Compare commits
3 Commits
| Author | SHA1 | Date | |
|---|---|---|---|
| 49c91e5d85 | |||
| 400bd41e56 | |||
| 1d1ebd385e |
+15
-1
@@ -4,7 +4,21 @@ __OLD__/
|
||||
.specstory/
|
||||
.history/
|
||||
.vscode/
|
||||
*.py[cod]
|
||||
.ipynb_checkpoints/
|
||||
.pytest_cache/
|
||||
|
||||
# Local environments
|
||||
.venv/
|
||||
venv/
|
||||
|
||||
# Local test data and generated analysis results
|
||||
data/*
|
||||
!data/.gitkeep
|
||||
results/*
|
||||
!results/.gitkeep
|
||||
|
||||
data
|
||||
|
||||
cvttpy
|
||||
results/
|
||||
tmp/
|
||||
|
||||
@@ -105,6 +105,10 @@ If verification cannot be run, report exactly what was skipped and why.
|
||||
|
||||
- Update `CHANGELOG.md` for every release with the release version, release
|
||||
date, Git tag, and a concise summary of notable changes.
|
||||
- Keep an `Unreleased` section at the top of `CHANGELOG.md` for changes that
|
||||
have not been included in a tagged release yet.
|
||||
- Move relevant entries from `Unreleased` into the dated release section when
|
||||
creating a release, and leave `Unreleased` present for future changes.
|
||||
- Use release headers in `YYYY-MM-DD vMAJOR.MINOR.PATCH` form.
|
||||
- Use version numbers in `MAJOR.MINOR.PATCH` form. Start this repository at
|
||||
`0.0.1`.
|
||||
@@ -112,6 +116,9 @@ If verification cannot be run, report exactly what was skipped and why.
|
||||
exactly. For example, version `0.0.1` must be tagged as `v0.0.1`.
|
||||
- Create the Git tag only after the changelog and any release-related version
|
||||
changes are complete.
|
||||
- When the user requests creating a release, treat that as explicit permission
|
||||
to commit the release changes, create the matching Git tag, and push both the
|
||||
branch and tag.
|
||||
- Do not push release commits or tags unless the user explicitly requests it.
|
||||
|
||||
## Mandatory background review
|
||||
|
||||
@@ -2,6 +2,56 @@
|
||||
|
||||
All notable changes to this project are documented in this file.
|
||||
|
||||
## Unreleased
|
||||
|
||||
No unreleased changes yet.
|
||||
|
||||
## 2026-07-28 v1.0.1
|
||||
|
||||
- Added the `spbt_day` notebook for interactive single-day backtest result
|
||||
analysis, including SQLite result file selection from the local data
|
||||
directory.
|
||||
- Added selector-pair loading and dense ranking by `mr_score.final`, preserving
|
||||
rows with invalid score JSON for inspection.
|
||||
- Added theoretical return calculation for ranked pairs from
|
||||
`trading_instructions`, including reusable helper functions and tests.
|
||||
- Added a Plotly histogram for visual analysis of total theoretical return by
|
||||
pair.
|
||||
- Moved notebook support code into reusable `scripts/spbt_day.py` helpers.
|
||||
- Adjusted notebook table outputs to show all relevant rows and reduce
|
||||
redundant intermediate displays.
|
||||
- Added an alphabetically sorted pair selector for individual pair analysis.
|
||||
- Added selected-pair theoretical execution tables and aligned TheoRet
|
||||
calculations with target-delta trade generation.
|
||||
- Added per-asset `strength` values to selected-pair theoretical execution
|
||||
tables.
|
||||
- Corrected theoretical execution size to use
|
||||
`10000 * strength / reference_price`.
|
||||
- Removed `:USD` quote suffixes from displayed pair names in notebook tables,
|
||||
chart hovers, and the pair selector dropdown while preserving full internal
|
||||
pair keys for calculations.
|
||||
- Added `num_trades` to pair TheoRet summaries, counting asset-level theoretical
|
||||
trades from effective `TARGET` and `CLOSE` instructions.
|
||||
- Added sortable interactive grids for the pair TheoRet and selected-pair
|
||||
theoretical execution tables.
|
||||
- Styled interactive dataframe grids with black text on white backgrounds for
|
||||
readability across notebook themes.
|
||||
- Added a selected-pair Plotly chart that overlays theoretical BUY/SELL
|
||||
executions on relative 1-minute market close data for both instruments.
|
||||
- Anchored the selected-pair market chart at trading-day midnight and normalized
|
||||
relative prices to each instrument's close at that timestamp.
|
||||
- Added a `min_pctg_change` threshold for ranked pair TheoRet calculations to
|
||||
skip small target-strength changes after a position is acquired.
|
||||
- Added a notebook input field for the minimum TARGET strength-change threshold.
|
||||
|
||||
## 2026-07-25 v0.0.9
|
||||
|
||||
- Added contributing guidance and Python dependency declarations.
|
||||
- Added placeholder files for active project directories.
|
||||
- Updated ignore rules for local data, generated results, caches, and local
|
||||
environments.
|
||||
- Documented unreleased changelog handling and release push behavior.
|
||||
|
||||
## 2026-07-25 v0.0.1
|
||||
|
||||
- Established the initial repository structure and project guidance.
|
||||
|
||||
@@ -0,0 +1,54 @@
|
||||
# Contributing
|
||||
|
||||
## Setup
|
||||
|
||||
Use the shared Python 3.12 virtual environment:
|
||||
|
||||
```bash
|
||||
source ~/.pyenv/python3.12-venv/bin/activate
|
||||
python -m pip install -r requirements.txt
|
||||
```
|
||||
|
||||
If you install another package for repository work, add its direct dependency
|
||||
to `requirements.txt`.
|
||||
|
||||
## Repository layout
|
||||
|
||||
- Put notebooks in `notebooks/`.
|
||||
- Put reusable Python and Bash utilities in `scripts/`.
|
||||
- Put local input files in `data/`.
|
||||
- Put generated artifacts in `results/`.
|
||||
|
||||
The contents of `data/` and `results/` are ignored. Do not force-add test
|
||||
databases, raw test results, generated exports, or notebook outputs.
|
||||
|
||||
`__SAV__/` is unrelated legacy material and is outside the active project.
|
||||
|
||||
## Working with notebooks
|
||||
|
||||
Notebooks must execute from top to bottom in a fresh kernel. Use relative paths,
|
||||
document data assumptions, and move reusable logic into tested scripts.
|
||||
|
||||
Before handing off a change:
|
||||
|
||||
```bash
|
||||
python -m pytest
|
||||
python -m pytest --nbmake notebooks
|
||||
jupyter nbconvert --ClearOutputPreprocessor.enabled=True --inplace path/to/notebook.ipynb
|
||||
```
|
||||
|
||||
Run only the checks relevant to the files present in the repository, and report
|
||||
anything that could not be run.
|
||||
|
||||
## Review requirement
|
||||
|
||||
Python scripts, Bash scripts, and notebook code-cell changes require review and
|
||||
approval by a separate background agent. Address material findings and rerun
|
||||
affected checks before completion. If a reviewer is unavailable, the change may
|
||||
be handed off only with the status `review pending`.
|
||||
|
||||
Documentation, dependency declarations, and ignore rules do not require this
|
||||
background review when no Python, Bash, or notebook code cells changed.
|
||||
|
||||
The `master` branch is not protected. That does not remove the review
|
||||
requirement or authorize an agent to commit or push without an explicit request.
|
||||
@@ -0,0 +1 @@
|
||||
|
||||
@@ -0,0 +1,357 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "single-day-title",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Single-Day Backtest Result Analysis\n",
|
||||
"\n",
|
||||
"This notebook analyzes the result of one single-day backtest stored in a SQLite database. Development is staged; Step 1 only selects the database file that later sections will read.\n",
|
||||
"\n",
|
||||
"Input assumptions for Step 1:\n",
|
||||
"\n",
|
||||
"- The default data directory is `data/` at the repository root.\n",
|
||||
"- SQLite result files usually use `.db`, `.sqlite`, or `.sqlite3` extensions.\n",
|
||||
"- The directory can be changed interactively if the result file lives elsewhere."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "imports-and-paths",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from pathlib import Path\n",
|
||||
"import importlib\n",
|
||||
"import sys\n",
|
||||
"\n",
|
||||
"from IPython.display import display\n",
|
||||
"import ipywidgets as widgets\n",
|
||||
"import pandas as pd\n",
|
||||
"\n",
|
||||
"START_DIR = Path.cwd().resolve()\n",
|
||||
"for candidate in (START_DIR, *START_DIR.parents):\n",
|
||||
" if (candidate / \"scripts\" / \"spbt_day.py\").exists():\n",
|
||||
" if str(candidate) not in sys.path:\n",
|
||||
" sys.path.insert(0, str(candidate))\n",
|
||||
" break\n",
|
||||
"\n",
|
||||
"import scripts.spbt_day as spbt_day\n",
|
||||
"\n",
|
||||
"spbt_day = importlib.reload(spbt_day)\n",
|
||||
"\n",
|
||||
"add_total_pnl = spbt_day.add_total_pnl\n",
|
||||
"calculate_pair_theo_executions = spbt_day.calculate_pair_theo_executions\n",
|
||||
"calculate_ranked_pairs_theo_ret = spbt_day.calculate_ranked_pairs_theo_ret\n",
|
||||
"create_database_file_selector = spbt_day.create_database_file_selector\n",
|
||||
"create_pair_name_dropdown = spbt_day.create_pair_name_dropdown\n",
|
||||
"create_pair_trades_market_plot = spbt_day.create_pair_trades_market_plot\n",
|
||||
"create_total_pnl_histogram = spbt_day.create_total_pnl_histogram\n",
|
||||
"find_repo_root = spbt_day.find_repo_root\n",
|
||||
"format_pair_name_for_display = spbt_day.format_pair_name_for_display\n",
|
||||
"format_pair_names_for_display = spbt_day.format_pair_names_for_display\n",
|
||||
"infer_trading_day_start_ns = spbt_day.infer_trading_day_start_ns\n",
|
||||
"load_selector_pair_rankings = spbt_day.load_selector_pair_rankings\n",
|
||||
"load_pair_market_data = spbt_day.load_pair_market_data\n",
|
||||
"load_trading_instructions = spbt_day.load_trading_instructions\n",
|
||||
"show_interactive_dataframe = spbt_day.show_interactive_dataframe\n",
|
||||
"\n",
|
||||
"REPO_ROOT = find_repo_root()\n",
|
||||
"DEFAULT_DATA_DIR = REPO_ROOT / \"data\"\n",
|
||||
"\n",
|
||||
"REPO_ROOT, DEFAULT_DATA_DIR"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "database-file-selector",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"db_selector = create_database_file_selector(\n",
|
||||
" default_data_dir=DEFAULT_DATA_DIR,\n",
|
||||
" repo_root=REPO_ROOT,\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"display(db_selector.widget)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "selected-database-helpers",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"selected_database_path = db_selector.selected_database_path\n",
|
||||
"connect_selected_database = db_selector.connect_selected_database\n",
|
||||
"\n",
|
||||
"# Later notebook sections can call selected_database_path() or connect_selected_database()."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "selector-pair-rankings-context",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Selector Pair Rankings\n",
|
||||
"\n",
|
||||
"Load `selector_pairs.pair_name` and `selector_pairs.mr_score` from the selected SQLite database. The JSON field `mr_score.final` is parsed as a numeric score and ranked descending with dense ranks, so tied scores share the same rank and the next distinct score gets the next rank.\n",
|
||||
"\n",
|
||||
"Rows with missing, malformed, non-numeric, or non-finite `mr_score.final` values are preserved, sorted after ranked rows, and marked in `mr_score_parse_status`."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "load-selector-pair-rankings",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"conn = connect_selected_database()\n",
|
||||
"try:\n",
|
||||
" selector_pair_rankings = load_selector_pair_rankings(conn)\n",
|
||||
"finally:\n",
|
||||
" conn.close()\n",
|
||||
"\n",
|
||||
"selector_pair_rankings_display = format_pair_names_for_display(\n",
|
||||
" selector_pair_rankings[[\"pair_rank\", \"pair_name\", \"mr_score_final\"]]\n",
|
||||
")\n",
|
||||
"with pd.option_context(\"display.max_rows\", None):\n",
|
||||
" display(selector_pair_rankings_display)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "theoretical-return-context",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Theoretical Return by Pair\n",
|
||||
"\n",
|
||||
"Load `trading_instructions` and calculate theoretical return for each ranked pair. Each pair starts from a fixed `$10,000` theoretical USD base. `TARGET` trades from the current theoretical position to the new target position, where target size is `10000 * strength / reference_price`; `CLOSE` liquidates the open position at the close row's `reference_price`; `HOLD` is ignored.\n",
|
||||
"\n",
|
||||
"`MIN_TARGET_STRENGTH_CHANGE_PCTG` can be raised above `0.0` to skip `TARGET` updates whose absolute percentage strength change is smaller than the threshold since the position was acquired. `num_trades` counts asset-level theoretical trades caused by effective `TARGET` and `CLOSE` rows. `realized_pnl` and `unrealized_pnl` are percentage returns relative to `$10,000`. The displayed dataframe is sorted by total return (`realized_pnl + unrealized_pnl`) ascending."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "target-change-threshold-input",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"min_target_change_input = widgets.FloatText(\n",
|
||||
" value=0.0,\n",
|
||||
" description=\"Mininal TARGET change (%)\",\n",
|
||||
" step=1.0,\n",
|
||||
" layout=widgets.Layout(width=\"420px\"),\n",
|
||||
" style={\"description_width\": \"190px\"},\n",
|
||||
")\n",
|
||||
"display(min_target_change_input)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "load-trading-instructions",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"conn = connect_selected_database()\n",
|
||||
"try:\n",
|
||||
" trading_instructions = load_trading_instructions(conn)\n",
|
||||
"finally:\n",
|
||||
" conn.close()\n",
|
||||
"\n",
|
||||
"print(f\"Loaded {len(trading_instructions):,} trading instruction rows.\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "calculate-pair-theoretical-returns",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"MIN_TARGET_STRENGTH_CHANGE_PCTG = float(min_target_change_input.value)\n",
|
||||
"\n",
|
||||
"pair_theo_ret = add_total_pnl(\n",
|
||||
" calculate_ranked_pairs_theo_ret(\n",
|
||||
" selector_pair_rankings,\n",
|
||||
" trading_instructions,\n",
|
||||
" min_pctg_change=MIN_TARGET_STRENGTH_CHANGE_PCTG,\n",
|
||||
" )\n",
|
||||
").sort_values(\n",
|
||||
" [\"total_pnl\", \"pair_name\"],\n",
|
||||
" ascending=[True, True],\n",
|
||||
" kind=\"mergesort\",\n",
|
||||
").drop(columns=\"total_pnl\").reset_index(drop=True)\n",
|
||||
"\n",
|
||||
"pair_theo_ret_display = format_pair_names_for_display(pair_theo_ret)\n",
|
||||
"\n",
|
||||
"show_interactive_dataframe(\n",
|
||||
" pair_theo_ret_display,\n",
|
||||
" table_id=\"pair-theo-ret-grid\",\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "theoretical-return-histogram-context",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Total Theoretical Return Distribution\n",
|
||||
"\n",
|
||||
"Plot the distribution of total theoretical return, calculated as `realized_pnl + unrealized_pnl`. Plotly chooses histogram bins automatically."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "plot-total-theoretical-return-histogram",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"total_pnl_histogram = create_total_pnl_histogram(pair_theo_ret)\n",
|
||||
"\n",
|
||||
"total_pnl_histogram"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "individual-pair-analysis-context",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Individual Pair Analysis\n",
|
||||
"\n",
|
||||
"Choose one pair for detailed follow-up analysis. Pair names are sorted alphabetically."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "choose-individual-pair",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pair_name_dropdown = create_pair_name_dropdown(selector_pair_rankings)\n",
|
||||
"display(pair_name_dropdown)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "selected-individual-pair",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"selected_pair_name = pair_name_dropdown.value\n",
|
||||
"format_pair_name_for_display(selected_pair_name)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "selected-pair-theo-executions-context",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Selected Pair Theoretical Executions\n",
|
||||
"\n",
|
||||
"Create the theoretical asset-level executions used by the PnL calculation for the selected pair. `TARGET` rows trade the position difference from the current theoretical position to the new target position, where target size is `10000 * strength / reference_price`; `CLOSE` rows flatten the current theoretical position. Positive size is `BUY`; negative size is `SELL`; USD value is signed as the opposite cash movement."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "selected-pair-theo-executions",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"selected_pair_theo_executions = calculate_pair_theo_executions(\n",
|
||||
" selected_pair_name,\n",
|
||||
" trading_instructions,\n",
|
||||
" min_pctg_change=MIN_TARGET_STRENGTH_CHANGE_PCTG,\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"selected_pair_theo_execution_columns = [\n",
|
||||
" \"time\",\n",
|
||||
" \"asset\",\n",
|
||||
" \"action\",\n",
|
||||
" \"side\",\n",
|
||||
" \"strength\",\n",
|
||||
" \"size\",\n",
|
||||
" \"price\",\n",
|
||||
" \"usd_value\",\n",
|
||||
"]\n",
|
||||
"selected_pair_theo_executions_display = selected_pair_theo_executions.reindex(\n",
|
||||
" columns=selected_pair_theo_execution_columns\n",
|
||||
")\n",
|
||||
"show_interactive_dataframe(\n",
|
||||
" selected_pair_theo_executions_display,\n",
|
||||
" table_id=\"selected-pair-theo-executions-grid\",\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "selected-pair-market-trades-context",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Selected Pair Trades on Market Data\n",
|
||||
"\n",
|
||||
"Load full available 1-minute market data for the selected pair's instruments from `ohlcv_1min`, starting at midnight UTC of the trading day inferred from `trading_instructions`. Close prices are shown as relative prices from each instrument's close at that midnight. Theoretical executions are overlaid at their execution `reference_price`, normalized by the same midnight close. Execution markers use execution timestamps directly and do not require a matching OHLCV row."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "selected-pair-market-trades-plot",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"trading_day_start_ns = infer_trading_day_start_ns(trading_instructions)\n",
|
||||
"\n",
|
||||
"conn = connect_selected_database()\n",
|
||||
"try:\n",
|
||||
" selected_pair_market_data = load_pair_market_data(\n",
|
||||
" conn,\n",
|
||||
" selected_pair_name,\n",
|
||||
" trading_day_start_ns=trading_day_start_ns,\n",
|
||||
" )\n",
|
||||
"finally:\n",
|
||||
" conn.close()\n",
|
||||
"\n",
|
||||
"selected_pair_market_trades_plot = create_pair_trades_market_plot(\n",
|
||||
" selected_pair_name,\n",
|
||||
" selected_pair_market_data,\n",
|
||||
" selected_pair_theo_executions,\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"selected_pair_market_trades_plot"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "python3.12-venv (3.12.13.final.0)",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.12.13"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
@@ -0,0 +1,12 @@
|
||||
# Interactive analysis
|
||||
ipykernel>=6.29,<7
|
||||
ipywidgets>=8.1,<9
|
||||
itables>=2.2,<3
|
||||
jupyter>=1.1,<2
|
||||
nbformat>=5.10,<6
|
||||
pandas>=2.2,<3
|
||||
plotly>=5.24,<7
|
||||
|
||||
# Verification
|
||||
nbmake>=1.5,<2
|
||||
pytest>=8,<9
|
||||
@@ -0,0 +1 @@
|
||||
|
||||
@@ -0,0 +1 @@
|
||||
|
||||
+1177
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user