Add scripts for environment setup and validation, and implement tests for portfolio performance exporter

- Created requirements.txt for dependencies including pandas, numpy, openpyxl, and yfinance.
- Added setup-env.sh script to set up a Python virtual environment and install required packages.
- Introduced validate-export.sh script to validate the exporter module and check expected fields.
- Implemented test cases in test_portfolio_performance_exporter.py to ensure correct CSV export functionality and data handling.
This commit is contained in:
2026-06-21 21:06:08 +03:00
parent c40724eae6
commit 68cfec926e
14 changed files with 3333 additions and 10 deletions
+32 -2
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@@ -2,10 +2,11 @@
This file is written for LLM agents and coding assistants. Follow it when a user asks you to install, use, or copy the XTB portfolio skills from this repository.
The repository ships two standalone, harness-neutral skill folders:
The repository ships three standalone, harness-neutral skill folders:
- `skills/xtb-portfolio-review`
- `skills/xtb-wealthfolio-export`
- `skills/xtb-portfolio-performance-export`
Each skill folder is self-contained: it includes `SKILL.md`, references, runnable scripts, Python source files, requirements, and offline Chart.js assets where needed. Users may copy a single skill folder without cloning the full repository.
@@ -30,6 +31,7 @@ For Codex:
mkdir -p "$HOME/.codex/skills"
cp -R skills/xtb-portfolio-review "$HOME/.codex/skills/"
cp -R skills/xtb-wealthfolio-export "$HOME/.codex/skills/"
cp -R skills/xtb-portfolio-performance-export "$HOME/.codex/skills/"
```
For a generic agent workspace, copy the skill folders to a user-chosen directory:
@@ -38,6 +40,7 @@ For a generic agent workspace, copy the skill folders to a user-chosen directory
mkdir -p ./agent-skills
cp -R skills/xtb-portfolio-review ./agent-skills/
cp -R skills/xtb-wealthfolio-export ./agent-skills/
cp -R skills/xtb-portfolio-performance-export ./agent-skills/
```
If only one workflow is needed, copy only that folder.
@@ -65,6 +68,14 @@ For Wealthfolio export:
/path/to/xtb-wealthfolio-export/scripts/export-wealthfolio.sh /path/to/report.xlsx
```
For Portfolio Performance export:
```bash
/path/to/xtb-portfolio-performance-export/scripts/setup-env.sh
/path/to/xtb-portfolio-performance-export/scripts/validate-export.sh
/path/to/xtb-portfolio-performance-export/scripts/export-portfolio-performance.sh /path/to/report.xlsx
```
The setup scripts create or reuse `.venv` in the current working directory. If network access or package installation requires approval, ask before running `setup-env.sh`.
## Use Without Installing
@@ -87,6 +98,10 @@ Read skills/xtb-portfolio-review/SKILL.md and use that skill to generate a portf
Read skills/xtb-wealthfolio-export/SKILL.md and use that skill to create a Wealthfolio CSV from my XTB export.
```
```text
Read skills/xtb-portfolio-performance-export/SKILL.md and use that skill to create Portfolio Performance CSV files from my XTB export.
```
## Skill Contents
Expected portable structure:
@@ -117,9 +132,23 @@ skills/
html_charts.py
requirements.txt
assets/
xtb-portfolio-performance-export/
SKILL.md
references/
scripts/
setup-env.sh
validate-export.sh
export-portfolio-performance.sh
exporter.py
main.py
html_charts.py
requirements.txt
```
Do not require the root-level `main.py`, `exporter.py`, or `html_charts.py` for copied skill usage. Those root files are repository compatibility shims only.
Do not require the root-level `main.py`, `exporter.py`,
`portfolio_performance_exporter.py`, or `html_charts.py` for copied skill
usage. Those root files are repository compatibility shims only.
## Verification Commands
@@ -128,6 +157,7 @@ From the repository root:
```bash
skills/xtb-portfolio-review/scripts/validate-review.sh
skills/xtb-wealthfolio-export/scripts/validate-export.sh
skills/xtb-portfolio-performance-export/scripts/validate-export.sh
```
If the full repository test suite is available:
+97 -6
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@@ -1,4 +1,4 @@
# XTB Portfolio Review & Wealthfolio Exporter
# XTB Portfolio Review & CSV Exporters
[![skills.sh](https://skills.sh/b/farcasclaudiu/xtb-investment-tools)](https://skills.sh/farcasclaudiu/xtb-investment-tools)
@@ -6,6 +6,8 @@ A set of Python tools that turn an **XTB brokerage report** (`.xlsx` export) int
1. A complete, human-readable **portfolio review** (console and a self-contained HTML report with interactive, offline charts and analysis tables).
2. A **Wealthfolio-compatible CSV** so the same XTB history can be imported into the [Wealthfolio](https://wealthfolio.app/) portfolio tracker.
3. **Portfolio Performance-compatible CSVs** split into Portfolio Transactions
and Account Transactions for import into [Portfolio Performance](https://www.portfolio-performance.info/).
The parser is generic for XTB exports in this format. Tests generate a small
synthetic workbook at runtime, while personal brokerage exports should stay
@@ -67,6 +69,23 @@ Use the XTB Wealthfolio export skill with EUR_demo_report.xlsx as the input file
and write the Wealthfolio CSV to results/EUR_demo_report_wealthfolio.csv.
```
Portfolio Performance export prompt examples:
```text
Use the XTB Portfolio Performance export skill to create and validate CSV files
from my XTB workbook.
```
```text
Use the XTB Portfolio Performance export skill and explain how to import the two
generated CSV files into Portfolio Performance.
```
```text
Use the XTB Portfolio Performance export skill with EUR_demo_report.xlsx as the
input file and write the CSV files to results/.
```
### Run the tools directly
From the repository root:
@@ -78,12 +97,16 @@ python3 -m venv .venv
.venv/bin/python main.py path/to/xtb-report.xlsx
.venv/bin/python exporter.py path/to/xtb-report.xlsx
.venv/bin/python portfolio_performance_exporter.py path/to/xtb-report.xlsx
```
Outputs are written to `results/`, including
`results/<stem>_review.html` for the portfolio review and
`results/<stem>_wealthfolio.csv` for the Wealthfolio import file. If there is
exactly one `.xlsx` file in the current folder, both tools can auto-detect it
`results/<stem>_wealthfolio.csv` for the Wealthfolio import file. The Portfolio
Performance exporter writes
`results/<stem>_portfolio_performance_portfolio_transactions.csv` and
`results/<stem>_portfolio_performance_account_transactions.csv`. If there is
exactly one `.xlsx` file in the current folder, the tools can auto-detect it
when the path is omitted. Add `--csv` to the portfolio review command only when
you want the extra per-section CSV exports.
@@ -113,7 +136,7 @@ Two quirks the code handles explicitly:
- **Stock-sale close notation**: some XTB stock-sale rows are written as
`CLOSE BUY ...` while the row type is `Stock sell` and the amount is positive
sale proceeds. The tools treat these as sales for holdings, cash flows, and
Wealthfolio export.
Wealthfolio and Portfolio Performance export.
---
@@ -124,8 +147,9 @@ Two quirks the code handles explicitly:
| File | Purpose |
| ------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `skills/xtb-portfolio-review/scripts/main.py` | **Portfolio review generator.** Parses the XTB report, reconstructs trades from Cash Operations comments, runs FIFO lot-matching for realized P/L, computes cash flows, holdings (cost basis), performance metrics, contribution/risk/income analysis, and reconciliation against the broker's `Total` row. Outputs a console report and a self-contained HTML report with interactive Chart.js charts and offline table tools (bundled inline, no internet required). |
| `skills/xtb-wealthfolio-export/scripts/exporter.py` | **XTB Wealthfolio CSV exporter.** Maps each Cash Operation to a Wealthfolio row (`date,symbol,quantity,activityType,unitPrice,currency,fee`). |
| `main.py`, `exporter.py`, `html_charts.py` | Thin compatibility entry points that preserve the original repo commands/imports while delegating to the bundled skill implementations. |
| `skills/xtb-wealthfolio-export/scripts/exporter.py` | **XTB -> Wealthfolio CSV exporter.** Maps each Cash Operation to a Wealthfolio row (`date,symbol,quantity,activityType,unitPrice,currency,fee`). |
| `skills/xtb-portfolio-performance-export/scripts/exporter.py` | **XTB -> Portfolio Performance CSV exporter.** Splits XTB cash operations into Portfolio Transactions and Account Transactions CSV files. |
| `main.py`, `exporter.py`, `portfolio_performance_exporter.py`, `html_charts.py` | Thin compatibility entry points that preserve the original repo commands/imports while delegating to the bundled skill implementations. |
### Tests
@@ -133,6 +157,7 @@ Two quirks the code handles explicitly:
| ------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `test_portfolio.py` | Unit + integration tests for `main.py` (parsing, FIFO realized P/L, cash-flow categorization, income, open positions, performance, analysis helpers, reconciliation against the generated synthetic workbook, HTML structure and interactions). |
| `test_exporter.py` | Tests for `exporter.py` (activity-type classification, the split-fill quantity parser, full row mapping, schema validation on the generated synthetic workbook, empty-input handling). |
| `test_portfolio_performance_exporter.py` | Tests for `portfolio_performance_exporter.py` (two-file CSV export, semicolon schema, transaction separation, account labels, split fills, empty-input handling). |
### Local inputs
@@ -157,6 +182,8 @@ automatically) and **named after the input report**: for input
| `results/<stem>_income.csv` | `main.py` | Income (dividends + interest) by month. |
| `results/<stem>_evolution.csv` | `main.py` | Daily cost / market value / realized P/L series (drives the evolution chart).|
| `results/<stem>_wealthfolio.csv` | `exporter.py` | Wealthfolio-importable transaction history. |
| `results/<stem>_portfolio_performance_portfolio_transactions.csv` | `portfolio_performance_exporter.py` | Portfolio Performance `Portfolio Transactions` import file for buys and sells. |
| `results/<stem>_portfolio_performance_account_transactions.csv` | `portfolio_performance_exporter.py` | Portfolio Performance `Account Transactions` import file for deposits, dividends, taxes, interest, fees, and transfers. |
---
@@ -187,6 +214,7 @@ copy/install/use instructions.
| ----- | ------- |
| `xtb-portfolio-review` | Generate and verify XTB portfolio review reports, including reconciliation, holdings, performance, income, risk, and data-quality caveats. |
| `xtb-wealthfolio-export` | Export and validate Wealthfolio-compatible CSV files from XTB reports, including activity mappings and import-readiness checks. |
| `xtb-portfolio-performance-export` | Export and validate Portfolio Performance-compatible CSV files from XTB reports, including import workflow instructions. |
Use the skill folder directly, or copy it into the skill/instruction directory
for your harness. With a generic LLM, ask it to read the relevant `SKILL.md`.
@@ -196,6 +224,7 @@ it in a new session:
```text
Use $xtb-portfolio-review to generate and verify an XTB portfolio report.
Use $xtb-wealthfolio-export to create and validate a Wealthfolio CSV from an XTB report.
Use $xtb-portfolio-performance-export to create and validate Portfolio Performance CSV files from an XTB report.
```
Each copied skill folder includes `scripts/requirements.txt` plus shell wrappers
@@ -205,6 +234,7 @@ want `.venv` and `results/` to live, install dependencies with:
```bash
skills/xtb-portfolio-review/scripts/setup-env.sh
skills/xtb-wealthfolio-export/scripts/setup-env.sh
skills/xtb-portfolio-performance-export/scripts/setup-env.sh
```
## Usage
@@ -254,6 +284,49 @@ The generated review HTML is a single offline file. It includes:
.venv/bin/python exporter.py EUR_other.xlsx -o my.csv # explicit input/output
```
### Export to Portfolio Performance CSV
```bash
.venv/bin/python portfolio_performance_exporter.py EUR_demo_report.xlsx
.venv/bin/python portfolio_performance_exporter.py EUR_demo_report.xlsx -o results
.venv/bin/python portfolio_performance_exporter.py EUR_demo_report.xlsx --securities-account "XTB" --cash-account "XTB (EUR)"
```
The exporter writes two UTF-8 semicolon-delimited files:
- `results/<stem>_portfolio_performance_portfolio_transactions.csv`
- `results/<stem>_portfolio_performance_account_transactions.csv`
Import them into Portfolio Performance in this order:
1. In Portfolio Performance, create or open the target portfolio file.
2. Ensure the Portfolio Performance `Securities Account` and `Deposit Account`
exist, or use the importer's account selection step to create/select them.
Defaults expected from the CSV are `XTB` and `XTB (<CCY>)`.
3. Import `results/<stem>_portfolio_performance_portfolio_transactions.csv`
first via `File > Import > CSV files`.
4. In the CSV wizard, select type `Portfolio Transactions`.
5. Use `UTF-8`, delimiter `semicolon`, and enable `First line contains header`.
6. Confirm mappings for `Date`, `Type`, `Shares`, `Ticker Symbol`,
`Security Name`, `Value`, `Fees`, `Taxes`, `Securities Account`, and
`Cash Account`. In the CSV importer, `Cash Account` maps to the Portfolio
Performance deposit account.
7. Finish that import and resolve any security matching prompts before
continuing.
8. Import `results/<stem>_portfolio_performance_account_transactions.csv` via
`File > Import > CSV files`.
9. In the CSV wizard, select type `Account Transactions`.
10. Use the same CSV settings: `UTF-8`, semicolon delimiter, first line header.
11. Confirm mappings for `Date`, `Type`, `Value`, `Ticker Symbol`,
`Security Name`, `Shares`, `Gross Amount`, `Currency Gross Amount`,
`Cash Account`, and `Offset Account`. In the CSV importer, `Cash Account`
maps to the Portfolio Performance deposit account.
12. Review Portfolio Performance's preview/status column before finishing,
especially transfers, taxes, and dividends.
Portfolio transactions should usually be imported before account transactions
so referenced securities exist before dividend rows are processed.
### Run the tests
```bash
@@ -318,6 +391,24 @@ only used for inline `BUY`/`SELL` commissions. Pure-cash rows use the `$CASH-<CC
(e.g. `$CASH-EUR`), while `DIVIDEND` keeps the security's real ticker. `BUY`/`SELL` leave
`amount` blank — Wealthfolio auto-calculates it as `quantity × unitPrice`.
### Portfolio Performance activity mapping
| XTB operation | Portfolio Performance import row |
| -------------------------------------------------- | -------------------------------- |
| `Stock purchase` / `OPEN BUY` | Portfolio `Buy` |
| `Stock sale` / `Stock sell` / `CLOSE SELL` / `OPEN SELL` | Portfolio `Sell` |
| `Stock sell` with `CLOSE BUY` | Portfolio `Sell` |
| `Deposit` / `Withdrawal` | Account `Deposit` / `Withdrawal` |
| `Dividend` | Account `Dividend` |
| `Free funds interest` | Account `Interest` |
| `Dividend tax` / `RO tax` / interest tax rows | Account `Taxes` |
| `Currency conversion` | Account `Fees` |
| `Subaccount transfer` / `Transfer` | Account `Transfer (Inbound/Outbound)` |
The Portfolio Performance exporter follows the app's documented import split:
use the `Portfolio Transactions` CSV for buys/sells and the `Account
Transactions` CSV for cash movements, income, taxes, fees, and transfers.
---
## Notes & limitations
+48
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@@ -0,0 +1,48 @@
"""Compatibility entry point for the XTB to Portfolio Performance export skill.
The canonical implementation lives in
`skills/xtb-portfolio-performance-export/scripts/exporter.py` so the skill
folder can be copied and used standalone by an LLM agent. This shim preserves
the repo API: `import portfolio_performance_exporter` and
`python portfolio_performance_exporter.py`.
"""
from __future__ import annotations
import importlib.util
import sys
from pathlib import Path
_IMPL_PATH = (
Path(__file__).resolve().parent
/ "skills"
/ "xtb-portfolio-performance-export"
/ "scripts"
/ "exporter.py"
)
def _load_impl():
module_name = (
__name__
if __name__ != "__main__"
else "_xtb_portfolio_performance_exporter_impl"
)
script_dir = _IMPL_PATH.parent
if str(script_dir) not in sys.path:
sys.path.insert(0, str(script_dir))
spec = importlib.util.spec_from_file_location(module_name, _IMPL_PATH)
if spec is None or spec.loader is None:
raise ImportError(f"Could not load XTB Portfolio Performance implementation at {_IMPL_PATH}")
module = importlib.util.module_from_spec(spec)
sys.modules[module_name] = module
if __name__ != "__main__":
sys.modules[__name__] = module
spec.loader.exec_module(module)
return module
_impl = _load_impl()
if __name__ == "__main__":
_impl.main_cli()
+3 -2
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@@ -4,10 +4,11 @@
"groupings": [
{
"title": "XTB Tools",
"description": "Skills for analyzing XTB brokerage exports and preparing Wealthfolio imports.",
"description": "Skills for analyzing XTB brokerage exports and preparing portfolio-app imports.",
"skills": [
"xtb-portfolio-review",
"xtb-wealthfolio-export"
"xtb-wealthfolio-export",
"xtb-portfolio-performance-export"
]
}
]
@@ -0,0 +1,43 @@
---
name: xtb-portfolio-performance-export
description: Use when converting XTB brokerage .xlsx exports to Portfolio Performance-compatible CSV files, validating Portfolio Transactions and Account Transactions outputs, or explaining the Portfolio Performance import workflow.
---
# XTB Portfolio Performance Export
Use this skill to create and validate Portfolio Performance CSV files from XTB
`Cash Operations` data using the bundled `exporter.py`.
## Workflow
1. Identify the target workbook. If omitted and exactly one non-lock `.xlsx`
exists, the exporter can auto-detect it.
2. Run exporter validation before trusting an import file:
`<skill-folder>/scripts/validate-export.sh`
3. Create the Portfolio Performance CSV files:
`<skill-folder>/scripts/export-portfolio-performance.sh <report.xlsx>`
4. If the user needs a custom directory, run:
`<skill-folder>/scripts/export-portfolio-performance.sh <report.xlsx> -o <output-dir>`
5. Inspect the generated CSV headers and a sample of rows before saying they
are import-ready.
6. Read `references/portfolio-performance-csv.md` before explaining import
steps, transaction mappings, or limitations.
## Outputs
- `results/<stem>_portfolio_performance_portfolio_transactions.csv`
- `results/<stem>_portfolio_performance_account_transactions.csv`
## Guardrails
- Import Portfolio Transactions before Account Transactions so securities
exist before dividend rows are matched.
- Use UTF-8, semicolon delimiter, and first-line header in Portfolio
Performance.
- Refer to Portfolio Performance UI accounts as `Deposit Account` and
`Securities Account`; keep CSV field names literal as `Cash Account` and
`Securities Account`.
- Do not claim the generated files are fully imported until the user has
reviewed Portfolio Performance's wizard preview/status column.
- Keep multi-currency caveats visible: this first exporter uses the account
currency and deterministic account labels, with optional CLI overrides.
@@ -0,0 +1,63 @@
# Portfolio Performance CSV Mapping
Load this when validating or explaining XTB to Portfolio Performance exports.
## Generated Files
- `<stem>_portfolio_performance_portfolio_transactions.csv`
- Import type: `Portfolio Transactions`
- Header: `Date;Type;Shares;Ticker Symbol;Security Name;Value;Fees;Taxes;Note;Securities Account;Cash Account`
- `<stem>_portfolio_performance_account_transactions.csv`
- Import type: `Account Transactions`
- Header: `Date;Type;Value;Ticker Symbol;Security Name;Shares;Gross Amount;Currency Gross Amount;Note;Cash Account;Offset Account`
Both files are UTF-8 CSV files with semicolon delimiters and a first-line
header.
Portfolio Performance's UI uses `Deposit Accounts` for cash/deposit accounts
and `Securities Accounts` for custody accounts. The CSV importer still names
the deposit-account field `Cash Account`; keep that header literal.
## XTB To Portfolio Performance Mapping
- `Stock purchase` or `OPEN BUY` -> Portfolio `Buy`
- `Stock sale`, `Stock sell`, `CLOSE SELL`, or `OPEN SELL` -> Portfolio `Sell`
- `Stock sell` with `CLOSE BUY` -> Portfolio `Sell`
- `Deposit` -> Account `Deposit`
- `Withdrawal` -> Account `Withdrawal`
- `Dividend` -> Account `Dividend`
- `Dividend tax`, `RO tax`, `Free funds interest tax`, or other tax-like rows -> Account `Taxes`
- `Free funds interest` -> Account `Interest`
- `Currency conversion` -> Account `Fees`
- `Subaccount transfer` or `Transfer` -> Account `Transfer (Inbound)` or `Transfer (Outbound)` by amount sign
## Import Steps
1. In Portfolio Performance, create or open the target portfolio file.
2. Ensure the Portfolio Performance `Securities Account` and `Deposit Account`
exist, or select/create them in the import wizard. The default CSV names are
`XTB` and `XTB (<CCY>)`.
3. Import the portfolio transactions CSV first with `File > Import > CSV files`.
4. Select type `Portfolio Transactions`.
5. Use `UTF-8`, delimiter `semicolon`, and enable `First line contains header`.
6. Confirm mappings for `Date`, `Type`, `Shares`, `Ticker Symbol`,
`Security Name`, `Value`, `Fees`, `Taxes`, `Securities Account`, and
`Cash Account`. In the CSV importer, `Cash Account` maps to the Portfolio
Performance deposit account.
7. Finish that import and resolve any security matching prompts.
8. Import the account transactions CSV with `File > Import > CSV files`.
9. Select type `Account Transactions`.
10. Use the same CSV settings: `UTF-8`, semicolon delimiter, first line header.
11. Confirm mappings for `Date`, `Type`, `Value`, `Ticker Symbol`,
`Security Name`, `Shares`, `Gross Amount`, `Currency Gross Amount`,
`Cash Account`, and `Offset Account`. In the CSV importer, `Cash Account`
maps to the Portfolio Performance deposit account.
12. Review the preview/status column before finishing, especially transfers,
taxes, and dividends.
## Limitations
- The exporter does not create Portfolio Performance `.xml` portfolio files.
- It does not generate Portfolio Performance JSON import configurations.
- Multi-currency gross amount and exchange-rate fields are left blank unless a
future XTB mapping can populate them safely.
@@ -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/exporter.py" "$@"
@@ -0,0 +1,305 @@
"""XTB report -> Portfolio Performance CSV exporter.
Portfolio Performance imports CSV files by import type. This exporter writes
two semicolon-delimited UTF-8 files:
* Portfolio Transactions: buys and sells
* Account Transactions: deposits, dividends, taxes, interest, and transfers
Run:
python exporter.py report.xlsx
python exporter.py report.xlsx --output-dir results
"""
from __future__ import annotations
import argparse
import csv
import re
from pathlib import Path
import pandas as pd
import main
from main import (
CONVERSION_RE,
DEPOSIT_RE,
DIVIDEND_RE,
DIVIDEND_TAX_RE,
INTEREST_RE,
PRICE_RE,
TRADE_COMMENT_RE,
WITHDRAW_RE,
find_column,
parse_executed_quantity,
parse_numeric,
)
PORTFOLIO_FIELDS = [
"Date",
"Type",
"Shares",
"Ticker Symbol",
"Security Name",
"Value",
"Fees",
"Taxes",
"Note",
"Securities Account",
"Cash Account",
]
ACCOUNT_FIELDS = [
"Date",
"Type",
"Value",
"Ticker Symbol",
"Security Name",
"Shares",
"Gross Amount",
"Currency Gross Amount",
"Note",
"Cash Account",
"Offset Account",
]
SHORT_OPEN_RE = re.compile(r"OPEN\s+SELL", re.IGNORECASE)
TRANSFER_RE = re.compile(r"\b(subaccount\s+transfer|transfer)\b", re.IGNORECASE)
TAX_RE = re.compile(r"\btax\b|withholding", re.IGNORECASE)
def default_cash_account(currency: str, account_prefix: str = "XTB") -> str:
return f"{account_prefix} ({currency})"
def _fmt_date(val) -> str:
dt = pd.to_datetime(val, errors="coerce")
if pd.isna(dt):
return ""
return dt.strftime("%Y-%m-%d")
def _fmt_decimal(val) -> str:
if val == "" or val is None:
return ""
num = float(val)
return f"{num:.6f}".rstrip("0").rstrip(".")
def _clean_text(val) -> str:
if val is None or pd.isna(val):
return ""
text = str(val).strip()
return "" if text.lower() == "nan" else text
def _trade_type(type_val: str, comment: str) -> str | None:
if not TRADE_COMMENT_RE.search(comment):
return None
lowered_comment = comment.lower()
lowered_type = type_val.lower()
is_sell = (
"close sell" in lowered_comment
or SHORT_OPEN_RE.search(comment)
or ("close buy" in lowered_comment and "sell" in lowered_type)
)
return "Sell" if is_sell else "Buy"
def _account_type(type_val: str, comment: str, amount: float) -> str | None:
text = f"{type_val} {comment}".lower()
if DIVIDEND_TAX_RE.search(text) or TAX_RE.search(text):
return "Taxes"
if DIVIDEND_RE.search(text):
return "Dividend"
if INTEREST_RE.search(text):
return "Interest"
if CONVERSION_RE.search(text):
return "Fees"
if WITHDRAW_RE.search(text):
return "Withdrawal"
if DEPOSIT_RE.search(text):
return "Deposit"
if TRANSFER_RE.search(text):
return "Transfer (Inbound)" if amount >= 0 else "Transfer (Outbound)"
return None
def build_rows(
cash_ops: pd.DataFrame,
currency: str,
*,
securities_account: str = "XTB",
cash_account: str | None = None,
account_prefix: str = "XTB",
) -> tuple[list[dict[str, str | float]], list[dict[str, str | float]]]:
"""Build Portfolio Performance portfolio/account transaction rows."""
cash_account = cash_account or default_cash_account(currency, account_prefix)
type_col = find_column(cash_ops, ["type", "operation"], required=False)
ticker_col = find_column(
cash_ops, ["ticker", "symbol", "instrument", "market"], required=False
)
name_col = find_column(cash_ops, ["instrument", "name", "description"], required=False)
amount_col = find_column(
cash_ops, ["amount", "value", "net_amount", "cash", "change", "payment"],
required=False,
)
date_col = find_column(
cash_ops, ["time", "date", "operation_date", "booking_date", "transaction_date"],
required=False,
)
comment_col = find_column(cash_ops, ["comment", "description", "details"], required=False)
if not (type_col and amount_col):
return [], []
portfolio_rows: list[dict[str, str | float]] = []
account_rows: list[dict[str, str | float]] = []
for _, row in cash_ops.iterrows():
type_val = _clean_text(row.get(type_col))
comment = _clean_text(row.get(comment_col)) if comment_col else ""
amount = float(parse_numeric(pd.Series([row[amount_col]])).iloc[0])
date = _fmt_date(row.get(date_col)) if date_col else ""
ticker = _clean_text(row.get(ticker_col)) if ticker_col else ""
security_name = _clean_text(row.get(name_col)) if name_col else ""
trade_type = _trade_type(type_val, comment)
if trade_type:
price = 0.0
price_match = PRICE_RE.search(comment)
if price_match:
price = float(parse_numeric(pd.Series([price_match.group(1)])).iloc[0])
shares = parse_executed_quantity(comment, amount, price)
portfolio_rows.append({
"Date": date,
"Type": trade_type,
"Shares": shares,
"Ticker Symbol": ticker,
"Security Name": security_name,
"Value": round(abs(amount), 6),
"Fees": "",
"Taxes": "",
"Note": comment,
"Securities Account": securities_account,
"Cash Account": cash_account,
})
continue
account_type = _account_type(type_val, comment, amount)
if account_type is None:
continue
account_rows.append({
"Date": date,
"Type": account_type,
"Value": round(abs(amount), 6),
"Ticker Symbol": ticker if account_type == "Dividend" else "",
"Security Name": security_name if account_type == "Dividend" else "",
"Shares": "",
"Gross Amount": "",
"Currency Gross Amount": "",
"Note": comment or type_val,
"Cash Account": cash_account,
"Offset Account": "",
})
return portfolio_rows, account_rows
def _write_csv(path: Path, fields: list[str], rows: list[dict[str, str | float]]) -> None:
path.parent.mkdir(parents=True, exist_ok=True)
with path.open("w", newline="", encoding="utf-8") as f:
writer = csv.DictWriter(f, fieldnames=fields, delimiter=";")
writer.writeheader()
for row in rows:
writer.writerow({
field: _fmt_decimal(row[field])
if isinstance(row.get(field), float)
else row.get(field, "")
for field in fields
})
def export(
xlsx_path: Path | str | None = None,
output_dir: Path | str | None = None,
*,
securities_account: str = "XTB",
cash_account: str | None = None,
account_prefix: str = "XTB",
) -> dict[str, Path]:
main.REPORT_FILE = main.resolve_report_file(xlsx_path)
currency = main.detect_currency()
_, cash_ops, _, _ = main.load_data()
portfolio_rows, account_rows = build_rows(
cash_ops,
currency,
securities_account=securities_account,
cash_account=cash_account,
account_prefix=account_prefix,
)
out_dir = Path(output_dir) if output_dir is not None else main.RESULTS_DIR
stem = main.REPORT_FILE.stem if main.REPORT_FILE else "portfolio"
outputs = {
"portfolio_transactions": out_dir
/ f"{stem}_portfolio_performance_portfolio_transactions.csv",
"account_transactions": out_dir
/ f"{stem}_portfolio_performance_account_transactions.csv",
}
_write_csv(outputs["portfolio_transactions"], PORTFOLIO_FIELDS, portfolio_rows)
_write_csv(outputs["account_transactions"], ACCOUNT_FIELDS, account_rows)
return outputs
def main_cli() -> None:
parser = argparse.ArgumentParser(description="Export XTB xlsx to Portfolio Performance CSVs.")
parser.add_argument(
"input",
nargs="?",
default=None,
help="Path to the XTB .xlsx report (auto-detected if omitted)",
)
parser.add_argument(
"-o",
"--output-dir",
default=None,
help="Output directory (default: results)",
)
parser.add_argument(
"--securities-account",
default="XTB",
help="Portfolio Performance securities account name (default: XTB)",
)
parser.add_argument(
"--cash-account",
default=None,
help="Portfolio Performance cash account name (default: XTB (<CCY>))",
)
parser.add_argument(
"--account-prefix",
default="XTB",
help="Prefix for the default cash account name (default: XTB)",
)
args = parser.parse_args()
try:
outputs = export(
args.input,
args.output_dir,
securities_account=args.securities_account,
cash_account=args.cash_account,
account_prefix=args.account_prefix,
)
except (FileNotFoundError, ValueError) as exc:
parser.error(str(exc))
for label, path in outputs.items():
print(f"Wrote {label}: {path.resolve()} ({path.stat().st_size} bytes)")
if __name__ == "__main__":
main_cli()
@@ -0,0 +1,365 @@
"""Interactive Chart.js charts for the self-contained HTML report.
This module is the only place that knows about Chart.js. It reads the vendored
UMD bundle from assets/ and builds Chart.js config dicts (pure functions) plus
an HTML fragment that inlines the bundle, the data (JSON), and a render script.
"""
from __future__ import annotations
import json
from pathlib import Path
from typing import Any
import pandas as pd
ASSETS_DIR = Path(__file__).resolve().parent / "assets"
CHARTJS_PATH = ASSETS_DIR / "chartjs.umd.min.js"
CHARTJS_VERSION_PATH = ASSETS_DIR / "chartjs.VERSION"
def load_chartjs_inline() -> str:
"""Return the minified Chart.js UMD source, vendored under assets/."""
if not CHARTJS_PATH.exists():
raise FileNotFoundError(
f"Chart.js bundle not found at {CHARTJS_PATH}. "
"Re-vendor it (see assets/chartjs.VERSION)."
)
return CHARTJS_PATH.read_text(encoding="utf-8")
def _iso(value: Any) -> str:
if hasattr(value, "isoformat"):
return value.isoformat()[:10]
return str(value)
def _round_series(values) -> list[float]:
return [round(float(v), 2) for v in values]
def evolution_chart_config(evolution_df: pd.DataFrame, currency: str) -> dict | None:
"""Build a Chart.js line-chart config for cost vs value over time.
Returns None when there is no evolution data (caller omits the card).
"""
if evolution_df is None or evolution_df.empty:
return None
labels = [_iso(d) for d in evolution_df.index]
return {
"type": "line",
"data": {
"labels": labels,
"datasets": [
{
"label": "Cost (invested)",
"data": _round_series(evolution_df["cost"]),
"borderColor": "#6b7280",
"backgroundColor": "#6b7280",
"borderWidth": 2,
"fill": False,
"pointRadius": 0,
"tension": 0.1,
},
{
"label": "Value (realized + unrealized)",
"data": _round_series(evolution_df["total_value"]),
"borderColor": "#2c5282",
"backgroundColor": "#2c5282",
"borderWidth": 2,
"fill": False,
"pointRadius": 0,
"tension": 0.1,
},
{
"label": "Cumulative realized P/L",
"data": _round_series(evolution_df["realized_pl"]),
"borderColor": "#f39c12",
"backgroundColor": "#f39c12",
"borderWidth": 1.5,
"borderDash": [6, 4],
"fill": False,
"pointRadius": 0,
"tension": 0.1,
},
],
},
"options": {
"responsive": True,
"maintainAspectRatio": False,
"interaction": {"mode": "index", "intersect": False},
"plugins": {
"legend": {"position": "bottom",
"labels": {"boxWidth": 12, "font": {"size": 12}}},
},
"scales": {
"x": {"ticks": {"maxRotation": 45, "autoSkip": True}},
"y": {"beginAtZero": False},
},
},
}
DOUGHNUT_COLORS = [
"#2c5282", "#1f9d55", "#f39c12", "#3498db", "#9b59b6",
"#e67e22", "#16a085", "#34495e", "#e3342f", "#7f8c8d",
]
def review_charts_config(
holdings: pd.DataFrame,
flows: dict[str, float],
income_by_period: pd.Series,
currency: str,
) -> dict:
"""Build Chart.js configs for the three review charts.
Returns {'holdings': cfg|None, 'cashflows': cfg|None, 'income': cfg|None}.
Each is None when its source data is empty.
"""
holdings_cfg = _holdings_config(holdings)
cashflows_cfg = _cashflows_config(flows)
income_cfg = _income_config(income_by_period)
return {"holdings": holdings_cfg, "cashflows": cashflows_cfg, "income": income_cfg}
def _holdings_config(holdings: pd.DataFrame) -> dict | None:
if holdings is None or holdings.empty:
return None
alloc_col = "market_value" if "market_value" in holdings.columns else "cost_basis"
filtered = holdings.loc[holdings[alloc_col] > 0]
if filtered.empty:
return None
values = _round_series(filtered[alloc_col])
return {
"type": "doughnut",
"data": {
"labels": [str(t) for t in filtered["ticker"].tolist()],
"datasets": [{
"data": values,
"backgroundColor": [DOUGHNUT_COLORS[i % len(DOUGHNUT_COLORS)]
for i in range(len(values))],
}],
},
"options": {
"responsive": True,
"maintainAspectRatio": False,
"plugins": {"legend": {"position": "right",
"labels": {"boxWidth": 12, "font": {"size": 11}}}},
},
}
def _cashflows_config(flows: dict[str, float]) -> dict | None:
if not flows:
return None
items = {
"Deposits": float(flows["deposits"]),
"Withdrawals": -float(flows["withdrawals"]),
"Interest": float(flows["interest"]),
"Dividends": float(flows["dividends"]),
"Div.tax": float(flows["dividend_tax"]),
"Invested": -float(flows["invested"]),
"Proceeds": float(flows["proceeds"]),
"FX fees": float(flows["conversion_fees"]),
"Fees": -float(flows["fees"]),
}
items = {k: v for k, v in items.items() if abs(v) > 1e-9}
if not items:
return None
labels = list(items.keys())
values = _round_series(items.values())
colors = ["#2ecc71" if v >= 0 else "#e74c3c" for v in items.values()]
return {
"type": "bar",
"data": {"labels": labels,
"datasets": [{"label": "Cash flows", "data": values,
"backgroundColor": colors}]},
"options": {
"responsive": True,
"maintainAspectRatio": False,
"plugins": {"legend": {"display": False}},
"scales": {"x": {"ticks": {"maxRotation": 30, "autoSkip": False}},
"y": {"beginAtZero": True}},
},
}
def _income_config(income_by_period: pd.Series) -> dict | None:
if income_by_period is None or income_by_period.empty:
return None
return {
"type": "bar",
"data": {
"labels": [str(i) for i in income_by_period.index],
"datasets": [{"label": "Income",
"data": _round_series(income_by_period.tolist()),
"backgroundColor": "#3498db"}],
},
"options": {
"responsive": True,
"maintainAspectRatio": False,
"plugins": {"legend": {"display": False}},
"scales": {"x": {"ticks": {"maxRotation": 45, "autoSkip": False}},
"y": {"beginAtZero": True}},
},
}
_RENDER_SCRIPT = r"""
function _bootPortfolioCharts() {
var block = document.getElementById('chart-data');
if (!block) { return; }
var data = JSON.parse(block.textContent);
var ccy = data.currency || 'EUR';
function fmt(v) {
try { return new Intl.NumberFormat('en-US', {style: 'currency', currency: ccy}).format(v); }
catch (e) { return String(v); }
}
function applyTooltip(cfg) {
if (!cfg || !cfg.options) { return; }
cfg.options.plugins = cfg.options.plugins || {};
cfg.options.plugins.tooltip = cfg.options.plugins.tooltip || {};
cfg.options.plugins.tooltip.callbacks = cfg.options.plugins.tooltip.callbacks || {};
if (cfg.type === 'doughnut' || cfg.type === 'pie') {
cfg.options.plugins.tooltip.callbacks.label = function (ctx) {
var total = (ctx.dataset && ctx.dataset.data)
? ctx.dataset.data.reduce(function (a, b) { return a + (typeof b === 'number' ? b : 0); }, 0)
: 0;
var v = (typeof ctx.parsed === 'number') ? ctx.parsed : ctx.raw;
var pct = total > 0 ? (v / total * 100) : 0;
return (ctx.label ? ctx.label + ': ' : '') + fmt(v) + ' (' + pct.toFixed(1) + '%)';
};
return;
}
cfg.options.plugins.tooltip.callbacks.label = function (ctx) {
var label = (ctx.dataset && ctx.dataset.label) ? ctx.dataset.label : '';
var v = (ctx.parsed && Object.prototype.hasOwnProperty.call(ctx.parsed, 'y'))
? ctx.parsed.y : (typeof ctx.parsed === 'number' ? ctx.parsed : ctx.raw);
return label ? (label + ': ' + fmt(v)) : fmt(v);
};
}
function mount(id, cfg, plugins) {
if (!cfg) { return; }
var el = document.getElementById(id);
if (!el) { return; }
applyTooltip(cfg);
var config = {type: cfg.type, data: cfg.data, options: cfg.options};
if (plugins && plugins.length) { config.plugins = plugins; }
new Chart(el.getContext('2d'), config);
}
var gainLossPlugin = {
id: 'gainLoss',
beforeDatasetsDraw: function (chart) {
var ds = chart.data.datasets;
if (ds.length < 2) { return; }
var meta0 = chart.getDatasetMeta(0);
var meta1 = chart.getDatasetMeta(1);
var cost = ds[0].data;
var value = ds[1].data;
if (!meta0 || !meta1 || !meta0.data || !meta1.data) { return; }
var ctx = chart.ctx;
ctx.save();
for (var i = 0; i < value.length - 1; i++) {
var a0 = meta0.data[i], a1 = meta0.data[i + 1];
var b0 = meta1.data[i], b1 = meta1.data[i + 1];
if (!a0 || !a1 || !b0 || !b1) { continue; }
var gain = (value[i] >= cost[i] && value[i + 1] >= cost[i + 1]);
ctx.beginPath();
ctx.moveTo(a0.x, a0.y); ctx.lineTo(a1.x, a1.y);
ctx.lineTo(b1.x, b1.y); ctx.lineTo(b0.x, b0.y);
ctx.closePath();
ctx.fillStyle = gain ? 'rgba(31,157,85,0.25)' : 'rgba(227,52,47,0.25)';
ctx.fill();
}
ctx.restore();
}
};
mount('evolution-chart', data.evolution, [gainLossPlugin]);
mount('holdings-chart', data.holdings);
mount('cashflows-chart', data.cashflows);
mount('income-chart', data.income);
}
if (document.readyState !== 'loading') { _bootPortfolioCharts(); }
else { document.addEventListener('DOMContentLoaded', _bootPortfolioCharts); }
"""
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
@@ -0,0 +1,12 @@
#!/usr/bin/env bash
set -euo pipefail
SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
PYTHON_BIN="${PYTHON:-python3}"
if [[ ! -d ".venv" ]]; then
"$PYTHON_BIN" -m venv .venv
fi
.venv/bin/python -m pip install --upgrade pip
.venv/bin/python -m pip install -r "$SCRIPT_DIR/requirements.txt"
@@ -0,0 +1,24 @@
#!/usr/bin/env bash
set -euo pipefail
SCRIPT_DIR="$(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
PYTHONDONTWRITEBYTECODE=1 "$PYTHON_BIN" - <<PY
import importlib.util
from pathlib import Path
script = Path("$SCRIPT_DIR") / "exporter.py"
spec = importlib.util.spec_from_file_location("xtb_portfolio_performance_exporter", script)
module = importlib.util.module_from_spec(spec)
spec.loader.exec_module(module)
assert module.PORTFOLIO_FIELDS[0] == "Date"
assert module.ACCOUNT_FIELDS[0] == "Date"
print("Portfolio Performance exporter loaded")
PY
+126
View File
@@ -0,0 +1,126 @@
import csv
import pandas as pd
import main
import portfolio_performance_exporter as pp
def _cash_ops(rows):
cols = ["Type", "Instrument", "Ticker", "Time", "Amount", "Comment", "Product"]
return main.clean_columns(pd.DataFrame(rows, columns=cols))
def _row(type_, instr="", ticker="", amount=0.0, comment="", time="2026-02-18 09:00:00"):
return [type_, instr, ticker, time, amount, comment, "My Trades"]
def _read_semicolon_csv(path):
with path.open(newline="", encoding="utf-8") as f:
return list(csv.DictReader(f, delimiter=";"))
def test_export_writes_two_semicolon_csvs_with_expected_headers(tmp_path):
outputs = pp.export(main.REPORT_FILE, output_dir=tmp_path)
portfolio_path = outputs["portfolio_transactions"]
account_path = outputs["account_transactions"]
assert portfolio_path.name == (
f"{main.REPORT_FILE.stem}_portfolio_performance_portfolio_transactions.csv"
)
assert account_path.name == (
f"{main.REPORT_FILE.stem}_portfolio_performance_account_transactions.csv"
)
with portfolio_path.open(encoding="utf-8") as f:
assert f.readline().strip() == ";".join(pp.PORTFOLIO_FIELDS)
with account_path.open(encoding="utf-8") as f:
assert f.readline().strip() == ";".join(pp.ACCOUNT_FIELDS)
portfolio_rows = _read_semicolon_csv(portfolio_path)
account_rows = _read_semicolon_csv(account_path)
assert [row["Type"] for row in portfolio_rows] == ["Buy", "Sell"]
assert [row["Type"] for row in account_rows] == ["Deposit", "Dividend", "Taxes"]
def test_build_rows_separates_trade_and_cash_activity():
ops = _cash_ops([
_row("Deposit", amount=4000.0, comment="deposit funds"),
_row("Stock purchase", "Demo Equity", "DEMO.DE", -500.0, "OPEN BUY 5 @ 100.00"),
_row("Dividend", "Demo Equity", "DEMO.DE", 10.0, "Dividend"),
_row("Dividend tax", "Demo Equity", "DEMO.DE", -1.5, "Dividend tax"),
_row("Free funds interest", amount=0.03, comment="Free funds interest"),
_row("RO tax", amount=-0.01, comment="Tax"),
_row("Stock sell", "Demo Equity", "DEMO.DE", 240.0, "CLOSE BUY 2 @ 120.00"),
])
portfolio_rows, account_rows = pp.build_rows(ops, "EUR")
assert [row["Type"] for row in portfolio_rows] == ["Buy", "Sell"]
assert portfolio_rows[0]["Shares"] == 5.0
assert portfolio_rows[0]["Ticker Symbol"] == "DEMO.DE"
assert portfolio_rows[0]["Security Name"] == "Demo Equity"
assert portfolio_rows[0]["Value"] == 500.0
assert portfolio_rows[0]["Securities Account"] == "XTB"
assert portfolio_rows[0]["Cash Account"] == "XTB (EUR)"
assert portfolio_rows[1]["Type"] == "Sell"
assert [row["Type"] for row in account_rows] == [
"Deposit",
"Dividend",
"Taxes",
"Interest",
"Taxes",
]
dividend = account_rows[1]
assert dividend["Ticker Symbol"] == "DEMO.DE"
assert dividend["Security Name"] == "Demo Equity"
assert dividend["Value"] == 10.0
def test_split_fill_quantity_uses_numerator():
ops = _cash_ops([
_row("Stock purchase", "S&P 500", "SPY.US", -14.31, "OPEN BUY 1/100 @ 14.3130"),
_row("Stock purchase", "S&P 500", "SPY.US", -1416.99, "OPEN BUY 99/100 @ 14.3130"),
])
portfolio_rows, account_rows = pp.build_rows(ops, "EUR")
assert account_rows == []
assert [row["Shares"] for row in portfolio_rows] == [1.0, 99.0]
def test_custom_account_names_are_used_in_rows():
ops = _cash_ops([
_row("Stock purchase", "Demo Equity", "DEMO.DE", -500.0, "OPEN BUY 5 @ 100.00"),
_row("Deposit", amount=4000.0, comment="deposit funds"),
])
portfolio_rows, account_rows = pp.build_rows(
ops,
"EUR",
securities_account="Broker Securities",
cash_account="Broker Cash EUR",
)
assert portfolio_rows[0]["Securities Account"] == "Broker Securities"
assert portfolio_rows[0]["Cash Account"] == "Broker Cash EUR"
assert account_rows[0]["Cash Account"] == "Broker Cash EUR"
def test_empty_input_writes_headers_only(tmp_path, monkeypatch):
empty = main.clean_columns(
pd.DataFrame(columns=["Type", "Instrument", "Ticker", "Time", "Amount", "Comment", "Product"])
)
monkeypatch.setattr(main, "load_data", lambda: (pd.DataFrame(), empty, pd.DataFrame(), 0.0))
outputs = pp.export(main.REPORT_FILE, output_dir=tmp_path)
for key, fields in (
("portfolio_transactions", pp.PORTFOLIO_FIELDS),
("account_transactions", pp.ACCOUNT_FIELDS),
):
with outputs[key].open(encoding="utf-8") as f:
assert f.readline().strip() == ";".join(fields)
assert f.read() == ""