Compare commits
3 Commits
8b28b8d5f9
...
8d58439f44
| Author | SHA1 | Date | |
|---|---|---|---|
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8d58439f44 | ||
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e74c25bb8d | ||
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fc24017638 |
@ -12,7 +12,10 @@
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# -------------------------------------
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# --- Current month - all files
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# -------------------------------------
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cd $(realpath $(dirname $0))
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mkdir -p ./data/crypto
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pushd ./data/crypto
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rsync -ahvv cvtt@hs01.cvtt.vpn:/works/cvtt/md_archive/crypto/sim/*.gz ./
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# -------------------------------------
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@ -25,4 +28,4 @@ for srcfname in $(ls *.db.gz); do
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rm -f ${tgtfile} && sqlite3 temp.db ".dump md_1min_bars" | sqlite3 ${tgtfile} && rm ${srcfname}
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done
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rm temp.db
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popd
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popd
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@ -12,7 +12,10 @@ if [ -z "${DatePattern}" ]; then
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fi
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FilePattern="${DatePattern}*.alpaca_sim_md.db.gz"
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cd $(realpath $(dirname $0))/..
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mkdir -p ./data/equity
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pushd ./data/equity
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Cmd="rsync -ahvv cvtt@hs01.cvtt.vpn:/works/cvtt/md_archive/equity/alpaca_md/sim/${FilePattern} ./"
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echo ${Cmd}
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eval ${Cmd}
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@ -27,4 +30,4 @@ for srcfname in $(ls *.db.gz); do
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rm -f ${tgtfile} && sqlite3 temp.db ".dump md_1min_bars" | sqlite3 ${tgtfile} && rm ${srcfname}
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done
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rm temp.db
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popd
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popd
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@ -15,7 +15,7 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### \ud83c\udfaf Key Features:\n",
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"### 🎯 Key Features:\n",
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"\n",
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"1. **Interactive Configuration**: \n",
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" - Easy switching between CRYPTO and EQUITY configurations\n",
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@ -43,7 +43,7 @@
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" - Re-run capabilities for different configurations\n",
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" - Support for both StaticFitStrategy and SlidingFitStrategy\n",
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"\n",
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"### \ud83d\ude80 How to Use:\n",
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"### 🚀 How to Use:\n",
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"\n",
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"1. **Start Jupyter**:\n",
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" ```bash\n",
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@ -74,7 +74,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -133,7 +133,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -185,16 +185,18 @@
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"\n",
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"# Load the specified configuration\n",
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"print(f\"\\nLoading {CONFIG_FILE} configuration using HJSON...\")\n",
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"CONFIG = load_config_from_file(CONFIG_FILE)\n",
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"test_config = load_config_from_file(CONFIG_FILE)\n",
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"assert test_config is not None\n",
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"BT_TEST_CONFIG = test_config\n",
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"\n",
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"if CONFIG:\n",
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" print(f\"\u2713 Successfully loaded {CONFIG['security_type']} configuration\")\n",
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" print(f\" Data directory: {CONFIG['data_directory']}\")\n",
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" print(f\" Database table: {CONFIG['db_table_name']}\")\n",
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" print(f\" Exchange: {CONFIG['exchange_id']}\")\n",
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" print(f\" Training window: {CONFIG['training_minutes']} minutes\")\n",
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" print(f\" Open threshold: {CONFIG['dis-equilibrium_open_trshld']}\")\n",
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" print(f\" Close threshold: {CONFIG['dis-equilibrium_close_trshld']}\")\n",
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"if BT_TEST_CONFIG:\n",
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" print(f\"✓ Successfully loaded {BT_TEST_CONFIG['security_type']} configuration\")\n",
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" print(f\" Data directory: {BT_TEST_CONFIG['data_directory']}\")\n",
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" print(f\" Database table: {BT_TEST_CONFIG['db_table_name']}\")\n",
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" print(f\" Exchange: {BT_TEST_CONFIG['exchange_id']}\")\n",
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" print(f\" Training window: {BT_TEST_CONFIG['training_minutes']} minutes\")\n",
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" print(f\" Open threshold: {BT_TEST_CONFIG['dis-equilibrium_open_trshld']}\")\n",
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" print(f\" Close threshold: {BT_TEST_CONFIG['dis-equilibrium_close_trshld']}\")\n",
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" \n",
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" # Automatically construct data file name based on date and config type\n",
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" # if CONFIG['security_type'] == \"CRYPTO\":\n",
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@ -205,25 +207,25 @@
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" # DATA_FILE = f\"{TRADING_DATE}.mktdata.db\" # Default fallback\n",
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"\n",
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" # Update CONFIG with the specific data file and instruments\n",
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" CONFIG[\"datafiles\"] = [DATA_FILE]\n",
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" CONFIG[\"instruments\"] = [SYMBOL_A, SYMBOL_B]\n",
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" BT_TEST_CONFIG[\"datafiles\"] = [DATA_FILE]\n",
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" BT_TEST_CONFIG[\"instruments\"] = [SYMBOL_A, SYMBOL_B]\n",
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" \n",
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" print(f\"\\nData Configuration:\")\n",
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" print(f\" Data File: {DATA_FILE}\")\n",
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" print(f\" Security Type: {CONFIG['security_type']}\")\n",
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" print(f\" Security Type: {BT_TEST_CONFIG['security_type']}\")\n",
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" \n",
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" # Verify data file exists\n",
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" import os\n",
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" data_file_path = f\"{CONFIG['data_directory']}/{DATA_FILE}\"\n",
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" data_file_path = f\"{BT_TEST_CONFIG['data_directory']}/{DATA_FILE}\"\n",
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" if os.path.exists(data_file_path):\n",
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" print(f\" \u2713 Data file found: {data_file_path}\")\n",
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" print(f\" ✓ Data file found: {data_file_path}\")\n",
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" else:\n",
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" print(f\" \u26a0 Data file not found: {data_file_path}\")\n",
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" print(f\" ⚠ Data file not found: {data_file_path}\")\n",
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" print(f\" Please check if the date and file exist in the data directory\")\n",
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" \n",
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" # List available files in the data directory\n",
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" try:\n",
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" data_dir = CONFIG['data_directory']\n",
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" data_dir = BT_TEST_CONFIG['data_directory']\n",
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" if os.path.exists(data_dir):\n",
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" available_files = [f for f in os.listdir(data_dir) if f.endswith('.db')]\n",
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" print(f\" Available files in {data_dir}:\")\n",
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@ -234,7 +236,7 @@
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" except Exception as e:\n",
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" print(f\" Could not list files in data directory: {e}\")\n",
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"else:\n",
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" print(\"\u26a0 Failed to load configuration. Please check the configuration file.\")\n",
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" print(\"⚠ Failed to load configuration. Please check the configuration file.\")\n",
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" print(\"Available configuration files:\")\n",
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" config_dir = \"../../configuration\"\n",
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" if os.path.exists(config_dir):\n",
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@ -289,10 +291,10 @@
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"outputs": [],
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"source": [
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"# Load market data\n",
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"datafile_path = f\"{CONFIG['data_directory']}/{DATA_FILE}\"\n",
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"datafile_path = f\"{BT_TEST_CONFIG['data_directory']}/{DATA_FILE}\"\n",
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"print(f\"Loading data from: {datafile_path}\")\n",
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"\n",
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"market_data_df = load_market_data(datafile_path, config=CONFIG)\n",
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"market_data_df = load_market_data(datafile_path, config=BT_TEST_CONFIG)\n",
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"\n",
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"print(f\"Loaded {len(market_data_df)} rows of market data\")\n",
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"print(f\"Symbols in data: {market_data_df['symbol'].unique()}\")\n",
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@ -303,7 +305,7 @@
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" market_data=market_data_df,\n",
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" symbol_a=SYMBOL_A,\n",
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" symbol_b=SYMBOL_B,\n",
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" price_column=CONFIG[\"price_column\"]\n",
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" price_column=BT_TEST_CONFIG[\"price_column\"]\n",
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")\n",
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"\n",
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"print(f\"\\nCreated trading pair: {pair}\")\n",
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@ -311,7 +313,7 @@
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"print(f\"Column names: {pair.colnames()}\")\n",
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"\n",
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"# Calculate maximum possible iterations for sliding window\n",
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"training_minutes = CONFIG[\"training_minutes\"]\n",
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"training_minutes = BT_TEST_CONFIG[\"training_minutes\"]\n",
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"max_iterations = len(pair.market_data_) - training_minutes\n",
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"print(f\"\\nSliding window analysis:\")\n",
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"print(f\" Training window size: {training_minutes} minutes\")\n",
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@ -354,13 +356,13 @@
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"\n",
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"# Initialize the strategy state\n",
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"pair.user_data_['state'] = PairState.INITIAL\n",
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"pair.user_data_[\"trades\"] = pd.DataFrame(columns=STRATEGY.TRADES_COLUMNS)\n",
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"pair.user_data_[\"trades\"] = pd.DataFrame(columns=pd.Index(STRATEGY.TRADES_COLUMNS, dtype=str))\n",
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"pair.user_data_[\"is_cointegrated\"] = False\n",
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"\n",
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"bt_result = BacktestResult(config=CONFIG)\n",
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"training_minutes = CONFIG[\"training_minutes\"]\n",
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"open_threshold = CONFIG[\"dis-equilibrium_open_trshld\"]\n",
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"close_threshold = CONFIG[\"dis-equilibrium_close_trshld\"]\n",
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"bt_result = BacktestResult(config=BT_TEST_CONFIG)\n",
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"training_minutes = BT_TEST_CONFIG[\"training_minutes\"]\n",
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"open_threshold = BT_TEST_CONFIG[\"dis-equilibrium_open_trshld\"]\n",
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"close_threshold = BT_TEST_CONFIG[\"dis-equilibrium_close_trshld\"]\n",
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"\n",
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"# Limit iterations for demonstration (change this to max_iterations for full run)\n",
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"max_demo_iterations = min(200, max_iterations) # Process first 200 minutes\n",
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@ -586,15 +588,21 @@
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"source": [
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"# Detailed analysis of how training windows evolve\n",
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"print(\"TRAINING WINDOW EVOLUTION ANALYSIS\")\n",
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"print(\"=\"*50)\n",
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"print(\"=\" * 50)\n",
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"\n",
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"# Analyze cointegration stability\n",
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"if len(cointegration_history) > 1:\n",
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" # Find cointegration change points\n",
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" change_points = []\n",
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" for i in range(1, len(cointegration_history)):\n",
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" if cointegration_history[i] != cointegration_history[i-1]:\n",
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" change_points.append((i, cointegration_history[i], valid_timestamps[i] if i < len(valid_timestamps) else None))\n",
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" if cointegration_history[i] != cointegration_history[i - 1]:\n",
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" change_points.append(\n",
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" (\n",
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" i,\n",
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" cointegration_history[i],\n",
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" valid_timestamps[i] if i < len(valid_timestamps) else None,\n",
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" )\n",
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" )\n",
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"\n",
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" print(f\"\\nCointegration Change Points:\")\n",
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" if change_points:\n",
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@ -608,25 +616,34 @@
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"\n",
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"# Analyze beta stability when cointegrated\n",
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"if valid_betas and len(valid_betas) > 10:\n",
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" beta_df = pd.DataFrame(valid_betas, columns=[f'Beta_{i}' for i in range(len(valid_betas[0]))])\n",
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" beta_df['timestamp'] = beta_timestamps\n",
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" beta_df = pd.DataFrame(\n",
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" valid_betas,\n",
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" columns=pd.Index([f\"Beta_{i}\" for i in range(len(valid_betas[0]))], dtype=str),\n",
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" )\n",
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" beta_df[\"timestamp\"] = beta_timestamps\n",
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"\n",
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" print(f\"\\nBeta Coefficient Analysis:\")\n",
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" print(f\" Number of valid beta estimates: {len(valid_betas)}\")\n",
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" print(f\" Beta statistics:\")\n",
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" for col in beta_df.columns[:-1]: # Exclude timestamp\n",
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" print(f\" {col}: Mean={beta_df[col].mean():.4f}, Std={beta_df[col].std():.4f}\")\n",
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" print(\n",
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" f\" {col}: Mean={beta_df[col].mean():.4f}, Std={beta_df[col].std():.4f}\"\n",
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" )\n",
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"\n",
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" # Check for beta regime changes\n",
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" beta_changes = []\n",
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" threshold = 0.1 # 10% change threshold\n",
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" for i in range(1, len(valid_betas)):\n",
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" if np.any(np.abs(np.array(valid_betas[i]) - np.array(valid_betas[i-1])) > threshold):\n",
|
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" if np.any(\n",
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" np.abs(np.array(valid_betas[i]) - np.array(valid_betas[i - 1])) > threshold\n",
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" ):\n",
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" beta_changes.append(i)\n",
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"\n",
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" print(f\" Significant beta changes (>{threshold*100}%): {len(beta_changes)}\")\n",
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" if beta_changes:\n",
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" print(f\" Change frequency: {len(beta_changes)/len(valid_betas)*100:.1f}% of cointegrated periods\")\n",
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" print(\n",
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" f\" Change frequency: {len(beta_changes)/len(valid_betas)*100:.1f}% of cointegrated periods\"\n",
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" )\n",
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"\n",
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"# Analyze dis-equilibrium characteristics\n",
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"if valid_scaled_diseq:\n",
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@ -640,20 +657,28 @@
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"\n",
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" # Threshold analysis\n",
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" open_breaches = sum(1 for x in valid_scaled_diseq if abs(x) >= open_threshold)\n",
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" close_opportunities = sum(1 for x in valid_scaled_diseq if abs(x) <= close_threshold)\n",
|
||||
" close_opportunities = sum(\n",
|
||||
" 1 for x in valid_scaled_diseq if abs(x) <= close_threshold\n",
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||||
" )\n",
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"\n",
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" print(f\" Open threshold breaches: {open_breaches} ({open_breaches/len(valid_scaled_diseq)*100:.1f}%)\")\n",
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" print(f\" Close opportunities: {close_opportunities} ({close_opportunities/len(valid_scaled_diseq)*100:.1f}%)\")\n",
|
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" print(\n",
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" f\" Open threshold breaches: {open_breaches} ({open_breaches/len(valid_scaled_diseq)*100:.1f}%)\"\n",
|
||||
" )\n",
|
||||
" print(\n",
|
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" f\" Close opportunities: {close_opportunities} ({close_opportunities/len(valid_scaled_diseq)*100:.1f}%)\"\n",
|
||||
" )\n",
|
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"\n",
|
||||
" # Mean reversion analysis\n",
|
||||
" zero_crossings = 0\n",
|
||||
" for i in range(1, len(valid_scaled_diseq)):\n",
|
||||
" if (valid_scaled_diseq[i-1] * valid_scaled_diseq[i]) < 0: # Sign change\n",
|
||||
" if (valid_scaled_diseq[i - 1] * valid_scaled_diseq[i]) < 0: # Sign change\n",
|
||||
" zero_crossings += 1\n",
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"\n",
|
||||
" print(f\" Zero crossings (mean reversion events): {zero_crossings}\")\n",
|
||||
" if zero_crossings > 0:\n",
|
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" print(f\" Average time between mean reversions: {len(valid_scaled_diseq)/zero_crossings:.1f} minutes\")"
|
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" print(\n",
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" f\" Average time between mean reversions: {len(valid_scaled_diseq)/zero_crossings:.1f} minutes\"\n",
|
||||
" )"
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||||
]
|
||||
},
|
||||
{
|
||||
@ -686,13 +711,13 @@
|
||||
" market_data=market_data_df,\n",
|
||||
" symbol_a=SYMBOL_A,\n",
|
||||
" symbol_b=SYMBOL_B,\n",
|
||||
" price_column=CONFIG[\"price_column\"]\n",
|
||||
" price_column=BT_TEST_CONFIG[\"price_column\"]\n",
|
||||
" )\n",
|
||||
"\n",
|
||||
" bt_result_full = BacktestResult(config=CONFIG)\n",
|
||||
" bt_result_full = BacktestResult(config=BT_TEST_CONFIG)\n",
|
||||
"\n",
|
||||
" # Run strategy\n",
|
||||
" pair_trades = STRATEGY.run_pair(config=CONFIG, pair=pair_full, bt_result=bt_result_full)\n",
|
||||
" pair_trades = STRATEGY.run_pair(config=BT_TEST_CONFIG, pair=pair_full, bt_result=bt_result_full)\n",
|
||||
"\n",
|
||||
" if pair_trades is not None and len(pair_trades) > 0:\n",
|
||||
" print(f\"\\nGenerated {len(pair_trades)} trading signals:\")\n",
|
||||
@ -741,9 +766,9 @@
|
||||
"print(\"=\"*40)\n",
|
||||
"\n",
|
||||
"print(f\"Current parameters:\")\n",
|
||||
"print(f\" Training window: {CONFIG['training_minutes']} minutes\")\n",
|
||||
"print(f\" Open threshold: {CONFIG['dis-equilibrium_open_trshld']}\")\n",
|
||||
"print(f\" Close threshold: {CONFIG['dis-equilibrium_close_trshld']}\")\n",
|
||||
"print(f\" Training window: {BT_TEST_CONFIG['training_minutes']} minutes\")\n",
|
||||
"print(f\" Open threshold: {BT_TEST_CONFIG['dis-equilibrium_open_trshld']}\")\n",
|
||||
"print(f\" Close threshold: {BT_TEST_CONFIG['dis-equilibrium_close_trshld']}\")\n",
|
||||
"\n",
|
||||
"# Recommendations based on observed data\n",
|
||||
"if valid_scaled_diseq:\n",
|
||||
@ -825,9 +850,9 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.12.3"
|
||||
"version": "3.12.9"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 4
|
||||
}
|
||||
}
|
||||
|
||||
@ -1,7 +1,7 @@
|
||||
|
||||
from typing import Any, Dict, List, Optional
|
||||
import pandas as pd #type:ignore
|
||||
from statsmodels.tsa.vector_ar.vecm import VECM #type:ignore
|
||||
import pandas as pd # type:ignore
|
||||
from statsmodels.tsa.vector_ar.vecm import VECM, VECMResults # type:ignore
|
||||
|
||||
|
||||
class TradingPair:
|
||||
market_data_: pd.DataFrame
|
||||
@ -9,79 +9,106 @@ class TradingPair:
|
||||
symbol_b_: str
|
||||
price_column_: str
|
||||
|
||||
training_mu_: Optional[float]
|
||||
training_std_: Optional[float]
|
||||
training_mu_: float
|
||||
training_std_: float
|
||||
|
||||
training_df_: Optional[pd.DataFrame]
|
||||
testing_df_: Optional[pd.DataFrame]
|
||||
training_df_: pd.DataFrame
|
||||
testing_df_: pd.DataFrame
|
||||
|
||||
vecm_fit_: Optional[VECM]
|
||||
vecm_fit_: VECMResults
|
||||
|
||||
user_data_: Dict[str, Any]
|
||||
|
||||
def __init__(self, market_data: pd.DataFrame, symbol_a: str, symbol_b: str, price_column: str):
|
||||
def __init__(
|
||||
self, market_data: pd.DataFrame, symbol_a: str, symbol_b: str, price_column: str
|
||||
):
|
||||
self.symbol_a_ = symbol_a
|
||||
self.symbol_b_ = symbol_b
|
||||
self.price_column_ = price_column
|
||||
self.market_data_ = self._transform_dataframe(market_data)[["tstamp"] + self.colnames()]
|
||||
|
||||
self.training_mu_ = None
|
||||
self.training_std_ = None
|
||||
self.training_df_ = None
|
||||
self.testing_df_ = None
|
||||
self.vecm_fit_ = None
|
||||
self.market_data_ = pd.DataFrame(
|
||||
self._transform_dataframe(market_data)[["tstamp"] + self.colnames()]
|
||||
)
|
||||
|
||||
|
||||
self.user_data_ = {}
|
||||
|
||||
def _transform_dataframe(self, df: pd.DataFrame):
|
||||
def _transform_dataframe(self, df: pd.DataFrame) -> pd.DataFrame:
|
||||
# Select only the columns we need
|
||||
df_selected = df[["tstamp", "symbol", self.price_column_]]
|
||||
df_selected: pd.DataFrame = pd.DataFrame(
|
||||
df[["tstamp", "symbol", self.price_column_]]
|
||||
)
|
||||
|
||||
# Start with unique timestamps
|
||||
result_df: pd.DataFrame = pd.DataFrame(df_selected["tstamp"]).drop_duplicates().reset_index(drop=True)
|
||||
result_df: pd.DataFrame = (
|
||||
pd.DataFrame(df_selected["tstamp"]).drop_duplicates().reset_index(drop=True)
|
||||
)
|
||||
|
||||
# For each unique symbol, add a corresponding close price column
|
||||
|
||||
symbols = df_selected["symbol"].unique()
|
||||
for symbol in symbols:
|
||||
# Filter rows for this symbol
|
||||
df_symbol = df_selected[df_selected["symbol"] == symbol].reset_index(drop=True)
|
||||
df_symbol = df_selected[df_selected["symbol"] == symbol].reset_index(
|
||||
drop=True
|
||||
)
|
||||
|
||||
# Create column name like "close-COIN"
|
||||
new_price_column = f"{self.price_column_}_{symbol}"
|
||||
|
||||
# Create temporary dataframe with timestamp and price
|
||||
temp_df = pd.DataFrame({
|
||||
"tstamp": df_symbol["tstamp"],
|
||||
new_price_column: df_symbol[self.price_column_]
|
||||
})
|
||||
temp_df = pd.DataFrame(
|
||||
{
|
||||
"tstamp": df_symbol["tstamp"],
|
||||
new_price_column: df_symbol[self.price_column_],
|
||||
}
|
||||
)
|
||||
|
||||
# Join with our result dataframe
|
||||
result_df = pd.merge(result_df, temp_df, on="tstamp", how="left")
|
||||
result_df = result_df.reset_index(drop=True) # do not dropna() since irrelevant symbol would affect dataset
|
||||
result_df = result_df.reset_index(
|
||||
drop=True
|
||||
) # do not dropna() since irrelevant symbol would affect dataset
|
||||
|
||||
return result_df
|
||||
def get_datasets(self, training_minutes: int, training_start_index: int = 0, testing_size: Optional[int] = None) -> None:
|
||||
|
||||
def get_datasets(
|
||||
self,
|
||||
training_minutes: int,
|
||||
training_start_index: int = 0,
|
||||
testing_size: Optional[int] = None,
|
||||
) -> None:
|
||||
|
||||
testing_start_index = training_start_index + training_minutes
|
||||
self.training_df_ = self.market_data_.iloc[training_start_index:testing_start_index, :].copy()
|
||||
self.training_df_ = self.market_data_.iloc[
|
||||
training_start_index:testing_start_index, :
|
||||
].copy()
|
||||
assert self.training_df_ is not None
|
||||
self.training_df_ = self.training_df_.dropna().reset_index(drop=True)
|
||||
|
||||
testing_start_index = training_start_index + training_minutes
|
||||
if testing_size is None:
|
||||
self.testing_df_ = self.market_data_.iloc[testing_start_index:, :].copy()
|
||||
else:
|
||||
self.testing_df_ = self.market_data_.iloc[testing_start_index:testing_start_index + testing_size, :].copy()
|
||||
self.testing_df_ = self.market_data_.iloc[
|
||||
testing_start_index : testing_start_index + testing_size, :
|
||||
].copy()
|
||||
assert self.testing_df_ is not None
|
||||
self.testing_df_ = self.testing_df_.dropna().reset_index(drop=True)
|
||||
|
||||
def colnames(self) -> List[str]:
|
||||
return [f"{self.price_column_}_{self.symbol_a_}", f"{self.price_column_}_{self.symbol_b_}"]
|
||||
return [
|
||||
f"{self.price_column_}_{self.symbol_a_}",
|
||||
f"{self.price_column_}_{self.symbol_b_}",
|
||||
]
|
||||
|
||||
def fit_VECM(self):
|
||||
assert self.training_df_ is not None
|
||||
vecm_df = self.training_df_[self.colnames()].reset_index(drop=True)
|
||||
vecm_model = VECM(vecm_df, coint_rank=1)
|
||||
vecm_fit = vecm_model.fit()
|
||||
|
||||
assert vecm_fit is not None
|
||||
|
||||
# URGENT check beta and alpha
|
||||
|
||||
# Check if the model converged properly
|
||||
@ -94,18 +121,23 @@ class TradingPair:
|
||||
pass
|
||||
|
||||
def check_cointegration_johansen(self):
|
||||
assert self.training_df_ is not None
|
||||
from statsmodels.tsa.vector_ar.vecm import coint_johansen
|
||||
|
||||
df = self.training_df_[self.colnames()].reset_index(drop=True)
|
||||
result = coint_johansen(df, det_order=0, k_ar_diff=1)
|
||||
print(f"{self}: lr1={result.lr1[0]} cvt={result.cvt[0, 1]}.")
|
||||
print(
|
||||
f"{self}: lr1={result.lr1[0]} > cvt={result.cvt[0, 1]}? {result.lr1[0] > result.cvt[0, 1]}"
|
||||
)
|
||||
is_cointegrated = result.lr1[0] > result.cvt[0, 1]
|
||||
|
||||
return is_cointegrated
|
||||
|
||||
|
||||
def check_cointegration(self):
|
||||
def check_cointegration_engle_granger(self):
|
||||
from statsmodels.tsa.stattools import coint
|
||||
|
||||
col1, col2 = self.colnames()
|
||||
assert self.training_df_ is not None
|
||||
series1 = self.training_df_[col1].reset_index(drop=True)
|
||||
series2 = self.training_df_[col2].reset_index(drop=True)
|
||||
|
||||
@ -116,10 +148,10 @@ class TradingPair:
|
||||
print(f"{self}: is_cointegrated={is_cointegrated} pvalue={pvalue}")
|
||||
return is_cointegrated
|
||||
|
||||
|
||||
def train_pair(self) -> bool:
|
||||
is_cointegrated = self.check_cointegration()
|
||||
if not is_cointegrated:
|
||||
is_cointegrated_johansen = self.check_cointegration_johansen()
|
||||
is_cointegrated_engle_granger = self.check_cointegration_engle_granger()
|
||||
if not is_cointegrated_johansen and not is_cointegrated_engle_granger:
|
||||
return False
|
||||
pass
|
||||
|
||||
@ -127,10 +159,13 @@ class TradingPair:
|
||||
self.fit_VECM()
|
||||
assert self.training_df_ is not None and self.vecm_fit_ is not None
|
||||
diseq_series = self.training_df_[self.colnames()] @ self.vecm_fit_.beta
|
||||
self.training_mu_ = diseq_series.mean().iloc[0]
|
||||
self.training_std_ = diseq_series.std().iloc[0]
|
||||
print(diseq_series.shape)
|
||||
self.training_mu_ = float(diseq_series[0].mean())
|
||||
self.training_std_ = float(diseq_series[0].std())
|
||||
|
||||
self.training_df_["dis-equilibrium"] = self.training_df_[self.colnames()] @ self.vecm_fit_.beta
|
||||
self.training_df_["dis-equilibrium"] = (
|
||||
self.training_df_[self.colnames()] @ self.vecm_fit_.beta
|
||||
)
|
||||
# Normalize the dis-equilibrium
|
||||
self.training_df_["scaled_dis-equilibrium"] = (
|
||||
diseq_series - self.training_mu_
|
||||
@ -138,7 +173,7 @@ class TradingPair:
|
||||
|
||||
return True
|
||||
|
||||
def predict(self) -> None:
|
||||
def predict(self) -> pd.DataFrame:
|
||||
assert self.testing_df_ is not None
|
||||
assert self.vecm_fit_ is not None
|
||||
predicted_prices = self.vecm_fit_.predict(steps=len(self.testing_df_))
|
||||
@ -148,23 +183,26 @@ class TradingPair:
|
||||
|
||||
self.predicted_df_ = pd.merge(
|
||||
self.testing_df_.reset_index(drop=True),
|
||||
pd.DataFrame(predicted_prices, columns=self.colnames()),
|
||||
pd.DataFrame(
|
||||
predicted_prices, columns=pd.Index(self.colnames()), dtype=float
|
||||
),
|
||||
left_index=True,
|
||||
right_index=True,
|
||||
suffixes=("", "_pred"),
|
||||
).dropna()
|
||||
|
||||
self.predicted_df_["disequilibrium"] = self.predicted_df_[self.colnames()] @ self.vecm_fit_.beta
|
||||
self.predicted_df_["disequilibrium"] = (
|
||||
self.predicted_df_[self.colnames()] @ self.vecm_fit_.beta
|
||||
)
|
||||
|
||||
self.predicted_df_["scaled_disequilibrium"] = (
|
||||
abs(self.predicted_df_["disequilibrium"] - self.training_mu_) / self.training_std_
|
||||
abs(self.predicted_df_["disequilibrium"] - self.training_mu_)
|
||||
/ self.training_std_
|
||||
)
|
||||
|
||||
# Reset index to ensure proper indexing
|
||||
self.predicted_df_ = self.predicted_df_.reset_index()
|
||||
return self.predicted_df_
|
||||
|
||||
|
||||
def __repr__(self) ->str:
|
||||
def __repr__(self) -> str:
|
||||
return f"{self.symbol_a_} & {self.symbol_b_}"
|
||||
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user