price history
investdaytip backtest --export-html # Export to HTML
investdaytip backtest --no-cache # Bypass SQLite cache
investdaytip backtest --cache-clear # Purge cache before runNote: Backtest only supports stocks (no ETFs). It simulates quarterly snapshots with a configurable reporting lag, scores each stock, and measures forward returns against a benchmark.
Use the included scripts/scoring_baseline.py to validate scoring changes objectively:
# Save baseline BEFORE your change
python scripts/scoring_baseline.py run \
--tag "before" -r us -n 10 \
-t "AAPL MSFT GOOGL META NVDA" \
--period 2y --interval-months 3 \
--min-market-cap 0
# Save baseline AFTER your change
python scripts/scoring_baseline.py run \
--tag "after" -r us -n 10 \
-t "AAPL MSFT GOOGL META NVDA" \
--period 2y --interval-months 3 \
--min-market-cap 0
# Compare side-by-side with decision rules
python scripts/scoring_baseline.py compare baseline-before.json baseline-after.jsonDecision rules:
- Ship it: Alpha ↑ AND Sharpe ↑ AND 12M win rate ↑
- Consider: Alpha ↑ OR Sharpe ↑ (mixed, review drawdown)
- Reject / iterate: Alpha ↓ AND Sharpe ↓
The --include-technical flag adds RSI-14 and MACD histogram to the Trend pillar (20% weight). Backtest validation across four scenarios shows mixed results — the flag helps concentrated lists but consistently hurts broad screens:
| Scenario | Without --include-technical |
With --include-technical |
Verdict |
|---|---|---|---|
US ($2B+) (59 tickers, -n 10) |
Alpha 10.32%, Sharpe 1.20 | Alpha 3.01%, Sharpe 1.06 | ❌ Worse — fundamentals filter already captures quality; technicals add noise |
US (no cap filter) (59 tickers, -n 10) |
Alpha 2.18%, Sharpe 0.45 | Alpha 1.89%, Sharpe 0.47 | |
US mega-caps ($200B+) (59 tickers, -n 2) |
Alpha −11.50%, Sharpe 0.69 | Alpha −5.08%, Sharpe 0.89 | ✅ Improved — alpha up 6.4pp, Sharpe +0.20, drawdown halved |
EU ($2B+) (66 tickers, -n 10) |
Alpha 4.59%, Sharpe 0.98 | Alpha −1.20%, Sharpe 0.75 | ❌ Worse — alpha, Sharpe, and win rate all decline |
Guidelines:
- ✅ Use it when analyzing a small, concentrated list of mega-caps (e.g., custom
-tlist with 5–15 tickers with market cap >$200B) - ❌ Avoid it when running broad-screen scans — the fundamental filter already selects the best candidates, and technicals dilute the signal
⚠️ Test it for your specific region and ticker list — results vary and may require different parameter calibration
The --min-market-cap filter controls the size of companies included in the analysis. Standard classification for reference:
| Category | Market Cap | Typical characteristics |
|---|---|---|
| Mega-cap | > $200B | Largest global companies (Apple, Microsoft, Google). Highest liquidity, most followed by analysts, technical indicators most reliable. |
| Large-cap | $10B – $200B | Established companies with stable fundamentals. Good balance of growth and stability. |
| Mid-cap | $2B – $10B | Growing companies, regional leaders. More volatile than large-caps but higher growth potential. |
| Small-cap | $300M – $2B | Niche players or regional companies. Higher volatility, less analyst coverage, fundamentals less reliable. |
| Micro-cap | < $300M | Early-stage or distressed companies. Very high risk, low liquidity. |
Why the default is $2B (mid-cap threshold):
The model is designed for long-term fundamental investing. Below $2B (small/micro-caps), companies tend to have:
- Incomplete or unreliable financial data in yfinance
- Higher volatility that overwhelms fundamental signals
- Lower liquidity, making technical indicators less meaningful
The $2B threshold strikes a balance: it includes mid-caps and above (where fundamentals are reliable) while filtering out the noisiest small/micro-caps. Use --min-market-cap 0 to include all tickers, or raise it to 10B/50B for a pure large/mega-cap focus.
Generate an interactive report that works offline (single file with inline CSS/JS):
investdaytip -n 25 -a all -r all --export-html investdaytip-report.htmlThe generated report includes filters for:
- Text search (ticker/name/sector)
- Asset class (
stock/etf) - Region (
us/eu/asia/superinvestor) - Minimum score
- Minimum 1M return (%)
- Minimum 1Y return (%)
When --superinvestor is enabled, an additional "Superinvestors" sortable column appears between 1Y and Score showing the number of tracked managers holding each ticker.
When --include-technical is enabled, "RSI" and "MACD" columns appear between 1Y (or Superinvestors) and Score.
It also includes:
- Click-to-sort columns (ascending/descending)
- Full-width responsive layout (uses available browser width)
- Pre-rendered rows + client-side interactivity (works even if JS is restricted)
- Direct platform links in table columns:
Ticker(Google Finance),T(TradingView),Y(Yahoo Finance)
Direct platform links (Ticker → Google Finance, T → TradingView, Y → Yahoo Finance) use exchange suffix mapping. See _normalize_exchange_hint() and _exchange_mapping() in src/investdaytip/html_export.py for the full list of ~20 supported exchanges.
from investdaytip import get_recommendations
# Get top 5 Asian stocks
picks = get_recommendations(top_n=5, region="asia", asset_class="stocks")
for s in picks:
print(f"{s.data.ticker} ({s.asset_type}) — score={s.total:.1f}")
print(f" Price: {s.data.current_price} {s.data.currency}")
print(f" 1M: {s.data.return_1m:.2%} 1Y: {s.data.return_12m:.2%}")
print(f" Why: {'; '.join(s.rationale[:3])}")
# Or filter by sector (prefix match, case-insensitive)
picks = get_recommendations(top_n=5, sector="Financial") # Financial + Financial Services
# Or mix regions and asset classes
picks = get_recommendations(top_n=10, region="all") # US + EU + Asia stocks & ETFsEach recommendation includes:
| Column | Meaning |
|---|---|
| Type | STOCK or ETF |
| Ticker / Name / Sector | Identification |
| Ticker link | Opens Google Finance in a new tab |
| T / Y | Opens TradingView / Yahoo Finance in a new tab |
| Price | Current price in native currency |
| % Today | Daily change vs previous close |
| P/E | Trailing price-to-earnings ratio (stocks; - when unavailable) |
| 1M Δ | % change vs ~22 trading days ago |
| 1Y Δ | % change vs ~252 trading days ago |
| RSI | RSI-14 (when --include-technical is enabled) |
| MACD | MACD histogram % vs price (when --include-technical is enabled) |
| Superinvestors | Number of managers holding the stock (HTML only; - if not in DataRoma universe) |
| Score | Composite 0-100 weighted score |
| Breakdown | Four sub-scores (shown in a compact single line) |
| Why | Top 3 rationale notes |
Each metric is normalized to 0-100 via piecewise-linear functions over empirically reasonable ranges. Missing data contributes a neutral 50 so a ticker isn't penalized for lacking a metric.
| Pillar | Weight | Metrics |
|---|---|---|
| Quality | 35% | ROE, profit margin, earnings & revenue growth |
| Value | 25% | trailing P/E, P/B, PEG |
| Health | 20% | Debt/Equity, current ratio, free cash flow |
| Trend | 20% | price vs SMA200, 12-month return, SMA200 slope (plus RSI-14 + MACD histogram when --include-technical is enabled) |
| Pillar | Weight | Metrics |
|---|---|---|
| Returns | 40% | 3y avg, 5y avg, 12m return |
| RiskAdj | 25% | Sharpe proxy (r12 - rf) / σ, annualized volatility |
| Size | 15% | AUM (log scale) |
| Cost/Yield | 20% | expense ratio (lower=better), dividend yield |
When no -t is given, InvestDayTip uses curated universes:
- US stocks — 58 large-caps across all S&P sectors (
src/investdaytip/universe.py) - US ETFs — 41 broad-market, factor, sector and bond ETFs (
etf_universe.py) - EU stocks — 65 large-caps from DAX, CAC, FTSE 100, IBEX, AEX, SMI, FTSE MIB, Nordics (
eu_universe.py) - EU UCITS ETFs — 38 broad, sector and bond UCITS ETFs (
eu_etf_universe.py) - Asia stocks — 76 large-caps from Japan, Hong Kong, Singapore, India, South Korea, Taiwan, and Australia (
asia_universe.py) - Asia ETFs — 20 broad-market, country-specific, and sector ETFs with significant Asian exposure (
asia_etf_universe.py) - Superinvestor stocks — 116 consensus picks held by ≥2 of ~82 top investors tracked by DataRoma 13F filings (
superinvestor_universe.py)
Tickers use Yahoo Finance suffixes:
- US: no suffix (AAPL, MSFT)
- EU:
.DEXetra ·.PAParis ·.ASAmsterdam ·.LLondon ·.MCMadrid ·.MIMilan ·.SWSwiss - Asia:
.TTokyo ·.HKHong Kong ·.SISingapore ·.NSNSE India ·.KSKorea ·.TWTaiwan ·.AXAustralia
The repository includes ready-to-use ticker sets in tickers-files-examples/ for thematic analyses:
artificial_intelligence_relevant_tickers.txtbiotech_relevant_tickers.txtenergy_relevant_tickers.txteu_etfs_relevant_tickers.txtfinancial_relevant_tickers.txthealth_relevant_tickers.txtpharma_relevant_tickers.txtquantum_computing_relevant_tickers.txtsemiconductors_relevant_tickers.txtspace_relevant_tickers.txtspain_relevant_tickers.txttechnology_relevant_tickers.txt
Example:
investdaytip -n 15 --tickers-file tickers-files-examples/artificial_intelligence_relevant_tickers.txt --export-htmlAfter export, preview the generated file in a browser by running:
./preview.shThen open the generated report from:
http://localhost:8000/<generated-file-name>.html
Requires the argcomplete package (installed automatically with pip install investdaytip):
# Enable for the current session:
eval "$(register-python-argcomplete investdaytip)"
# Make it permanent (add to ~/.zshrc or ~/.bashrc):
echo 'eval "$(register-python-argcomplete investdaytip)"' >> ~/.zshrcOnce enabled, try investdaytip -<TAB> or investdaytip --region <TAB>.
All market data is fetched live from Yahoo Finance via the yfinance library. Fundamentals come from Ticker.info, prices and trend metrics from Ticker.history(period="2y").
Superinvestor data is scraped from DataRoma (https://www.dataroma.com) — 13F filings from ~82 legendary investors. The data is fetched once and cached for 7 days. Use --superinvestor to enable this data and display the "Superinvestors" column in both the HTML report and the CLI table (disabled by default to avoid the ~80 HTTP requests).
Ticker normalization: When a company has multiple share classes (e.g., Alphabet's GOOGL and GOOG), DataRoma holdings are normalized to a single ticker (GOOGL) to avoid duplicate counting. This ensures the "Superinvestors" column reflects unique manager positions, not duplicated entries.
InvestDayTip includes an SQLite cache (~/.investdaytip/cache.db) created automatically on first fetch:
| Data | TTL |
|---|---|
| Prices & history | 15 minutes |
| Fundamentals (info dict) | 1 day |
| Financial statements (balance sheet, income, cash flow) | 7 days |
| Dividends | 7 days |
| Fear & Greed Index | 1 hour |
| Superinvestor holdings | 7 days |
The cache uses per-thread SQLite connections with a write lock to support concurrent fetches safely. Use --no-cache to bypass the cache for a single run, or --cache-clear to purge all entries. Both flags work on the main and backtest subcommands. Caching is automatically disabled when running tests.
preview.sh # Local static server for generated HTML reports
scripts/
└── scoring_baseline.py # Backtest before/after comparison tool
src/investdaytip/
├── __init__.py # Public API: get_recommendations
├── main.py # CLI entry point + rich table rendering
├── advisor.py # Interactive advisor: market pulse (VIX + macro), portfolio review, buy recs
├── backtest.py # Historical stock scoring validation (stocks only)
├── dataroma.py # DataRoma superinvestor holdings scraper
├── html_export.py # Self-contained HTML report exporter
├── recommender.py # Concurrent orchestration
├── cache.py # SQLite caching layer (per-thread connections, WAL mode)
├── data_source.py # yfinance wrapper + dataclasses (StockData / EtfData)
├── scoring.py # Pure scoring functions (score_stock, score_etf)
├── sentiment.py # CNN Fear & Greed Index fetch
├── universe.py # US stock universe
├── etf_universe.py # US ETF universe
├── superinvestor_universe.py # Superinvestor consensus universe (DataRoma 13F)
├── eu_universe.py # EU stock universe
├── eu_etf_universe.py # EU UCITS ETF universe
├── asia_universe.py # Asia stock universe
└── asia_etf_universe.py # Asia ETF universe
portfolios/ # Portfolio ticker files
advisor_recommendations/ # Advisor-generated HTML reports (git-ignored)
tests/
├── test_integration.py # End-to-end integration tests (sorting, filters, CLI, export)
├── test_advisor.py # Advisor tests (VIX, macro regime, bubble risk)
├── test_backtest.py # Backtest engine tests (fetch, snapshots, scoring, cache, interpret)
├── test_recommender.py # Recommendation orchestration tests
├── test_main.py # CLI helper tests
├── test_html_export.py # HTML export tests
├── test_scoring.py # Stock scoring tests
├── test_etf_scoring.py # ETF scoring tests
├── test_sentiment.py # Fear & Greed sentiment tests
├── test_cache.py # SQLite cache tests
├── test_universes.py # Universe integrity tests
└── test_data_source.py # Data fetching tests
tickers-files-examples/
├── semiconductors_relevant_tickers.txt
├── artificial_intelligence_relevant_tickers.txt
├── quantum_computing_relevant_tickers.txt
├── energy_relevant_tickers.txt
├── space_relevant_tickers.txt
├── technology_relevant_tickers.txt
├── spanish_relevant_tickers.txt
├── pharma_relevant_tickers.txt
├── biotech_relevant_tickers.txt
├── health_relevant_tickers.txt
└── financial_relevant_tickers.txt
- No currency normalization — prices, market caps and AUM stay in native currency
- Currency filter (
-c) compares against yfinance's reported currency field; tickers with a missing/unknown currency are always kept (a missing field shouldn't silently drop a candidate) min_market_capfilter (--min-market-cap) is applied against raw native-currency figures; when active, tickers with a missing market cap are excluded- Some European tickers change Yahoo symbols over time; if a ticker is delisted in Yahoo it's silently skipped
- ETF expense ratios are sometimes missing in
yfinance— the scorer falls back to a slightly optimistic default (60) in that case - Long-term, fundamental-driven model: not suitable for short-term/day trading signals
pip install -e ".[dev]" # pytest, ruff, mypy
pytest -q # run the suite
ruff check src tests # lint
mypy # type-check176 tests across 12 test files. The scoring engine is purely functional
and tested without network calls; an autouse guard in tests/conftest.py fails
fast if a test reaches yfinance unmocked. Integration tests mock the full
recommend() → main() → export pipeline.
Contributions are welcome! See CONTRIBUTING.md.
This is not financial advice. InvestDayTip is an educational tool that applies a deterministic scoring model to publicly available data. Always do your own research and consult a licensed advisor before making investment decisions.