lg_changes
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"security_type": "EQUITY",
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"data_directory": "./data/equity",
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"datafiles": [
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"202506*.mktdata.ohlcv.db",
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"20250618.mktdata.ohlcv.db",
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],
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"db_table_name": "md_1min_bars",
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"exchange_id": "ALPACA",
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@ -19,8 +19,8 @@
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"dis-equilibrium_close_trshld": 1.0,
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"training_minutes": 120,
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"funding_per_pair": 2000.0,
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"fit_method_class": "pt_trading.sliding_fit.SlidingFit",
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# "fit_method_class": "pt_trading.static_fit.StaticFit",
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# "fit_method_class": "pt_trading.sliding_fit.SlidingFit",
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"fit_method_class": "pt_trading.static_fit.StaticFit",
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"exclude_instruments": ["CAN"],
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"close_outstanding_positions": false
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26
configuration/equity_lg.cfg
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26
configuration/equity_lg.cfg
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{
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"security_type": "EQUITY",
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"data_directory": "./data/equity",
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"datafiles": [
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"20250602.mktdata.ohlcv.db",
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],
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"db_table_name": "md_1min_bars",
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"exchange_id": "ALPACA",
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"instrument_id_pfx": "STOCK-",
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"trading_hours": {
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"begin_session": "9:30:00",
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"end_session": "16:00:00",
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"timezone": "America/New_York"
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},
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"price_column": "close",
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"min_required_points": 30,
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"zero_threshold": 1e-10,
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"dis-equilibrium_open_trshld": 2.0,
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"dis-equilibrium_close_trshld": 1.0,
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"training_minutes": 120,
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"funding_per_pair": 2000.0,
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"fit_method_class": "pt_trading.fit_methods.StaticFit",
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"exclude_instruments": ["CAN"]
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}
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# "fit_method_class": "pt_trading.fit_methods.SlidingFit",
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# "fit_method_class": "pt_trading.fit_methods.StaticFit",
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115
lg_notes.md
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115
lg_notes.md
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07.11.2025
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pairs_trading/configuration <---- directory for config
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equity_lg.cfg <-------- copy of equity.cfg
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How to run a Program: TRIANGLEsquare ----> triangle EQUITY backtest
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Results are in > results (timestamp table for all runs)
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table "...timestamp... .pt_backtest_results.equity.db"
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going to table using sqlite
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> sqlite3 '/home/coder/results/20250721_175750.pt_backtest_results.equity.db'
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sqlite> .databases
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main: /home/coder/results/20250717_180122.pt_backtest_results.equity.db r/w
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sqlite> .tables
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config outstanding_positions pt_bt_results
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sqlite> PRAGMA table_info('pt_bt_results');
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0|date|DATE|0||0
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1|pair|TEXT|0||0
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2|symbol|TEXT|0||0
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3|open_time|DATETIME|0||0
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4|open_side|TEXT|0||0
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5|open_price|REAL|0||0
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6|open_quantity|INTEGER|0||0
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7|open_disequilibrium|REAL|0||0
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8|close_time|DATETIME|0||0
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9|close_side|TEXT|0||0
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10|close_price|REAL|0||0
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11|close_quantity|INTEGER|0||0
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12|close_disequilibrium|REAL|0||0
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13|symbol_return|REAL|0||0
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14|pair_return|REAL|0||0
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select count(*) as cnt from pt_bt_results;
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8
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select * from pt_bt_results;
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select
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date, close_time, pair, symbol, symbol_return, pair_return
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from pt_bt_results ;
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select date, sum(symbol_return) as daily_return
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from pt_bt_results where date = '2025-06-18' group by date;
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.quit
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sqlite3 '/home/coder/results/20250717_172435.pt_backtest_results.equity.db'
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sqlite> select date, sum(symbol_return) as daily_return
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from pt_bt_results group by date;
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2025-06-02|1.29845390060828
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...
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2025-06-18|-43.5084977104115 <========== ????? ==========>
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2025-06-20|11.8605547517183
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select
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date, close_time, pair, symbol, symbol_return, pair_return
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from pt_bt_results ;
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select date, close_time, pair, symbol, symbol_return, pair_return
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from pt_bt_results where date = '2025-06-18';
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./scripts/load_equity_pair_intraday.sh -A NVDA -B QQQ -d 20250701 -T ./intraday_md
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to inspect exactly what sources, formats, and processing steps you can open the script with:
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head -n 50 ./scripts/load_equity_pair_intraday.sh
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✓ Data file found: /home/coder/pairs_trading/data/crypto/20250605.mktdata.ohlcv.db
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sqlite3 '/home/coder/results/20250722_201930.pt_backtest_results.crypto.db'
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sqlite3 '/home/coder/results/xxxxxxxx_yyyyyy.pt_backtest_results.pseudo.db'
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11111111
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=== At your terminal, run these commands:
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sqlite3 '/home/coder/results/20250722_201930.pt_backtest_results.crypto.db'
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=== Then inside the SQLite prompt:
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.mode csv
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.headers on
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.output results_20250722.csv
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SELECT * FROM pt_bt_results;
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.output stdout
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.quit
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cd /home/coder/
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# === mode csv formats output as CSV
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# === headers on includes column names
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# === output my_table.csv directs output to that file
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# === Run your SELECT query, then revert output
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# === Open my_table.csv in Excel directly
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# ======== Using scp (Secure Copy)
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# === On your local machine, open a terminal and run:
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scp cvtt@953f6e8df266:/home/coder/results_20250722.csv ~/Downloads/
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# ===== convert cvs pandas dataframe ====== -->
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import pandas as pd
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# Replace with the actual path to your CSV file
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file_path = '/home/coder/results_20250722.csv'
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# Read the CSV file into a DataFrame
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df = pd.read_csv(file_path)
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# Show the first few rows
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print(df.head())
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16
research/notebooks/pt_pair_backtest.ipynb
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16
research/notebooks/pt_pair_backtest.ipynb
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{
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"cells": [],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"name": "python",
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"version": "3.12.5"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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