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"# 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."
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"data": {
"text/plain": [
"(PosixPath('/home/oleg/develop/research/stat_pairs_backtest'),\n",
" PosixPath('/home/oleg/develop/research/stat_pairs_backtest/data'))"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from html import escape\n",
"from pathlib import Path\n",
"import importlib\n",
"import sys\n",
"\n",
"from IPython.display import clear_output, display\n",
"import ipywidgets as widgets\n",
"import pandas as pd\n",
"import panel as pn\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",
"pn.extension(\"tabulator\", \"plotly\")\n",
"\n",
"ANALYZE_BUTTON_COLUMN = spbt_day.ANALYZE_BUTTON_COLUMN\n",
"SELECTED_PAIR_EXECUTION_DISPLAY_COLUMNS = spbt_day.SELECTED_PAIR_EXECUTION_DISPLAY_COLUMNS\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_theo_ret_analyze_grid = spbt_day.create_pair_theo_ret_analyze_grid\n",
"create_pair_trades_market_plot = spbt_day.create_pair_trades_market_plot\n",
"create_selected_pair_executions_grid = spbt_day.create_selected_pair_executions_grid\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",
"format_pair_theo_ret_for_analyze_grid = spbt_day.format_pair_theo_ret_for_analyze_grid\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",
"pair_name_from_analyze_event = spbt_day.pair_name_from_analyze_event\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": 22,
"id": "database-file-selector",
"metadata": {},
"outputs": [
{
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"model_id": "66eae60bfc7044b9a5fa01eab6b1e6ae",
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"text/plain": [
"VBox(children=(HBox(children=(Text(value='/home/oleg/develop/research/stat_pairs_backtest/data', continuous_up…"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"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": 23,
"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": 24,
"id": "load-selector-pair-rankings",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>pair_rank</th>\n",
" <th>pair_name</th>\n",
" <th>mr_score_final</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>ETH-SOL</td>\n",
" <td>0.714349</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>BTC-SOL</td>\n",
" <td>0.630029</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>SOL-XLM</td>\n",
" <td>0.546467</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>BTC-ETH</td>\n",
" <td>0.457146</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>ETH-XLM</td>\n",
" <td>0.371887</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" pair_rank pair_name mr_score_final\n",
"0 1 ETH-SOL 0.714349\n",
"1 2 BTC-SOL 0.630029\n",
"2 3 SOL-XLM 0.546467\n",
"3 4 BTC-ETH 0.457146\n",
"4 5 ETH-XLM 0.371887"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"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. SP Quant result databases store `action`, `quote_asset`, `assets`, `scaled_disequilibrium`, and `beta` as explicit columns; legacy databases with packed JSON `data` are still accepted by the loader. 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": 25,
"id": "target-change-threshold-input",
"metadata": {},
"outputs": [
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"FloatText(value=0.0, description='Mininal TARGET change (%)', layout=Layout(width='420px'), step=1.0, style=De…"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"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": 26,
"id": "load-trading-instructions",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Loaded 8,244 trading instruction rows.\n"
]
}
],
"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": 27,
"id": "calculate-pair-theoretical-returns",
"metadata": {},
"outputs": [
{
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"BokehModel(combine_events=True, render_bundle={'docs_json': {'142c4f6b-90f8-4610-b62f-602c1db7a505': {'version…"
]
},
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"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_theo_ret_for_analyze_grid(pair_theo_ret)\n",
"pair_theo_ret_grid = create_pair_theo_ret_analyze_grid(\n",
" pair_theo_ret_display,\n",
" height=520,\n",
")\n",
"display(pair_theo_ret_grid)"
]
},
{
"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": 28,
"id": "plot-total-theoretical-return-histogram",
"metadata": {},
"outputs": [
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"source": [
"total_pnl_histogram = create_total_pnl_histogram(pair_theo_ret)\n",
"total_pnl_histogram.update_layout(height=360)\n",
"\n",
"display(total_pnl_histogram)"
]
},
{
"cell_type": "markdown",
"id": "individual-pair-analysis-context",
"metadata": {},
"source": [
"## Individual Pair Analysis\n",
"\n",
"Select a pair from the dropdown and click Analyze to load detailed follow-up analysis. The Pair TheoRet grid Analyze buttons use the same callback when the notebook frontend supports Tabulator button events. The selected-pair execution table includes instruction-level `scaled_disequilibrium` and `beta`. The market/trade chart is loaded from `market` and is not calculated until a pair is analyzed."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "individual-pair-analysis",
"metadata": {},
"outputs": [
{
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"HBox(children=(Dropdown(description='Pair', layout=Layout(width='100%'), options=(('BTC-ETH', 'BTC:USD-ETH:USD…"
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"HTML(value='<h3>Trades on Market Data</h3>')"
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]
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"source": [
"selected_pair_name = None\n",
"selected_pair_theo_executions = pd.DataFrame()\n",
"selected_pair_theo_executions_display = pd.DataFrame(\n",
" columns=SELECTED_PAIR_EXECUTION_DISPLAY_COLUMNS\n",
")\n",
"selected_pair_market_data = pd.DataFrame()\n",
"selected_pair_market_trades_plot = None\n",
"\n",
"pair_name_dropdown = create_pair_name_dropdown(pair_theo_ret)\n",
"analyze_selected_pair_button = widgets.Button(\n",
" description=\"Analyze\",\n",
" icon=\"search\",\n",
" button_style=\"primary\",\n",
" tooltip=\"Analyze the selected pair\",\n",
")\n",
"selected_pair_message = widgets.HTML(\n",
" value=\"Choose a pair and click Analyze to load individual-pair details.\"\n",
")\n",
"selected_pair_theo_executions_grid = create_selected_pair_executions_grid(\n",
" selected_pair_theo_executions_display,\n",
" height=360,\n",
")\n",
"selected_pair_market_trades_plot_output = widgets.Output(\n",
" layout=widgets.Layout(width=\"100%\"),\n",
")\n",
"\n",
"\n",
"def update_selected_pair(pair_name):\n",
" global selected_pair_name\n",
" global selected_pair_theo_executions\n",
" global selected_pair_theo_executions_display\n",
" global selected_pair_market_data\n",
" global selected_pair_market_trades_plot\n",
"\n",
" try:\n",
" selected_pair_name = pair_name\n",
" selected_pair_message.value = (\n",
" f\"Selected pair: <b>{escape(format_pair_name_for_display(selected_pair_name))}</b>\"\n",
" )\n",
"\n",
" 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",
" selected_pair_theo_executions_display = selected_pair_theo_executions.reindex(\n",
" columns=SELECTED_PAIR_EXECUTION_DISPLAY_COLUMNS\n",
" )\n",
" selected_pair_theo_executions_grid.value = selected_pair_theo_executions_display\n",
"\n",
" trading_day_start_ns = infer_trading_day_start_ns(trading_instructions)\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",
" selected_pair_market_trades_plot.update_layout(height=520)\n",
" with selected_pair_market_trades_plot_output:\n",
" clear_output(wait=True)\n",
" display(selected_pair_market_trades_plot)\n",
" except Exception as exc:\n",
" selected_pair_message.value = f\"<b>Error:</b> {escape(str(exc))}\"\n",
" with selected_pair_market_trades_plot_output:\n",
" clear_output(wait=True)\n",
"\n",
"\n",
"def analyze_selected_pair_click(_event):\n",
" update_selected_pair(pair_name_dropdown.value)\n",
"\n",
"\n",
"def analyze_pair_click(event):\n",
" update_selected_pair(pair_name_from_analyze_event(pair_theo_ret_grid, event))\n",
"\n",
"\n",
"analyze_selected_pair_button.on_click(analyze_selected_pair_click)\n",
"pair_theo_ret_grid.on_click(analyze_pair_click, column=ANALYZE_BUTTON_COLUMN)\n",
"\n",
"display(widgets.HBox([pair_name_dropdown, analyze_selected_pair_button]))\n",
"\n",
"display(selected_pair_message)\n",
"display(pn.Column(\"### Theoretical Executions\", selected_pair_theo_executions_grid))\n",
"display(widgets.HTML(value=\"<h3>Trades on Market Data</h3>\"))\n",
"display(selected_pair_market_trades_plot_output)"
]
}
],
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