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https://github.com/farcasclaudiu/xtb-investment-tools.git
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Initial commit
This commit is contained in:
@@ -0,0 +1,42 @@
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---
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name: xtb-portfolio-review
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description: Use when analyzing XTB brokerage .xlsx exports with the local portfolio review tool, generating or checking HTML/CSV reports, validating cash reconciliation, reviewing holdings, risk, income, performance, or explaining report outputs from main.py.
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---
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# XTB Portfolio Review
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Use this skill to run and assess XTB portfolio reviews from a copied skill folder. The skill bundles the required Python tools in `scripts/`, so it can run without the original repository as long as Python dependencies are installed.
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## Workflow
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1. Identify the target workbook. If the user does not name one and exactly one non-lock `.xlsx` exists in the current working directory, use it.
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2. Ensure dependencies are available:
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`<skill-folder>/scripts/setup-env.sh`
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3. Validate the bundled tools:
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`<skill-folder>/scripts/validate-review.sh`
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4. Generate the review from the directory where outputs should be written:
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`<skill-folder>/scripts/run-review.sh <report.xlsx>`
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5. Inspect `results/` outputs named from the workbook stem, especially `_review.html`, `_holdings.csv`, `_cash_flows.csv`, `_performance.csv`, `_income.csv`, and `_evolution.csv`.
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6. Check whether computed ending cash reconciles to the broker `Total` row within EUR/USD/etc. `0.01`.
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7. Report findings with caveats: cost-priced tickers, missing live prices, cash mismatch, XIRR availability, concentration, income tax drag, and any generated file paths.
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## Bundled Tools
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- `scripts/main.py`: standalone XTB portfolio review generator.
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- `scripts/html_charts.py`: offline Chart.js report rendering helper.
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- `scripts/assets/chartjs.umd.min.js`: vendored Chart.js bundle for self-contained HTML.
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- `scripts/run-review.sh`: shell wrapper that runs the bundled review tool with `--csv`.
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- `scripts/validate-review.sh`: dependency and asset smoke check.
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- `scripts/setup-env.sh`: creates `.venv` in the current working directory and installs dependencies.
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- `scripts/requirements.txt`: Python dependencies.
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## References
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- Read `references/xtb-format.md` when parsing behavior, report assumptions, or XTB edge cases matter.
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- Read `references/validation-checklist.md` before claiming a generated portfolio review is correct or ready to use.
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## Guardrails
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- Do not treat the generated report as investment advice; describe what the tool computed and the data-quality limits.
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- Prefer the bundled validation script and generated CSVs over eyeballing the HTML alone.
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- Preserve offline/self-contained HTML behavior; do not introduce CDN dependencies when modifying the report.
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# Portfolio Review Validation Checklist
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Load this before saying an XTB portfolio review is ready.
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## Commands
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- Install dependencies:
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`<skill-folder>/scripts/setup-env.sh`
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- Validate bundled tools:
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`<skill-folder>/scripts/validate-review.sh`
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- Generate report and CSVs:
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`<skill-folder>/scripts/run-review.sh <report.xlsx>`
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- If working inside the original project repository, full tests are also useful:
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`.venv/bin/python -m pytest -q`
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## Required Checks
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- The command exits successfully and writes `results/<stem>_review.html`.
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- CSV side outputs exist when `--csv` was used.
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- Cash reconciliation is `[OK]` or the mismatch is explicitly reported.
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- Holdings with live-price failures are visible as cost fallbacks.
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- The HTML remains self-contained/offline: no CDN script or stylesheet dependency.
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- The report includes methodology/data-quality notes for pricing and reconciliation.
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## Useful Output Files
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- `_holdings.csv`: shares, cost basis, market value, allocation, unrealized P/L, price source.
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- `_cash_flows.csv`: deposits, withdrawals, invested, proceeds, dividends, tax, fees, ending cash.
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- `_realized_pl.csv`: realized profit/loss by ticker.
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- `_performance.csv`: portfolio value, total gain, return metrics, income yield.
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- `_income.csv`: dividend and interest income over time.
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- `_evolution.csv`: daily cost/value/realized series for charts.
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## Reporting Style
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Summarize computed facts and data-quality status. Avoid recommendations to buy, sell, rebalance, or time markets unless the user explicitly asks for financial planning context, and still frame it as educational analysis rather than advice.
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# XTB Report Format Notes
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Load this when XTB parsing details matter.
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## Workbook Layout
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- XTB exports are `.xlsx` files.
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- Metadata is in rows 1-4.
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- Column headers begin on row 5, so pandas should use `header=4`.
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- Main sheets:
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- `Cash Operations`: trades, deposits, withdrawals, dividends, taxes, interest, conversions, and broker `Total` row.
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- `Closed Positions`: realized trade summary; can be empty for still-open accounts.
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- `Open Positions`: optional live/open-position sheet.
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## Trade Reconstruction
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- The review reconstructs trades primarily from `Cash Operations` comments such as `OPEN BUY 6 @ 301.50` and `CLOSE SELL 2 @ 100.00`.
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- Use the real `Ticker` column as the instrument key, not only descriptive instrument text.
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- Process trades chronologically before FIFO matching.
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- Split-fill notation like `OPEN BUY 1/100 @ 14.3130` means executed quantity is `1`; use the numerator, not `0.01`.
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- Some XTB stock sales appear as `CLOSE BUY` while the row type is `Stock sell` and amount is positive. Treat these economically as sales.
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## Valuation
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- Live prices come from `yfinance` daily closes at or before the report end date.
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- Use trusted same-instrument symbol aliases only. Do not substitute a different share class as a proxy.
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- If no trusted price exists, hold the ticker at cost and surface `price_source = cost` plus the reason.
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## Cash And Performance
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- Reconciliation compares computed ending cash with the broker `Total` row.
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- Dividends and interest are internal cash flows unless withdrawn; do not count them as external cash flows for XIRR.
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- XIRR may be `n/a` if the cash-flow signs or solver conditions are insufficient.
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4.5.1
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File diff suppressed because one or more lines are too long
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"""Interactive Chart.js charts for the self-contained HTML report.
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This module is the only place that knows about Chart.js. It reads the vendored
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UMD bundle from assets/ and builds Chart.js config dicts (pure functions) plus
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an HTML fragment that inlines the bundle, the data (JSON), and a render script.
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"""
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from __future__ import annotations
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import json
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from pathlib import Path
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from typing import Any
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import pandas as pd
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ASSETS_DIR = Path(__file__).resolve().parent / "assets"
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CHARTJS_PATH = ASSETS_DIR / "chartjs.umd.min.js"
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CHARTJS_VERSION_PATH = ASSETS_DIR / "chartjs.VERSION"
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def load_chartjs_inline() -> str:
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"""Return the minified Chart.js UMD source, vendored under assets/."""
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if not CHARTJS_PATH.exists():
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raise FileNotFoundError(
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f"Chart.js bundle not found at {CHARTJS_PATH}. "
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"Re-vendor it (see assets/chartjs.VERSION)."
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)
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return CHARTJS_PATH.read_text(encoding="utf-8")
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def _iso(value: Any) -> str:
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if hasattr(value, "isoformat"):
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return value.isoformat()[:10]
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return str(value)
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def _round_series(values) -> list[float]:
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return [round(float(v), 2) for v in values]
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def evolution_chart_config(evolution_df: pd.DataFrame, currency: str) -> dict | None:
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"""Build a Chart.js line-chart config for cost vs value over time.
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Returns None when there is no evolution data (caller omits the card).
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"""
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if evolution_df is None or evolution_df.empty:
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return None
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labels = [_iso(d) for d in evolution_df.index]
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return {
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"type": "line",
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"data": {
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"labels": labels,
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"datasets": [
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{
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"label": "Cost (invested)",
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"data": _round_series(evolution_df["cost"]),
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"borderColor": "#6b7280",
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"backgroundColor": "#6b7280",
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"borderWidth": 2,
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"fill": False,
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"pointRadius": 0,
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"tension": 0.1,
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},
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{
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"label": "Value (realized + unrealized)",
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"data": _round_series(evolution_df["total_value"]),
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"borderColor": "#2c5282",
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"backgroundColor": "#2c5282",
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"borderWidth": 2,
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"fill": False,
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"pointRadius": 0,
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"tension": 0.1,
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},
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{
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"label": "Cumulative realized P/L",
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"data": _round_series(evolution_df["realized_pl"]),
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"borderColor": "#f39c12",
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"backgroundColor": "#f39c12",
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"borderWidth": 1.5,
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"borderDash": [6, 4],
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"fill": False,
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"pointRadius": 0,
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"tension": 0.1,
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},
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],
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},
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"options": {
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"responsive": True,
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"maintainAspectRatio": False,
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"interaction": {"mode": "index", "intersect": False},
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"plugins": {
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"legend": {"position": "bottom",
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"labels": {"boxWidth": 12, "font": {"size": 12}}},
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},
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"scales": {
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"x": {"ticks": {"maxRotation": 45, "autoSkip": True}},
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"y": {"beginAtZero": False},
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},
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},
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}
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DOUGHNUT_COLORS = [
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"#2c5282", "#1f9d55", "#f39c12", "#3498db", "#9b59b6",
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"#e67e22", "#16a085", "#34495e", "#e3342f", "#7f8c8d",
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]
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def review_charts_config(
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holdings: pd.DataFrame,
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flows: dict[str, float],
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income_by_period: pd.Series,
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currency: str,
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) -> dict:
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"""Build Chart.js configs for the three review charts.
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Returns {'holdings': cfg|None, 'cashflows': cfg|None, 'income': cfg|None}.
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Each is None when its source data is empty.
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"""
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holdings_cfg = _holdings_config(holdings)
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cashflows_cfg = _cashflows_config(flows)
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income_cfg = _income_config(income_by_period)
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return {"holdings": holdings_cfg, "cashflows": cashflows_cfg, "income": income_cfg}
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def _holdings_config(holdings: pd.DataFrame) -> dict | None:
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if holdings is None or holdings.empty:
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return None
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alloc_col = "market_value" if "market_value" in holdings.columns else "cost_basis"
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filtered = holdings.loc[holdings[alloc_col] > 0]
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if filtered.empty:
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return None
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values = _round_series(filtered[alloc_col])
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return {
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"type": "doughnut",
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"data": {
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"labels": [str(t) for t in filtered["ticker"].tolist()],
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"datasets": [{
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"data": values,
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"backgroundColor": [DOUGHNUT_COLORS[i % len(DOUGHNUT_COLORS)]
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for i in range(len(values))],
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}],
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},
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"options": {
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"responsive": True,
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"maintainAspectRatio": False,
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"plugins": {"legend": {"position": "right",
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"labels": {"boxWidth": 12, "font": {"size": 11}}}},
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},
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}
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def _cashflows_config(flows: dict[str, float]) -> dict | None:
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if not flows:
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return None
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items = {
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"Deposits": float(flows["deposits"]),
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"Withdrawals": -float(flows["withdrawals"]),
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"Interest": float(flows["interest"]),
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"Dividends": float(flows["dividends"]),
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"Div.tax": float(flows["dividend_tax"]),
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"Invested": -float(flows["invested"]),
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"Proceeds": float(flows["proceeds"]),
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"FX fees": float(flows["conversion_fees"]),
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"Fees": -float(flows["fees"]),
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}
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items = {k: v for k, v in items.items() if abs(v) > 1e-9}
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if not items:
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return None
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labels = list(items.keys())
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values = _round_series(items.values())
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colors = ["#2ecc71" if v >= 0 else "#e74c3c" for v in items.values()]
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return {
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"type": "bar",
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"data": {"labels": labels,
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"datasets": [{"label": "Cash flows", "data": values,
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"backgroundColor": colors}]},
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"options": {
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"responsive": True,
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"maintainAspectRatio": False,
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"plugins": {"legend": {"display": False}},
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"scales": {"x": {"ticks": {"maxRotation": 30, "autoSkip": False}},
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"y": {"beginAtZero": True}},
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},
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}
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def _income_config(income_by_period: pd.Series) -> dict | None:
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if income_by_period is None or income_by_period.empty:
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return None
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return {
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"type": "bar",
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"data": {
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"labels": [str(i) for i in income_by_period.index],
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"datasets": [{"label": "Income",
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"data": _round_series(income_by_period.tolist()),
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"backgroundColor": "#3498db"}],
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},
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"options": {
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"responsive": True,
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"maintainAspectRatio": False,
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"plugins": {"legend": {"display": False}},
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"scales": {"x": {"ticks": {"maxRotation": 45, "autoSkip": False}},
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"y": {"beginAtZero": True}},
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},
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}
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_RENDER_SCRIPT = r"""
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function _bootPortfolioCharts() {
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var block = document.getElementById('chart-data');
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if (!block) { return; }
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var data = JSON.parse(block.textContent);
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var ccy = data.currency || 'EUR';
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function fmt(v) {
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try { return new Intl.NumberFormat('en-US', {style: 'currency', currency: ccy}).format(v); }
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catch (e) { return String(v); }
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}
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function applyTooltip(cfg) {
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if (!cfg || !cfg.options) { return; }
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cfg.options.plugins = cfg.options.plugins || {};
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cfg.options.plugins.tooltip = cfg.options.plugins.tooltip || {};
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cfg.options.plugins.tooltip.callbacks = cfg.options.plugins.tooltip.callbacks || {};
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if (cfg.type === 'doughnut' || cfg.type === 'pie') {
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cfg.options.plugins.tooltip.callbacks.label = function (ctx) {
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var total = (ctx.dataset && ctx.dataset.data)
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? ctx.dataset.data.reduce(function (a, b) { return a + (typeof b === 'number' ? b : 0); }, 0)
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: 0;
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var v = (typeof ctx.parsed === 'number') ? ctx.parsed : ctx.raw;
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var pct = total > 0 ? (v / total * 100) : 0;
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return (ctx.label ? ctx.label + ': ' : '') + fmt(v) + ' (' + pct.toFixed(1) + '%)';
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};
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return;
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}
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cfg.options.plugins.tooltip.callbacks.label = function (ctx) {
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var label = (ctx.dataset && ctx.dataset.label) ? ctx.dataset.label : '';
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var v = (ctx.parsed && Object.prototype.hasOwnProperty.call(ctx.parsed, 'y'))
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? ctx.parsed.y : (typeof ctx.parsed === 'number' ? ctx.parsed : ctx.raw);
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return label ? (label + ': ' + fmt(v)) : fmt(v);
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};
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}
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function mount(id, cfg, plugins) {
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if (!cfg) { return; }
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var el = document.getElementById(id);
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if (!el) { return; }
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applyTooltip(cfg);
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var config = {type: cfg.type, data: cfg.data, options: cfg.options};
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if (plugins && plugins.length) { config.plugins = plugins; }
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new Chart(el.getContext('2d'), config);
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}
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var gainLossPlugin = {
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id: 'gainLoss',
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beforeDatasetsDraw: function (chart) {
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var ds = chart.data.datasets;
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if (ds.length < 2) { return; }
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||||
var meta0 = chart.getDatasetMeta(0);
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var meta1 = chart.getDatasetMeta(1);
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var cost = ds[0].data;
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var value = ds[1].data;
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if (!meta0 || !meta1 || !meta0.data || !meta1.data) { return; }
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var ctx = chart.ctx;
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ctx.save();
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for (var i = 0; i < value.length - 1; i++) {
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var a0 = meta0.data[i], a1 = meta0.data[i + 1];
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var b0 = meta1.data[i], b1 = meta1.data[i + 1];
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||||
if (!a0 || !a1 || !b0 || !b1) { continue; }
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var gain = (value[i] >= cost[i] && value[i + 1] >= cost[i + 1]);
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ctx.beginPath();
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ctx.moveTo(a0.x, a0.y); ctx.lineTo(a1.x, a1.y);
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ctx.lineTo(b1.x, b1.y); ctx.lineTo(b0.x, b0.y);
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ctx.closePath();
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ctx.fillStyle = gain ? 'rgba(31,157,85,0.25)' : 'rgba(227,52,47,0.25)';
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ctx.fill();
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}
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ctx.restore();
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||||
}
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||||
};
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mount('evolution-chart', data.evolution, [gainLossPlugin]);
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mount('holdings-chart', data.holdings);
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mount('cashflows-chart', data.cashflows);
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mount('income-chart', data.income);
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}
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if (document.readyState !== 'loading') { _bootPortfolioCharts(); }
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else { document.addEventListener('DOMContentLoaded', _bootPortfolioCharts); }
|
||||
"""
|
||||
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||||
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||||
def render_charts_block(
|
||||
evolution_cfg: dict | None, review_cfg: dict, currency: str
|
||||
) -> str:
|
||||
"""Return the HTML fragment: canvases + inlined Chart.js + JSON + render script.
|
||||
|
||||
Returns "" when there is nothing to render.
|
||||
"""
|
||||
holdings_cfg = review_cfg.get("holdings") if review_cfg else None
|
||||
cashflows_cfg = review_cfg.get("cashflows") if review_cfg else None
|
||||
income_cfg = review_cfg.get("income") if review_cfg else None
|
||||
|
||||
if evolution_cfg is None and not any([holdings_cfg, cashflows_cfg, income_cfg]):
|
||||
return ""
|
||||
|
||||
parts: list[str] = []
|
||||
|
||||
if evolution_cfg is not None:
|
||||
parts.append(
|
||||
"<div class='card chart full' id='charts'>\n"
|
||||
" <h2>Portfolio Evolution — Cost vs Value</h2>\n"
|
||||
" <div class='chart-wrap' style='height:380px'>"
|
||||
"<canvas id='evolution-chart'></canvas></div>\n"
|
||||
"</div>"
|
||||
)
|
||||
|
||||
grid_cells = []
|
||||
if holdings_cfg is not None:
|
||||
grid_cells.append(
|
||||
"<div><h3>Holdings Allocation</h3>"
|
||||
"<div class='chart-wrap' style='height:300px'>"
|
||||
"<canvas id='holdings-chart'></canvas></div></div>"
|
||||
)
|
||||
else:
|
||||
grid_cells.append("<div><h3>Holdings Allocation</h3>"
|
||||
"<p class='muted'>No open positions.</p></div>")
|
||||
if cashflows_cfg is not None:
|
||||
grid_cells.append(
|
||||
"<div><h3>Cash Flows</h3>"
|
||||
"<div class='chart-wrap' style='height:300px'>"
|
||||
"<canvas id='cashflows-chart'></canvas></div></div>"
|
||||
)
|
||||
else:
|
||||
grid_cells.append("<div><h3>Cash Flows</h3>"
|
||||
"<p class='muted'>No cash flows.</p></div>")
|
||||
# Income is optional: the income cell is omitted entirely when there is no
|
||||
# income data, unlike holdings/cashflows which always render a cell with a
|
||||
# muted fallback.
|
||||
if income_cfg is not None:
|
||||
grid_cells.append(
|
||||
"<div><h3>Income Over Time</h3>"
|
||||
"<div class='chart-wrap' style='height:300px'>"
|
||||
"<canvas id='income-chart'></canvas></div></div>"
|
||||
)
|
||||
charts_id_attr = " id='charts'" if evolution_cfg is None else ""
|
||||
parts.append(
|
||||
f"<div class='card chart full'{charts_id_attr}>\n"
|
||||
" <h2>Charts</h2>\n"
|
||||
" <div class='chart-grid'>\n " +
|
||||
"\n ".join(grid_cells) + "\n </div>\n"
|
||||
"</div>"
|
||||
)
|
||||
|
||||
payload = {
|
||||
"currency": currency,
|
||||
"evolution": evolution_cfg,
|
||||
"holdings": holdings_cfg,
|
||||
"cashflows": cashflows_cfg,
|
||||
"income": income_cfg,
|
||||
}
|
||||
# Escape < and > so the JSON is always safe to inline inside a <script>
|
||||
# block, even if a label ever contained the literal "</script>".
|
||||
data_json = json.dumps(payload).replace("<", "\\u003c").replace(">", "\\u003e")
|
||||
|
||||
parts.append(
|
||||
"<script>\n" + load_chartjs_inline() + "\n</script>\n"
|
||||
"<script type='application/json' id='chart-data'>" + data_json + "</script>\n"
|
||||
"<script>\n" + _RENDER_SCRIPT + "\n</script>"
|
||||
)
|
||||
return "\n".join(parts)
|
||||
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,4 @@
|
||||
pandas>=2.2,<4
|
||||
numpy>=1.26,<3
|
||||
openpyxl>=3.1,<4
|
||||
yfinance>=0.2,<2
|
||||
+13
@@ -0,0 +1,13 @@
|
||||
#!/usr/bin/env bash
|
||||
set -euo pipefail
|
||||
|
||||
SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
|
||||
if [[ -n "${PYTHON:-}" ]]; then
|
||||
PYTHON_BIN="$PYTHON"
|
||||
elif [[ -x ".venv/bin/python" ]]; then
|
||||
PYTHON_BIN=".venv/bin/python"
|
||||
else
|
||||
PYTHON_BIN="python3"
|
||||
fi
|
||||
|
||||
exec "$PYTHON_BIN" "$SCRIPT_DIR/main.py" "$@" --csv
|
||||
+15
@@ -0,0 +1,15 @@
|
||||
#!/usr/bin/env bash
|
||||
set -euo pipefail
|
||||
|
||||
SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
|
||||
VENV_DIR="${VENV_DIR:-.venv}"
|
||||
PYTHON_BOOTSTRAP="${PYTHON:-python3}"
|
||||
|
||||
if [[ ! -x "$VENV_DIR/bin/python" ]]; then
|
||||
"$PYTHON_BOOTSTRAP" -m venv "$VENV_DIR"
|
||||
fi
|
||||
|
||||
"$VENV_DIR/bin/python" -m pip install --upgrade pip
|
||||
"$VENV_DIR/bin/python" -m pip install -r "$SCRIPT_DIR/requirements.txt"
|
||||
|
||||
echo "Environment ready: $VENV_DIR"
|
||||
+35
@@ -0,0 +1,35 @@
|
||||
#!/usr/bin/env bash
|
||||
set -euo pipefail
|
||||
|
||||
SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
|
||||
if [[ -n "${PYTHON:-}" ]]; then
|
||||
PYTHON_BIN="$PYTHON"
|
||||
elif [[ -x ".venv/bin/python" ]]; then
|
||||
PYTHON_BIN=".venv/bin/python"
|
||||
else
|
||||
PYTHON_BIN="python3"
|
||||
fi
|
||||
|
||||
"$PYTHON_BIN" - <<PY
|
||||
import importlib.util
|
||||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
script_dir = Path("$SCRIPT_DIR")
|
||||
sys.path.insert(0, str(script_dir))
|
||||
|
||||
for module in ("pandas", "openpyxl", "yfinance"):
|
||||
if importlib.util.find_spec(module) is None:
|
||||
raise SystemExit(
|
||||
f"Missing dependency: {module}. Install with: "
|
||||
f"{sys.executable} -m pip install -r {script_dir / 'requirements.txt'}"
|
||||
)
|
||||
|
||||
import main
|
||||
import html_charts
|
||||
|
||||
if not html_charts.CHARTJS_PATH.exists():
|
||||
raise SystemExit(f"Missing Chart.js asset: {html_charts.CHARTJS_PATH}")
|
||||
|
||||
print("XTB portfolio review skill tools are importable.")
|
||||
PY
|
||||
Reference in New Issue
Block a user