investdaytip 0.1.1

A tool that suggests 5 stock investment recommendations at the moment you run it.

InvestDayTip

A multi-factor analysis tool that suggests long-term stock & ETF buy recommendations from US, European, and Asian markets, computed live from public market data.

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Features

• 📈 Multi-factor scoring — composite 0-100 score per asset
• 🏦 Stocks & ETFs — auto-detected and scored with dedicated models
• 🌍 US, European & Asian markets — S&P 500, DAX, CAC 40, FTSE 100, Nikkei 225, Hang Seng, NSE, and more
• 💱 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 advisor subcommand
• 🤖 AI-powered advisor — chat with an intelligent investment advisor that analyzes markets, reviews portfolios, and recommends buys — powered by OpenCode agents

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Installation

From PyPI (recommended)

pip install investdaytip

From source (for development)

git 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]"

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Quick start

pip install investdaytip
investdaytip

That's it. You'll see the top 5 buys scored across 300+ stocks & ETFs.

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Usage

CLI

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 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 --workers 20            # More parallelism
investdaytip --min-market-cap 1B     # Raise min market cap to $1B
investdaytip --min-market-cap 0      # Disable market-cap filter
investdaytip --help

./preview.sh                         # Serve generated HTML files on localhost:8000

Options

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} 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
--export-html [PATH]	Export recommendations to self-contained HTML (investDayTip-aaaammdd-hhmm.html if omitted)	disabled
--min-market-cap VALUE	Minimum market cap (1B, 500M, 0 to disable)	2B
--workers N	Parallel fetch threads	10

Advisor subcommand

Interactive market analysis, 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 portfolio

See investdaytip advisor --help for all options.

OpenCode AI Agent

🤖 Chat with an AI investment advisor that checks VIX, reviews portfolios, and recommends buys — see .opencode/agents/advisor.md.

No memorizing flags. From the project root:

@advisor  what's the market pulse?

HTML export

Generate an interactive report that works offline (single file with inline CSS/JS):

investdaytip -n 25 -a all -r all --export-html investdaytip-report.html

The generated report includes filters for:

• Text search (ticker/name/sector)
• Asset class (stock / etf)
• Region (us / eu / asia)
• Minimum score
• Minimum 1M return (%)
• Minimum 1Y return (%)

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.

Programmatic API

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 mix regions and asset classes
picks = get_recommendations(top_n=10, region="all")  # US + EU + Asia stocks & ETFs

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Output

Each 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
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
Score	Composite 0-100 weighted score
Breakdown	Four sub-scores (shown in a compact single line)
Why	Top 3 rationale notes

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Scoring Model

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.

Stocks (Graham/Buffett + Momentum)

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

ETFs

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

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Universes

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 — 24 broad-market, country-specific, and sector ETFs with significant Asian exposure (asia_etf_universe.py)

Tickers use Yahoo Finance suffixes:

• US: no suffix (AAPL, MSFT)
• EU: .DE Xetra · .PA Paris · .AS Amsterdam · .L London · .MC Madrid · .MI Milan · .SW Swiss
• Asia: .T Tokyo · .HK Hong Kong · .SI Singapore · .NS NSE India · .KS Korea · .TW Taiwan · .AX Australia

Ticker File Examples

The repository includes ready-to-use ticker sets in tickers-files-examples/ for thematic analyses:

• artificial_intelligence_relevant_tickers.txt
• biotech_relevant_tickers.txt
• energy_relevant_tickers.txt
• eu_etfs_relevant_tickers.txt
• financial_relevant_tickers.txt
• health_relevant_tickers.txt
• pharma_relevant_tickers.txt
• quantum_computing_relevant_tickers.txt
• semiconductors_relevant_tickers.txt
• space_relevant_tickers.txt
• spanish_relevant_tickers.txt
• technology_relevant_tickers.txt

Example:

investdaytip -n 15 --tickers-file tickers-files-examples/artificial_intelligence_relevant_tickers.txt --export-html

After export, preview the generated file in a browser by running:

./preview.sh

Then open the generated report from:

http://localhost:8000/<generated-file-name>.html

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Data Source

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").

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Project Structure

preview.sh                # Local static server for generated HTML reports
src/investdaytip/
├── __init__.py            # Public API: get_recommendations
├── main.py                # CLI entry point + rich table rendering
├── advisor.py             # Interactive advisor: market pulse, portfolio review, buy recs
├── html_export.py         # Self-contained HTML report exporter
├── recommender.py         # Concurrent orchestration
├── data_source.py         # yfinance wrapper + dataclasses (StockData / EtfData)
├── scoring.py             # Pure scoring functions (score_stock, score_etf)
├── universe.py            # US stock universe
├── etf_universe.py        # US ETF universe
├── 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_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
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

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Limitations & Caveats

• 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 pass only when -c all
• min_market_cap filter (--min-market-cap) is applied against raw native-currency figures
• 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

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Testing

pytest -q

The scoring engine is purely functional and tested without network calls.

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Contributing

Contributions are welcome! See CONTRIBUTING.md.

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Disclaimer

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.

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License

MIT