gavindiaz/Forex-Quantitative-Trading-Strategy-Development
1
0
Fork 0
mirror of https://github.com/aguru-venkata-saisantosh-patnaik/Forex-Quantitative-Trading-Strategy-Development.git synced 2026-07-27 18:47:48 +00:00
Code Issues Packages Projects Releases Wiki Activity
Files
main
Forex-Quantitative-Trading-…/full_developed_strategy.ipynb
T
Aguru Venkata Saisantosh Patnaik 56241d7165 Add files via upload
2025-09-10 17:50:41 +05:30

9123 lines
1.4 MiB
Plaintext
Raw Permalink Blame History

This file contains ambiguous Unicode characters
This file contains Unicode characters that might be confused with other characters. If you think that this is intentional, you can safely ignore this warning. Use the Escape button to reveal them.
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Reading given data"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 424
},
"id": "RlMur3_ELMtB",
"outputId": "e63f3eaf-88cc-428d-dbd5-cacde139ae8c"
},
"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>Date</th>\n",
" <th>Open</th>\n",
" <th>High</th>\n",
" <th>Low</th>\n",
" <th>Close</th>\n",
" <th>Volume</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2004-12-31 20:00:00</td>\n",
" <td>1.35460</td>\n",
" <td>1.35860</td>\n",
" <td>1.35370</td>\n",
" <td>1.35710</td>\n",
" <td>409.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2004-12-31 21:00:00</td>\n",
" <td>1.35720</td>\n",
" <td>1.35850</td>\n",
" <td>1.35600</td>\n",
" <td>1.35650</td>\n",
" <td>304.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2004-12-31 22:00:00</td>\n",
" <td>1.35660</td>\n",
" <td>1.35710</td>\n",
" <td>1.35520</td>\n",
" <td>1.35540</td>\n",
" <td>272.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2004-12-31 23:00:00</td>\n",
" <td>1.35540</td>\n",
" <td>1.35630</td>\n",
" <td>1.35520</td>\n",
" <td>1.35620</td>\n",
" <td>84.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2005-01-03 01:00:00</td>\n",
" <td>1.35790</td>\n",
" <td>1.35810</td>\n",
" <td>1.35390</td>\n",
" <td>1.35470</td>\n",
" <td>318.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>126570</th>\n",
" <td>2025-08-04 07:00:00</td>\n",
" <td>1.15793</td>\n",
" <td>1.15855</td>\n",
" <td>1.15741</td>\n",
" <td>1.15748</td>\n",
" <td>1245.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>126571</th>\n",
" <td>2025-08-04 08:00:00</td>\n",
" <td>1.15748</td>\n",
" <td>1.15824</td>\n",
" <td>1.15668</td>\n",
" <td>1.15706</td>\n",
" <td>1925.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>126572</th>\n",
" <td>2025-08-04 09:00:00</td>\n",
" <td>1.15707</td>\n",
" <td>1.15834</td>\n",
" <td>1.15630</td>\n",
" <td>1.15718</td>\n",
" <td>3556.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>126573</th>\n",
" <td>2025-08-04 10:00:00</td>\n",
" <td>1.15719</td>\n",
" <td>1.15787</td>\n",
" <td>1.15506</td>\n",
" <td>1.15636</td>\n",
" <td>3699.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>126574</th>\n",
" <td>2025-08-04 11:00:00</td>\n",
" <td>1.15637</td>\n",
" <td>1.15654</td>\n",
" <td>1.15495</td>\n",
" <td>1.15544</td>\n",
" <td>2637.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>126575 rows × 6 columns</p>\n",
"</div>"
],
"text/plain": [
" Date Open High Low Close Volume\n",
"0 2004-12-31 20:00:00 1.35460 1.35860 1.35370 1.35710 409.0\n",
"1 2004-12-31 21:00:00 1.35720 1.35850 1.35600 1.35650 304.0\n",
"2 2004-12-31 22:00:00 1.35660 1.35710 1.35520 1.35540 272.0\n",
"3 2004-12-31 23:00:00 1.35540 1.35630 1.35520 1.35620 84.0\n",
"4 2005-01-03 01:00:00 1.35790 1.35810 1.35390 1.35470 318.0\n",
"... ... ... ... ... ... ...\n",
"126570 2025-08-04 07:00:00 1.15793 1.15855 1.15741 1.15748 1245.0\n",
"126571 2025-08-04 08:00:00 1.15748 1.15824 1.15668 1.15706 1925.0\n",
"126572 2025-08-04 09:00:00 1.15707 1.15834 1.15630 1.15718 3556.0\n",
"126573 2025-08-04 10:00:00 1.15719 1.15787 1.15506 1.15636 3699.0\n",
"126574 2025-08-04 11:00:00 1.15637 1.15654 1.15495 1.15544 2637.0\n",
"\n",
"[126575 rows x 6 columns]"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"df = pd.read_csv('data.csv')\n",
"df\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Dds9bbs-Lf4V"
},
"source": [
"# Making Features for the Model"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "mlyVnjaPLkOP"
},
"source": [
"## Session Labeling (Asian, NY, London) (Ordinal Encoding)\n",
"\n",
"* The label is 0 when none of the sessions are trading in a given time\n",
"* The label is 1 when only 1 session is trading in a given time\n",
"* The label is 2 when only 2 sessions are trading in a given time\n",
"* The label is 3 when all 3 sessions are trading in a given time"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 424
},
"id": "QLIgfSAWLZ13",
"outputId": "57032e3f-2c30-4699-8f4d-9f412195b8c1"
},
"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>Date</th>\n",
" <th>Open</th>\n",
" <th>High</th>\n",
" <th>Low</th>\n",
" <th>Close</th>\n",
" <th>Volume</th>\n",
" <th>Hour</th>\n",
" <th>Session</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2004-12-31 20:00:00</td>\n",
" <td>1.35460</td>\n",
" <td>1.35860</td>\n",
" <td>1.35370</td>\n",
" <td>1.35710</td>\n",
" <td>409.0</td>\n",
" <td>20</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2004-12-31 21:00:00</td>\n",
" <td>1.35720</td>\n",
" <td>1.35850</td>\n",
" <td>1.35600</td>\n",
" <td>1.35650</td>\n",
" <td>304.0</td>\n",
" <td>21</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2004-12-31 22:00:00</td>\n",
" <td>1.35660</td>\n",
" <td>1.35710</td>\n",
" <td>1.35520</td>\n",
" <td>1.35540</td>\n",
" <td>272.0</td>\n",
" <td>22</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2004-12-31 23:00:00</td>\n",
" <td>1.35540</td>\n",
" <td>1.35630</td>\n",
" <td>1.35520</td>\n",
" <td>1.35620</td>\n",
" <td>84.0</td>\n",
" <td>23</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2005-01-03 01:00:00</td>\n",
" <td>1.35790</td>\n",
" <td>1.35810</td>\n",
" <td>1.35390</td>\n",
" <td>1.35470</td>\n",
" <td>318.0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>126570</th>\n",
" <td>2025-08-04 07:00:00</td>\n",
" <td>1.15793</td>\n",
" <td>1.15855</td>\n",
" <td>1.15741</td>\n",
" <td>1.15748</td>\n",
" <td>1245.0</td>\n",
" <td>7</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>126571</th>\n",
" <td>2025-08-04 08:00:00</td>\n",
" <td>1.15748</td>\n",
" <td>1.15824</td>\n",
" <td>1.15668</td>\n",
" <td>1.15706</td>\n",
" <td>1925.0</td>\n",
" <td>8</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>126572</th>\n",
" <td>2025-08-04 09:00:00</td>\n",
" <td>1.15707</td>\n",
" <td>1.15834</td>\n",
" <td>1.15630</td>\n",
" <td>1.15718</td>\n",
" <td>3556.0</td>\n",
" <td>9</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>126573</th>\n",
" <td>2025-08-04 10:00:00</td>\n",
" <td>1.15719</td>\n",
" <td>1.15787</td>\n",
" <td>1.15506</td>\n",
" <td>1.15636</td>\n",
" <td>3699.0</td>\n",
" <td>10</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>126574</th>\n",
" <td>2025-08-04 11:00:00</td>\n",
" <td>1.15637</td>\n",
" <td>1.15654</td>\n",
" <td>1.15495</td>\n",
" <td>1.15544</td>\n",
" <td>2637.0</td>\n",
" <td>11</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>126575 rows × 8 columns</p>\n",
"</div>"
],
"text/plain": [
" Date Open High Low Close Volume Hour \\\n",
"0 2004-12-31 20:00:00 1.35460 1.35860 1.35370 1.35710 409.0 20 \n",
"1 2004-12-31 21:00:00 1.35720 1.35850 1.35600 1.35650 304.0 21 \n",
"2 2004-12-31 22:00:00 1.35660 1.35710 1.35520 1.35540 272.0 22 \n",
"3 2004-12-31 23:00:00 1.35540 1.35630 1.35520 1.35620 84.0 23 \n",
"4 2005-01-03 01:00:00 1.35790 1.35810 1.35390 1.35470 318.0 1 \n",
"... ... ... ... ... ... ... ... \n",
"126570 2025-08-04 07:00:00 1.15793 1.15855 1.15741 1.15748 1245.0 7 \n",
"126571 2025-08-04 08:00:00 1.15748 1.15824 1.15668 1.15706 1925.0 8 \n",
"126572 2025-08-04 09:00:00 1.15707 1.15834 1.15630 1.15718 3556.0 9 \n",
"126573 2025-08-04 10:00:00 1.15719 1.15787 1.15506 1.15636 3699.0 10 \n",
"126574 2025-08-04 11:00:00 1.15637 1.15654 1.15495 1.15544 2637.0 11 \n",
"\n",
" Session \n",
"0 1 \n",
"1 1 \n",
"2 0 \n",
"3 0 \n",
"4 1 \n",
"... ... \n",
"126570 1 \n",
"126571 2 \n",
"126572 1 \n",
"126573 1 \n",
"126574 1 \n",
"\n",
"[126575 rows x 8 columns]"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['Date'] = pd.to_datetime(df['Date'])\n",
"\n",
"# Extract time\n",
"df['Hour'] = df['Date'].dt.hour\n",
"\n",
"def label_hour(h):\n",
" if 0 <= h < 8:\n",
" return 1\n",
" elif 8 <= h < 9:\n",
" return 2\n",
" elif 9 <= h < 13:\n",
" return 1\n",
" elif 13 <= h < 17:\n",
" return 2\n",
" elif 17 <= h < 22:\n",
" return 1\n",
" elif 22 <= h <= 23:\n",
" return 0\n",
"\n",
"df['Session'] = df['Hour'].apply(label_hour)\n",
"\n",
"df"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "TljgWYOkMYbi"
},
"source": [
"## Labeling Quarter (Ordinal Encoding)\n",
"\n",
"* 1 for April, May, June\n",
"* 2 for July, August, Spetember\n",
"* 3 for October, November, December\n",
"* 4 for January, Febrauary, March"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "zO2xcjh7MPzM",
"outputId": "18ff470a-2562-48f7-f4e6-eb7e09c08885"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Date Open High Low Close Volume Hour \\\n",
"0 2004-12-31 20:00:00 1.35460 1.35860 1.35370 1.35710 409.0 20 \n",
"1 2004-12-31 21:00:00 1.35720 1.35850 1.35600 1.35650 304.0 21 \n",
"2 2004-12-31 22:00:00 1.35660 1.35710 1.35520 1.35540 272.0 22 \n",
"3 2004-12-31 23:00:00 1.35540 1.35630 1.35520 1.35620 84.0 23 \n",
"4 2005-01-03 01:00:00 1.35790 1.35810 1.35390 1.35470 318.0 1 \n",
"... ... ... ... ... ... ... ... \n",
"126570 2025-08-04 07:00:00 1.15793 1.15855 1.15741 1.15748 1245.0 7 \n",
"126571 2025-08-04 08:00:00 1.15748 1.15824 1.15668 1.15706 1925.0 8 \n",
"126572 2025-08-04 09:00:00 1.15707 1.15834 1.15630 1.15718 3556.0 9 \n",
"126573 2025-08-04 10:00:00 1.15719 1.15787 1.15506 1.15636 3699.0 10 \n",
"126574 2025-08-04 11:00:00 1.15637 1.15654 1.15495 1.15544 2637.0 11 \n",
"\n",
" Session Month Quarter \n",
"0 1 12 4 \n",
"1 1 12 4 \n",
"2 0 12 4 \n",
"3 0 12 4 \n",
"4 1 1 1 \n",
"... ... ... ... \n",
"126570 1 8 3 \n",
"126571 2 8 3 \n",
"126572 1 8 3 \n",
"126573 1 8 3 \n",
"126574 1 8 3 \n",
"\n",
"[126575 rows x 10 columns]\n"
]
}
],
"source": [
"df['Month'] = df['Date'].dt.month\n",
"\n",
"# Function to assign quarter labels\n",
"def label_quarter(m):\n",
" if 1 <= m <= 3:\n",
" return 1\n",
" elif 4 <= m <= 6:\n",
" return 2\n",
" elif 7 <= m <= 9:\n",
" return 3\n",
" elif 10 <= m <= 12:\n",
" return 4\n",
"\n",
"# Apply function\n",
"df['Quarter'] = df['Month'].apply(label_quarter)\n",
"\n",
"print(df)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "AdeG3Dk9NNiP"
},
"source": [
"## Capturing the cyclic nature of Months, Date(1,2,...,31), Day of Week (Mon, Tue, Wed,...,Sun), Hour of the day (0,1,2,3,...,23)\n",
"\n",
"* Applying sine transformation to the above mentioned fields so that the machine understands that december is closer to january than to June and other details hidden in DateTime in a similar fashion.\n",
"* Truncating the values upto 5 decimal points because the OHLCV is of the precision upto 5 decimal points"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "GAwVGdAzMurb",
"outputId": "5bdaad6f-efaa-454f-de09-cae01d39fad0"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Date Open High Low Close Volume Hour \\\n",
"0 2004-12-31 20:00:00 1.35460 1.35860 1.35370 1.35710 409.0 20 \n",
"1 2004-12-31 21:00:00 1.35720 1.35850 1.35600 1.35650 304.0 21 \n",
"2 2004-12-31 22:00:00 1.35660 1.35710 1.35520 1.35540 272.0 22 \n",
"3 2004-12-31 23:00:00 1.35540 1.35630 1.35520 1.35620 84.0 23 \n",
"4 2005-01-03 01:00:00 1.35790 1.35810 1.35390 1.35470 318.0 1 \n",
"... ... ... ... ... ... ... ... \n",
"126570 2025-08-04 07:00:00 1.15793 1.15855 1.15741 1.15748 1245.0 7 \n",
"126571 2025-08-04 08:00:00 1.15748 1.15824 1.15668 1.15706 1925.0 8 \n",
"126572 2025-08-04 09:00:00 1.15707 1.15834 1.15630 1.15718 3556.0 9 \n",
"126573 2025-08-04 10:00:00 1.15719 1.15787 1.15506 1.15636 3699.0 10 \n",
"126574 2025-08-04 11:00:00 1.15637 1.15654 1.15495 1.15544 2637.0 11 \n",
"\n",
" Session Month Quarter Month_sin \n",
"0 1 12 4 -0.00000 \n",
"1 1 12 4 -0.00000 \n",
"2 0 12 4 -0.00000 \n",
"3 0 12 4 -0.00000 \n",
"4 1 1 1 0.50000 \n",
"... ... ... ... ... \n",
"126570 1 8 3 -0.86603 \n",
"126571 2 8 3 -0.86603 \n",
"126572 1 8 3 -0.86603 \n",
"126573 1 8 3 -0.86603 \n",
"126574 1 8 3 -0.86603 \n",
"\n",
"[126575 rows x 11 columns]\n"
]
}
],
"source": [
"import numpy as np\n",
"\n",
"# Apply sine transformation (cyclical encoding)\n",
"df['Month_sin'] = np.round(np.sin(2 * np.pi * df['Month'] / 12),5)\n",
"\n",
"print(df)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "j9rjsyofNtYj",
"outputId": "96b4c886-a6c3-4778-b449-c4cf465b8e87"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Date Open High Low Close Volume Hour \\\n",
"0 2004-12-31 20:00:00 1.35460 1.35860 1.35370 1.35710 409.0 20 \n",
"1 2004-12-31 21:00:00 1.35720 1.35850 1.35600 1.35650 304.0 21 \n",
"2 2004-12-31 22:00:00 1.35660 1.35710 1.35520 1.35540 272.0 22 \n",
"3 2004-12-31 23:00:00 1.35540 1.35630 1.35520 1.35620 84.0 23 \n",
"4 2005-01-03 01:00:00 1.35790 1.35810 1.35390 1.35470 318.0 1 \n",
"... ... ... ... ... ... ... ... \n",
"126570 2025-08-04 07:00:00 1.15793 1.15855 1.15741 1.15748 1245.0 7 \n",
"126571 2025-08-04 08:00:00 1.15748 1.15824 1.15668 1.15706 1925.0 8 \n",
"126572 2025-08-04 09:00:00 1.15707 1.15834 1.15630 1.15718 3556.0 9 \n",
"126573 2025-08-04 10:00:00 1.15719 1.15787 1.15506 1.15636 3699.0 10 \n",
"126574 2025-08-04 11:00:00 1.15637 1.15654 1.15495 1.15544 2637.0 11 \n",
"\n",
" Session Month Quarter Month_sin Hour_sin \n",
"0 1 12 4 -0.00000 -0.86603 \n",
"1 1 12 4 -0.00000 -0.70711 \n",
"2 0 12 4 -0.00000 -0.50000 \n",
"3 0 12 4 -0.00000 -0.25882 \n",
"4 1 1 1 0.50000 0.25882 \n",
"... ... ... ... ... ... \n",
"126570 1 8 3 -0.86603 0.96593 \n",
"126571 2 8 3 -0.86603 0.86603 \n",
"126572 1 8 3 -0.86603 0.70711 \n",
"126573 1 8 3 -0.86603 0.50000 \n",
"126574 1 8 3 -0.86603 0.25882 \n",
"\n",
"[126575 rows x 12 columns]\n"
]
}
],
"source": [
"# Apply sine transformation and round to 5 decimals\n",
"df['Hour_sin'] = np.round(np.sin(2 * np.pi * df['Hour'] / 24), 5)\n",
"\n",
"print(df)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "ZJCsFhVrOjIc",
"outputId": "23685120-b1a2-457e-dd9a-b5c00bf8185a"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Date Open High Low Close Volume Hour \\\n",
"0 2004-12-31 20:00:00 1.35460 1.35860 1.35370 1.35710 409.0 20 \n",
"1 2004-12-31 21:00:00 1.35720 1.35850 1.35600 1.35650 304.0 21 \n",
"2 2004-12-31 22:00:00 1.35660 1.35710 1.35520 1.35540 272.0 22 \n",
"3 2004-12-31 23:00:00 1.35540 1.35630 1.35520 1.35620 84.0 23 \n",
"4 2005-01-03 01:00:00 1.35790 1.35810 1.35390 1.35470 318.0 1 \n",
"... ... ... ... ... ... ... ... \n",
"126570 2025-08-04 07:00:00 1.15793 1.15855 1.15741 1.15748 1245.0 7 \n",
"126571 2025-08-04 08:00:00 1.15748 1.15824 1.15668 1.15706 1925.0 8 \n",
"126572 2025-08-04 09:00:00 1.15707 1.15834 1.15630 1.15718 3556.0 9 \n",
"126573 2025-08-04 10:00:00 1.15719 1.15787 1.15506 1.15636 3699.0 10 \n",
"126574 2025-08-04 11:00:00 1.15637 1.15654 1.15495 1.15544 2637.0 11 \n",
"\n",
" Session Month Quarter Month_sin Hour_sin Day Date_sin \n",
"0 1 12 4 -0.00000 -0.86603 31 0.20791 \n",
"1 1 12 4 -0.00000 -0.70711 31 0.20791 \n",
"2 0 12 4 -0.00000 -0.50000 31 0.20791 \n",
"3 0 12 4 -0.00000 -0.25882 31 0.20791 \n",
"4 1 1 1 0.50000 0.25882 3 0.58779 \n",
"... ... ... ... ... ... ... ... \n",
"126570 1 8 3 -0.86603 0.96593 4 0.74314 \n",
"126571 2 8 3 -0.86603 0.86603 4 0.74314 \n",
"126572 1 8 3 -0.86603 0.70711 4 0.74314 \n",
"126573 1 8 3 -0.86603 0.50000 4 0.74314 \n",
"126574 1 8 3 -0.86603 0.25882 4 0.74314 \n",
"\n",
"[126575 rows x 14 columns]\n"
]
}
],
"source": [
"# Ensure Date column is datetime type\n",
"df[\"Date\"] = pd.to_datetime(df[\"Date\"])\n",
"\n",
"# Extract only the day number (1–31)\n",
"df[\"Day\"] = df[\"Date\"].dt.day\n",
"\n",
"\n",
"# Apply sine transformation and round to 5 decimals\n",
"df['Date_sin'] = np.round(np.sin(2 * np.pi * df['Day'] / 30), 5)\n",
"\n",
"print(df)\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 424
},
"id": "ZCNbAVrhPHE6",
"outputId": "ef6a6bd9-3794-43c2-d07d-36fccd5cc88a"
},
"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>Date</th>\n",
" <th>Open</th>\n",
" <th>High</th>\n",
" <th>Low</th>\n",
" <th>Close</th>\n",
" <th>Volume</th>\n",
" <th>Hour</th>\n",
" <th>Session</th>\n",
" <th>Month</th>\n",
" <th>Quarter</th>\n",
" <th>Month_sin</th>\n",
" <th>Hour_sin</th>\n",
" <th>Day</th>\n",
" <th>Date_sin</th>\n",
" <th>DOW</th>\n",
" <th>DOW_sin</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2004-12-31 20:00:00</td>\n",
" <td>1.35460</td>\n",
" <td>1.35860</td>\n",
" <td>1.35370</td>\n",
" <td>1.35710</td>\n",
" <td>409.0</td>\n",
" <td>20</td>\n",
" <td>1</td>\n",
" <td>12</td>\n",
" <td>4</td>\n",
" <td>-0.00000</td>\n",
" <td>-0.86603</td>\n",
" <td>31</td>\n",
" <td>0.20791</td>\n",
" <td>4</td>\n",
" <td>-0.433884</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2004-12-31 21:00:00</td>\n",
" <td>1.35720</td>\n",
" <td>1.35850</td>\n",
" <td>1.35600</td>\n",
" <td>1.35650</td>\n",
" <td>304.0</td>\n",
" <td>21</td>\n",
" <td>1</td>\n",
" <td>12</td>\n",
" <td>4</td>\n",
" <td>-0.00000</td>\n",
" <td>-0.70711</td>\n",
" <td>31</td>\n",
" <td>0.20791</td>\n",
" <td>4</td>\n",
" <td>-0.433884</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2004-12-31 22:00:00</td>\n",
" <td>1.35660</td>\n",
" <td>1.35710</td>\n",
" <td>1.35520</td>\n",
" <td>1.35540</td>\n",
" <td>272.0</td>\n",
" <td>22</td>\n",
" <td>0</td>\n",
" <td>12</td>\n",
" <td>4</td>\n",
" <td>-0.00000</td>\n",
" <td>-0.50000</td>\n",
" <td>31</td>\n",
" <td>0.20791</td>\n",
" <td>4</td>\n",
" <td>-0.433884</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2004-12-31 23:00:00</td>\n",
" <td>1.35540</td>\n",
" <td>1.35630</td>\n",
" <td>1.35520</td>\n",
" <td>1.35620</td>\n",
" <td>84.0</td>\n",
" <td>23</td>\n",
" <td>0</td>\n",
" <td>12</td>\n",
" <td>4</td>\n",
" <td>-0.00000</td>\n",
" <td>-0.25882</td>\n",
" <td>31</td>\n",
" <td>0.20791</td>\n",
" <td>4</td>\n",
" <td>-0.433884</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2005-01-03 01:00:00</td>\n",
" <td>1.35790</td>\n",
" <td>1.35810</td>\n",
" <td>1.35390</td>\n",
" <td>1.35470</td>\n",
" <td>318.0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0.50000</td>\n",
" <td>0.25882</td>\n",
" <td>3</td>\n",
" <td>0.58779</td>\n",
" <td>0</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>126570</th>\n",
" <td>2025-08-04 07:00:00</td>\n",
" <td>1.15793</td>\n",
" <td>1.15855</td>\n",
" <td>1.15741</td>\n",
" <td>1.15748</td>\n",
" <td>1245.0</td>\n",
" <td>7</td>\n",
" <td>1</td>\n",
" <td>8</td>\n",
" <td>3</td>\n",
" <td>-0.86603</td>\n",
" <td>0.96593</td>\n",
" <td>4</td>\n",
" <td>0.74314</td>\n",
" <td>0</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>126571</th>\n",
" <td>2025-08-04 08:00:00</td>\n",
" <td>1.15748</td>\n",
" <td>1.15824</td>\n",
" <td>1.15668</td>\n",
" <td>1.15706</td>\n",
" <td>1925.0</td>\n",
" <td>8</td>\n",
" <td>2</td>\n",
" <td>8</td>\n",
" <td>3</td>\n",
" <td>-0.86603</td>\n",
" <td>0.86603</td>\n",
" <td>4</td>\n",
" <td>0.74314</td>\n",
" <td>0</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>126572</th>\n",
" <td>2025-08-04 09:00:00</td>\n",
" <td>1.15707</td>\n",
" <td>1.15834</td>\n",
" <td>1.15630</td>\n",
" <td>1.15718</td>\n",
" <td>3556.0</td>\n",
" <td>9</td>\n",
" <td>1</td>\n",
" <td>8</td>\n",
" <td>3</td>\n",
" <td>-0.86603</td>\n",
" <td>0.70711</td>\n",
" <td>4</td>\n",
" <td>0.74314</td>\n",
" <td>0</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>126573</th>\n",
" <td>2025-08-04 10:00:00</td>\n",
" <td>1.15719</td>\n",
" <td>1.15787</td>\n",
" <td>1.15506</td>\n",
" <td>1.15636</td>\n",
" <td>3699.0</td>\n",
" <td>10</td>\n",
" <td>1</td>\n",
" <td>8</td>\n",
" <td>3</td>\n",
" <td>-0.86603</td>\n",
" <td>0.50000</td>\n",
" <td>4</td>\n",
" <td>0.74314</td>\n",
" <td>0</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>126574</th>\n",
" <td>2025-08-04 11:00:00</td>\n",
" <td>1.15637</td>\n",
" <td>1.15654</td>\n",
" <td>1.15495</td>\n",
" <td>1.15544</td>\n",
" <td>2637.0</td>\n",
" <td>11</td>\n",
" <td>1</td>\n",
" <td>8</td>\n",
" <td>3</td>\n",
" <td>-0.86603</td>\n",
" <td>0.25882</td>\n",
" <td>4</td>\n",
" <td>0.74314</td>\n",
" <td>0</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>126575 rows × 16 columns</p>\n",
"</div>"
],
"text/plain": [
" Date Open High Low Close Volume Hour \\\n",
"0 2004-12-31 20:00:00 1.35460 1.35860 1.35370 1.35710 409.0 20 \n",
"1 2004-12-31 21:00:00 1.35720 1.35850 1.35600 1.35650 304.0 21 \n",
"2 2004-12-31 22:00:00 1.35660 1.35710 1.35520 1.35540 272.0 22 \n",
"3 2004-12-31 23:00:00 1.35540 1.35630 1.35520 1.35620 84.0 23 \n",
"4 2005-01-03 01:00:00 1.35790 1.35810 1.35390 1.35470 318.0 1 \n",
"... ... ... ... ... ... ... ... \n",
"126570 2025-08-04 07:00:00 1.15793 1.15855 1.15741 1.15748 1245.0 7 \n",
"126571 2025-08-04 08:00:00 1.15748 1.15824 1.15668 1.15706 1925.0 8 \n",
"126572 2025-08-04 09:00:00 1.15707 1.15834 1.15630 1.15718 3556.0 9 \n",
"126573 2025-08-04 10:00:00 1.15719 1.15787 1.15506 1.15636 3699.0 10 \n",
"126574 2025-08-04 11:00:00 1.15637 1.15654 1.15495 1.15544 2637.0 11 \n",
"\n",
" Session Month Quarter Month_sin Hour_sin Day Date_sin DOW \\\n",
"0 1 12 4 -0.00000 -0.86603 31 0.20791 4 \n",
"1 1 12 4 -0.00000 -0.70711 31 0.20791 4 \n",
"2 0 12 4 -0.00000 -0.50000 31 0.20791 4 \n",
"3 0 12 4 -0.00000 -0.25882 31 0.20791 4 \n",
"4 1 1 1 0.50000 0.25882 3 0.58779 0 \n",
"... ... ... ... ... ... ... ... ... \n",
"126570 1 8 3 -0.86603 0.96593 4 0.74314 0 \n",
"126571 2 8 3 -0.86603 0.86603 4 0.74314 0 \n",
"126572 1 8 3 -0.86603 0.70711 4 0.74314 0 \n",
"126573 1 8 3 -0.86603 0.50000 4 0.74314 0 \n",
"126574 1 8 3 -0.86603 0.25882 4 0.74314 0 \n",
"\n",
" DOW_sin \n",
"0 -0.433884 \n",
"1 -0.433884 \n",
"2 -0.433884 \n",
"3 -0.433884 \n",
"4 0.000000 \n",
"... ... \n",
"126570 0.000000 \n",
"126571 0.000000 \n",
"126572 0.000000 \n",
"126573 0.000000 \n",
"126574 0.000000 \n",
"\n",
"[126575 rows x 16 columns]"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# make sure Date is datetime\n",
"df[\"Date\"] = pd.to_datetime(df[\"Date\"])\n",
"\n",
"# get day of week (Monday=0, Sunday=6)\n",
"df[\"DOW\"] = df[\"Date\"].dt.weekday\n",
"\n",
"# sine transformation (map 1–7 into a circle)\n",
"df[\"DOW_sin\"] = np.sin(2 * np.pi * df[\"DOW\"] / 7)\n",
"\n",
"df\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 424
},
"id": "P46xfd-tTdbP",
"outputId": "b94c3546-a576-4f07-f569-2e683064cede"
},
"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>Date</th>\n",
" <th>Open</th>\n",
" <th>High</th>\n",
" <th>Low</th>\n",
" <th>Close</th>\n",
" <th>Volume</th>\n",
" <th>Session</th>\n",
" <th>Quarter</th>\n",
" <th>Month_sin</th>\n",
" <th>Hour_sin</th>\n",
" <th>Date_sin</th>\n",
" <th>DOW_sin</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2004-12-31 20:00:00</td>\n",
" <td>1.35460</td>\n",
" <td>1.35860</td>\n",
" <td>1.35370</td>\n",
" <td>1.35710</td>\n",
" <td>409.0</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>-0.00000</td>\n",
" <td>-0.86603</td>\n",
" <td>0.20791</td>\n",
" <td>-0.433884</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2004-12-31 21:00:00</td>\n",
" <td>1.35720</td>\n",
" <td>1.35850</td>\n",
" <td>1.35600</td>\n",
" <td>1.35650</td>\n",
" <td>304.0</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>-0.00000</td>\n",
" <td>-0.70711</td>\n",
" <td>0.20791</td>\n",
" <td>-0.433884</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2004-12-31 22:00:00</td>\n",
" <td>1.35660</td>\n",
" <td>1.35710</td>\n",
" <td>1.35520</td>\n",
" <td>1.35540</td>\n",
" <td>272.0</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>-0.00000</td>\n",
" <td>-0.50000</td>\n",
" <td>0.20791</td>\n",
" <td>-0.433884</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2004-12-31 23:00:00</td>\n",
" <td>1.35540</td>\n",
" <td>1.35630</td>\n",
" <td>1.35520</td>\n",
" <td>1.35620</td>\n",
" <td>84.0</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>-0.00000</td>\n",
" <td>-0.25882</td>\n",
" <td>0.20791</td>\n",
" <td>-0.433884</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2005-01-03 01:00:00</td>\n",
" <td>1.35790</td>\n",
" <td>1.35810</td>\n",
" <td>1.35390</td>\n",
" <td>1.35470</td>\n",
" <td>318.0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0.50000</td>\n",
" <td>0.25882</td>\n",
" <td>0.58779</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>126570</th>\n",
" <td>2025-08-04 07:00:00</td>\n",
" <td>1.15793</td>\n",
" <td>1.15855</td>\n",
" <td>1.15741</td>\n",
" <td>1.15748</td>\n",
" <td>1245.0</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>-0.86603</td>\n",
" <td>0.96593</td>\n",
" <td>0.74314</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>126571</th>\n",
" <td>2025-08-04 08:00:00</td>\n",
" <td>1.15748</td>\n",
" <td>1.15824</td>\n",
" <td>1.15668</td>\n",
" <td>1.15706</td>\n",
" <td>1925.0</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>-0.86603</td>\n",
" <td>0.86603</td>\n",
" <td>0.74314</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>126572</th>\n",
" <td>2025-08-04 09:00:00</td>\n",
" <td>1.15707</td>\n",
" <td>1.15834</td>\n",
" <td>1.15630</td>\n",
" <td>1.15718</td>\n",
" <td>3556.0</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>-0.86603</td>\n",
" <td>0.70711</td>\n",
" <td>0.74314</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>126573</th>\n",
" <td>2025-08-04 10:00:00</td>\n",
" <td>1.15719</td>\n",
" <td>1.15787</td>\n",
" <td>1.15506</td>\n",
" <td>1.15636</td>\n",
" <td>3699.0</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>-0.86603</td>\n",
" <td>0.50000</td>\n",
" <td>0.74314</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>126574</th>\n",
" <td>2025-08-04 11:00:00</td>\n",
" <td>1.15637</td>\n",
" <td>1.15654</td>\n",
" <td>1.15495</td>\n",
" <td>1.15544</td>\n",
" <td>2637.0</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>-0.86603</td>\n",
" <td>0.25882</td>\n",
" <td>0.74314</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>126575 rows × 12 columns</p>\n",
"</div>"
],
"text/plain": [
" Date Open High Low Close Volume \\\n",
"0 2004-12-31 20:00:00 1.35460 1.35860 1.35370 1.35710 409.0 \n",
"1 2004-12-31 21:00:00 1.35720 1.35850 1.35600 1.35650 304.0 \n",
"2 2004-12-31 22:00:00 1.35660 1.35710 1.35520 1.35540 272.0 \n",
"3 2004-12-31 23:00:00 1.35540 1.35630 1.35520 1.35620 84.0 \n",
"4 2005-01-03 01:00:00 1.35790 1.35810 1.35390 1.35470 318.0 \n",
"... ... ... ... ... ... ... \n",
"126570 2025-08-04 07:00:00 1.15793 1.15855 1.15741 1.15748 1245.0 \n",
"126571 2025-08-04 08:00:00 1.15748 1.15824 1.15668 1.15706 1925.0 \n",
"126572 2025-08-04 09:00:00 1.15707 1.15834 1.15630 1.15718 3556.0 \n",
"126573 2025-08-04 10:00:00 1.15719 1.15787 1.15506 1.15636 3699.0 \n",
"126574 2025-08-04 11:00:00 1.15637 1.15654 1.15495 1.15544 2637.0 \n",
"\n",
" Session Quarter Month_sin Hour_sin Date_sin DOW_sin \n",
"0 1 4 -0.00000 -0.86603 0.20791 -0.433884 \n",
"1 1 4 -0.00000 -0.70711 0.20791 -0.433884 \n",
"2 0 4 -0.00000 -0.50000 0.20791 -0.433884 \n",
"3 0 4 -0.00000 -0.25882 0.20791 -0.433884 \n",
"4 1 1 0.50000 0.25882 0.58779 0.000000 \n",
"... ... ... ... ... ... ... \n",
"126570 1 3 -0.86603 0.96593 0.74314 0.000000 \n",
"126571 2 3 -0.86603 0.86603 0.74314 0.000000 \n",
"126572 1 3 -0.86603 0.70711 0.74314 0.000000 \n",
"126573 1 3 -0.86603 0.50000 0.74314 0.000000 \n",
"126574 1 3 -0.86603 0.25882 0.74314 0.000000 \n",
"\n",
"[126575 rows x 12 columns]"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = df.drop([\"Hour\", \"Month\", \"Day\", \"DOW\"], axis=1)\n",
"df"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "OjEVy_bnUSi8"
},
"source": [
"## Using Alternate Data as features\n",
"\n",
"### Interest Rate Differential of Yield-Par curve of 2Yr and 10Yr US Treasuary Bonds\n",
"\n",
"* US has been selected because the data provided during the assignment did not contain any explicit mention of the currency pair. So, as a backup plan, US has been choosen because of the dependency of almost all currency pairs on it due to its importance as the primary global trading currency\n",
"\n",
"The data for this has been taken from : \"https://home.treasury.gov/resource-center/data-chart-center/interest-rates/TextView?type=daily_treasury_yield_curve&field_tdr_date_value_month=202508\""
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 424
},
"id": "aa43BXfRUHxw",
"outputId": "4b94663a-c506-49e8-ba49-583a3cebfea1"
},
"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>Date</th>\n",
" <th>1 Mo</th>\n",
" <th>2 Mo</th>\n",
" <th>3 Mo</th>\n",
" <th>4 Mo</th>\n",
" <th>6 Mo</th>\n",
" <th>1 Yr</th>\n",
" <th>2 Yr</th>\n",
" <th>3 Yr</th>\n",
" <th>5 Yr</th>\n",
" <th>7 Yr</th>\n",
" <th>10 Yr</th>\n",
" <th>20 Yr</th>\n",
" <th>30 Yr</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>12/31/24</td>\n",
" <td>4.40</td>\n",
" <td>4.39</td>\n",
" <td>4.37</td>\n",
" <td>4.32</td>\n",
" <td>4.24</td>\n",
" <td>4.16</td>\n",
" <td>4.25</td>\n",
" <td>4.27</td>\n",
" <td>4.38</td>\n",
" <td>4.48</td>\n",
" <td>4.58</td>\n",
" <td>4.86</td>\n",
" <td>4.78</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>12/30/24</td>\n",
" <td>4.43</td>\n",
" <td>4.42</td>\n",
" <td>4.37</td>\n",
" <td>4.33</td>\n",
" <td>4.25</td>\n",
" <td>4.17</td>\n",
" <td>4.24</td>\n",
" <td>4.29</td>\n",
" <td>4.37</td>\n",
" <td>4.46</td>\n",
" <td>4.55</td>\n",
" <td>4.84</td>\n",
" <td>4.77</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>12/27/24</td>\n",
" <td>4.44</td>\n",
" <td>4.43</td>\n",
" <td>4.31</td>\n",
" <td>4.35</td>\n",
" <td>4.29</td>\n",
" <td>4.20</td>\n",
" <td>4.31</td>\n",
" <td>4.36</td>\n",
" <td>4.45</td>\n",
" <td>4.53</td>\n",
" <td>4.62</td>\n",
" <td>4.89</td>\n",
" <td>4.82</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>12/26/24</td>\n",
" <td>4.45</td>\n",
" <td>4.45</td>\n",
" <td>4.35</td>\n",
" <td>4.37</td>\n",
" <td>4.31</td>\n",
" <td>4.23</td>\n",
" <td>4.30</td>\n",
" <td>4.35</td>\n",
" <td>4.42</td>\n",
" <td>4.49</td>\n",
" <td>4.58</td>\n",
" <td>4.83</td>\n",
" <td>4.76</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>12/24/24</td>\n",
" <td>4.44</td>\n",
" <td>4.44</td>\n",
" <td>4.40</td>\n",
" <td>4.38</td>\n",
" <td>4.30</td>\n",
" <td>4.24</td>\n",
" <td>4.29</td>\n",
" <td>4.36</td>\n",
" <td>4.43</td>\n",
" <td>4.52</td>\n",
" <td>4.59</td>\n",
" <td>4.84</td>\n",
" <td>4.76</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8752</th>\n",
" <td>1/8/90</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>7.79</td>\n",
" <td>NaN</td>\n",
" <td>7.88</td>\n",
" <td>7.81</td>\n",
" <td>7.90</td>\n",
" <td>7.95</td>\n",
" <td>7.92</td>\n",
" <td>8.05</td>\n",
" <td>8.02</td>\n",
" <td>NaN</td>\n",
" <td>8.09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8753</th>\n",
" <td>1/5/90</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>7.79</td>\n",
" <td>NaN</td>\n",
" <td>7.85</td>\n",
" <td>7.79</td>\n",
" <td>7.90</td>\n",
" <td>7.94</td>\n",
" <td>7.92</td>\n",
" <td>8.03</td>\n",
" <td>7.99</td>\n",
" <td>NaN</td>\n",
" <td>8.06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8754</th>\n",
" <td>1/4/90</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>7.84</td>\n",
" <td>NaN</td>\n",
" <td>7.90</td>\n",
" <td>7.82</td>\n",
" <td>7.92</td>\n",
" <td>7.93</td>\n",
" <td>7.91</td>\n",
" <td>8.02</td>\n",
" <td>7.98</td>\n",
" <td>NaN</td>\n",
" <td>8.04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8755</th>\n",
" <td>1/3/90</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>7.89</td>\n",
" <td>NaN</td>\n",
" <td>7.94</td>\n",
" <td>7.85</td>\n",
" <td>7.94</td>\n",
" <td>7.96</td>\n",
" <td>7.92</td>\n",
" <td>8.04</td>\n",
" <td>7.99</td>\n",
" <td>NaN</td>\n",
" <td>8.04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8756</th>\n",
" <td>1/2/90</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>7.83</td>\n",
" <td>NaN</td>\n",
" <td>7.89</td>\n",
" <td>7.81</td>\n",
" <td>7.87</td>\n",
" <td>7.90</td>\n",
" <td>7.87</td>\n",
" <td>7.98</td>\n",
" <td>7.94</td>\n",
" <td>NaN</td>\n",
" <td>8.00</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>8757 rows × 14 columns</p>\n",
"</div>"
],
"text/plain": [
" Date 1 Mo 2 Mo 3 Mo 4 Mo 6 Mo 1 Yr 2 Yr 3 Yr 5 Yr 7 Yr \\\n",
"0 12/31/24 4.40 4.39 4.37 4.32 4.24 4.16 4.25 4.27 4.38 4.48 \n",
"1 12/30/24 4.43 4.42 4.37 4.33 4.25 4.17 4.24 4.29 4.37 4.46 \n",
"2 12/27/24 4.44 4.43 4.31 4.35 4.29 4.20 4.31 4.36 4.45 4.53 \n",
"3 12/26/24 4.45 4.45 4.35 4.37 4.31 4.23 4.30 4.35 4.42 4.49 \n",
"4 12/24/24 4.44 4.44 4.40 4.38 4.30 4.24 4.29 4.36 4.43 4.52 \n",
"... ... ... ... ... ... ... ... ... ... ... ... \n",
"8752 1/8/90 NaN NaN 7.79 NaN 7.88 7.81 7.90 7.95 7.92 8.05 \n",
"8753 1/5/90 NaN NaN 7.79 NaN 7.85 7.79 7.90 7.94 7.92 8.03 \n",
"8754 1/4/90 NaN NaN 7.84 NaN 7.90 7.82 7.92 7.93 7.91 8.02 \n",
"8755 1/3/90 NaN NaN 7.89 NaN 7.94 7.85 7.94 7.96 7.92 8.04 \n",
"8756 1/2/90 NaN NaN 7.83 NaN 7.89 7.81 7.87 7.90 7.87 7.98 \n",
"\n",
" 10 Yr 20 Yr 30 Yr \n",
"0 4.58 4.86 4.78 \n",
"1 4.55 4.84 4.77 \n",
"2 4.62 4.89 4.82 \n",
"3 4.58 4.83 4.76 \n",
"4 4.59 4.84 4.76 \n",
"... ... ... ... \n",
"8752 8.02 NaN 8.09 \n",
"8753 7.99 NaN 8.06 \n",
"8754 7.98 NaN 8.04 \n",
"8755 7.99 NaN 8.04 \n",
"8756 7.94 NaN 8.00 \n",
"\n",
"[8757 rows x 14 columns]"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rate_df = pd.read_csv('interest_rates.csv')\n",
"rate_df"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 424
},
"id": "9timicWWVNkh",
"outputId": "ea843e32-0649-4954-dd50-545e9f9b0a41"
},
"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>Open</th>\n",
" <th>High</th>\n",
" <th>Low</th>\n",
" <th>Close</th>\n",
" <th>Volume</th>\n",
" <th>Session</th>\n",
" <th>Quarter</th>\n",
" <th>Month_sin</th>\n",
" <th>Hour_sin</th>\n",
" <th>Date_sin</th>\n",
" <th>DOW_sin</th>\n",
" <th>DateTime</th>\n",
" <th>Diff_10Y_2Y</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1.35460</td>\n",
" <td>1.35860</td>\n",
" <td>1.35370</td>\n",
" <td>1.35710</td>\n",
" <td>409.0</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>-0.0</td>\n",
" <td>-0.86603</td>\n",
" <td>0.20791</td>\n",
" <td>-0.433884</td>\n",
" <td>2004-12-31 20:00:00</td>\n",
" <td>0.048333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1.35720</td>\n",
" <td>1.35850</td>\n",
" <td>1.35600</td>\n",
" <td>1.35650</td>\n",
" <td>304.0</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>-0.0</td>\n",
" <td>-0.70711</td>\n",
" <td>0.20791</td>\n",
" <td>-0.433884</td>\n",
" <td>2004-12-31 21:00:00</td>\n",
" <td>0.048333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1.35660</td>\n",
" <td>1.35710</td>\n",
" <td>1.35520</td>\n",
" <td>1.35540</td>\n",
" <td>272.0</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>-0.0</td>\n",
" <td>-0.50000</td>\n",
" <td>0.20791</td>\n",
" <td>-0.433884</td>\n",
" <td>2004-12-31 22:00:00</td>\n",
" <td>0.048333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1.35540</td>\n",
" <td>1.35630</td>\n",
" <td>1.35520</td>\n",
" <td>1.35620</td>\n",
" <td>84.0</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>-0.0</td>\n",
" <td>-0.25882</td>\n",
" <td>0.20791</td>\n",
" <td>-0.433884</td>\n",
" <td>2004-12-31 23:00:00</td>\n",
" <td>0.048333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1.35790</td>\n",
" <td>1.35810</td>\n",
" <td>1.35390</td>\n",
" <td>1.35470</td>\n",
" <td>318.0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0.5</td>\n",
" <td>0.25882</td>\n",
" <td>0.58779</td>\n",
" <td>0.000000</td>\n",
" <td>2005-01-03 01:00:00</td>\n",
" <td>0.047083</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>122928</th>\n",
" <td>1.03502</td>\n",
" <td>1.03548</td>\n",
" <td>1.03438</td>\n",
" <td>1.03490</td>\n",
" <td>2323.0</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>-0.0</td>\n",
" <td>-0.96593</td>\n",
" <td>0.20791</td>\n",
" <td>0.781831</td>\n",
" <td>2024-12-31 19:00:00</td>\n",
" <td>0.013750</td>\n",
" </tr>\n",
" <tr>\n",
" <th>122929</th>\n",
" <td>1.03489</td>\n",
" <td>1.03566</td>\n",
" <td>1.03455</td>\n",
" <td>1.03529</td>\n",
" <td>1900.0</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>-0.0</td>\n",
" <td>-0.86603</td>\n",
" <td>0.20791</td>\n",
" <td>0.781831</td>\n",
" <td>2024-12-31 20:00:00</td>\n",
" <td>0.013750</td>\n",
" </tr>\n",
" <tr>\n",
" <th>122930</th>\n",
" <td>1.03526</td>\n",
" <td>1.03645</td>\n",
" <td>1.03516</td>\n",
" <td>1.03547</td>\n",
" <td>1445.0</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>-0.0</td>\n",
" <td>-0.70711</td>\n",
" <td>0.20791</td>\n",
" <td>0.781831</td>\n",
" <td>2024-12-31 21:00:00</td>\n",
" <td>0.013750</td>\n",
" </tr>\n",
" <tr>\n",
" <th>122931</th>\n",
" <td>1.03547</td>\n",
" <td>1.03631</td>\n",
" <td>1.03544</td>\n",
" <td>1.03582</td>\n",
" <td>1208.0</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>-0.0</td>\n",
" <td>-0.50000</td>\n",
" <td>0.20791</td>\n",
" <td>0.781831</td>\n",
" <td>2024-12-31 22:00:00</td>\n",
" <td>0.013750</td>\n",
" </tr>\n",
" <tr>\n",
" <th>122932</th>\n",
" <td>1.03585</td>\n",
" <td>1.03608</td>\n",
" <td>1.03489</td>\n",
" <td>1.03493</td>\n",
" <td>616.0</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>-0.0</td>\n",
" <td>-0.25882</td>\n",
" <td>0.20791</td>\n",
" <td>0.781831</td>\n",
" <td>2024-12-31 23:00:00</td>\n",
" <td>0.013750</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>118632 rows × 13 columns</p>\n",
"</div>"
],
"text/plain": [
" Open High Low Close Volume Session Quarter \\\n",
"0 1.35460 1.35860 1.35370 1.35710 409.0 1 4 \n",
"1 1.35720 1.35850 1.35600 1.35650 304.0 1 4 \n",
"2 1.35660 1.35710 1.35520 1.35540 272.0 0 4 \n",
"3 1.35540 1.35630 1.35520 1.35620 84.0 0 4 \n",
"4 1.35790 1.35810 1.35390 1.35470 318.0 1 1 \n",
"... ... ... ... ... ... ... ... \n",
"122928 1.03502 1.03548 1.03438 1.03490 2323.0 1 4 \n",
"122929 1.03489 1.03566 1.03455 1.03529 1900.0 1 4 \n",
"122930 1.03526 1.03645 1.03516 1.03547 1445.0 1 4 \n",
"122931 1.03547 1.03631 1.03544 1.03582 1208.0 0 4 \n",
"122932 1.03585 1.03608 1.03489 1.03493 616.0 0 4 \n",
"\n",
" Month_sin Hour_sin Date_sin DOW_sin DateTime \\\n",
"0 -0.0 -0.86603 0.20791 -0.433884 2004-12-31 20:00:00 \n",
"1 -0.0 -0.70711 0.20791 -0.433884 2004-12-31 21:00:00 \n",
"2 -0.0 -0.50000 0.20791 -0.433884 2004-12-31 22:00:00 \n",
"3 -0.0 -0.25882 0.20791 -0.433884 2004-12-31 23:00:00 \n",
"4 0.5 0.25882 0.58779 0.000000 2005-01-03 01:00:00 \n",
"... ... ... ... ... ... \n",
"122928 -0.0 -0.96593 0.20791 0.781831 2024-12-31 19:00:00 \n",
"122929 -0.0 -0.86603 0.20791 0.781831 2024-12-31 20:00:00 \n",
"122930 -0.0 -0.70711 0.20791 0.781831 2024-12-31 21:00:00 \n",
"122931 -0.0 -0.50000 0.20791 0.781831 2024-12-31 22:00:00 \n",
"122932 -0.0 -0.25882 0.20791 0.781831 2024-12-31 23:00:00 \n",
"\n",
" Diff_10Y_2Y \n",
"0 0.048333 \n",
"1 0.048333 \n",
"2 0.048333 \n",
"3 0.048333 \n",
"4 0.047083 \n",
"... ... \n",
"122928 0.013750 \n",
"122929 0.013750 \n",
"122930 0.013750 \n",
"122931 0.013750 \n",
"122932 0.013750 \n",
"\n",
"[118632 rows x 13 columns]"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"\n",
"# Convert Date to datetime\n",
"rate_df['Date'] = pd.to_datetime(rate_df['Date'], format='%m/%d/%y', errors='coerce')\n",
"\n",
"# Create interest rate differential column (10 Yr - 2 Yr)\n",
"rate_df['Diff_10Y_2Y'] = (rate_df['10 Yr'] - rate_df['2 Yr'])/24\n",
"\n",
"# keep a copy of original datetime\n",
"df['DateTime'] = df['Date']\n",
"\n",
"# extract just the date (without time) for merging\n",
"df['Date'] = df['Date'].dt.date\n",
"rate_df['Date'] = rate_df['Date'].dt.date\n",
"\n",
"# merge on just the date\n",
"merged = pd.merge(df, rate_df, on='Date', how='left')\n",
"\n",
"# restore datetime with hours\n",
"merged['Date'] = merged['DateTime']\n",
"merged = merged.drop(columns=['Date'])\n",
"\n",
"df = merged\n",
"\n",
"df = df.drop(columns=['1 Mo', '2 Mo', '3 Mo', '4 Mo', '6 Mo', '1 Yr', '2 Yr', '3 Yr', '5 Yr', '7 Yr', '10 Yr', '20 Yr', '30 Yr'])\n",
"\n",
"df = df.dropna()\n",
"\n",
"df\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "dqhkZF0OXUww"
},
"source": [
"### Oil and Gold\n",
"\n",
"* It is often seen that the currency values changes majorly when major global trading commodities such as oil and Gold change.\n",
"* They can be seen as a leading indicator of volume and change in price for the forex trading pairs.\n",
"\n",
"The data for Gold has been taken from : \"https://www.kaggle.com/datasets/novandraanugrah/xauusd-gold-price-historical-data-2004-2024\"\n",
"\n",
"The data for oil has been taken from : \"https://finance.yahoo.com/quote/CL%3DF/history/?guccounter=1&guce_referrer=aHR0cHM6Ly93d3cuZ29vZ2xlLmNvbS8&guce_referrer_sig=AQAAAHBu3O2YAG4BmOqSpxUDAQmwfsfej5nBk9-VmpIWBpBNgHZ1tYYoD_v3BTwELQ3cousJCEKhSI3XsDuRq6vgSSFUHkx4tDVdKSnRKkPws55F1t8K4QRVCnjfk9AGgPfoaR9Yaxt6TvQATiAjAZwx17brwzdzjm01jDiV98-3sGo6\""
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 424
},
"id": "3zTFOYjqVq0w",
"outputId": "f81d7a32-dcb6-426b-ed97-4550ec3f6470"
},
"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>Date</th>\n",
" <th>Oil_Volume</th>\n",
" <th>Oil_Close</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Aug 21, 2025</td>\n",
" <td>196698</td>\n",
" <td>63.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Aug 20, 2025</td>\n",
" <td>99484</td>\n",
" <td>63.21</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Aug 19, 2025</td>\n",
" <td>99484</td>\n",
" <td>62.35</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Aug 18, 2025</td>\n",
" <td>95113</td>\n",
" <td>63.42</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Aug 15, 2025</td>\n",
" <td>197390</td>\n",
" <td>62.80</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6271</th>\n",
" <td>Aug 29, 2000</td>\n",
" <td>49131</td>\n",
" <td>32.72</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6272</th>\n",
" <td>Aug 28, 2000</td>\n",
" <td>46770</td>\n",
" <td>32.87</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6273</th>\n",
" <td>Aug 25, 2000</td>\n",
" <td>44601</td>\n",
" <td>32.05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6274</th>\n",
" <td>Aug 24, 2000</td>\n",
" <td>72978</td>\n",
" <td>31.63</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6275</th>\n",
" <td>Aug 23, 2000</td>\n",
" <td>79385</td>\n",
" <td>32.05</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>6276 rows × 3 columns</p>\n",
"</div>"
],
"text/plain": [
" Date Oil_Volume Oil_Close\n",
"0 Aug 21, 2025 196698 63.50\n",
"1 Aug 20, 2025 99484 63.21\n",
"2 Aug 19, 2025 99484 62.35\n",
"3 Aug 18, 2025 95113 63.42\n",
"4 Aug 15, 2025 197390 62.80\n",
"... ... ... ...\n",
"6271 Aug 29, 2000 49131 32.72\n",
"6272 Aug 28, 2000 46770 32.87\n",
"6273 Aug 25, 2000 44601 32.05\n",
"6274 Aug 24, 2000 72978 31.63\n",
"6275 Aug 23, 2000 79385 32.05\n",
"\n",
"[6276 rows x 3 columns]"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"oil_df = pd.read_csv('oil.csv')\n",
"oil_df = oil_df.rename(columns={\n",
" \"Volume\": \"Oil_Volume\",\n",
" \"Adj Close\": \"Oil_Close\"\n",
"})\n",
"\n",
"\n",
"oil_df"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 898
},
"id": "zCFvrZJTY0Gz",
"outputId": "11eb17e6-b5b5-4885-873e-c91acd61f6f1"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/var/folders/l_/j4rfg9014yvd4jfvv441w26r0000gn/T/ipykernel_16708/2363661686.py:16: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" df['Oil_Volume'] = (df['Oil_Volume']/24)\n"
]
},
{
"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>Open</th>\n",
" <th>High</th>\n",
" <th>Low</th>\n",
" <th>Close</th>\n",
" <th>Volume</th>\n",
" <th>Session</th>\n",
" <th>Quarter</th>\n",
" <th>Month_sin</th>\n",
" <th>Hour_sin</th>\n",
" <th>Date_sin</th>\n",
" <th>DOW_sin</th>\n",
" <th>DateTime</th>\n",
" <th>Diff_10Y_2Y</th>\n",
" <th>Oil_Volume</th>\n",
" <th>Oil_Close</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1.35790</td>\n",
" <td>1.35810</td>\n",
" <td>1.35390</td>\n",
" <td>1.35470</td>\n",
" <td>318.0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0.5</td>\n",
" <td>0.25882</td>\n",
" <td>0.58779</td>\n",
" <td>0.000000</td>\n",
" <td>2005-01-03 01:00:00</td>\n",
" <td>0.047083</td>\n",
" <td>2895.166667</td>\n",
" <td>42.12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>1.35460</td>\n",
" <td>1.35510</td>\n",
" <td>1.35290</td>\n",
" <td>1.35380</td>\n",
" <td>338.0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0.5</td>\n",
" <td>0.50000</td>\n",
" <td>0.58779</td>\n",
" <td>0.000000</td>\n",
" <td>2005-01-03 02:00:00</td>\n",
" <td>0.047083</td>\n",
" <td>2895.166667</td>\n",
" <td>42.12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>1.35390</td>\n",
" <td>1.35390</td>\n",
" <td>1.34980</td>\n",
" <td>1.35040</td>\n",
" <td>356.0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0.5</td>\n",
" <td>0.70711</td>\n",
" <td>0.58779</td>\n",
" <td>0.000000</td>\n",
" <td>2005-01-03 03:00:00</td>\n",
" <td>0.047083</td>\n",
" <td>2895.166667</td>\n",
" <td>42.12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>1.35040</td>\n",
" <td>1.35060</td>\n",
" <td>1.33850</td>\n",
" <td>1.33990</td>\n",
" <td>545.0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0.5</td>\n",
" <td>0.86603</td>\n",
" <td>0.58779</td>\n",
" <td>0.000000</td>\n",
" <td>2005-01-03 04:00:00</td>\n",
" <td>0.047083</td>\n",
" <td>2895.166667</td>\n",
" <td>42.12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>1.33990</td>\n",
" <td>1.34450</td>\n",
" <td>1.33990</td>\n",
" <td>1.34430</td>\n",
" <td>385.0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0.5</td>\n",
" <td>0.96593</td>\n",
" <td>0.58779</td>\n",
" <td>0.000000</td>\n",
" <td>2005-01-03 05:00:00</td>\n",
" <td>0.047083</td>\n",
" <td>2895.166667</td>\n",
" <td>42.12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>118627</th>\n",
" <td>1.03502</td>\n",
" <td>1.03548</td>\n",
" <td>1.03438</td>\n",
" <td>1.03490</td>\n",
" <td>2323.0</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>-0.0</td>\n",
" <td>-0.96593</td>\n",
" <td>0.20791</td>\n",
" <td>0.781831</td>\n",
" <td>2024-12-31 19:00:00</td>\n",
" <td>0.013750</td>\n",
" <td>6298.125000</td>\n",
" <td>71.72</td>\n",
" </tr>\n",
" <tr>\n",
" <th>118628</th>\n",
" <td>1.03489</td>\n",
" <td>1.03566</td>\n",
" <td>1.03455</td>\n",
" <td>1.03529</td>\n",
" <td>1900.0</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>-0.0</td>\n",
" <td>-0.86603</td>\n",
" <td>0.20791</td>\n",
" <td>0.781831</td>\n",
" <td>2024-12-31 20:00:00</td>\n",
" <td>0.013750</td>\n",
" <td>6298.125000</td>\n",
" <td>71.72</td>\n",
" </tr>\n",
" <tr>\n",
" <th>118629</th>\n",
" <td>1.03526</td>\n",
" <td>1.03645</td>\n",
" <td>1.03516</td>\n",
" <td>1.03547</td>\n",
" <td>1445.0</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>-0.0</td>\n",
" <td>-0.70711</td>\n",
" <td>0.20791</td>\n",
" <td>0.781831</td>\n",
" <td>2024-12-31 21:00:00</td>\n",
" <td>0.013750</td>\n",
" <td>6298.125000</td>\n",
" <td>71.72</td>\n",
" </tr>\n",
" <tr>\n",
" <th>118630</th>\n",
" <td>1.03547</td>\n",
" <td>1.03631</td>\n",
" <td>1.03544</td>\n",
" <td>1.03582</td>\n",
" <td>1208.0</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>-0.0</td>\n",
" <td>-0.50000</td>\n",
" <td>0.20791</td>\n",
" <td>0.781831</td>\n",
" <td>2024-12-31 22:00:00</td>\n",
" <td>0.013750</td>\n",
" <td>6298.125000</td>\n",
" <td>71.72</td>\n",
" </tr>\n",
" <tr>\n",
" <th>118631</th>\n",
" <td>1.03585</td>\n",
" <td>1.03608</td>\n",
" <td>1.03489</td>\n",
" <td>1.03493</td>\n",
" <td>616.0</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>-0.0</td>\n",
" <td>-0.25882</td>\n",
" <td>0.20791</td>\n",
" <td>0.781831</td>\n",
" <td>2024-12-31 23:00:00</td>\n",
" <td>0.013750</td>\n",
" <td>6298.125000</td>\n",
" <td>71.72</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>118393 rows × 15 columns</p>\n",
"</div>"
],
"text/plain": [
" Open High Low Close Volume Session Quarter \\\n",
"4 1.35790 1.35810 1.35390 1.35470 318.0 1 1 \n",
"5 1.35460 1.35510 1.35290 1.35380 338.0 1 1 \n",
"6 1.35390 1.35390 1.34980 1.35040 356.0 1 1 \n",
"7 1.35040 1.35060 1.33850 1.33990 545.0 1 1 \n",
"8 1.33990 1.34450 1.33990 1.34430 385.0 1 1 \n",
"... ... ... ... ... ... ... ... \n",
"118627 1.03502 1.03548 1.03438 1.03490 2323.0 1 4 \n",
"118628 1.03489 1.03566 1.03455 1.03529 1900.0 1 4 \n",
"118629 1.03526 1.03645 1.03516 1.03547 1445.0 1 4 \n",
"118630 1.03547 1.03631 1.03544 1.03582 1208.0 0 4 \n",
"118631 1.03585 1.03608 1.03489 1.03493 616.0 0 4 \n",
"\n",
" Month_sin Hour_sin Date_sin DOW_sin DateTime \\\n",
"4 0.5 0.25882 0.58779 0.000000 2005-01-03 01:00:00 \n",
"5 0.5 0.50000 0.58779 0.000000 2005-01-03 02:00:00 \n",
"6 0.5 0.70711 0.58779 0.000000 2005-01-03 03:00:00 \n",
"7 0.5 0.86603 0.58779 0.000000 2005-01-03 04:00:00 \n",
"8 0.5 0.96593 0.58779 0.000000 2005-01-03 05:00:00 \n",
"... ... ... ... ... ... \n",
"118627 -0.0 -0.96593 0.20791 0.781831 2024-12-31 19:00:00 \n",
"118628 -0.0 -0.86603 0.20791 0.781831 2024-12-31 20:00:00 \n",
"118629 -0.0 -0.70711 0.20791 0.781831 2024-12-31 21:00:00 \n",
"118630 -0.0 -0.50000 0.20791 0.781831 2024-12-31 22:00:00 \n",
"118631 -0.0 -0.25882 0.20791 0.781831 2024-12-31 23:00:00 \n",
"\n",
" Diff_10Y_2Y Oil_Volume Oil_Close \n",
"4 0.047083 2895.166667 42.12 \n",
"5 0.047083 2895.166667 42.12 \n",
"6 0.047083 2895.166667 42.12 \n",
"7 0.047083 2895.166667 42.12 \n",
"8 0.047083 2895.166667 42.12 \n",
"... ... ... ... \n",
"118627 0.013750 6298.125000 71.72 \n",
"118628 0.013750 6298.125000 71.72 \n",
"118629 0.013750 6298.125000 71.72 \n",
"118630 0.013750 6298.125000 71.72 \n",
"118631 0.013750 6298.125000 71.72 \n",
"\n",
"[118393 rows x 15 columns]"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"\n",
"# Convert both to datetime64[ns]\n",
"oil_df['Date'] = pd.to_datetime(oil_df['Date'], errors='coerce')\n",
"df['Date'] = pd.to_datetime(df['DateTime']).dt.normalize() # strips time, keeps dtype datetime64[ns]\n",
"\n",
"# Now merge\n",
"newmerge = pd.merge(df, oil_df, on='Date', how='left')\n",
"\n",
"newmerge = newmerge.drop(columns=['Date'])\n",
"\n",
"df = newmerge\n",
"\n",
"df = df.dropna()\n",
"\n",
"df['Oil_Volume'] = (df['Oil_Volume']/24)\n",
"\n",
"df\n"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 424
},
"id": "aQeTiaPtfGTx",
"outputId": "06f350ab-55f2-46f0-847c-0f88c640df86"
},
"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>Date</th>\n",
" <th>Open</th>\n",
" <th>High</th>\n",
" <th>Low</th>\n",
" <th>Close</th>\n",
" <th>Volume</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2004.06.11 07:00</td>\n",
" <td>384.00</td>\n",
" <td>384.30</td>\n",
" <td>383.30</td>\n",
" <td>383.80</td>\n",
" <td>44</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2004.06.11 08:00</td>\n",
" <td>383.80</td>\n",
" <td>384.30</td>\n",
" <td>383.10</td>\n",
" <td>383.10</td>\n",
" <td>41</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2004.06.11 09:00</td>\n",
" <td>383.10</td>\n",
" <td>384.10</td>\n",
" <td>382.80</td>\n",
" <td>383.10</td>\n",
" <td>55</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2004.06.11 10:00</td>\n",
" <td>383.00</td>\n",
" <td>383.80</td>\n",
" <td>383.00</td>\n",
" <td>383.60</td>\n",
" <td>33</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2004.06.11 11:00</td>\n",
" <td>383.60</td>\n",
" <td>383.80</td>\n",
" <td>383.50</td>\n",
" <td>383.60</td>\n",
" <td>23</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>122651</th>\n",
" <td>2025.07.15 15:00</td>\n",
" <td>3354.84</td>\n",
" <td>3360.22</td>\n",
" <td>3346.34</td>\n",
" <td>3348.77</td>\n",
" <td>55313</td>\n",
" </tr>\n",
" <tr>\n",
" <th>122652</th>\n",
" <td>2025.07.15 16:00</td>\n",
" <td>3348.78</td>\n",
" <td>3352.36</td>\n",
" <td>3334.35</td>\n",
" <td>3346.50</td>\n",
" <td>79108</td>\n",
" </tr>\n",
" <tr>\n",
" <th>122653</th>\n",
" <td>2025.07.15 17:00</td>\n",
" <td>3346.49</td>\n",
" <td>3352.05</td>\n",
" <td>3342.43</td>\n",
" <td>3347.89</td>\n",
" <td>54849</td>\n",
" </tr>\n",
" <tr>\n",
" <th>122654</th>\n",
" <td>2025.07.15 18:00</td>\n",
" <td>3347.88</td>\n",
" <td>3349.87</td>\n",
" <td>3325.34</td>\n",
" <td>3328.88</td>\n",
" <td>63341</td>\n",
" </tr>\n",
" <tr>\n",
" <th>122655</th>\n",
" <td>2025.07.15 19:00</td>\n",
" <td>3328.87</td>\n",
" <td>3328.89</td>\n",
" <td>3320.17</td>\n",
" <td>3327.27</td>\n",
" <td>36550</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>122656 rows × 6 columns</p>\n",
"</div>"
],
"text/plain": [
" Date Open High Low Close Volume\n",
"0 2004.06.11 07:00 384.00 384.30 383.30 383.80 44\n",
"1 2004.06.11 08:00 383.80 384.30 383.10 383.10 41\n",
"2 2004.06.11 09:00 383.10 384.10 382.80 383.10 55\n",
"3 2004.06.11 10:00 383.00 383.80 383.00 383.60 33\n",
"4 2004.06.11 11:00 383.60 383.80 383.50 383.60 23\n",
"... ... ... ... ... ... ...\n",
"122651 2025.07.15 15:00 3354.84 3360.22 3346.34 3348.77 55313\n",
"122652 2025.07.15 16:00 3348.78 3352.36 3334.35 3346.50 79108\n",
"122653 2025.07.15 17:00 3346.49 3352.05 3342.43 3347.89 54849\n",
"122654 2025.07.15 18:00 3347.88 3349.87 3325.34 3328.88 63341\n",
"122655 2025.07.15 19:00 3328.87 3328.89 3320.17 3327.27 36550\n",
"\n",
"[122656 rows x 6 columns]"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gold_df = pd.read_csv('gold.csv', sep=\";\")\n",
"gold_df"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 791
},
"id": "pRr9VzVvgsCI",
"outputId": "a573b200-5696-49b3-8a14-ff20938f32b6"
},
"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>Open</th>\n",
" <th>High</th>\n",
" <th>Low</th>\n",
" <th>Close</th>\n",
" <th>Volume</th>\n",
" <th>Session</th>\n",
" <th>Quarter</th>\n",
" <th>Month_sin</th>\n",
" <th>Hour_sin</th>\n",
" <th>Date_sin</th>\n",
" <th>DOW_sin</th>\n",
" <th>DateTime</th>\n",
" <th>Diff_10Y_2Y</th>\n",
" <th>Oil_Volume</th>\n",
" <th>Oil_Close</th>\n",
" <th>Gold_Close</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1.35790</td>\n",
" <td>1.35810</td>\n",
" <td>1.35390</td>\n",
" <td>1.35470</td>\n",
" <td>318.0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0.5</td>\n",
" <td>0.25882</td>\n",
" <td>0.58779</td>\n",
" <td>0.000000</td>\n",
" <td>2005-01-03 01:00:00</td>\n",
" <td>0.047083</td>\n",
" <td>2895.166667</td>\n",
" <td>42.12</td>\n",
" <td>435.60</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1.35460</td>\n",
" <td>1.35510</td>\n",
" <td>1.35290</td>\n",
" <td>1.35380</td>\n",
" <td>338.0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0.5</td>\n",
" <td>0.50000</td>\n",
" <td>0.58779</td>\n",
" <td>0.000000</td>\n",
" <td>2005-01-03 02:00:00</td>\n",
" <td>0.047083</td>\n",
" <td>2895.166667</td>\n",
" <td>42.12</td>\n",
" <td>435.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1.35390</td>\n",
" <td>1.35390</td>\n",
" <td>1.34980</td>\n",
" <td>1.35040</td>\n",
" <td>356.0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0.5</td>\n",
" <td>0.70711</td>\n",
" <td>0.58779</td>\n",
" <td>0.000000</td>\n",
" <td>2005-01-03 03:00:00</td>\n",
" <td>0.047083</td>\n",
" <td>2895.166667</td>\n",
" <td>42.12</td>\n",
" <td>435.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1.35040</td>\n",
" <td>1.35060</td>\n",
" <td>1.33850</td>\n",
" <td>1.33990</td>\n",
" <td>545.0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0.5</td>\n",
" <td>0.86603</td>\n",
" <td>0.58779</td>\n",
" <td>0.000000</td>\n",
" <td>2005-01-03 04:00:00</td>\n",
" <td>0.047083</td>\n",
" <td>2895.166667</td>\n",
" <td>42.12</td>\n",
" <td>433.60</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1.33990</td>\n",
" <td>1.34450</td>\n",
" <td>1.33990</td>\n",
" <td>1.34430</td>\n",
" <td>385.0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0.5</td>\n",
" <td>0.96593</td>\n",
" <td>0.58779</td>\n",
" <td>0.000000</td>\n",
" <td>2005-01-03 05:00:00</td>\n",
" <td>0.047083</td>\n",
" <td>2895.166667</td>\n",
" <td>42.12</td>\n",
" <td>433.60</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>118388</th>\n",
" <td>1.03502</td>\n",
" <td>1.03548</td>\n",
" <td>1.03438</td>\n",
" <td>1.03490</td>\n",
" <td>2323.0</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>-0.0</td>\n",
" <td>-0.96593</td>\n",
" <td>0.20791</td>\n",
" <td>0.781831</td>\n",
" <td>2024-12-31 19:00:00</td>\n",
" <td>0.013750</td>\n",
" <td>6298.125000</td>\n",
" <td>71.72</td>\n",
" <td>2624.38</td>\n",
" </tr>\n",
" <tr>\n",
" <th>118389</th>\n",
" <td>1.03489</td>\n",
" <td>1.03566</td>\n",
" <td>1.03455</td>\n",
" <td>1.03529</td>\n",
" <td>1900.0</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>-0.0</td>\n",
" <td>-0.86603</td>\n",
" <td>0.20791</td>\n",
" <td>0.781831</td>\n",
" <td>2024-12-31 20:00:00</td>\n",
" <td>0.013750</td>\n",
" <td>6298.125000</td>\n",
" <td>71.72</td>\n",
" <td>2624.81</td>\n",
" </tr>\n",
" <tr>\n",
" <th>118390</th>\n",
" <td>1.03526</td>\n",
" <td>1.03645</td>\n",
" <td>1.03516</td>\n",
" <td>1.03547</td>\n",
" <td>1445.0</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>-0.0</td>\n",
" <td>-0.70711</td>\n",
" <td>0.20791</td>\n",
" <td>0.781831</td>\n",
" <td>2024-12-31 21:00:00</td>\n",
" <td>0.013750</td>\n",
" <td>6298.125000</td>\n",
" <td>71.72</td>\n",
" <td>2623.85</td>\n",
" </tr>\n",
" <tr>\n",
" <th>118391</th>\n",
" <td>1.03547</td>\n",
" <td>1.03631</td>\n",
" <td>1.03544</td>\n",
" <td>1.03582</td>\n",
" <td>1208.0</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>-0.0</td>\n",
" <td>-0.50000</td>\n",
" <td>0.20791</td>\n",
" <td>0.781831</td>\n",
" <td>2024-12-31 22:00:00</td>\n",
" <td>0.013750</td>\n",
" <td>6298.125000</td>\n",
" <td>71.72</td>\n",
" <td>2623.36</td>\n",
" </tr>\n",
" <tr>\n",
" <th>118392</th>\n",
" <td>1.03585</td>\n",
" <td>1.03608</td>\n",
" <td>1.03489</td>\n",
" <td>1.03493</td>\n",
" <td>616.0</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>-0.0</td>\n",
" <td>-0.25882</td>\n",
" <td>0.20791</td>\n",
" <td>0.781831</td>\n",
" <td>2024-12-31 23:00:00</td>\n",
" <td>0.013750</td>\n",
" <td>6298.125000</td>\n",
" <td>71.72</td>\n",
" <td>2624.61</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>113016 rows × 16 columns</p>\n",
"</div>"
],
"text/plain": [
" Open High Low Close Volume Session Quarter \\\n",
"0 1.35790 1.35810 1.35390 1.35470 318.0 1 1 \n",
"1 1.35460 1.35510 1.35290 1.35380 338.0 1 1 \n",
"2 1.35390 1.35390 1.34980 1.35040 356.0 1 1 \n",
"3 1.35040 1.35060 1.33850 1.33990 545.0 1 1 \n",
"4 1.33990 1.34450 1.33990 1.34430 385.0 1 1 \n",
"... ... ... ... ... ... ... ... \n",
"118388 1.03502 1.03548 1.03438 1.03490 2323.0 1 4 \n",
"118389 1.03489 1.03566 1.03455 1.03529 1900.0 1 4 \n",
"118390 1.03526 1.03645 1.03516 1.03547 1445.0 1 4 \n",
"118391 1.03547 1.03631 1.03544 1.03582 1208.0 0 4 \n",
"118392 1.03585 1.03608 1.03489 1.03493 616.0 0 4 \n",
"\n",
" Month_sin Hour_sin Date_sin DOW_sin DateTime \\\n",
"0 0.5 0.25882 0.58779 0.000000 2005-01-03 01:00:00 \n",
"1 0.5 0.50000 0.58779 0.000000 2005-01-03 02:00:00 \n",
"2 0.5 0.70711 0.58779 0.000000 2005-01-03 03:00:00 \n",
"3 0.5 0.86603 0.58779 0.000000 2005-01-03 04:00:00 \n",
"4 0.5 0.96593 0.58779 0.000000 2005-01-03 05:00:00 \n",
"... ... ... ... ... ... \n",
"118388 -0.0 -0.96593 0.20791 0.781831 2024-12-31 19:00:00 \n",
"118389 -0.0 -0.86603 0.20791 0.781831 2024-12-31 20:00:00 \n",
"118390 -0.0 -0.70711 0.20791 0.781831 2024-12-31 21:00:00 \n",
"118391 -0.0 -0.50000 0.20791 0.781831 2024-12-31 22:00:00 \n",
"118392 -0.0 -0.25882 0.20791 0.781831 2024-12-31 23:00:00 \n",
"\n",
" Diff_10Y_2Y Oil_Volume Oil_Close Gold_Close \n",
"0 0.047083 2895.166667 42.12 435.60 \n",
"1 0.047083 2895.166667 42.12 435.50 \n",
"2 0.047083 2895.166667 42.12 435.00 \n",
"3 0.047083 2895.166667 42.12 433.60 \n",
"4 0.047083 2895.166667 42.12 433.60 \n",
"... ... ... ... ... \n",
"118388 0.013750 6298.125000 71.72 2624.38 \n",
"118389 0.013750 6298.125000 71.72 2624.81 \n",
"118390 0.013750 6298.125000 71.72 2623.85 \n",
"118391 0.013750 6298.125000 71.72 2623.36 \n",
"118392 0.013750 6298.125000 71.72 2624.61 \n",
"\n",
"[113016 rows x 16 columns]"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gold_df = gold_df.drop(columns=['Open', 'High', 'Low', 'Volume'])\n",
"\n",
"gold_df = gold_df.rename(columns={\n",
" \"Close\": \"Gold_Close\",\n",
" \"Date\":\"DateTime\"\n",
"})\n",
"\n",
"# Convert gold_df['Date'] to proper datetime\n",
"gold_df['DateTime'] = pd.to_datetime(gold_df['DateTime'], format=\"%Y.%m.%d %H:%M\", errors=\"coerce\")\n",
"\n",
"# Merge on DateOnly\n",
"merged = pd.merge(df, gold_df, on='DateTime', how='left')\n",
"\n",
"df = merged\n",
"\n",
"df = df.dropna()\n",
"\n",
"df\n",
"\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "MyqzbrPmjL4J"
},
"source": [
"## Technical Indicators\n",
"\n",
"* I have chosen only bounded indicators because i do not want the machine to get confused.\n",
"* All the chosen Indicators are selected keeping in mind that their primary function is to help understand the machine the nature of the market when combined with the previously made features.\n",
"\n",
"* The Features selected can be broadly classified into 3 categories:\n",
"\n",
" * Trend :\n",
" * ADX - Average Directional Index (Strength Only)\n",
" * CCI - Commodity Channel Index (Strength and Direction)\n",
" \n",
" * Momentum :\n",
" * PPO - Percent Price Oscillator (Strength and Direction)\n",
" * CMO - Chande Momentum Oscillator (Strength and Direction)\n",
"\n",
" * Volatility:\n",
" * Chaikin Volatility (Intra-Bar)\n",
" * Keltner Channel Width (Inter-Bar)\n",
"\n",
"* Here I am calculating them and then in order to find the most responsive lookback periods, graphs showing ACF (Auto-Correlation Function) and PACF (Partial Auto-Correlation Factor)\n",
"\n",
"* Based on ACF, PACF and standard lookback periods, we will calculate indicators for multiple lookback periods.\n",
"\n",
"* Based on all these indicators, new secondary indicators will be calculated from primary features to catch as micro market structures and repetitive macro market structures"
]
},
{
"cell_type": "code",
"execution_count": 118,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "2jBOM8xwhBg2",
"outputId": "ddd7d7d6-7eb8-4317-fb44-539ed00c6232"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/var/folders/l_/j4rfg9014yvd4jfvv441w26r0000gn/T/ipykernel_54914/3209065174.py:9: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" df['ADX_14'] = talib.ADX(df['High'], df['Low'], df['Close'], timeperiod=14)\n",
"/var/folders/l_/j4rfg9014yvd4jfvv441w26r0000gn/T/ipykernel_54914/3209065174.py:10: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" df['CCI_14'] = talib.CCI(df['High'], df['Low'], df['Close'], timeperiod=14)\n",
"/var/folders/l_/j4rfg9014yvd4jfvv441w26r0000gn/T/ipykernel_54914/3209065174.py:11: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" df['PPO_12_26'] = talib.PPO(df['Close'], fastperiod=12, slowperiod=26, matype=0)\n",
"/var/folders/l_/j4rfg9014yvd4jfvv441w26r0000gn/T/ipykernel_54914/3209065174.py:12: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" df['CMO_14'] = talib.CMO(df['Close'], timeperiod=14)\n",
"/var/folders/l_/j4rfg9014yvd4jfvv441w26r0000gn/T/ipykernel_54914/3209065174.py:13: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" df['ChaikinVol_3_10'] = talib.ADOSC(df['High'], df['Low'], df['Close'], df['Volume'], fastperiod=3, slowperiod=10)\n",
"/var/folders/l_/j4rfg9014yvd4jfvv441w26r0000gn/T/ipykernel_54914/3209065174.py:14: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" df['KeltnerWidth_20'] = (talib.EMA(df['High'] - df['Low'], timeperiod=20) / talib.EMA(df['Close'], timeperiod=20))\n"
]
},
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 1200x500 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 1200x500 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 1200x500 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 1200x500 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 1200x500 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 1200x500 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"from statsmodels.graphics.tsaplots import plot_acf, plot_pacf\n",
"import talib\n",
"\n",
"# Example: df contains columns ['Open', 'High', 'Low', 'Close', 'Volume']\n",
"\n",
"# Compute indicators using TA-Lib\n",
"df['ADX_14'] = talib.ADX(df['High'], df['Low'], df['Close'], timeperiod=14)\n",
"df['CCI_14'] = talib.CCI(df['High'], df['Low'], df['Close'], timeperiod=14)\n",
"df['PPO_12_26'] = talib.PPO(df['Close'], fastperiod=12, slowperiod=26, matype=0)\n",
"df['CMO_14'] = talib.CMO(df['Close'], timeperiod=14)\n",
"df['ChaikinVol_3_10'] = talib.ADOSC(df['High'], df['Low'], df['Close'], df['Volume'], fastperiod=3, slowperiod=10)\n",
"df['KeltnerWidth_20'] = (talib.EMA(df['High'] - df['Low'], timeperiod=20) / talib.EMA(df['Close'], timeperiod=20))\n",
"\n",
"# Select only indicator columns\n",
"indicators = ['ADX_14', 'CCI_14', 'PPO_12_26', 'CMO_14', 'ChaikinVol_3_10', 'KeltnerWidth_20']\n",
"\n",
"# Loop through each indicator and plot ACF + PACF\n",
"for ind in indicators:\n",
" plt.figure(figsize=(12, 5))\n",
"\n",
" plt.subplot(1, 2, 1)\n",
" plot_acf(df[ind].dropna(), lags=40, ax=plt.gca())\n",
" plt.title(f'ACF of {ind}')\n",
"\n",
" plt.subplot(1, 2, 2)\n",
" plot_pacf(df[ind].dropna(), lags=40, ax=plt.gca(), method='ywm')\n",
" plt.title(f'PACF of {ind}')\n",
"\n",
" plt.tight_layout()\n",
" plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": 119,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 444
},
"id": "kR6NuS-VxeuX",
"outputId": "c58ceb4c-4b02-45b2-a2d2-adfb8bddd8eb"
},
"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>Open</th>\n",
" <th>High</th>\n",
" <th>Low</th>\n",
" <th>Close</th>\n",
" <th>Volume</th>\n",
" <th>Session</th>\n",
" <th>Quarter</th>\n",
" <th>Month_sin</th>\n",
" <th>Hour_sin</th>\n",
" <th>Date_sin</th>\n",
" <th>...</th>\n",
" <th>Diff_10Y_2Y</th>\n",
" <th>Oil_Volume</th>\n",
" <th>Oil_Close</th>\n",
" <th>Gold_Close</th>\n",
" <th>ADX_14</th>\n",
" <th>CCI_14</th>\n",
" <th>PPO_12_26</th>\n",
" <th>CMO_14</th>\n",
" <th>ChaikinVol_3_10</th>\n",
" <th>KeltnerWidth_20</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1.35790</td>\n",
" <td>1.35810</td>\n",
" <td>1.35390</td>\n",
" <td>1.35470</td>\n",
" <td>318.0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0.5</td>\n",
" <td>0.25882</td>\n",
" <td>0.58779</td>\n",
" <td>...</td>\n",
" <td>0.047083</td>\n",
" <td>2895.166667</td>\n",
" <td>42.12</td>\n",
" <td>435.60</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1.35460</td>\n",
" <td>1.35510</td>\n",
" <td>1.35290</td>\n",
" <td>1.35380</td>\n",
" <td>338.0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0.5</td>\n",
" <td>0.50000</td>\n",
" <td>0.58779</td>\n",
" <td>...</td>\n",
" <td>0.047083</td>\n",
" <td>2895.166667</td>\n",
" <td>42.12</td>\n",
" <td>435.50</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1.35390</td>\n",
" <td>1.35390</td>\n",
" <td>1.34980</td>\n",
" <td>1.35040</td>\n",
" <td>356.0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0.5</td>\n",
" <td>0.70711</td>\n",
" <td>0.58779</td>\n",
" <td>...</td>\n",
" <td>0.047083</td>\n",
" <td>2895.166667</td>\n",
" <td>42.12</td>\n",
" <td>435.00</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1.35040</td>\n",
" <td>1.35060</td>\n",
" <td>1.33850</td>\n",
" <td>1.33990</td>\n",
" <td>545.0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0.5</td>\n",
" <td>0.86603</td>\n",
" <td>0.58779</td>\n",
" <td>...</td>\n",
" <td>0.047083</td>\n",
" <td>2895.166667</td>\n",
" <td>42.12</td>\n",
" <td>433.60</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1.33990</td>\n",
" <td>1.34450</td>\n",
" <td>1.33990</td>\n",
" <td>1.34430</td>\n",
" <td>385.0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0.5</td>\n",
" <td>0.96593</td>\n",
" <td>0.58779</td>\n",
" <td>...</td>\n",
" <td>0.047083</td>\n",
" <td>2895.166667</td>\n",
" <td>42.12</td>\n",
" <td>433.60</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>118388</th>\n",
" <td>1.03502</td>\n",
" <td>1.03548</td>\n",
" <td>1.03438</td>\n",
" <td>1.03490</td>\n",
" <td>2323.0</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>-0.0</td>\n",
" <td>-0.96593</td>\n",
" <td>0.20791</td>\n",
" <td>...</td>\n",
" <td>0.013750</td>\n",
" <td>6298.125000</td>\n",
" <td>71.72</td>\n",
" <td>2624.38</td>\n",
" <td>23.611404</td>\n",
" <td>-160.337614</td>\n",
" <td>-0.051720</td>\n",
" <td>-49.033438</td>\n",
" <td>-2699.177784</td>\n",
" <td>0.001465</td>\n",
" </tr>\n",
" <tr>\n",
" <th>118389</th>\n",
" <td>1.03489</td>\n",
" <td>1.03566</td>\n",
" <td>1.03455</td>\n",
" <td>1.03529</td>\n",
" <td>1900.0</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>-0.0</td>\n",
" <td>-0.86603</td>\n",
" <td>0.20791</td>\n",
" <td>...</td>\n",
" <td>0.013750</td>\n",
" <td>6298.125000</td>\n",
" <td>71.72</td>\n",
" <td>2624.81</td>\n",
" <td>25.551370</td>\n",
" <td>-119.355906</td>\n",
" <td>-0.082496</td>\n",
" <td>-43.107288</td>\n",
" <td>-2306.287257</td>\n",
" <td>0.001427</td>\n",
" </tr>\n",
" <tr>\n",
" <th>118390</th>\n",
" <td>1.03526</td>\n",
" <td>1.03645</td>\n",
" <td>1.03516</td>\n",
" <td>1.03547</td>\n",
" <td>1445.0</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>-0.0</td>\n",
" <td>-0.70711</td>\n",
" <td>0.20791</td>\n",
" <td>...</td>\n",
" <td>0.013750</td>\n",
" <td>6298.125000</td>\n",
" <td>71.72</td>\n",
" <td>2623.85</td>\n",
" <td>26.385304</td>\n",
" <td>-85.267741</td>\n",
" <td>-0.116771</td>\n",
" <td>-40.333690</td>\n",
" <td>-2174.693523</td>\n",
" <td>0.001410</td>\n",
" </tr>\n",
" <tr>\n",
" <th>118391</th>\n",
" <td>1.03547</td>\n",
" <td>1.03631</td>\n",
" <td>1.03544</td>\n",
" <td>1.03582</td>\n",
" <td>1208.0</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>-0.0</td>\n",
" <td>-0.50000</td>\n",
" <td>0.20791</td>\n",
" <td>...</td>\n",
" <td>0.013750</td>\n",
" <td>6298.125000</td>\n",
" <td>71.72</td>\n",
" <td>2623.36</td>\n",
" <td>27.159672</td>\n",
" <td>-69.016843</td>\n",
" <td>-0.151568</td>\n",
" <td>-34.860425</td>\n",
" <td>-1971.758013</td>\n",
" <td>0.001356</td>\n",
" </tr>\n",
" <tr>\n",
" <th>118392</th>\n",
" <td>1.03585</td>\n",
" <td>1.03608</td>\n",
" <td>1.03489</td>\n",
" <td>1.03493</td>\n",
" <td>616.0</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>-0.0</td>\n",
" <td>-0.25882</td>\n",
" <td>0.20791</td>\n",
" <td>...</td>\n",
" <td>0.013750</td>\n",
" <td>6298.125000</td>\n",
" <td>71.72</td>\n",
" <td>2624.61</td>\n",
" <td>28.181968</td>\n",
" <td>-73.068540</td>\n",
" <td>-0.186740</td>\n",
" <td>-41.146290</td>\n",
" <td>-1892.311742</td>\n",
" <td>0.001336</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>113016 rows × 22 columns</p>\n",
"</div>"
],
"text/plain": [
" Open High Low Close Volume Session Quarter \\\n",
"0 1.35790 1.35810 1.35390 1.35470 318.0 1 1 \n",
"1 1.35460 1.35510 1.35290 1.35380 338.0 1 1 \n",
"2 1.35390 1.35390 1.34980 1.35040 356.0 1 1 \n",
"3 1.35040 1.35060 1.33850 1.33990 545.0 1 1 \n",
"4 1.33990 1.34450 1.33990 1.34430 385.0 1 1 \n",
"... ... ... ... ... ... ... ... \n",
"118388 1.03502 1.03548 1.03438 1.03490 2323.0 1 4 \n",
"118389 1.03489 1.03566 1.03455 1.03529 1900.0 1 4 \n",
"118390 1.03526 1.03645 1.03516 1.03547 1445.0 1 4 \n",
"118391 1.03547 1.03631 1.03544 1.03582 1208.0 0 4 \n",
"118392 1.03585 1.03608 1.03489 1.03493 616.0 0 4 \n",
"\n",
" Month_sin Hour_sin Date_sin ... Diff_10Y_2Y Oil_Volume \\\n",
"0 0.5 0.25882 0.58779 ... 0.047083 2895.166667 \n",
"1 0.5 0.50000 0.58779 ... 0.047083 2895.166667 \n",
"2 0.5 0.70711 0.58779 ... 0.047083 2895.166667 \n",
"3 0.5 0.86603 0.58779 ... 0.047083 2895.166667 \n",
"4 0.5 0.96593 0.58779 ... 0.047083 2895.166667 \n",
"... ... ... ... ... ... ... \n",
"118388 -0.0 -0.96593 0.20791 ... 0.013750 6298.125000 \n",
"118389 -0.0 -0.86603 0.20791 ... 0.013750 6298.125000 \n",
"118390 -0.0 -0.70711 0.20791 ... 0.013750 6298.125000 \n",
"118391 -0.0 -0.50000 0.20791 ... 0.013750 6298.125000 \n",
"118392 -0.0 -0.25882 0.20791 ... 0.013750 6298.125000 \n",
"\n",
" Oil_Close Gold_Close ADX_14 CCI_14 PPO_12_26 CMO_14 \\\n",
"0 42.12 435.60 NaN NaN NaN NaN \n",
"1 42.12 435.50 NaN NaN NaN NaN \n",
"2 42.12 435.00 NaN NaN NaN NaN \n",
"3 42.12 433.60 NaN NaN NaN NaN \n",
"4 42.12 433.60 NaN NaN NaN NaN \n",
"... ... ... ... ... ... ... \n",
"118388 71.72 2624.38 23.611404 -160.337614 -0.051720 -49.033438 \n",
"118389 71.72 2624.81 25.551370 -119.355906 -0.082496 -43.107288 \n",
"118390 71.72 2623.85 26.385304 -85.267741 -0.116771 -40.333690 \n",
"118391 71.72 2623.36 27.159672 -69.016843 -0.151568 -34.860425 \n",
"118392 71.72 2624.61 28.181968 -73.068540 -0.186740 -41.146290 \n",
"\n",
" ChaikinVol_3_10 KeltnerWidth_20 \n",
"0 NaN NaN \n",
"1 NaN NaN \n",
"2 NaN NaN \n",
"3 NaN NaN \n",
"4 NaN NaN \n",
"... ... ... \n",
"118388 -2699.177784 0.001465 \n",
"118389 -2306.287257 0.001427 \n",
"118390 -2174.693523 0.001410 \n",
"118391 -1971.758013 0.001356 \n",
"118392 -1892.311742 0.001336 \n",
"\n",
"[113016 rows x 22 columns]"
]
},
"execution_count": 119,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": 120,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "-oXlHPKm5btt",
"outputId": "f89f8f33-3b6d-4f91-de95-a3e060a06d97"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/var/folders/l_/j4rfg9014yvd4jfvv441w26r0000gn/T/ipykernel_54914/1728166818.py:1: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" df['ADX_2'] = talib.ADX(df['High'], df['Low'], df['Close'], timeperiod=2)\n",
"/var/folders/l_/j4rfg9014yvd4jfvv441w26r0000gn/T/ipykernel_54914/1728166818.py:2: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" df['ADX_5'] = talib.ADX(df['High'], df['Low'], df['Close'], timeperiod=5)\n",
"/var/folders/l_/j4rfg9014yvd4jfvv441w26r0000gn/T/ipykernel_54914/1728166818.py:3: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" df['CCI_2'] = talib.CCI(df['High'], df['Low'], df['Close'], timeperiod=2)\n",
"/var/folders/l_/j4rfg9014yvd4jfvv441w26r0000gn/T/ipykernel_54914/1728166818.py:4: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" df['CCI_8'] = talib.CCI(df['High'], df['Low'], df['Close'], timeperiod=8)\n",
"/var/folders/l_/j4rfg9014yvd4jfvv441w26r0000gn/T/ipykernel_54914/1728166818.py:5: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" df['CCI_20'] = talib.CCI(df['High'], df['Low'], df['Close'], timeperiod=20)\n",
"/var/folders/l_/j4rfg9014yvd4jfvv441w26r0000gn/T/ipykernel_54914/1728166818.py:6: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" df['PPO_2_6'] = talib.PPO(df['Close'], fastperiod=2, slowperiod=6, matype=0)\n",
"/var/folders/l_/j4rfg9014yvd4jfvv441w26r0000gn/T/ipykernel_54914/1728166818.py:7: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" df['CMO_2'] = talib.CMO(df['Close'], timeperiod=2)\n",
"/var/folders/l_/j4rfg9014yvd4jfvv441w26r0000gn/T/ipykernel_54914/1728166818.py:8: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" df['CMO54'] = talib.CMO(df['Close'], timeperiod=5)\n",
"/var/folders/l_/j4rfg9014yvd4jfvv441w26r0000gn/T/ipykernel_54914/1728166818.py:9: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" df['ChaikinVol_2_5'] = talib.ADOSC(df['High'], df['Low'], df['Close'], df['Volume'], fastperiod=2, slowperiod=5)\n",
"/var/folders/l_/j4rfg9014yvd4jfvv441w26r0000gn/T/ipykernel_54914/1728166818.py:10: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" df['KeltnerWidth_2'] = (talib.EMA(df['High'] - df['Low'], timeperiod=2) / talib.EMA(df['Close'], timeperiod=2))\n",
"/var/folders/l_/j4rfg9014yvd4jfvv441w26r0000gn/T/ipykernel_54914/1728166818.py:11: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" df['KeltnerWidth_15'] = (talib.EMA(df['High'] - df['Low'], timeperiod=15) / talib.EMA(df['Close'], timeperiod=15))\n",
"/var/folders/l_/j4rfg9014yvd4jfvv441w26r0000gn/T/ipykernel_54914/1728166818.py:12: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" df['KeltnerWidth_24'] = (talib.EMA(df['High'] - df['Low'], timeperiod=24) / talib.EMA(df['Close'], timeperiod=24))\n"
]
}
],
"source": [
"df['ADX_2'] = talib.ADX(df['High'], df['Low'], df['Close'], timeperiod=2)\n",
"df['ADX_5'] = talib.ADX(df['High'], df['Low'], df['Close'], timeperiod=5)\n",
"df['CCI_2'] = talib.CCI(df['High'], df['Low'], df['Close'], timeperiod=2)\n",
"df['CCI_8'] = talib.CCI(df['High'], df['Low'], df['Close'], timeperiod=8)\n",
"df['CCI_20'] = talib.CCI(df['High'], df['Low'], df['Close'], timeperiod=20)\n",
"df['PPO_2_6'] = talib.PPO(df['Close'], fastperiod=2, slowperiod=6, matype=0)\n",
"df['CMO_2'] = talib.CMO(df['Close'], timeperiod=2)\n",
"df['CMO54'] = talib.CMO(df['Close'], timeperiod=5)\n",
"df['ChaikinVol_2_5'] = talib.ADOSC(df['High'], df['Low'], df['Close'], df['Volume'], fastperiod=2, slowperiod=5)\n",
"df['KeltnerWidth_2'] = (talib.EMA(df['High'] - df['Low'], timeperiod=2) / talib.EMA(df['Close'], timeperiod=2))\n",
"df['KeltnerWidth_15'] = (talib.EMA(df['High'] - df['Low'], timeperiod=15) / talib.EMA(df['Close'], timeperiod=15))\n",
"df['KeltnerWidth_24'] = (talib.EMA(df['High'] - df['Low'], timeperiod=24) / talib.EMA(df['Close'], timeperiod=24))"
]
},
{
"cell_type": "code",
"execution_count": 121,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 444
},
"id": "G0IqqeYr6zo4",
"outputId": "9c95e7d5-f43e-4232-d741-34cd44558b51"
},
"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>Open</th>\n",
" <th>High</th>\n",
" <th>Low</th>\n",
" <th>Close</th>\n",
" <th>Volume</th>\n",
" <th>Session</th>\n",
" <th>Quarter</th>\n",
" <th>Month_sin</th>\n",
" <th>Hour_sin</th>\n",
" <th>Date_sin</th>\n",
" <th>...</th>\n",
" <th>CCI_2</th>\n",
" <th>CCI_8</th>\n",
" <th>CCI_20</th>\n",
" <th>PPO_2_6</th>\n",
" <th>CMO_2</th>\n",
" <th>CMO54</th>\n",
" <th>ChaikinVol_2_5</th>\n",
" <th>KeltnerWidth_2</th>\n",
" <th>KeltnerWidth_15</th>\n",
" <th>KeltnerWidth_24</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1.35790</td>\n",
" <td>1.35810</td>\n",
" <td>1.35390</td>\n",
" <td>1.35470</td>\n",
" <td>318.0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0.5</td>\n",
" <td>0.25882</td>\n",
" <td>0.58779</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1.35460</td>\n",
" <td>1.35510</td>\n",
" <td>1.35290</td>\n",
" <td>1.35380</td>\n",
" <td>338.0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0.5</td>\n",
" <td>0.50000</td>\n",
" <td>0.58779</td>\n",
" <td>...</td>\n",
" <td>-66.666667</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.002363</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1.35390</td>\n",
" <td>1.35390</td>\n",
" <td>1.34980</td>\n",
" <td>1.35040</td>\n",
" <td>356.0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0.5</td>\n",
" <td>0.70711</td>\n",
" <td>0.58779</td>\n",
" <td>...</td>\n",
" <td>-66.666667</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-100.000000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.002811</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1.35040</td>\n",
" <td>1.35060</td>\n",
" <td>1.33850</td>\n",
" <td>1.33990</td>\n",
" <td>545.0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0.5</td>\n",
" <td>0.86603</td>\n",
" <td>0.58779</td>\n",
" <td>...</td>\n",
" <td>-66.666667</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-100.000000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.006945</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1.33990</td>\n",
" <td>1.34450</td>\n",
" <td>1.33990</td>\n",
" <td>1.34430</td>\n",
" <td>385.0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0.5</td>\n",
" <td>0.96593</td>\n",
" <td>0.58779</td>\n",
" <td>...</td>\n",
" <td>-66.666667</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-17.948718</td>\n",
" <td>NaN</td>\n",
" <td>-99.117404</td>\n",
" <td>0.004596</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>118388</th>\n",
" <td>1.03502</td>\n",
" <td>1.03548</td>\n",
" <td>1.03438</td>\n",
" <td>1.03490</td>\n",
" <td>2323.0</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>-0.0</td>\n",
" <td>-0.96593</td>\n",
" <td>0.20791</td>\n",
" <td>...</td>\n",
" <td>-66.666667</td>\n",
" <td>-108.467310</td>\n",
" <td>-217.330577</td>\n",
" <td>-0.188861</td>\n",
" <td>-99.184418</td>\n",
" <td>-86.385833</td>\n",
" <td>-1741.904757</td>\n",
" <td>0.001345</td>\n",
" <td>0.001520</td>\n",
" <td>0.001433</td>\n",
" </tr>\n",
" <tr>\n",
" <th>118389</th>\n",
" <td>1.03489</td>\n",
" <td>1.03566</td>\n",
" <td>1.03455</td>\n",
" <td>1.03529</td>\n",
" <td>1900.0</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>-0.0</td>\n",
" <td>-0.86603</td>\n",
" <td>0.20791</td>\n",
" <td>...</td>\n",
" <td>66.666667</td>\n",
" <td>-76.572200</td>\n",
" <td>-161.486199</td>\n",
" <td>-0.112263</td>\n",
" <td>-18.839266</td>\n",
" <td>-65.275344</td>\n",
" <td>-1063.099374</td>\n",
" <td>0.001163</td>\n",
" <td>0.001464</td>\n",
" <td>0.001404</td>\n",
" </tr>\n",
" <tr>\n",
" <th>118390</th>\n",
" <td>1.03526</td>\n",
" <td>1.03645</td>\n",
" <td>1.03516</td>\n",
" <td>1.03547</td>\n",
" <td>1445.0</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>-0.0</td>\n",
" <td>-0.70711</td>\n",
" <td>0.20791</td>\n",
" <td>...</td>\n",
" <td>66.666667</td>\n",
" <td>-46.257822</td>\n",
" <td>-118.798827</td>\n",
" <td>-0.023335</td>\n",
" <td>13.404055</td>\n",
" <td>-55.138058</td>\n",
" <td>-926.177387</td>\n",
" <td>0.001218</td>\n",
" <td>0.001437</td>\n",
" <td>0.001392</td>\n",
" </tr>\n",
" <tr>\n",
" <th>118391</th>\n",
" <td>1.03547</td>\n",
" <td>1.03631</td>\n",
" <td>1.03544</td>\n",
" <td>1.03582</td>\n",
" <td>1208.0</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>-0.0</td>\n",
" <td>-0.50000</td>\n",
" <td>0.20791</td>\n",
" <td>...</td>\n",
" <td>66.666667</td>\n",
" <td>-26.847505</td>\n",
" <td>-97.839834</td>\n",
" <td>0.030747</td>\n",
" <td>57.863488</td>\n",
" <td>-35.010678</td>\n",
" <td>-740.844959</td>\n",
" <td>0.000966</td>\n",
" <td>0.001363</td>\n",
" <td>0.001347</td>\n",
" </tr>\n",
" <tr>\n",
" <th>118392</th>\n",
" <td>1.03585</td>\n",
" <td>1.03608</td>\n",
" <td>1.03489</td>\n",
" <td>1.03493</td>\n",
" <td>616.0</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>-0.0</td>\n",
" <td>-0.25882</td>\n",
" <td>0.20791</td>\n",
" <td>...</td>\n",
" <td>-66.666667</td>\n",
" <td>-50.481804</td>\n",
" <td>-99.278507</td>\n",
" <td>0.013201</td>\n",
" <td>-56.283445</td>\n",
" <td>-53.986053</td>\n",
" <td>-726.557174</td>\n",
" <td>0.001089</td>\n",
" <td>0.001336</td>\n",
" <td>0.001332</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>113016 rows × 34 columns</p>\n",
"</div>"
],
"text/plain": [
" Open High Low Close Volume Session Quarter \\\n",
"0 1.35790 1.35810 1.35390 1.35470 318.0 1 1 \n",
"1 1.35460 1.35510 1.35290 1.35380 338.0 1 1 \n",
"2 1.35390 1.35390 1.34980 1.35040 356.0 1 1 \n",
"3 1.35040 1.35060 1.33850 1.33990 545.0 1 1 \n",
"4 1.33990 1.34450 1.33990 1.34430 385.0 1 1 \n",
"... ... ... ... ... ... ... ... \n",
"118388 1.03502 1.03548 1.03438 1.03490 2323.0 1 4 \n",
"118389 1.03489 1.03566 1.03455 1.03529 1900.0 1 4 \n",
"118390 1.03526 1.03645 1.03516 1.03547 1445.0 1 4 \n",
"118391 1.03547 1.03631 1.03544 1.03582 1208.0 0 4 \n",
"118392 1.03585 1.03608 1.03489 1.03493 616.0 0 4 \n",
"\n",
" Month_sin Hour_sin Date_sin ... CCI_2 CCI_8 CCI_20 \\\n",
"0 0.5 0.25882 0.58779 ... NaN NaN NaN \n",
"1 0.5 0.50000 0.58779 ... -66.666667 NaN NaN \n",
"2 0.5 0.70711 0.58779 ... -66.666667 NaN NaN \n",
"3 0.5 0.86603 0.58779 ... -66.666667 NaN NaN \n",
"4 0.5 0.96593 0.58779 ... -66.666667 NaN NaN \n",
"... ... ... ... ... ... ... ... \n",
"118388 -0.0 -0.96593 0.20791 ... -66.666667 -108.467310 -217.330577 \n",
"118389 -0.0 -0.86603 0.20791 ... 66.666667 -76.572200 -161.486199 \n",
"118390 -0.0 -0.70711 0.20791 ... 66.666667 -46.257822 -118.798827 \n",
"118391 -0.0 -0.50000 0.20791 ... 66.666667 -26.847505 -97.839834 \n",
"118392 -0.0 -0.25882 0.20791 ... -66.666667 -50.481804 -99.278507 \n",
"\n",
" PPO_2_6 CMO_2 CMO54 ChaikinVol_2_5 KeltnerWidth_2 \\\n",
"0 NaN NaN NaN NaN NaN \n",
"1 NaN NaN NaN NaN 0.002363 \n",
"2 NaN -100.000000 NaN NaN 0.002811 \n",
"3 NaN -100.000000 NaN NaN 0.006945 \n",
"4 NaN -17.948718 NaN -99.117404 0.004596 \n",
"... ... ... ... ... ... \n",
"118388 -0.188861 -99.184418 -86.385833 -1741.904757 0.001345 \n",
"118389 -0.112263 -18.839266 -65.275344 -1063.099374 0.001163 \n",
"118390 -0.023335 13.404055 -55.138058 -926.177387 0.001218 \n",
"118391 0.030747 57.863488 -35.010678 -740.844959 0.000966 \n",
"118392 0.013201 -56.283445 -53.986053 -726.557174 0.001089 \n",
"\n",
" KeltnerWidth_15 KeltnerWidth_24 \n",
"0 NaN NaN \n",
"1 NaN NaN \n",
"2 NaN NaN \n",
"3 NaN NaN \n",
"4 NaN NaN \n",
"... ... ... \n",
"118388 0.001520 0.001433 \n",
"118389 0.001464 0.001404 \n",
"118390 0.001437 0.001392 \n",
"118391 0.001363 0.001347 \n",
"118392 0.001336 0.001332 \n",
"\n",
"[113016 rows x 34 columns]"
]
},
"execution_count": 121,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": 122,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "i44En6xf7NE8",
"outputId": "621a2c38-a80a-4294-c9f6-be83253cac38"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/var/folders/l_/j4rfg9014yvd4jfvv441w26r0000gn/T/ipykernel_54914/4128738175.py:1: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" df['ADX_diff_3'] = df['ADX_5'] - df['ADX_2']\n",
"/var/folders/l_/j4rfg9014yvd4jfvv441w26r0000gn/T/ipykernel_54914/4128738175.py:2: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" df['ADX_diff_9'] = df['ADX_14'] - df['ADX_5']\n",
"/var/folders/l_/j4rfg9014yvd4jfvv441w26r0000gn/T/ipykernel_54914/4128738175.py:3: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" df['ADX_diff_12'] = df['ADX_14'] - df['ADX_2']\n",
"/var/folders/l_/j4rfg9014yvd4jfvv441w26r0000gn/T/ipykernel_54914/4128738175.py:4: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" df['ADX_ratio_2_5'] = df['ADX_2'] / df['ADX_5']\n",
"/var/folders/l_/j4rfg9014yvd4jfvv441w26r0000gn/T/ipykernel_54914/4128738175.py:5: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" df['ADX_ratio_5_14'] = df['ADX_5'] / df['ADX_14']\n",
"/var/folders/l_/j4rfg9014yvd4jfvv441w26r0000gn/T/ipykernel_54914/4128738175.py:6: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" df['ADX_ratio_2_14'] = df['ADX_2'] / df['ADX_14']\n"
]
}
],
"source": [
"df['ADX_diff_3'] = df['ADX_5'] - df['ADX_2']\n",
"df['ADX_diff_9'] = df['ADX_14'] - df['ADX_5']\n",
"df['ADX_diff_12'] = df['ADX_14'] - df['ADX_2']\n",
"df['ADX_ratio_2_5'] = df['ADX_2'] / df['ADX_5']\n",
"df['ADX_ratio_5_14'] = df['ADX_5'] / df['ADX_14']\n",
"df['ADX_ratio_2_14'] = df['ADX_2'] / df['ADX_14']"
]
},
{
"cell_type": "code",
"execution_count": 123,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "-38A7MWM9tAJ",
"outputId": "400182aa-25fa-4900-e078-2324dfff8d81"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/var/folders/l_/j4rfg9014yvd4jfvv441w26r0000gn/T/ipykernel_54914/448491090.py:1: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" df['CCI_diff_6'] = df['CCI_8'] - df['CCI_2']\n",
"/var/folders/l_/j4rfg9014yvd4jfvv441w26r0000gn/T/ipykernel_54914/448491090.py:2: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" df['CCI_diff_12'] = df['CCI_20'] - df['CCI_8']\n",
"/var/folders/l_/j4rfg9014yvd4jfvv441w26r0000gn/T/ipykernel_54914/448491090.py:3: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" df['CCI_diff_18'] = df['CCI_20'] - df['CCI_2']\n",
"/var/folders/l_/j4rfg9014yvd4jfvv441w26r0000gn/T/ipykernel_54914/448491090.py:4: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" df['CCI_ratio_2_8'] = df['CCI_2'] / df['CCI_8']\n",
"/var/folders/l_/j4rfg9014yvd4jfvv441w26r0000gn/T/ipykernel_54914/448491090.py:5: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" df['CCI_ratio_8_20'] = df['CCI_8'] / df['CCI_20']\n",
"/var/folders/l_/j4rfg9014yvd4jfvv441w26r0000gn/T/ipykernel_54914/448491090.py:6: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" df['CCI_ratio_2_20'] = df['CCI_2'] / df['CCI_20']\n"
]
}
],
"source": [
"df['CCI_diff_6'] = df['CCI_8'] - df['CCI_2']\n",
"df['CCI_diff_12'] = df['CCI_20'] - df['CCI_8']\n",
"df['CCI_diff_18'] = df['CCI_20'] - df['CCI_2']\n",
"df['CCI_ratio_2_8'] = df['CCI_2'] / df['CCI_8']\n",
"df['CCI_ratio_8_20'] = df['CCI_8'] / df['CCI_20']\n",
"df['CCI_ratio_2_20'] = df['CCI_2'] / df['CCI_20']"
]
},
{
"cell_type": "code",
"execution_count": 124,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "qMYkeMtQ-YJY",
"outputId": "a7d43769-b59f-4dd0-db3a-c138d1cf2ed4"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/var/folders/l_/j4rfg9014yvd4jfvv441w26r0000gn/T/ipykernel_54914/2351237557.py:1: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" df['CMO_diff_12'] = df['CMO_14'] - df['ADX_2']\n",
"/var/folders/l_/j4rfg9014yvd4jfvv441w26r0000gn/T/ipykernel_54914/2351237557.py:2: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" df['CMO_ratio_2_14'] = df['CCI_2'] / df['CCI_14']\n",
"/var/folders/l_/j4rfg9014yvd4jfvv441w26r0000gn/T/ipykernel_54914/2351237557.py:3: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" df['KeltnerWidth_diff_13'] = df['KeltnerWidth_15'] - df['KeltnerWidth_2']\n",
"/var/folders/l_/j4rfg9014yvd4jfvv441w26r0000gn/T/ipykernel_54914/2351237557.py:4: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" df['KeltnerWidth_diff_22'] = df['KeltnerWidth_24'] - df['KeltnerWidth_2']\n",
"/var/folders/l_/j4rfg9014yvd4jfvv441w26r0000gn/T/ipykernel_54914/2351237557.py:5: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" df['KeltnerWidth_diff_9'] = df['KeltnerWidth_24'] - df['KeltnerWidth_15']\n",
"/var/folders/l_/j4rfg9014yvd4jfvv441w26r0000gn/T/ipykernel_54914/2351237557.py:6: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" df['KeltnerWidth_ratio_2_15'] = df['KeltnerWidth_2'] / df['KeltnerWidth_15']\n",
"/var/folders/l_/j4rfg9014yvd4jfvv441w26r0000gn/T/ipykernel_54914/2351237557.py:7: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" df['KeltnerWidth_ratio_15_24'] = df['KeltnerWidth_15'] / df['KeltnerWidth_24']\n",
"/var/folders/l_/j4rfg9014yvd4jfvv441w26r0000gn/T/ipykernel_54914/2351237557.py:8: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" df['KeltnerWidth_ratio_2_24'] = df['KeltnerWidth_2'] / df['KeltnerWidth_24']\n"
]
}
],
"source": [
"df['CMO_diff_12'] = df['CMO_14'] - df['ADX_2']\n",
"df['CMO_ratio_2_14'] = df['CCI_2'] / df['CCI_14']\n",
"df['KeltnerWidth_diff_13'] = df['KeltnerWidth_15'] - df['KeltnerWidth_2']\n",
"df['KeltnerWidth_diff_22'] = df['KeltnerWidth_24'] - df['KeltnerWidth_2']\n",
"df['KeltnerWidth_diff_9'] = df['KeltnerWidth_24'] - df['KeltnerWidth_15']\n",
"df['KeltnerWidth_ratio_2_15'] = df['KeltnerWidth_2'] / df['KeltnerWidth_15']\n",
"df['KeltnerWidth_ratio_15_24'] = df['KeltnerWidth_15'] / df['KeltnerWidth_24']\n",
"df['KeltnerWidth_ratio_2_24'] = df['KeltnerWidth_2'] / df['KeltnerWidth_24']"
]
},
{
"cell_type": "code",
"execution_count": 125,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 444
},
"id": "G8zjp6uf_deD",
"outputId": "3f1b4738-fc34-45b9-ce55-cbc6d71052ed"
},
"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>Open</th>\n",
" <th>High</th>\n",
" <th>Low</th>\n",
" <th>Close</th>\n",
" <th>Volume</th>\n",
" <th>Session</th>\n",
" <th>Quarter</th>\n",
" <th>Month_sin</th>\n",
" <th>Hour_sin</th>\n",
" <th>Date_sin</th>\n",
" <th>...</th>\n",
" <th>CCI_ratio_8_20</th>\n",
" <th>CCI_ratio_2_20</th>\n",
" <th>CMO_diff_12</th>\n",
" <th>CMO_ratio_2_14</th>\n",
" <th>KeltnerWidth_diff_13</th>\n",
" <th>KeltnerWidth_diff_22</th>\n",
" <th>KeltnerWidth_diff_9</th>\n",
" <th>KeltnerWidth_ratio_2_15</th>\n",
" <th>KeltnerWidth_ratio_15_24</th>\n",
" <th>KeltnerWidth_ratio_2_24</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>1.34880</td>\n",
" <td>1.34960</td>\n",
" <td>1.34820</td>\n",
" <td>1.34860</td>\n",
" <td>331.0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0.5</td>\n",
" <td>0.96593</td>\n",
" <td>0.74314</td>\n",
" <td>...</td>\n",
" <td>-32.989639</td>\n",
" <td>-17.057143</td>\n",
" <td>-82.813262</td>\n",
" <td>0.470803</td>\n",
" <td>0.000697</td>\n",
" <td>0.001281</td>\n",
" <td>0.000583</td>\n",
" <td>0.609548</td>\n",
" <td>0.753818</td>\n",
" <td>0.459488</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>1.34840</td>\n",
" <td>1.34970</td>\n",
" <td>1.34810</td>\n",
" <td>1.34910</td>\n",
" <td>324.0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0.5</td>\n",
" <td>1.00000</td>\n",
" <td>0.74314</td>\n",
" <td>...</td>\n",
" <td>10.711532</td>\n",
" <td>7.213889</td>\n",
" <td>-86.398829</td>\n",
" <td>0.511278</td>\n",
" <td>0.000558</td>\n",
" <td>0.001121</td>\n",
" <td>0.000564</td>\n",
" <td>0.674174</td>\n",
" <td>0.752199</td>\n",
" <td>0.507113</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>1.34920</td>\n",
" <td>1.34950</td>\n",
" <td>1.34590</td>\n",
" <td>1.34600</td>\n",
" <td>345.0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0.5</td>\n",
" <td>0.96593</td>\n",
" <td>0.74314</td>\n",
" <td>...</td>\n",
" <td>0.617531</td>\n",
" <td>1.143775</td>\n",
" <td>-99.281747</td>\n",
" <td>1.652174</td>\n",
" <td>-0.000335</td>\n",
" <td>0.000140</td>\n",
" <td>0.000475</td>\n",
" <td>1.183191</td>\n",
" <td>0.793926</td>\n",
" <td>0.939366</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>1.34610</td>\n",
" <td>1.34760</td>\n",
" <td>1.34420</td>\n",
" <td>1.34680</td>\n",
" <td>462.0</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>0.5</td>\n",
" <td>0.86603</td>\n",
" <td>0.74314</td>\n",
" <td>...</td>\n",
" <td>0.902003</td>\n",
" <td>0.639500</td>\n",
" <td>-100.097442</td>\n",
" <td>0.608775</td>\n",
" <td>-0.000487</td>\n",
" <td>-0.000081</td>\n",
" <td>0.000406</td>\n",
" <td>1.254089</td>\n",
" <td>0.825234</td>\n",
" <td>1.034918</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td>1.34690</td>\n",
" <td>1.34720</td>\n",
" <td>1.34110</td>\n",
" <td>1.34210</td>\n",
" <td>512.0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0.5</td>\n",
" <td>0.70711</td>\n",
" <td>0.74314</td>\n",
" <td>...</td>\n",
" <td>0.755647</td>\n",
" <td>0.264265</td>\n",
" <td>-123.234395</td>\n",
" <td>0.279337</td>\n",
" <td>-0.001585</td>\n",
" <td>-0.001329</td>\n",
" <td>0.000256</td>\n",
" <td>1.705978</td>\n",
" <td>0.897678</td>\n",
" <td>1.531419</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>118388</th>\n",
" <td>1.03502</td>\n",
" <td>1.03548</td>\n",
" <td>1.03438</td>\n",
" <td>1.03490</td>\n",
" <td>2323.0</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>-0.0</td>\n",
" <td>-0.96593</td>\n",
" <td>0.20791</td>\n",
" <td>...</td>\n",
" <td>0.499089</td>\n",
" <td>0.306752</td>\n",
" <td>-146.708669</td>\n",
" <td>0.415789</td>\n",
" <td>0.000175</td>\n",
" <td>0.000087</td>\n",
" <td>-0.000087</td>\n",
" <td>0.885021</td>\n",
" <td>1.060976</td>\n",
" <td>0.938986</td>\n",
" </tr>\n",
" <tr>\n",
" <th>118389</th>\n",
" <td>1.03489</td>\n",
" <td>1.03566</td>\n",
" <td>1.03455</td>\n",
" <td>1.03529</td>\n",
" <td>1900.0</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>-0.0</td>\n",
" <td>-0.86603</td>\n",
" <td>0.20791</td>\n",
" <td>...</td>\n",
" <td>0.474172</td>\n",
" <td>-0.412832</td>\n",
" <td>-120.580881</td>\n",
" <td>-0.558554</td>\n",
" <td>0.000301</td>\n",
" <td>0.000241</td>\n",
" <td>-0.000060</td>\n",
" <td>0.794353</td>\n",
" <td>1.042922</td>\n",
" <td>0.828449</td>\n",
" </tr>\n",
" <tr>\n",
" <th>118390</th>\n",
" <td>1.03526</td>\n",
" <td>1.03645</td>\n",
" <td>1.03516</td>\n",
" <td>1.03547</td>\n",
" <td>1445.0</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>-0.0</td>\n",
" <td>-0.70711</td>\n",
" <td>0.20791</td>\n",
" <td>...</td>\n",
" <td>0.389379</td>\n",
" <td>-0.561173</td>\n",
" <td>-101.534169</td>\n",
" <td>-0.781851</td>\n",
" <td>0.000219</td>\n",
" <td>0.000173</td>\n",
" <td>-0.000046</td>\n",
" <td>0.847724</td>\n",
" <td>1.032793</td>\n",
" <td>0.875523</td>\n",
" </tr>\n",
" <tr>\n",
" <th>118391</th>\n",
" <td>1.03547</td>\n",
" <td>1.03631</td>\n",
" <td>1.03544</td>\n",
" <td>1.03582</td>\n",
" <td>1208.0</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>-0.0</td>\n",
" <td>-0.50000</td>\n",
" <td>0.20791</td>\n",
" <td>...</td>\n",
" <td>0.274403</td>\n",
" <td>-0.681386</td>\n",
" <td>-87.924347</td>\n",
" <td>-0.965948</td>\n",
" <td>0.000397</td>\n",
" <td>0.000381</td>\n",
" <td>-0.000015</td>\n",
" <td>0.708939</td>\n",
" <td>1.011219</td>\n",
" <td>0.716892</td>\n",
" </tr>\n",
" <tr>\n",
" <th>118392</th>\n",
" <td>1.03585</td>\n",
" <td>1.03608</td>\n",
" <td>1.03489</td>\n",
" <td>1.03493</td>\n",
" <td>616.0</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>-0.0</td>\n",
" <td>-0.25882</td>\n",
" <td>0.20791</td>\n",
" <td>...</td>\n",
" <td>0.508487</td>\n",
" <td>0.671512</td>\n",
" <td>-91.887164</td>\n",
" <td>0.912385</td>\n",
" <td>0.000248</td>\n",
" <td>0.000243</td>\n",
" <td>-0.000004</td>\n",
" <td>0.814725</td>\n",
" <td>1.003260</td>\n",
" <td>0.817381</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>112986 rows × 54 columns</p>\n",
"</div>"
],
"text/plain": [
" Open High Low Close Volume Session Quarter \\\n",
"28 1.34880 1.34960 1.34820 1.34860 331.0 1 1 \n",
"29 1.34840 1.34970 1.34810 1.34910 324.0 1 1 \n",
"30 1.34920 1.34950 1.34590 1.34600 345.0 1 1 \n",
"31 1.34610 1.34760 1.34420 1.34680 462.0 2 1 \n",
"32 1.34690 1.34720 1.34110 1.34210 512.0 1 1 \n",
"... ... ... ... ... ... ... ... \n",
"118388 1.03502 1.03548 1.03438 1.03490 2323.0 1 4 \n",
"118389 1.03489 1.03566 1.03455 1.03529 1900.0 1 4 \n",
"118390 1.03526 1.03645 1.03516 1.03547 1445.0 1 4 \n",
"118391 1.03547 1.03631 1.03544 1.03582 1208.0 0 4 \n",
"118392 1.03585 1.03608 1.03489 1.03493 616.0 0 4 \n",
"\n",
" Month_sin Hour_sin Date_sin ... CCI_ratio_8_20 CCI_ratio_2_20 \\\n",
"28 0.5 0.96593 0.74314 ... -32.989639 -17.057143 \n",
"29 0.5 1.00000 0.74314 ... 10.711532 7.213889 \n",
"30 0.5 0.96593 0.74314 ... 0.617531 1.143775 \n",
"31 0.5 0.86603 0.74314 ... 0.902003 0.639500 \n",
"32 0.5 0.70711 0.74314 ... 0.755647 0.264265 \n",
"... ... ... ... ... ... ... \n",
"118388 -0.0 -0.96593 0.20791 ... 0.499089 0.306752 \n",
"118389 -0.0 -0.86603 0.20791 ... 0.474172 -0.412832 \n",
"118390 -0.0 -0.70711 0.20791 ... 0.389379 -0.561173 \n",
"118391 -0.0 -0.50000 0.20791 ... 0.274403 -0.681386 \n",
"118392 -0.0 -0.25882 0.20791 ... 0.508487 0.671512 \n",
"\n",
" CMO_diff_12 CMO_ratio_2_14 KeltnerWidth_diff_13 \\\n",
"28 -82.813262 0.470803 0.000697 \n",
"29 -86.398829 0.511278 0.000558 \n",
"30 -99.281747 1.652174 -0.000335 \n",
"31 -100.097442 0.608775 -0.000487 \n",
"32 -123.234395 0.279337 -0.001585 \n",
"... ... ... ... \n",
"118388 -146.708669 0.415789 0.000175 \n",
"118389 -120.580881 -0.558554 0.000301 \n",
"118390 -101.534169 -0.781851 0.000219 \n",
"118391 -87.924347 -0.965948 0.000397 \n",
"118392 -91.887164 0.912385 0.000248 \n",
"\n",
" KeltnerWidth_diff_22 KeltnerWidth_diff_9 KeltnerWidth_ratio_2_15 \\\n",
"28 0.001281 0.000583 0.609548 \n",
"29 0.001121 0.000564 0.674174 \n",
"30 0.000140 0.000475 1.183191 \n",
"31 -0.000081 0.000406 1.254089 \n",
"32 -0.001329 0.000256 1.705978 \n",
"... ... ... ... \n",
"118388 0.000087 -0.000087 0.885021 \n",
"118389 0.000241 -0.000060 0.794353 \n",
"118390 0.000173 -0.000046 0.847724 \n",
"118391 0.000381 -0.000015 0.708939 \n",
"118392 0.000243 -0.000004 0.814725 \n",
"\n",
" KeltnerWidth_ratio_15_24 KeltnerWidth_ratio_2_24 \n",
"28 0.753818 0.459488 \n",
"29 0.752199 0.507113 \n",
"30 0.793926 0.939366 \n",
"31 0.825234 1.034918 \n",
"32 0.897678 1.531419 \n",
"... ... ... \n",
"118388 1.060976 0.938986 \n",
"118389 1.042922 0.828449 \n",
"118390 1.032793 0.875523 \n",
"118391 1.011219 0.716892 \n",
"118392 1.003260 0.817381 \n",
"\n",
"[112986 rows x 54 columns]"
]
},
"execution_count": 125,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.dropna()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "c4OdjOyWBoj8"
},
"source": [
"# Current Features Correlation Heat Map along with Pearson Clustered Feature Correlation\n",
"\n",
"* It can be seen that most features are uncorrleated and even the clustering shows that there are some cross features that are show correlation. We will filter among these after finding feature after finding feature importance in later stages"
]
},
{
"cell_type": "code",
"execution_count": 126,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "WdT8GMFj_-vR",
"outputId": "072f5126-2b33-4320-c102-5e4461cfe3f8"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/var/folders/l_/j4rfg9014yvd4jfvv441w26r0000gn/T/ipykernel_54914/1159260896.py:31: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
" corr = df.corr(method='pearson')\n"
]
},
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 1400x1200 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 1400x1400 with 4 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"\n",
"# --- 1) List of features you provided ---\n",
"features = [\n",
" 'Session', 'Quarter', 'Month_sin', 'Hour_sin', 'Date_sin', 'DOW_sin',\n",
" 'Diff_10Y_2Y', 'Oil_Volume', 'Oil_Close', 'Gold_Close',\n",
" 'ADX_14', 'CCI_14', 'PPO_12_26', 'CMO_14', 'ChaikinVol_3_10', 'KeltnerWidth_20',\n",
" 'ADX_2', 'ADX_5', 'CCI_2', 'CCI_8', 'CCI_20', 'PPO_2_6', 'CMO_2', 'CMO54',\n",
" 'ChaikinVol_2_5', 'KeltnerWidth_2', 'KeltnerWidth_15', 'KeltnerWidth_24',\n",
" 'ADX_diff_3', 'ADX_diff_9', 'ADX_diff_12', 'ADX_ratio_2_5', 'ADX_ratio_5_14', 'ADX_ratio_2_14',\n",
" 'CCI_diff_6', 'CCI_diff_12', 'CCI_diff_18', 'CCI_ratio_2_8', 'CCI_ratio_8_20', 'CCI_ratio_2_20',\n",
" 'CMO_diff_12', 'CMO_ratio_2_14',\n",
" 'KeltnerWidth_diff_13', 'KeltnerWidth_diff_22', 'KeltnerWidth_diff_9',\n",
" 'KeltnerWidth_ratio_2_15', 'KeltnerWidth_ratio_15_24', 'KeltnerWidth_ratio_2_24'\n",
"]\n",
"\n",
"# --- 2) Keep only columns that exist in df ---\n",
"features = [c for c in features if c in df.columns]\n",
"\n",
"# --- 3) Coerce to numeric (if any are strings) & drop non-numeric/constant cols ---\n",
"df_feat = df[features].apply(pd.to_numeric, errors='coerce')\n",
"\n",
"# # Drop columns that are all NaN or constant (std == 0)\n",
"non_constant = df_feat.columns[(df_feat.nunique(dropna=True) > 1)]\n",
"df_feat = df_feat[non_constant].dropna(how='all')\n",
"\n",
"# --- 4) Correlation matrix ---\n",
"corr = df.corr(method='pearson')\n",
"\n",
"# --- 5) Pretty heatmap (masked upper triangle for readability) ---\n",
"mask = np.triu(np.ones_like(corr, dtype=bool))\n",
"plt.figure(figsize=(14, 12))\n",
"sns.heatmap(\n",
" corr, mask=mask, annot=False, fmt=\".2f\",\n",
" cmap=\"coolwarm\", vmin=-1, vmax=1,\n",
" square=True, linewidths=0.5, cbar_kws={\"shrink\": 0.8}\n",
")\n",
"plt.title(\"Feature Correlation Heatmap (Pearson)\")\n",
"plt.tight_layout()\n",
"plt.show()\n",
"\n",
"# --- 6) (Optional) Clustered heatmap to group similar features ---\n",
"# Comment out if you don't want it\n",
"sns.clustermap(\n",
" corr.fillna(0), cmap=\"coolwarm\", vmin=-1, vmax=1,\n",
" figsize=(14, 14), linewidths=0.3, annot=False,\n",
" cbar_kws={\"shrink\": 0.8}\n",
")\n",
"plt.suptitle(\"Clustered Feature Correlation (Pearson)\", y=1.02)\n",
"plt.show()\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "SyWvRmwZPS6A"
},
"source": [
"# Underlying Trading Strategy Signal Generation as a Feature\n",
"\n",
"* First signals will be generated then based on PnL, we will prepare the Target signal feature for the machine such that it understands the good and bad trades and gives out its own signal.\n",
"* To do this we will label the trades using PnL such that trades with return greater than 0.1% will be label as Profit and trades with return less than -0.1% will be labeld as loss and trades with return between -0.1% and 0.1% will be labeled neutral.\n",
"* So during profit label, the target signal variable will not change the signal. During loss label, the target signal variable will be reversed and during neutral label, the signal will be 0 to not do anything."
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "KyO7p78wby5Y",
"outputId": "34d4b0af-2d34-41a3-f611-395da6a011e9"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/var/folders/l_/j4rfg9014yvd4jfvv441w26r0000gn/T/ipykernel_16708/2837943448.py:15: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" df[col] = pd.to_numeric(df[col], errors=\"coerce\")\n"
]
}
],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"# ---------------- Parameters (your specification) ----------------\n",
"EMA_WIN = 500 # rolling EMA window\n",
"SMA_WIN = 2000 # rolling SMA window\n",
"\n",
"TP_PCT = 0.005 # 1% take-profit (applies to both long & short)\n",
"SL_PCT = 0.0 # stop-loss (0 disables SL). Set >0 if you want an SL.\n",
"MAX_HOLD_LONG = 240 # bars to force-close long (time-based exit)\n",
"MAX_HOLD_SHORT = 240 # bars to force-close short (time-based exit)\n",
"\n",
"# ---------------- Pre-req: df must exist with Open, High, Low, Close ----------------\n",
"for col in [\"Open\", \"High\", \"Low\", \"Close\"]:\n",
" df[col] = pd.to_numeric(df[col], errors=\"coerce\")\n",
"df = df.dropna(subset=[\"Open\", \"High\", \"Low\", \"Close\"]).copy()\n",
"n = len(df)\n",
"\n",
"# ---------------- Indicators (no lookahead) ----------------\n",
"# EMA aligned to current bar, SMA aligned to current bar\n",
"df[\"ema\"] = df[\"Close\"].ewm(span=EMA_WIN, adjust=False, min_periods=EMA_WIN).mean()\n",
"df[\"sma\"] = df[\"Close\"].rolling(window=SMA_WIN, min_periods=SMA_WIN).mean()\n",
"\n",
"# ---------------- schedule/action arrays ----------------\n",
"# Action codes:\n",
"# 0 none, 1=open long (OL), 2=close long (CL), 3=open short (OS), 4=close short (CS)\n",
"act = np.zeros(n, dtype=np.int8)\n",
"\n",
"def action_to_signal(code: int) -> int:\n",
" \"\"\"Map internal action codes to single-column signals:\n",
" 1 or 4 -> +1 (buy), 2 or 3 -> -1 (sell)\n",
" \"\"\"\n",
" if code in (1, 4):\n",
" return +1\n",
" if code in (2, 3):\n",
" return -1\n",
" return 0\n",
"\n",
"def place_action(idx: int, code: int, prefer_close=True):\n",
" \"\"\"Place action at idx or push forward to next free bar.\n",
" If prefer_close and placing a close onto an existing open,\n",
" swap and push the open forward (so closes override opens).\n",
" \"\"\"\n",
" if idx >= n:\n",
" return\n",
" j = idx\n",
" while j < n:\n",
" if act[j] == 0:\n",
" act[j] = code\n",
" return\n",
" if prefer_close and code in (2, 4) and act[j] in (1, 3):\n",
" existing = act[j]\n",
" act[j] = code\n",
" j2 = j + 1\n",
" while j2 < n and act[j2] != 0:\n",
" j2 += 1\n",
" if j2 < n:\n",
" act[j2] = existing\n",
" return\n",
" j += 1\n",
" # ran out of room -> dropped\n",
"\n",
"# ---------------- convenience arrays ----------------\n",
"O = df[\"Open\"].to_numpy()\n",
"H = df[\"High\"].to_numpy()\n",
"L = df[\"Low\"].to_numpy()\n",
"C = df[\"Close\"].to_numpy()\n",
"EMA = df[\"ema\"].to_numpy()\n",
"SMA = df[\"sma\"].to_numpy()\n",
"\n",
"# ---------------- position state ----------------\n",
"long_open = False\n",
"long_entry_idx = None\n",
"long_entry_px = None\n",
"long_close_pending = False\n",
"\n",
"short_open = False\n",
"short_entry_idx = None\n",
"short_entry_px = None\n",
"short_close_pending = False\n",
"\n",
"# ---------------- Walk bars ----------------\n",
"for i in range(n):\n",
" # 0) Execute scheduled action for THIS bar\n",
" code = int(act[i])\n",
" if code == 1: # open long\n",
" long_open = True\n",
" long_entry_idx = i\n",
" long_entry_px = C[i]\n",
" long_close_pending = False\n",
" elif code == 2: # close long\n",
" long_open = False\n",
" long_entry_idx = None\n",
" long_entry_px = None\n",
" long_close_pending = False\n",
" elif code == 3: # open short\n",
" short_open = True\n",
" short_entry_idx = i\n",
" short_entry_px = C[i]\n",
" short_close_pending = False\n",
" elif code == 4: # close short\n",
" short_open = False\n",
" short_entry_idx = None\n",
" short_entry_px = None\n",
" short_close_pending = False\n",
"\n",
" # 1) Manage LONG exits: TP / SL (if enabled) / time-based -> schedule close NEXT bar\n",
" if long_open and not long_close_pending and long_entry_px is not None:\n",
" tp = long_entry_px * (1.0 + TP_PCT)\n",
" sl = long_entry_px * (1.0 - SL_PCT) if SL_PCT > 0 else None\n",
" hit_tp = H[i] >= tp\n",
" hit_sl = (L[i] <= sl) if (SL_PCT > 0) else False\n",
" timeup = (MAX_HOLD_LONG > 0) and (i - long_entry_idx >= MAX_HOLD_LONG)\n",
"\n",
" # conservative intrabar rule: if both touched, treat as SL (worse case)\n",
" if hit_tp and hit_sl:\n",
" hit_tp = False\n",
" hit_sl = True\n",
"\n",
" if hit_tp or hit_sl or timeup:\n",
" place_action(i + 1, 2, prefer_close=True) # close long next bar\n",
" long_close_pending = True\n",
"\n",
" # 2) Manage SHORT exits: TP / SL (if enabled) / time-based -> schedule close NEXT bar\n",
" if short_open and not short_close_pending and short_entry_px is not None:\n",
" tp_s = short_entry_px * (1.0 - TP_PCT) # for short, TP is lower price\n",
" sl_s = short_entry_px * (1.0 + SL_PCT) if SL_PCT > 0 else None\n",
" hit_tp_s = L[i] <= tp_s\n",
" hit_sl_s = (H[i] >= sl_s) if (SL_PCT > 0) else False\n",
" timeup_s = (MAX_HOLD_SHORT > 0) and (i - short_entry_idx >= MAX_HOLD_SHORT)\n",
"\n",
" # conservative intrabar rule: if both touched, treat as SL (worse case)\n",
" if hit_tp_s and hit_sl_s:\n",
" hit_tp_s = False\n",
" hit_sl_s = True\n",
"\n",
" if hit_tp_s or hit_sl_s or timeup_s:\n",
" place_action(i + 1, 4, prefer_close=True) # close short next bar\n",
" short_close_pending = True\n",
"\n",
" # 3) Entry logic (based on indicator cross; place entry for NEXT bar)\n",
" # Only open if flat (no long and no short currently open) and there is room\n",
" if (not long_open) and (not short_open) and (i + 1 < n):\n",
" # require both indicators to be not-NaN for comparison\n",
" if not np.isnan(EMA[i]) and not np.isnan(SMA[i]):\n",
" if EMA[i] > SMA[i]:\n",
" place_action(i + 1, 1, prefer_close=False) # open long next bar\n",
" elif SMA[i] > EMA[i]:\n",
" place_action(i + 1, 3, prefer_close=False) # open short next bar\n",
" # if equal -> no action\n",
"\n",
"# 4) Force-close any leftover positions at the end (close on last bar)\n",
"# If a close was already scheduled beyond last bar it was dropped; so ensure we close at last bar\n",
"if long_open:\n",
" place_action(n - 1, 2, prefer_close=True)\n",
"if short_open:\n",
" place_action(n - 1, 4, prefer_close=True)\n",
"\n",
"# 5) Build single-column signal from actions\n",
"df[\"signal\"] = np.fromiter((action_to_signal(x) for x in act), dtype=int, count=n)\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"id": "wds1reib6iYp"
},
"outputs": [],
"source": [
"import numpy as np\n",
"\n",
"# Ensure the column exists\n",
"df['pnl'] = np.nan\n",
"\n",
"pos = 0 # 0 = flat, +1 = long, -1 = short\n",
"entry_i = None\n",
"entry_px = None\n",
"\n",
"# (optional) keep a trade id to inspect later\n",
"trade_id = np.full(len(df), np.nan, dtype=float)\n",
"tid = 0\n",
"\n",
"sig = df['signal'].to_numpy()\n",
"px = df['Close'].to_numpy()\n",
"\n",
"for i in range(len(df)):\n",
" s = sig[i]\n",
"\n",
" if pos == 0:\n",
" if s == 1: # open long\n",
" pos = 1\n",
" entry_i = i\n",
" entry_px = px[i]\n",
" tid += 1\n",
" elif s == -1: # open short\n",
" pos = -1\n",
" entry_i = i\n",
" entry_px = px[i]\n",
" tid += 1\n",
"\n",
" elif pos == 1:\n",
" if s == -1: # close long\n",
" pnl = ((px[i] - entry_px) / entry_px)*100\n",
" df.iloc[entry_i:i+1, df.columns.get_loc('pnl')] = pnl\n",
" trade_id[entry_i:i+1] = tid\n",
" pos = 0\n",
" entry_i = entry_px = None\n",
"\n",
" elif pos == -1:\n",
" if s == 1: # close short (using your same formula)\n",
" pnl = ((-px[i] +entry_px) / entry_px)*100\n",
" df.iloc[entry_i:i+1, df.columns.get_loc('pnl')] = pnl\n",
" trade_id[entry_i:i+1] = tid\n",
" pos = 0\n",
" entry_i = entry_px = None\n",
"\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"id": "9jyhLNi1-Nug"
},
"outputs": [],
"source": [
"\n",
"\n",
"df['pnl_label'] = np.where(df['pnl'] > 0.1, 'Profit',np.where(df['pnl'] < -0.1, 'Loss', 'Neutral'))\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "FOXl4QunjFgY"
},
"source": [
"## Target Signal Creation based on the signal and pnl of the underlying strategy"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"id": "7Za0BDCYTzqO"
},
"outputs": [],
"source": [
"import numpy as np\n",
"\n",
"# target_signal rules:\n",
"# - 'Neutral' -> 0\n",
"# - 'Loss' -> reverse sign\n",
"# - 'Profit' -> keep as is\n",
"df['target_signal'] = np.select(\n",
" [\n",
" df['pnl_label'].eq('Neutral'),\n",
" df['pnl_label'].eq('Loss')\n",
" ],\n",
" [\n",
" 0,\n",
" -df['signal']\n",
" ],\n",
" default=df['signal']\n",
").astype(int)\n"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"id": "RpEyiY6Ni7QO"
},
"outputs": [],
"source": [
"df = df.drop(columns=['pnl', 'trade_id', 'pnl_label'])"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"id": "CFdQFcb8lIL3"
},
"outputs": [],
"source": [
"df = df.iloc[2000:].reset_index(drop=True)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# 70% train, 30% test (time-order safe, no shuffling)\n",
"split_idx = int(len(df) * 0.7)\n",
"\n",
"train_df = df.iloc[:split_idx].copy()\n",
"test_df = df.iloc[split_idx:].copy()\n",
"\n",
"test_df = test_df.drop(columns=[\"target_signal\"])"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "OG3V9ZArkU1J"
},
"source": [
"# Final Feature Selection by importance and theory using random forest\n",
"\n",
"* In order to avoid any kind of look-ahead bias, I have split the df to 2 parts, one for training with 70% and the other to test with 30% and named them train_data and test_data.\n",
"* In the testing, we have further removed target_signal column also."
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"id": "waCH26Xpr4vD"
},
"outputs": [],
"source": [
"train_df.fillna(0, inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [],
"source": [
"# Remove rows where all columns are 0\n",
"train_df = train_df.loc[~(train_df.eq(0).all(axis=1))].copy()\n"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"id": "8djjW2uWkUjy"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Top features by OUT-OF-SAMPLE permutation importance (mean across folds):\n",
"\n"
]
},
{
"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>model_importance_mean</th>\n",
" <th>model_importance_std</th>\n",
" <th>perm_importance_mean</th>\n",
" <th>perm_importance_std</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>signal</th>\n",
" <td>0.306649</td>\n",
" <td>0.042522</td>\n",
" <td>0.011702</td>\n",
" <td>0.002480</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CMO_14</th>\n",
" <td>0.028196</td>\n",
" <td>0.000615</td>\n",
" <td>0.001137</td>\n",
" <td>0.000429</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ADX_5</th>\n",
" <td>0.028298</td>\n",
" <td>0.001057</td>\n",
" <td>0.000819</td>\n",
" <td>0.000296</td>\n",
" </tr>\n",
" <tr>\n",
" <th>PPO_2_6</th>\n",
" <td>0.027918</td>\n",
" <td>0.000813</td>\n",
" <td>0.000595</td>\n",
" <td>0.000526</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CCI_ratio_2_20</th>\n",
" <td>0.016868</td>\n",
" <td>0.002119</td>\n",
" <td>0.000582</td>\n",
" <td>0.000256</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CCI_20</th>\n",
" <td>0.022840</td>\n",
" <td>0.001432</td>\n",
" <td>0.000391</td>\n",
" <td>0.000319</td>\n",
" </tr>\n",
" <tr>\n",
" <th>KeltnerWidth_ratio_15_24</th>\n",
" <td>0.020487</td>\n",
" <td>0.000306</td>\n",
" <td>0.000365</td>\n",
" <td>0.000174</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CMO_ratio_2_14</th>\n",
" <td>0.015399</td>\n",
" <td>0.002409</td>\n",
" <td>0.000341</td>\n",
" <td>0.000282</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CCI_ratio_8_20</th>\n",
" <td>0.015631</td>\n",
" <td>0.001033</td>\n",
" <td>0.000318</td>\n",
" <td>0.000115</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CCI_diff_12</th>\n",
" <td>0.012387</td>\n",
" <td>0.001230</td>\n",
" <td>0.000307</td>\n",
" <td>0.000082</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CCI_diff_18</th>\n",
" <td>0.015434</td>\n",
" <td>0.001561</td>\n",
" <td>0.000304</td>\n",
" <td>0.000150</td>\n",
" </tr>\n",
" <tr>\n",
" <th>KeltnerWidth_diff_9</th>\n",
" <td>0.021018</td>\n",
" <td>0.001042</td>\n",
" <td>0.000277</td>\n",
" <td>0.000094</td>\n",
" </tr>\n",
" <tr>\n",
" <th>PPO_12_26</th>\n",
" <td>0.014241</td>\n",
" <td>0.000926</td>\n",
" <td>0.000264</td>\n",
" <td>0.000109</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CCI_8</th>\n",
" <td>0.019527</td>\n",
" <td>0.000839</td>\n",
" <td>0.000193</td>\n",
" <td>0.000339</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CCI_14</th>\n",
" <td>0.020120</td>\n",
" <td>0.002310</td>\n",
" <td>0.000188</td>\n",
" <td>0.000213</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CMO_diff_12</th>\n",
" <td>0.015694</td>\n",
" <td>0.001227</td>\n",
" <td>0.000171</td>\n",
" <td>0.000283</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ADX_ratio_2_5</th>\n",
" <td>0.014738</td>\n",
" <td>0.002739</td>\n",
" <td>0.000170</td>\n",
" <td>0.000203</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ADX_14</th>\n",
" <td>0.013390</td>\n",
" <td>0.000597</td>\n",
" <td>0.000154</td>\n",
" <td>0.000059</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CCI_diff_6</th>\n",
" <td>0.013123</td>\n",
" <td>0.001673</td>\n",
" <td>0.000154</td>\n",
" <td>0.000133</td>\n",
" </tr>\n",
" <tr>\n",
" <th>KeltnerWidth_15</th>\n",
" <td>0.012065</td>\n",
" <td>0.001431</td>\n",
" <td>0.000144</td>\n",
" <td>0.000094</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CMO54</th>\n",
" <td>0.014536</td>\n",
" <td>0.000912</td>\n",
" <td>0.000120</td>\n",
" <td>0.000225</td>\n",
" </tr>\n",
" <tr>\n",
" <th>KeltnerWidth_24</th>\n",
" <td>0.010461</td>\n",
" <td>0.001092</td>\n",
" <td>0.000073</td>\n",
" <td>0.000060</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CCI_ratio_2_8</th>\n",
" <td>0.012882</td>\n",
" <td>0.001430</td>\n",
" <td>0.000070</td>\n",
" <td>0.000183</td>\n",
" </tr>\n",
" <tr>\n",
" <th>KeltnerWidth_2</th>\n",
" <td>0.015248</td>\n",
" <td>0.001092</td>\n",
" <td>0.000059</td>\n",
" <td>0.000102</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ADX_2</th>\n",
" <td>0.015177</td>\n",
" <td>0.003281</td>\n",
" <td>0.000051</td>\n",
" <td>0.000124</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" model_importance_mean model_importance_std \\\n",
"signal 0.306649 0.042522 \n",
"CMO_14 0.028196 0.000615 \n",
"ADX_5 0.028298 0.001057 \n",
"PPO_2_6 0.027918 0.000813 \n",
"CCI_ratio_2_20 0.016868 0.002119 \n",
"CCI_20 0.022840 0.001432 \n",
"KeltnerWidth_ratio_15_24 0.020487 0.000306 \n",
"CMO_ratio_2_14 0.015399 0.002409 \n",
"CCI_ratio_8_20 0.015631 0.001033 \n",
"CCI_diff_12 0.012387 0.001230 \n",
"CCI_diff_18 0.015434 0.001561 \n",
"KeltnerWidth_diff_9 0.021018 0.001042 \n",
"PPO_12_26 0.014241 0.000926 \n",
"CCI_8 0.019527 0.000839 \n",
"CCI_14 0.020120 0.002310 \n",
"CMO_diff_12 0.015694 0.001227 \n",
"ADX_ratio_2_5 0.014738 0.002739 \n",
"ADX_14 0.013390 0.000597 \n",
"CCI_diff_6 0.013123 0.001673 \n",
"KeltnerWidth_15 0.012065 0.001431 \n",
"CMO54 0.014536 0.000912 \n",
"KeltnerWidth_24 0.010461 0.001092 \n",
"CCI_ratio_2_8 0.012882 0.001430 \n",
"KeltnerWidth_2 0.015248 0.001092 \n",
"ADX_2 0.015177 0.003281 \n",
"\n",
" perm_importance_mean perm_importance_std \n",
"signal 0.011702 0.002480 \n",
"CMO_14 0.001137 0.000429 \n",
"ADX_5 0.000819 0.000296 \n",
"PPO_2_6 0.000595 0.000526 \n",
"CCI_ratio_2_20 0.000582 0.000256 \n",
"CCI_20 0.000391 0.000319 \n",
"KeltnerWidth_ratio_15_24 0.000365 0.000174 \n",
"CMO_ratio_2_14 0.000341 0.000282 \n",
"CCI_ratio_8_20 0.000318 0.000115 \n",
"CCI_diff_12 0.000307 0.000082 \n",
"CCI_diff_18 0.000304 0.000150 \n",
"KeltnerWidth_diff_9 0.000277 0.000094 \n",
"PPO_12_26 0.000264 0.000109 \n",
"CCI_8 0.000193 0.000339 \n",
"CCI_14 0.000188 0.000213 \n",
"CMO_diff_12 0.000171 0.000283 \n",
"ADX_ratio_2_5 0.000170 0.000203 \n",
"ADX_14 0.000154 0.000059 \n",
"CCI_diff_6 0.000154 0.000133 \n",
"KeltnerWidth_15 0.000144 0.000094 \n",
"CMO54 0.000120 0.000225 \n",
"KeltnerWidth_24 0.000073 0.000060 \n",
"CCI_ratio_2_8 0.000070 0.000183 \n",
"KeltnerWidth_2 0.000059 0.000102 \n",
"ADX_2 0.000051 0.000124 "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# --- Feature importance for a trading dataset (reproducible, no lookahead) ---\n",
"\n",
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"from sklearn.inspection import permutation_importance\n",
"from sklearn.model_selection import TimeSeriesSplit\n",
"\n",
"# ---------------- 0) Reproducibility ----------------\n",
"SEED = 42\n",
"np.random.seed(SEED)\n",
"\n",
"# ---------------- 1) Prepare features/target ----------------\n",
"df = train_df.copy()\n",
"\n",
"# Ensure target exists and is binary/ternary int-like\n",
"y = pd.to_numeric(df['target_signal'], errors='coerce').astype('Int64')\n",
"\n",
"# X: all columns except the target. (You said all others are features.)\n",
"X = df.drop(columns=['target_signal']).copy()\n",
"\n",
"# Keep only numeric columns (coerce any stray objects to numeric without peeking ahead)\n",
"for col in X.columns:\n",
" if not pd.api.types.is_numeric_dtype(X[col]):\n",
" X[col] = pd.to_numeric(X[col], errors='coerce')\n",
"\n",
"# Replace inf with NaN, then forward-fill ONLY (uses past info => no lookahead).\n",
"# Drop remaining NaNs (typically at the very start where ffill has nothing to fill).\n",
"X = X.replace([np.inf, -np.inf], np.nan).ffill()\n",
"valid_mask = ~X.isna().any(axis=1) & y.notna()\n",
"X = X.loc[valid_mask]\n",
"y = y.loc[valid_mask].astype(int)\n",
"\n",
"# ---------------- 2) Walk-forward CV with a gap ----------------\n",
"# Use TimeSeriesSplit with a small 'gap' to reduce leakage from overlapping windows.\n",
"n_splits = 5\n",
"gap_bars = 5 # adjust if your target uses future bars that can overlap\n",
"\n",
"tscv = TimeSeriesSplit(n_splits=n_splits, gap=gap_bars)\n",
"\n",
"# Model choice: tree-based (handles scales, interactions). Set random_state for reproducibility.\n",
"rf = RandomForestClassifier(\n",
" n_estimators=600,\n",
" max_depth=None,\n",
" min_samples_leaf=3,\n",
" class_weight=\"balanced_subsample\",\n",
" random_state=SEED,\n",
" n_jobs=-1,\n",
")\n",
"\n",
"# ---------------- 3) Collect importances across folds ----------------\n",
"feat_names = X.columns.to_list()\n",
"all_model_imps = []\n",
"all_perm_imps = []\n",
"\n",
"for fold, (train_idx, test_idx) in enumerate(tscv.split(X), start=1):\n",
" X_tr, y_tr = X.iloc[train_idx], y.iloc[train_idx]\n",
" X_te, y_te = X.iloc[test_idx], y.iloc[test_idx]\n",
"\n",
" # Fit only on past data\n",
" rf.fit(X_tr, y_tr)\n",
"\n",
" # Built-in (in-sample) model importances\n",
" all_model_imps.append(pd.Series(rf.feature_importances_, index=feat_names))\n",
"\n",
" # Out-of-sample permutation importances on the validation fold\n",
" perm = permutation_importance(\n",
" rf, X_te, y_te,\n",
" n_repeats=10,\n",
" random_state=SEED,\n",
" n_jobs=-1,\n",
" scoring=None # default = estimator's score (accuracy). Change if you prefer f1, roc_auc, etc.\n",
" )\n",
" all_perm_imps.append(pd.Series(perm.importances_mean, index=feat_names))\n",
"\n",
"# ---------------- 4) Aggregate & present ----------------\n",
"model_imp_df = pd.concat(all_model_imps, axis=1)\n",
"perm_imp_df = pd.concat(all_perm_imps, axis=1)\n",
"\n",
"model_imp_mean = model_imp_df.mean(axis=1).rename(\"model_importance_mean\")\n",
"model_imp_std = model_imp_df.std(axis=1).rename(\"model_importance_std\")\n",
"\n",
"perm_imp_mean = perm_imp_df.mean(axis=1).rename(\"perm_importance_mean\")\n",
"perm_imp_std = perm_imp_df.std(axis=1).rename(\"perm_importance_std\")\n",
"\n",
"importance = pd.concat([model_imp_mean, model_imp_std, perm_imp_mean, perm_imp_std], axis=1)\n",
"importance = importance.sort_values(\"perm_importance_mean\", ascending=False)\n",
"\n",
"# Show top features (permutation-based is the most trustworthy OOS signal)\n",
"top_k = 25\n",
"print(\"\\nTop features by OUT-OF-SAMPLE permutation importance (mean across folds):\\n\")\n",
"display(importance.head(top_k))\n",
"\n",
"# If you also want the full table:\n",
"importance.to_csv(\"feature_importance_timeseries.csv\")\n"
]
},
{
"cell_type": "code",
"execution_count": 51,
"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>model_importance_mean</th>\n",
" <th>model_importance_std</th>\n",
" <th>perm_importance_mean</th>\n",
" <th>perm_importance_std</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>signal</th>\n",
" <td>0.306649</td>\n",
" <td>0.042522</td>\n",
" <td>1.170155e-02</td>\n",
" <td>0.002480</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ADX_5</th>\n",
" <td>0.028298</td>\n",
" <td>0.001057</td>\n",
" <td>8.187225e-04</td>\n",
" <td>0.000296</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CMO_14</th>\n",
" <td>0.028196</td>\n",
" <td>0.000615</td>\n",
" <td>1.136943e-03</td>\n",
" <td>0.000429</td>\n",
" </tr>\n",
" <tr>\n",
" <th>PPO_2_6</th>\n",
" <td>0.027918</td>\n",
" <td>0.000813</td>\n",
" <td>5.947324e-04</td>\n",
" <td>0.000526</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CCI_20</th>\n",
" <td>0.022840</td>\n",
" <td>0.001432</td>\n",
" <td>3.908241e-04</td>\n",
" <td>0.000319</td>\n",
" </tr>\n",
" <tr>\n",
" <th>KeltnerWidth_diff_9</th>\n",
" <td>0.021018</td>\n",
" <td>0.001042</td>\n",
" <td>2.765119e-04</td>\n",
" <td>0.000094</td>\n",
" </tr>\n",
" <tr>\n",
" <th>KeltnerWidth_ratio_15_24</th>\n",
" <td>0.020487</td>\n",
" <td>0.000306</td>\n",
" <td>3.645632e-04</td>\n",
" <td>0.000174</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CCI_14</th>\n",
" <td>0.020120</td>\n",
" <td>0.002310</td>\n",
" <td>1.884606e-04</td>\n",
" <td>0.000213</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CCI_8</th>\n",
" <td>0.019527</td>\n",
" <td>0.000839</td>\n",
" <td>1.930949e-04</td>\n",
" <td>0.000339</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CCI_ratio_2_20</th>\n",
" <td>0.016868</td>\n",
" <td>0.002119</td>\n",
" <td>5.823743e-04</td>\n",
" <td>0.000256</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CMO_diff_12</th>\n",
" <td>0.015694</td>\n",
" <td>0.001227</td>\n",
" <td>1.714683e-04</td>\n",
" <td>0.000283</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CCI_ratio_8_20</th>\n",
" <td>0.015631</td>\n",
" <td>0.001033</td>\n",
" <td>3.182204e-04</td>\n",
" <td>0.000115</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CCI_diff_18</th>\n",
" <td>0.015434</td>\n",
" <td>0.001561</td>\n",
" <td>3.043176e-04</td>\n",
" <td>0.000150</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CMO_ratio_2_14</th>\n",
" <td>0.015399</td>\n",
" <td>0.002409</td>\n",
" <td>3.413918e-04</td>\n",
" <td>0.000282</td>\n",
" </tr>\n",
" <tr>\n",
" <th>KeltnerWidth_2</th>\n",
" <td>0.015248</td>\n",
" <td>0.001092</td>\n",
" <td>5.870086e-05</td>\n",
" <td>0.000102</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ADX_2</th>\n",
" <td>0.015177</td>\n",
" <td>0.003281</td>\n",
" <td>5.097706e-05</td>\n",
" <td>0.000124</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ADX_ratio_2_5</th>\n",
" <td>0.014738</td>\n",
" <td>0.002739</td>\n",
" <td>1.699235e-04</td>\n",
" <td>0.000203</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CMO54</th>\n",
" <td>0.014536</td>\n",
" <td>0.000912</td>\n",
" <td>1.204912e-04</td>\n",
" <td>0.000225</td>\n",
" </tr>\n",
" <tr>\n",
" <th>PPO_12_26</th>\n",
" <td>0.014241</td>\n",
" <td>0.000926</td>\n",
" <td>2.641539e-04</td>\n",
" <td>0.000109</td>\n",
" </tr>\n",
" <tr>\n",
" <th>KeltnerWidth_ratio_2_24</th>\n",
" <td>0.014073</td>\n",
" <td>0.001852</td>\n",
" <td>1.699235e-05</td>\n",
" <td>0.000338</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ADX_14</th>\n",
" <td>0.013390</td>\n",
" <td>0.000597</td>\n",
" <td>1.544759e-04</td>\n",
" <td>0.000059</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CCI_diff_6</th>\n",
" <td>0.013123</td>\n",
" <td>0.001673</td>\n",
" <td>1.544759e-04</td>\n",
" <td>0.000133</td>\n",
" </tr>\n",
" <tr>\n",
" <th>KeltnerWidth_ratio_2_15</th>\n",
" <td>0.012956</td>\n",
" <td>0.001455</td>\n",
" <td>-4.943230e-05</td>\n",
" <td>0.000295</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CCI_ratio_2_8</th>\n",
" <td>0.012882</td>\n",
" <td>0.001430</td>\n",
" <td>6.951417e-05</td>\n",
" <td>0.000183</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CCI_diff_12</th>\n",
" <td>0.012387</td>\n",
" <td>0.001230</td>\n",
" <td>3.074071e-04</td>\n",
" <td>0.000082</td>\n",
" </tr>\n",
" <tr>\n",
" <th>KeltnerWidth_15</th>\n",
" <td>0.012065</td>\n",
" <td>0.001431</td>\n",
" <td>1.436626e-04</td>\n",
" <td>0.000094</td>\n",
" </tr>\n",
" <tr>\n",
" <th>KeltnerWidth_diff_22</th>\n",
" <td>0.011980</td>\n",
" <td>0.001152</td>\n",
" <td>4.219037e-17</td>\n",
" <td>0.000229</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ChaikinVol_2_5</th>\n",
" <td>0.011735</td>\n",
" <td>0.001426</td>\n",
" <td>7.723797e-06</td>\n",
" <td>0.000190</td>\n",
" </tr>\n",
" <tr>\n",
" <th>KeltnerWidth_diff_13</th>\n",
" <td>0.011701</td>\n",
" <td>0.001045</td>\n",
" <td>-7.723797e-05</td>\n",
" <td>0.000233</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ChaikinVol_3_10</th>\n",
" <td>0.011406</td>\n",
" <td>0.002300</td>\n",
" <td>3.861899e-05</td>\n",
" <td>0.000201</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ADX_diff_9</th>\n",
" <td>0.011238</td>\n",
" <td>0.001878</td>\n",
" <td>-5.561134e-05</td>\n",
" <td>0.000059</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Gold_Close</th>\n",
" <td>0.010996</td>\n",
" <td>0.003362</td>\n",
" <td>-2.317139e-05</td>\n",
" <td>0.000044</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Unnamed: 0</th>\n",
" <td>0.010731</td>\n",
" <td>0.002601</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>KeltnerWidth_20</th>\n",
" <td>0.010705</td>\n",
" <td>0.001163</td>\n",
" <td>4.943230e-05</td>\n",
" <td>0.000129</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CMO_2</th>\n",
" <td>0.010696</td>\n",
" <td>0.000290</td>\n",
" <td>4.016374e-05</td>\n",
" <td>0.000088</td>\n",
" </tr>\n",
" <tr>\n",
" <th>KeltnerWidth_24</th>\n",
" <td>0.010461</td>\n",
" <td>0.001092</td>\n",
" <td>7.260369e-05</td>\n",
" <td>0.000060</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Volume</th>\n",
" <td>0.010460</td>\n",
" <td>0.001554</td>\n",
" <td>-7.723797e-06</td>\n",
" <td>0.000081</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ADX_diff_3</th>\n",
" <td>0.010061</td>\n",
" <td>0.000414</td>\n",
" <td>-6.951417e-05</td>\n",
" <td>0.000113</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ADX_ratio_5_14</th>\n",
" <td>0.009508</td>\n",
" <td>0.000785</td>\n",
" <td>-4.634278e-05</td>\n",
" <td>0.000120</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ADX_diff_12</th>\n",
" <td>0.009228</td>\n",
" <td>0.000512</td>\n",
" <td>-4.479802e-05</td>\n",
" <td>0.000100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Diff_10Y_2Y</th>\n",
" <td>0.009158</td>\n",
" <td>0.001091</td>\n",
" <td>-4.634278e-05</td>\n",
" <td>0.000097</td>\n",
" </tr>\n",
" <tr>\n",
" <th>sma</th>\n",
" <td>0.009080</td>\n",
" <td>0.000535</td>\n",
" <td>-7.723797e-06</td>\n",
" <td>0.000075</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ADX_ratio_2_14</th>\n",
" <td>0.009070</td>\n",
" <td>0.000448</td>\n",
" <td>-3.398471e-05</td>\n",
" <td>0.000109</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Open</th>\n",
" <td>0.008671</td>\n",
" <td>0.000402</td>\n",
" <td>-3.089519e-05</td>\n",
" <td>0.000070</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Oil_Close</th>\n",
" <td>0.008671</td>\n",
" <td>0.000734</td>\n",
" <td>-1.699235e-05</td>\n",
" <td>0.000033</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Oil_Volume</th>\n",
" <td>0.008659</td>\n",
" <td>0.000408</td>\n",
" <td>-5.715610e-05</td>\n",
" <td>0.000074</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Low</th>\n",
" <td>0.008550</td>\n",
" <td>0.000231</td>\n",
" <td>-2.471615e-05</td>\n",
" <td>0.000024</td>\n",
" </tr>\n",
" <tr>\n",
" <th>High</th>\n",
" <td>0.008299</td>\n",
" <td>0.000378</td>\n",
" <td>-4.016374e-05</td>\n",
" <td>0.000047</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Close</th>\n",
" <td>0.008296</td>\n",
" <td>0.000433</td>\n",
" <td>-1.544759e-05</td>\n",
" <td>0.000058</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ema</th>\n",
" <td>0.007906</td>\n",
" <td>0.000204</td>\n",
" <td>-2.317139e-05</td>\n",
" <td>0.000040</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Hour_sin</th>\n",
" <td>0.006267</td>\n",
" <td>0.000311</td>\n",
" <td>1.235808e-05</td>\n",
" <td>0.000065</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Date_sin</th>\n",
" <td>0.005902</td>\n",
" <td>0.000935</td>\n",
" <td>-4.634278e-05</td>\n",
" <td>0.000030</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Month_sin</th>\n",
" <td>0.003765</td>\n",
" <td>0.000437</td>\n",
" <td>-2.008187e-05</td>\n",
" <td>0.000048</td>\n",
" </tr>\n",
" <tr>\n",
" <th>DOW_sin</th>\n",
" <td>0.003258</td>\n",
" <td>0.000196</td>\n",
" <td>1.544759e-06</td>\n",
" <td>0.000045</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Quarter</th>\n",
" <td>0.002403</td>\n",
" <td>0.000197</td>\n",
" <td>-1.235808e-05</td>\n",
" <td>0.000057</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Session</th>\n",
" <td>0.001260</td>\n",
" <td>0.000224</td>\n",
" <td>-2.162663e-05</td>\n",
" <td>0.000037</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CCI_2</th>\n",
" <td>0.000981</td>\n",
" <td>0.000159</td>\n",
" <td>7.723797e-06</td>\n",
" <td>0.000022</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" model_importance_mean model_importance_std \\\n",
"signal 0.306649 0.042522 \n",
"ADX_5 0.028298 0.001057 \n",
"CMO_14 0.028196 0.000615 \n",
"PPO_2_6 0.027918 0.000813 \n",
"CCI_20 0.022840 0.001432 \n",
"KeltnerWidth_diff_9 0.021018 0.001042 \n",
"KeltnerWidth_ratio_15_24 0.020487 0.000306 \n",
"CCI_14 0.020120 0.002310 \n",
"CCI_8 0.019527 0.000839 \n",
"CCI_ratio_2_20 0.016868 0.002119 \n",
"CMO_diff_12 0.015694 0.001227 \n",
"CCI_ratio_8_20 0.015631 0.001033 \n",
"CCI_diff_18 0.015434 0.001561 \n",
"CMO_ratio_2_14 0.015399 0.002409 \n",
"KeltnerWidth_2 0.015248 0.001092 \n",
"ADX_2 0.015177 0.003281 \n",
"ADX_ratio_2_5 0.014738 0.002739 \n",
"CMO54 0.014536 0.000912 \n",
"PPO_12_26 0.014241 0.000926 \n",
"KeltnerWidth_ratio_2_24 0.014073 0.001852 \n",
"ADX_14 0.013390 0.000597 \n",
"CCI_diff_6 0.013123 0.001673 \n",
"KeltnerWidth_ratio_2_15 0.012956 0.001455 \n",
"CCI_ratio_2_8 0.012882 0.001430 \n",
"CCI_diff_12 0.012387 0.001230 \n",
"KeltnerWidth_15 0.012065 0.001431 \n",
"KeltnerWidth_diff_22 0.011980 0.001152 \n",
"ChaikinVol_2_5 0.011735 0.001426 \n",
"KeltnerWidth_diff_13 0.011701 0.001045 \n",
"ChaikinVol_3_10 0.011406 0.002300 \n",
"ADX_diff_9 0.011238 0.001878 \n",
"Gold_Close 0.010996 0.003362 \n",
"Unnamed: 0 0.010731 0.002601 \n",
"KeltnerWidth_20 0.010705 0.001163 \n",
"CMO_2 0.010696 0.000290 \n",
"KeltnerWidth_24 0.010461 0.001092 \n",
"Volume 0.010460 0.001554 \n",
"ADX_diff_3 0.010061 0.000414 \n",
"ADX_ratio_5_14 0.009508 0.000785 \n",
"ADX_diff_12 0.009228 0.000512 \n",
"Diff_10Y_2Y 0.009158 0.001091 \n",
"sma 0.009080 0.000535 \n",
"ADX_ratio_2_14 0.009070 0.000448 \n",
"Open 0.008671 0.000402 \n",
"Oil_Close 0.008671 0.000734 \n",
"Oil_Volume 0.008659 0.000408 \n",
"Low 0.008550 0.000231 \n",
"High 0.008299 0.000378 \n",
"Close 0.008296 0.000433 \n",
"ema 0.007906 0.000204 \n",
"Hour_sin 0.006267 0.000311 \n",
"Date_sin 0.005902 0.000935 \n",
"Month_sin 0.003765 0.000437 \n",
"DOW_sin 0.003258 0.000196 \n",
"Quarter 0.002403 0.000197 \n",
"Session 0.001260 0.000224 \n",
"CCI_2 0.000981 0.000159 \n",
"\n",
" perm_importance_mean perm_importance_std \n",
"signal 1.170155e-02 0.002480 \n",
"ADX_5 8.187225e-04 0.000296 \n",
"CMO_14 1.136943e-03 0.000429 \n",
"PPO_2_6 5.947324e-04 0.000526 \n",
"CCI_20 3.908241e-04 0.000319 \n",
"KeltnerWidth_diff_9 2.765119e-04 0.000094 \n",
"KeltnerWidth_ratio_15_24 3.645632e-04 0.000174 \n",
"CCI_14 1.884606e-04 0.000213 \n",
"CCI_8 1.930949e-04 0.000339 \n",
"CCI_ratio_2_20 5.823743e-04 0.000256 \n",
"CMO_diff_12 1.714683e-04 0.000283 \n",
"CCI_ratio_8_20 3.182204e-04 0.000115 \n",
"CCI_diff_18 3.043176e-04 0.000150 \n",
"CMO_ratio_2_14 3.413918e-04 0.000282 \n",
"KeltnerWidth_2 5.870086e-05 0.000102 \n",
"ADX_2 5.097706e-05 0.000124 \n",
"ADX_ratio_2_5 1.699235e-04 0.000203 \n",
"CMO54 1.204912e-04 0.000225 \n",
"PPO_12_26 2.641539e-04 0.000109 \n",
"KeltnerWidth_ratio_2_24 1.699235e-05 0.000338 \n",
"ADX_14 1.544759e-04 0.000059 \n",
"CCI_diff_6 1.544759e-04 0.000133 \n",
"KeltnerWidth_ratio_2_15 -4.943230e-05 0.000295 \n",
"CCI_ratio_2_8 6.951417e-05 0.000183 \n",
"CCI_diff_12 3.074071e-04 0.000082 \n",
"KeltnerWidth_15 1.436626e-04 0.000094 \n",
"KeltnerWidth_diff_22 4.219037e-17 0.000229 \n",
"ChaikinVol_2_5 7.723797e-06 0.000190 \n",
"KeltnerWidth_diff_13 -7.723797e-05 0.000233 \n",
"ChaikinVol_3_10 3.861899e-05 0.000201 \n",
"ADX_diff_9 -5.561134e-05 0.000059 \n",
"Gold_Close -2.317139e-05 0.000044 \n",
"Unnamed: 0 0.000000e+00 0.000000 \n",
"KeltnerWidth_20 4.943230e-05 0.000129 \n",
"CMO_2 4.016374e-05 0.000088 \n",
"KeltnerWidth_24 7.260369e-05 0.000060 \n",
"Volume -7.723797e-06 0.000081 \n",
"ADX_diff_3 -6.951417e-05 0.000113 \n",
"ADX_ratio_5_14 -4.634278e-05 0.000120 \n",
"ADX_diff_12 -4.479802e-05 0.000100 \n",
"Diff_10Y_2Y -4.634278e-05 0.000097 \n",
"sma -7.723797e-06 0.000075 \n",
"ADX_ratio_2_14 -3.398471e-05 0.000109 \n",
"Open -3.089519e-05 0.000070 \n",
"Oil_Close -1.699235e-05 0.000033 \n",
"Oil_Volume -5.715610e-05 0.000074 \n",
"Low -2.471615e-05 0.000024 \n",
"High -4.016374e-05 0.000047 \n",
"Close -1.544759e-05 0.000058 \n",
"ema -2.317139e-05 0.000040 \n",
"Hour_sin 1.235808e-05 0.000065 \n",
"Date_sin -4.634278e-05 0.000030 \n",
"Month_sin -2.008187e-05 0.000048 \n",
"DOW_sin 1.544759e-06 0.000045 \n",
"Quarter -1.235808e-05 0.000057 \n",
"Session -2.162663e-05 0.000037 \n",
"CCI_2 7.723797e-06 0.000022 "
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Or descending (largest → smallest)\n",
"df_sorted_desc = importance.sort_values(by=\"model_importance_mean\", ascending=False)\n",
"\n",
"df_sorted_desc"
]
},
{
"cell_type": "code",
"execution_count": 52,
"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>model_importance_mean</th>\n",
" <th>model_importance_std</th>\n",
" <th>perm_importance_mean</th>\n",
" <th>perm_importance_std</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>signal</th>\n",
" <td>0.306649</td>\n",
" <td>0.042522</td>\n",
" <td>1.170155e-02</td>\n",
" <td>0.002480</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CMO_14</th>\n",
" <td>0.028196</td>\n",
" <td>0.000615</td>\n",
" <td>1.136943e-03</td>\n",
" <td>0.000429</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ADX_5</th>\n",
" <td>0.028298</td>\n",
" <td>0.001057</td>\n",
" <td>8.187225e-04</td>\n",
" <td>0.000296</td>\n",
" </tr>\n",
" <tr>\n",
" <th>PPO_2_6</th>\n",
" <td>0.027918</td>\n",
" <td>0.000813</td>\n",
" <td>5.947324e-04</td>\n",
" <td>0.000526</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CCI_ratio_2_20</th>\n",
" <td>0.016868</td>\n",
" <td>0.002119</td>\n",
" <td>5.823743e-04</td>\n",
" <td>0.000256</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CCI_20</th>\n",
" <td>0.022840</td>\n",
" <td>0.001432</td>\n",
" <td>3.908241e-04</td>\n",
" <td>0.000319</td>\n",
" </tr>\n",
" <tr>\n",
" <th>KeltnerWidth_ratio_15_24</th>\n",
" <td>0.020487</td>\n",
" <td>0.000306</td>\n",
" <td>3.645632e-04</td>\n",
" <td>0.000174</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CMO_ratio_2_14</th>\n",
" <td>0.015399</td>\n",
" <td>0.002409</td>\n",
" <td>3.413918e-04</td>\n",
" <td>0.000282</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CCI_ratio_8_20</th>\n",
" <td>0.015631</td>\n",
" <td>0.001033</td>\n",
" <td>3.182204e-04</td>\n",
" <td>0.000115</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CCI_diff_12</th>\n",
" <td>0.012387</td>\n",
" <td>0.001230</td>\n",
" <td>3.074071e-04</td>\n",
" <td>0.000082</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CCI_diff_18</th>\n",
" <td>0.015434</td>\n",
" <td>0.001561</td>\n",
" <td>3.043176e-04</td>\n",
" <td>0.000150</td>\n",
" </tr>\n",
" <tr>\n",
" <th>KeltnerWidth_diff_9</th>\n",
" <td>0.021018</td>\n",
" <td>0.001042</td>\n",
" <td>2.765119e-04</td>\n",
" <td>0.000094</td>\n",
" </tr>\n",
" <tr>\n",
" <th>PPO_12_26</th>\n",
" <td>0.014241</td>\n",
" <td>0.000926</td>\n",
" <td>2.641539e-04</td>\n",
" <td>0.000109</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CCI_8</th>\n",
" <td>0.019527</td>\n",
" <td>0.000839</td>\n",
" <td>1.930949e-04</td>\n",
" <td>0.000339</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CCI_14</th>\n",
" <td>0.020120</td>\n",
" <td>0.002310</td>\n",
" <td>1.884606e-04</td>\n",
" <td>0.000213</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CMO_diff_12</th>\n",
" <td>0.015694</td>\n",
" <td>0.001227</td>\n",
" <td>1.714683e-04</td>\n",
" <td>0.000283</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ADX_ratio_2_5</th>\n",
" <td>0.014738</td>\n",
" <td>0.002739</td>\n",
" <td>1.699235e-04</td>\n",
" <td>0.000203</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ADX_14</th>\n",
" <td>0.013390</td>\n",
" <td>0.000597</td>\n",
" <td>1.544759e-04</td>\n",
" <td>0.000059</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CCI_diff_6</th>\n",
" <td>0.013123</td>\n",
" <td>0.001673</td>\n",
" <td>1.544759e-04</td>\n",
" <td>0.000133</td>\n",
" </tr>\n",
" <tr>\n",
" <th>KeltnerWidth_15</th>\n",
" <td>0.012065</td>\n",
" <td>0.001431</td>\n",
" <td>1.436626e-04</td>\n",
" <td>0.000094</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CMO54</th>\n",
" <td>0.014536</td>\n",
" <td>0.000912</td>\n",
" <td>1.204912e-04</td>\n",
" <td>0.000225</td>\n",
" </tr>\n",
" <tr>\n",
" <th>KeltnerWidth_24</th>\n",
" <td>0.010461</td>\n",
" <td>0.001092</td>\n",
" <td>7.260369e-05</td>\n",
" <td>0.000060</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CCI_ratio_2_8</th>\n",
" <td>0.012882</td>\n",
" <td>0.001430</td>\n",
" <td>6.951417e-05</td>\n",
" <td>0.000183</td>\n",
" </tr>\n",
" <tr>\n",
" <th>KeltnerWidth_2</th>\n",
" <td>0.015248</td>\n",
" <td>0.001092</td>\n",
" <td>5.870086e-05</td>\n",
" <td>0.000102</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ADX_2</th>\n",
" <td>0.015177</td>\n",
" <td>0.003281</td>\n",
" <td>5.097706e-05</td>\n",
" <td>0.000124</td>\n",
" </tr>\n",
" <tr>\n",
" <th>KeltnerWidth_20</th>\n",
" <td>0.010705</td>\n",
" <td>0.001163</td>\n",
" <td>4.943230e-05</td>\n",
" <td>0.000129</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CMO_2</th>\n",
" <td>0.010696</td>\n",
" <td>0.000290</td>\n",
" <td>4.016374e-05</td>\n",
" <td>0.000088</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ChaikinVol_3_10</th>\n",
" <td>0.011406</td>\n",
" <td>0.002300</td>\n",
" <td>3.861899e-05</td>\n",
" <td>0.000201</td>\n",
" </tr>\n",
" <tr>\n",
" <th>KeltnerWidth_ratio_2_24</th>\n",
" <td>0.014073</td>\n",
" <td>0.001852</td>\n",
" <td>1.699235e-05</td>\n",
" <td>0.000338</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Hour_sin</th>\n",
" <td>0.006267</td>\n",
" <td>0.000311</td>\n",
" <td>1.235808e-05</td>\n",
" <td>0.000065</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ChaikinVol_2_5</th>\n",
" <td>0.011735</td>\n",
" <td>0.001426</td>\n",
" <td>7.723797e-06</td>\n",
" <td>0.000190</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CCI_2</th>\n",
" <td>0.000981</td>\n",
" <td>0.000159</td>\n",
" <td>7.723797e-06</td>\n",
" <td>0.000022</td>\n",
" </tr>\n",
" <tr>\n",
" <th>DOW_sin</th>\n",
" <td>0.003258</td>\n",
" <td>0.000196</td>\n",
" <td>1.544759e-06</td>\n",
" <td>0.000045</td>\n",
" </tr>\n",
" <tr>\n",
" <th>KeltnerWidth_diff_22</th>\n",
" <td>0.011980</td>\n",
" <td>0.001152</td>\n",
" <td>4.219037e-17</td>\n",
" <td>0.000229</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Unnamed: 0</th>\n",
" <td>0.010731</td>\n",
" <td>0.002601</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Volume</th>\n",
" <td>0.010460</td>\n",
" <td>0.001554</td>\n",
" <td>-7.723797e-06</td>\n",
" <td>0.000081</td>\n",
" </tr>\n",
" <tr>\n",
" <th>sma</th>\n",
" <td>0.009080</td>\n",
" <td>0.000535</td>\n",
" <td>-7.723797e-06</td>\n",
" <td>0.000075</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Quarter</th>\n",
" <td>0.002403</td>\n",
" <td>0.000197</td>\n",
" <td>-1.235808e-05</td>\n",
" <td>0.000057</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Close</th>\n",
" <td>0.008296</td>\n",
" <td>0.000433</td>\n",
" <td>-1.544759e-05</td>\n",
" <td>0.000058</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Oil_Close</th>\n",
" <td>0.008671</td>\n",
" <td>0.000734</td>\n",
" <td>-1.699235e-05</td>\n",
" <td>0.000033</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Month_sin</th>\n",
" <td>0.003765</td>\n",
" <td>0.000437</td>\n",
" <td>-2.008187e-05</td>\n",
" <td>0.000048</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Session</th>\n",
" <td>0.001260</td>\n",
" <td>0.000224</td>\n",
" <td>-2.162663e-05</td>\n",
" <td>0.000037</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ema</th>\n",
" <td>0.007906</td>\n",
" <td>0.000204</td>\n",
" <td>-2.317139e-05</td>\n",
" <td>0.000040</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Gold_Close</th>\n",
" <td>0.010996</td>\n",
" <td>0.003362</td>\n",
" <td>-2.317139e-05</td>\n",
" <td>0.000044</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Low</th>\n",
" <td>0.008550</td>\n",
" <td>0.000231</td>\n",
" <td>-2.471615e-05</td>\n",
" <td>0.000024</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Open</th>\n",
" <td>0.008671</td>\n",
" <td>0.000402</td>\n",
" <td>-3.089519e-05</td>\n",
" <td>0.000070</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ADX_ratio_2_14</th>\n",
" <td>0.009070</td>\n",
" <td>0.000448</td>\n",
" <td>-3.398471e-05</td>\n",
" <td>0.000109</td>\n",
" </tr>\n",
" <tr>\n",
" <th>High</th>\n",
" <td>0.008299</td>\n",
" <td>0.000378</td>\n",
" <td>-4.016374e-05</td>\n",
" <td>0.000047</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ADX_diff_12</th>\n",
" <td>0.009228</td>\n",
" <td>0.000512</td>\n",
" <td>-4.479802e-05</td>\n",
" <td>0.000100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ADX_ratio_5_14</th>\n",
" <td>0.009508</td>\n",
" <td>0.000785</td>\n",
" <td>-4.634278e-05</td>\n",
" <td>0.000120</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Date_sin</th>\n",
" <td>0.005902</td>\n",
" <td>0.000935</td>\n",
" <td>-4.634278e-05</td>\n",
" <td>0.000030</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Diff_10Y_2Y</th>\n",
" <td>0.009158</td>\n",
" <td>0.001091</td>\n",
" <td>-4.634278e-05</td>\n",
" <td>0.000097</td>\n",
" </tr>\n",
" <tr>\n",
" <th>KeltnerWidth_ratio_2_15</th>\n",
" <td>0.012956</td>\n",
" <td>0.001455</td>\n",
" <td>-4.943230e-05</td>\n",
" <td>0.000295</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ADX_diff_9</th>\n",
" <td>0.011238</td>\n",
" <td>0.001878</td>\n",
" <td>-5.561134e-05</td>\n",
" <td>0.000059</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Oil_Volume</th>\n",
" <td>0.008659</td>\n",
" <td>0.000408</td>\n",
" <td>-5.715610e-05</td>\n",
" <td>0.000074</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ADX_diff_3</th>\n",
" <td>0.010061</td>\n",
" <td>0.000414</td>\n",
" <td>-6.951417e-05</td>\n",
" <td>0.000113</td>\n",
" </tr>\n",
" <tr>\n",
" <th>KeltnerWidth_diff_13</th>\n",
" <td>0.011701</td>\n",
" <td>0.001045</td>\n",
" <td>-7.723797e-05</td>\n",
" <td>0.000233</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" model_importance_mean model_importance_std \\\n",
"signal 0.306649 0.042522 \n",
"CMO_14 0.028196 0.000615 \n",
"ADX_5 0.028298 0.001057 \n",
"PPO_2_6 0.027918 0.000813 \n",
"CCI_ratio_2_20 0.016868 0.002119 \n",
"CCI_20 0.022840 0.001432 \n",
"KeltnerWidth_ratio_15_24 0.020487 0.000306 \n",
"CMO_ratio_2_14 0.015399 0.002409 \n",
"CCI_ratio_8_20 0.015631 0.001033 \n",
"CCI_diff_12 0.012387 0.001230 \n",
"CCI_diff_18 0.015434 0.001561 \n",
"KeltnerWidth_diff_9 0.021018 0.001042 \n",
"PPO_12_26 0.014241 0.000926 \n",
"CCI_8 0.019527 0.000839 \n",
"CCI_14 0.020120 0.002310 \n",
"CMO_diff_12 0.015694 0.001227 \n",
"ADX_ratio_2_5 0.014738 0.002739 \n",
"ADX_14 0.013390 0.000597 \n",
"CCI_diff_6 0.013123 0.001673 \n",
"KeltnerWidth_15 0.012065 0.001431 \n",
"CMO54 0.014536 0.000912 \n",
"KeltnerWidth_24 0.010461 0.001092 \n",
"CCI_ratio_2_8 0.012882 0.001430 \n",
"KeltnerWidth_2 0.015248 0.001092 \n",
"ADX_2 0.015177 0.003281 \n",
"KeltnerWidth_20 0.010705 0.001163 \n",
"CMO_2 0.010696 0.000290 \n",
"ChaikinVol_3_10 0.011406 0.002300 \n",
"KeltnerWidth_ratio_2_24 0.014073 0.001852 \n",
"Hour_sin 0.006267 0.000311 \n",
"ChaikinVol_2_5 0.011735 0.001426 \n",
"CCI_2 0.000981 0.000159 \n",
"DOW_sin 0.003258 0.000196 \n",
"KeltnerWidth_diff_22 0.011980 0.001152 \n",
"Unnamed: 0 0.010731 0.002601 \n",
"Volume 0.010460 0.001554 \n",
"sma 0.009080 0.000535 \n",
"Quarter 0.002403 0.000197 \n",
"Close 0.008296 0.000433 \n",
"Oil_Close 0.008671 0.000734 \n",
"Month_sin 0.003765 0.000437 \n",
"Session 0.001260 0.000224 \n",
"ema 0.007906 0.000204 \n",
"Gold_Close 0.010996 0.003362 \n",
"Low 0.008550 0.000231 \n",
"Open 0.008671 0.000402 \n",
"ADX_ratio_2_14 0.009070 0.000448 \n",
"High 0.008299 0.000378 \n",
"ADX_diff_12 0.009228 0.000512 \n",
"ADX_ratio_5_14 0.009508 0.000785 \n",
"Date_sin 0.005902 0.000935 \n",
"Diff_10Y_2Y 0.009158 0.001091 \n",
"KeltnerWidth_ratio_2_15 0.012956 0.001455 \n",
"ADX_diff_9 0.011238 0.001878 \n",
"Oil_Volume 0.008659 0.000408 \n",
"ADX_diff_3 0.010061 0.000414 \n",
"KeltnerWidth_diff_13 0.011701 0.001045 \n",
"\n",
" perm_importance_mean perm_importance_std \n",
"signal 1.170155e-02 0.002480 \n",
"CMO_14 1.136943e-03 0.000429 \n",
"ADX_5 8.187225e-04 0.000296 \n",
"PPO_2_6 5.947324e-04 0.000526 \n",
"CCI_ratio_2_20 5.823743e-04 0.000256 \n",
"CCI_20 3.908241e-04 0.000319 \n",
"KeltnerWidth_ratio_15_24 3.645632e-04 0.000174 \n",
"CMO_ratio_2_14 3.413918e-04 0.000282 \n",
"CCI_ratio_8_20 3.182204e-04 0.000115 \n",
"CCI_diff_12 3.074071e-04 0.000082 \n",
"CCI_diff_18 3.043176e-04 0.000150 \n",
"KeltnerWidth_diff_9 2.765119e-04 0.000094 \n",
"PPO_12_26 2.641539e-04 0.000109 \n",
"CCI_8 1.930949e-04 0.000339 \n",
"CCI_14 1.884606e-04 0.000213 \n",
"CMO_diff_12 1.714683e-04 0.000283 \n",
"ADX_ratio_2_5 1.699235e-04 0.000203 \n",
"ADX_14 1.544759e-04 0.000059 \n",
"CCI_diff_6 1.544759e-04 0.000133 \n",
"KeltnerWidth_15 1.436626e-04 0.000094 \n",
"CMO54 1.204912e-04 0.000225 \n",
"KeltnerWidth_24 7.260369e-05 0.000060 \n",
"CCI_ratio_2_8 6.951417e-05 0.000183 \n",
"KeltnerWidth_2 5.870086e-05 0.000102 \n",
"ADX_2 5.097706e-05 0.000124 \n",
"KeltnerWidth_20 4.943230e-05 0.000129 \n",
"CMO_2 4.016374e-05 0.000088 \n",
"ChaikinVol_3_10 3.861899e-05 0.000201 \n",
"KeltnerWidth_ratio_2_24 1.699235e-05 0.000338 \n",
"Hour_sin 1.235808e-05 0.000065 \n",
"ChaikinVol_2_5 7.723797e-06 0.000190 \n",
"CCI_2 7.723797e-06 0.000022 \n",
"DOW_sin 1.544759e-06 0.000045 \n",
"KeltnerWidth_diff_22 4.219037e-17 0.000229 \n",
"Unnamed: 0 0.000000e+00 0.000000 \n",
"Volume -7.723797e-06 0.000081 \n",
"sma -7.723797e-06 0.000075 \n",
"Quarter -1.235808e-05 0.000057 \n",
"Close -1.544759e-05 0.000058 \n",
"Oil_Close -1.699235e-05 0.000033 \n",
"Month_sin -2.008187e-05 0.000048 \n",
"Session -2.162663e-05 0.000037 \n",
"ema -2.317139e-05 0.000040 \n",
"Gold_Close -2.317139e-05 0.000044 \n",
"Low -2.471615e-05 0.000024 \n",
"Open -3.089519e-05 0.000070 \n",
"ADX_ratio_2_14 -3.398471e-05 0.000109 \n",
"High -4.016374e-05 0.000047 \n",
"ADX_diff_12 -4.479802e-05 0.000100 \n",
"ADX_ratio_5_14 -4.634278e-05 0.000120 \n",
"Date_sin -4.634278e-05 0.000030 \n",
"Diff_10Y_2Y -4.634278e-05 0.000097 \n",
"KeltnerWidth_ratio_2_15 -4.943230e-05 0.000295 \n",
"ADX_diff_9 -5.561134e-05 0.000059 \n",
"Oil_Volume -5.715610e-05 0.000074 \n",
"ADX_diff_3 -6.951417e-05 0.000113 \n",
"KeltnerWidth_diff_13 -7.723797e-05 0.000233 "
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Or descending (largest → smallest)\n",
"df_sorted_desc = importance.sort_values(by=\"perm_importance_mean\", ascending=False)\n",
"\n",
"\n",
"df_sorted_desc"
]
},
{
"cell_type": "code",
"execution_count": 64,
"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>Hour_sin</th>\n",
" <th>DOW_sin</th>\n",
" <th>ADX_14</th>\n",
" <th>CCI_14</th>\n",
" <th>PPO_12_26</th>\n",
" <th>CMO_14</th>\n",
" <th>ADX_2</th>\n",
" <th>ADX_5</th>\n",
" <th>CCI_8</th>\n",
" <th>CCI_20</th>\n",
" <th>...</th>\n",
" <th>CCI_diff_18</th>\n",
" <th>CCI_ratio_8_20</th>\n",
" <th>CCI_ratio_2_20</th>\n",
" <th>CMO_diff_12</th>\n",
" <th>CMO_ratio_2_14</th>\n",
" <th>KeltnerWidth_diff_9</th>\n",
" <th>KeltnerWidth_ratio_15_24</th>\n",
" <th>KeltnerWidth_ratio_2_24</th>\n",
" <th>signal</th>\n",
" <th>target_signal</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.86603</td>\n",
" <td>0.974928</td>\n",
" <td>25.930420</td>\n",
" <td>39.315068</td>\n",
" <td>0.121403</td>\n",
" <td>7.355190</td>\n",
" <td>41.651610</td>\n",
" <td>42.154713</td>\n",
" <td>-6.549708</td>\n",
" <td>59.300273</td>\n",
" <td>...</td>\n",
" <td>125.966939</td>\n",
" <td>-0.110450</td>\n",
" <td>-1.124222</td>\n",
" <td>-34.296419</td>\n",
" <td>-1.695703</td>\n",
" <td>0.000011</td>\n",
" <td>0.993302</td>\n",
" <td>1.370979</td>\n",
" <td>-1.0</td>\n",
" <td>-1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.96593</td>\n",
" <td>0.974928</td>\n",
" <td>25.909992</td>\n",
" <td>-65.160075</td>\n",
" <td>0.107986</td>\n",
" <td>8.428157</td>\n",
" <td>31.880359</td>\n",
" <td>37.044387</td>\n",
" <td>-132.473118</td>\n",
" <td>23.189994</td>\n",
" <td>...</td>\n",
" <td>89.856660</td>\n",
" <td>-5.712512</td>\n",
" <td>-2.874803</td>\n",
" <td>-23.452201</td>\n",
" <td>1.023121</td>\n",
" <td>0.000036</td>\n",
" <td>0.977000</td>\n",
" <td>0.906158</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1.00000</td>\n",
" <td>0.974928</td>\n",
" <td>25.381184</td>\n",
" <td>-146.502058</td>\n",
" <td>0.095970</td>\n",
" <td>3.275771</td>\n",
" <td>48.925969</td>\n",
" <td>30.959935</td>\n",
" <td>-167.017544</td>\n",
" <td>-12.596221</td>\n",
" <td>...</td>\n",
" <td>54.070446</td>\n",
" <td>13.259337</td>\n",
" <td>5.292593</td>\n",
" <td>-45.650197</td>\n",
" <td>0.455056</td>\n",
" <td>0.000055</td>\n",
" <td>0.963263</td>\n",
" <td>0.758581</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.96593</td>\n",
" <td>0.974928</td>\n",
" <td>25.225738</td>\n",
" <td>-88.888889</td>\n",
" <td>0.085549</td>\n",
" <td>7.984586</td>\n",
" <td>35.433883</td>\n",
" <td>27.694438</td>\n",
" <td>-79.830149</td>\n",
" <td>-3.902146</td>\n",
" <td>...</td>\n",
" <td>-70.568813</td>\n",
" <td>20.458011</td>\n",
" <td>-17.084615</td>\n",
" <td>-27.449297</td>\n",
" <td>-0.750000</td>\n",
" <td>0.000060</td>\n",
" <td>0.959495</td>\n",
" <td>0.815246</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.86603</td>\n",
" <td>0.974928</td>\n",
" <td>26.060597</td>\n",
" <td>86.264929</td>\n",
" <td>0.084842</td>\n",
" <td>24.751330</td>\n",
" <td>59.426680</td>\n",
" <td>33.035738</td>\n",
" <td>67.208672</td>\n",
" <td>96.026205</td>\n",
" <td>...</td>\n",
" <td>29.359538</td>\n",
" <td>0.699899</td>\n",
" <td>0.694255</td>\n",
" <td>-34.675350</td>\n",
" <td>0.772813</td>\n",
" <td>0.000025</td>\n",
" <td>0.983683</td>\n",
" <td>1.174859</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>77682</th>\n",
" <td>-1.00000</td>\n",
" <td>0.433884</td>\n",
" <td>33.262839</td>\n",
" <td>-172.486839</td>\n",
" <td>-0.129321</td>\n",
" <td>-67.588764</td>\n",
" <td>97.465878</td>\n",
" <td>70.027283</td>\n",
" <td>-111.789483</td>\n",
" <td>-233.479963</td>\n",
" <td>...</td>\n",
" <td>-166.813296</td>\n",
" <td>0.478797</td>\n",
" <td>0.285535</td>\n",
" <td>-165.054643</td>\n",
" <td>0.386503</td>\n",
" <td>-0.000233</td>\n",
" <td>1.176570</td>\n",
" <td>1.682584</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>77683</th>\n",
" <td>-0.96593</td>\n",
" <td>0.433884</td>\n",
" <td>36.313543</td>\n",
" <td>-125.872774</td>\n",
" <td>-0.160356</td>\n",
" <td>-51.571693</td>\n",
" <td>98.306065</td>\n",
" <td>74.257578</td>\n",
" <td>-82.237004</td>\n",
" <td>-172.632282</td>\n",
" <td>...</td>\n",
" <td>-239.298949</td>\n",
" <td>0.476371</td>\n",
" <td>-0.386177</td>\n",
" <td>-149.877758</td>\n",
" <td>-0.529635</td>\n",
" <td>-0.000231</td>\n",
" <td>1.169284</td>\n",
" <td>1.484021</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>77684</th>\n",
" <td>-0.86603</td>\n",
" <td>0.433884</td>\n",
" <td>39.146339</td>\n",
" <td>-103.217265</td>\n",
" <td>-0.188601</td>\n",
" <td>-54.267195</td>\n",
" <td>98.726158</td>\n",
" <td>77.641815</td>\n",
" <td>-68.714640</td>\n",
" <td>-140.468984</td>\n",
" <td>...</td>\n",
" <td>-73.802317</td>\n",
" <td>0.489180</td>\n",
" <td>0.474601</td>\n",
" <td>-152.993353</td>\n",
" <td>0.645887</td>\n",
" <td>-0.000180</td>\n",
" <td>1.135452</td>\n",
" <td>0.940292</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>77685</th>\n",
" <td>-0.70711</td>\n",
" <td>0.433884</td>\n",
" <td>42.314998</td>\n",
" <td>-125.764047</td>\n",
" <td>-0.234321</td>\n",
" <td>-65.243593</td>\n",
" <td>99.321072</td>\n",
" <td>81.298451</td>\n",
" <td>-123.954156</td>\n",
" <td>-160.044002</td>\n",
" <td>...</td>\n",
" <td>-93.377336</td>\n",
" <td>0.774500</td>\n",
" <td>0.416552</td>\n",
" <td>-164.564665</td>\n",
" <td>0.530093</td>\n",
" <td>-0.000271</td>\n",
" <td>1.177650</td>\n",
" <td>1.960267</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>77686</th>\n",
" <td>-0.50000</td>\n",
" <td>0.433884</td>\n",
" <td>45.257324</td>\n",
" <td>-118.285292</td>\n",
" <td>-0.287115</td>\n",
" <td>-65.720496</td>\n",
" <td>99.618529</td>\n",
" <td>84.223761</td>\n",
" <td>-137.559041</td>\n",
" <td>-148.659237</td>\n",
" <td>...</td>\n",
" <td>-81.992570</td>\n",
" <td>0.925331</td>\n",
" <td>0.448453</td>\n",
" <td>-165.339025</td>\n",
" <td>0.563609</td>\n",
" <td>-0.000205</td>\n",
" <td>1.139376</td>\n",
" <td>1.040766</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>77687 rows × 26 columns</p>\n",
"</div>"
],
"text/plain": [
" Hour_sin DOW_sin ADX_14 CCI_14 PPO_12_26 CMO_14 \\\n",
"0 0.86603 0.974928 25.930420 39.315068 0.121403 7.355190 \n",
"1 0.96593 0.974928 25.909992 -65.160075 0.107986 8.428157 \n",
"2 1.00000 0.974928 25.381184 -146.502058 0.095970 3.275771 \n",
"3 0.96593 0.974928 25.225738 -88.888889 0.085549 7.984586 \n",
"4 0.86603 0.974928 26.060597 86.264929 0.084842 24.751330 \n",
"... ... ... ... ... ... ... \n",
"77682 -1.00000 0.433884 33.262839 -172.486839 -0.129321 -67.588764 \n",
"77683 -0.96593 0.433884 36.313543 -125.872774 -0.160356 -51.571693 \n",
"77684 -0.86603 0.433884 39.146339 -103.217265 -0.188601 -54.267195 \n",
"77685 -0.70711 0.433884 42.314998 -125.764047 -0.234321 -65.243593 \n",
"77686 -0.50000 0.433884 45.257324 -118.285292 -0.287115 -65.720496 \n",
"\n",
" ADX_2 ADX_5 CCI_8 CCI_20 ... CCI_diff_18 \\\n",
"0 41.651610 42.154713 -6.549708 59.300273 ... 125.966939 \n",
"1 31.880359 37.044387 -132.473118 23.189994 ... 89.856660 \n",
"2 48.925969 30.959935 -167.017544 -12.596221 ... 54.070446 \n",
"3 35.433883 27.694438 -79.830149 -3.902146 ... -70.568813 \n",
"4 59.426680 33.035738 67.208672 96.026205 ... 29.359538 \n",
"... ... ... ... ... ... ... \n",
"77682 97.465878 70.027283 -111.789483 -233.479963 ... -166.813296 \n",
"77683 98.306065 74.257578 -82.237004 -172.632282 ... -239.298949 \n",
"77684 98.726158 77.641815 -68.714640 -140.468984 ... -73.802317 \n",
"77685 99.321072 81.298451 -123.954156 -160.044002 ... -93.377336 \n",
"77686 99.618529 84.223761 -137.559041 -148.659237 ... -81.992570 \n",
"\n",
" CCI_ratio_8_20 CCI_ratio_2_20 CMO_diff_12 CMO_ratio_2_14 \\\n",
"0 -0.110450 -1.124222 -34.296419 -1.695703 \n",
"1 -5.712512 -2.874803 -23.452201 1.023121 \n",
"2 13.259337 5.292593 -45.650197 0.455056 \n",
"3 20.458011 -17.084615 -27.449297 -0.750000 \n",
"4 0.699899 0.694255 -34.675350 0.772813 \n",
"... ... ... ... ... \n",
"77682 0.478797 0.285535 -165.054643 0.386503 \n",
"77683 0.476371 -0.386177 -149.877758 -0.529635 \n",
"77684 0.489180 0.474601 -152.993353 0.645887 \n",
"77685 0.774500 0.416552 -164.564665 0.530093 \n",
"77686 0.925331 0.448453 -165.339025 0.563609 \n",
"\n",
" KeltnerWidth_diff_9 KeltnerWidth_ratio_15_24 KeltnerWidth_ratio_2_24 \\\n",
"0 0.000011 0.993302 1.370979 \n",
"1 0.000036 0.977000 0.906158 \n",
"2 0.000055 0.963263 0.758581 \n",
"3 0.000060 0.959495 0.815246 \n",
"4 0.000025 0.983683 1.174859 \n",
"... ... ... ... \n",
"77682 -0.000233 1.176570 1.682584 \n",
"77683 -0.000231 1.169284 1.484021 \n",
"77684 -0.000180 1.135452 0.940292 \n",
"77685 -0.000271 1.177650 1.960267 \n",
"77686 -0.000205 1.139376 1.040766 \n",
"\n",
" signal target_signal \n",
"0 -1.0 -1.0 \n",
"1 0.0 0.0 \n",
"2 0.0 0.0 \n",
"3 0.0 0.0 \n",
"4 0.0 0.0 \n",
"... ... ... \n",
"77682 0.0 0.0 \n",
"77683 0.0 0.0 \n",
"77684 0.0 0.0 \n",
"77685 0.0 0.0 \n",
"77686 1.0 1.0 \n",
"\n",
"[77687 rows x 26 columns]"
]
},
"execution_count": 64,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_df[\"CCI_ratio_8_20\"] = pd.to_numeric(train_df[\"CCI_ratio_8_20\"].replace(\"#NAME?\", 0), errors=\"coerce\")\n",
"train_df\n"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [],
"source": [
"cols_to_drop = [\"Quarter\", \"Month_sin\", \"Date_sin\", \"Close\", \"ema\", \"Open\", \"Oil_Close\", \"CCI_2\",\n",
" \"KeltnerWidth_diff_22\",\"KeltnerWidth_ratio_2_15\", \"ADX_diff_12\", \"ADX_diff_3\",\"ADX_diff_9\",\n",
" \"CMO_2\", \"KeltnerWidth_diff_13\", \"ADX_ratio_2_14\", \"ADX_ratio_5_14\", \"ChaikinVol_2_5\", \"CCI_ratio_2_8\",\n",
" \"Volume\", \"CMO_2\", \"KeltnerWidth_15\", \"KeltnerWidth_20\", \"KeltnerWidth_24\",\n",
" \"ChaikinVol_3_10\", \"Unnamed: 0\", \"High\", \"Low\", \"sma\", \"Session\", \"Gold_Close\", \"Oil_Volume\", \"Diff_10Y_2Y\"]\n",
"train_df = train_df.drop(columns=cols_to_drop)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Model Training and Optimisation"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"=== Hyperparameter Tuning (Randomized Search) ===\n",
"[01/30] BA=0.8559 | F1m=0.8482 | Acc=0.9946 | best_n_estimators≈1001\n",
"[02/30] BA=0.8510 | F1m=0.8434 | Acc=0.9944 | best_n_estimators≈844\n",
"[03/30] BA=0.8579 | F1m=0.8503 | Acc=0.9946 | best_n_estimators≈470\n",
"[04/30] BA=0.8478 | F1m=0.8403 | Acc=0.9943 | best_n_estimators≈181\n",
"[05/30] BA=0.8528 | F1m=0.8451 | Acc=0.9945 | best_n_estimators≈1021\n",
"[06/30] BA=0.8542 | F1m=0.8466 | Acc=0.9945 | best_n_estimators≈1374\n",
"[07/30] BA=0.8589 | F1m=0.8512 | Acc=0.9947 | best_n_estimators≈253\n",
"[08/30] BA=0.8544 | F1m=0.8466 | Acc=0.9945 | best_n_estimators≈371\n",
"[09/30] BA=0.8551 | F1m=0.8475 | Acc=0.9945 | best_n_estimators≈800\n",
"[10/30] BA=0.8564 | F1m=0.8487 | Acc=0.9946 | best_n_estimators≈821\n",
"[11/30] BA=0.8498 | F1m=0.8422 | Acc=0.9944 | best_n_estimators≈133\n",
"[12/30] BA=0.8421 | F1m=0.8346 | Acc=0.9941 | best_n_estimators≈144\n",
"[13/30] BA=0.8523 | F1m=0.8445 | Acc=0.9945 | best_n_estimators≈612\n",
"[14/30] BA=0.8519 | F1m=0.8442 | Acc=0.9945 | best_n_estimators≈193\n",
"[15/30] BA=0.8521 | F1m=0.8443 | Acc=0.9945 | best_n_estimators≈423\n",
"[16/30] BA=0.8522 | F1m=0.8445 | Acc=0.9945 | best_n_estimators≈429\n",
"[17/30] BA=0.8535 | F1m=0.8458 | Acc=0.9945 | best_n_estimators≈394\n",
"[18/30] BA=0.8620 | F1m=0.8543 | Acc=0.9948 | best_n_estimators≈360\n",
"[19/30] BA=0.8537 | F1m=0.8460 | Acc=0.9945 | best_n_estimators≈116\n",
"[20/30] BA=0.8515 | F1m=0.8438 | Acc=0.9944 | best_n_estimators≈216\n",
"[21/30] BA=0.8515 | F1m=0.8438 | Acc=0.9944 | best_n_estimators≈142\n",
"[22/30] BA=0.8599 | F1m=0.8523 | Acc=0.9947 | best_n_estimators≈666\n",
"[23/30] BA=0.8618 | F1m=0.8541 | Acc=0.9947 | best_n_estimators≈222\n",
"[24/30] BA=0.8560 | F1m=0.8483 | Acc=0.9946 | best_n_estimators≈646\n",
"[25/30] BA=0.8560 | F1m=0.8484 | Acc=0.9946 | best_n_estimators≈487\n",
"[26/30] BA=0.8516 | F1m=0.8439 | Acc=0.9945 | best_n_estimators≈465\n",
"[27/30] BA=0.8539 | F1m=0.8460 | Acc=0.9945 | best_n_estimators≈577\n",
"[28/30] BA=0.8529 | F1m=0.8452 | Acc=0.9945 | best_n_estimators≈1658\n",
"[29/30] BA=0.8571 | F1m=0.8493 | Acc=0.9946 | best_n_estimators≈250\n",
"[30/30] BA=0.8524 | F1m=0.8446 | Acc=0.9945 | best_n_estimators≈869\n",
"\n",
"=== Best Params (by balanced_accuracy) ===\n",
"{'eta': 0.03, 'max_depth': 4, 'min_child_weight': 2.0, 'subsample': 0.8, 'colsample_bytree': 1.0, 'lambda': 1.0, 'alpha': 0.1, 'gamma': 0.1}\n",
"Best mean_best_iters: 360\n",
"\n",
"Top-5 tuning results:\n",
" iter eta max_depth min_child_weight subsample colsample_bytree \\\n",
"0 18 0.03 4 2.0 0.8 1.0 \n",
"1 23 0.05 4 5.0 0.8 1.0 \n",
"2 22 0.02 4 1.0 0.6 0.9 \n",
"3 7 0.05 7 5.0 0.6 0.7 \n",
"4 3 0.03 4 5.0 0.8 0.8 \n",
"\n",
" lambda alpha gamma mean_best_iters accuracy balanced_accuracy \\\n",
"0 1.0 0.1 0.1 360 0.994763 0.861978 \n",
"1 0.5 0.1 0.3 222 0.994748 0.861802 \n",
"2 3.0 0.3 0.1 666 0.994686 0.859866 \n",
"3 2.0 0.1 0.0 253 0.994655 0.858883 \n",
"4 1.0 0.0 0.5 470 0.994624 0.857905 \n",
"\n",
" f1_macro \n",
"0 0.854291 \n",
"1 0.854148 \n",
"2 0.852296 \n",
"3 0.851186 \n",
"4 0.850265 \n"
]
}
],
"source": [
"# === Robust, reproducible no-lookahead XGBoost (xgb.train) with tuning ===\n",
"\n",
"import numpy as np\n",
"import pandas as pd\n",
"from sklearn.model_selection import TimeSeriesSplit\n",
"from sklearn.metrics import accuracy_score, balanced_accuracy_score, f1_score, classification_report\n",
"from sklearn.utils.class_weight import compute_class_weight\n",
"import xgboost as xgb\n",
"\n",
"# ---------------- Config ----------------\n",
"SEED = 42\n",
"np.random.seed(SEED)\n",
"\n",
"N_ITER = 30 # hyperparam search iterations\n",
"N_SPLITS = 5 # TimeSeries CV splits\n",
"GAP_BARS = 5 # purge gap between train/valid\n",
"NUM_BOOST_ROUND = 3000\n",
"EARLY_STOP_ROUNDS = 150\n",
"\n",
"# ---------------- 1) Load ----------------\n",
"df = train_df.copy()\n",
"\n",
"# Target\n",
"y = pd.to_numeric(df[\"target_signal\"], errors=\"coerce\")\n",
"valid_idx = y.notna()\n",
"df = df.loc[valid_idx].copy()\n",
"y = y.loc[valid_idx]\n",
"\n",
"# Features\n",
"X = df.drop(columns=[\"target_signal\"]).copy()\n",
"\n",
"# Replace ±inf with 0 (no NaNs)\n",
"X = X.replace([np.inf, -np.inf], 0)\n",
"\n",
"# Ensure numeric array\n",
"X_np = X.to_numpy(dtype=float, copy=False)\n",
"\n",
"# Label encoding (supports {-1,0,1} or binary)\n",
"valid_labels = sorted(y.unique())\n",
"label_to_idx = {lab: i for i, lab in enumerate(valid_labels)}\n",
"idx_to_label = {i: lab for lab, i in label_to_idx.items()}\n",
"y_enc = pd.Series(y.map(label_to_idx).astype(int)).reset_index(drop=True)\n",
"\n",
"num_classes = len(valid_labels)\n",
"is_multiclass = num_classes > 2\n",
"\n",
"# ---------------- 2) XGBoost params ----------------\n",
"OBJECTIVE = \"multi:softprob\" if is_multiclass else \"binary:logistic\"\n",
"EVAL_METRIC = \"mlogloss\" if is_multiclass else \"logloss\"\n",
"\n",
"BASE_PARAMS = {\n",
" \"objective\": OBJECTIVE,\n",
" \"eval_metric\": EVAL_METRIC,\n",
" \"eta\": 0.03,\n",
" \"max_depth\": 6, # <- INT\n",
" \"min_child_weight\": 1.0,\n",
" \"subsample\": 0.8,\n",
" \"colsample_bytree\": 0.8,\n",
" \"lambda\": 1.0,\n",
" \"alpha\": 0.0,\n",
" \"gamma\": 0.0,\n",
" \"tree_method\": \"hist\",\n",
" \"seed\": SEED,\n",
" \"verbosity\": 0,\n",
"}\n",
"if is_multiclass:\n",
" BASE_PARAMS[\"num_class\"] = num_classes\n",
"\n",
"# ---------------- 3) Search space ----------------\n",
"# Keep integer params as Python ints to start with\n",
"SEARCH_SPACE = {\n",
" \"eta\": np.array([0.01, 0.015, 0.02, 0.03, 0.05, 0.07], dtype=float),\n",
" \"max_depth\": np.array([4, 5, 6, 7, 8], dtype=int), # <- ints\n",
" \"min_child_weight\": np.array([1.0, 2.0, 3.0, 5.0], dtype=float),\n",
" \"subsample\": np.array([0.6, 0.7, 0.8, 0.9, 1.0], dtype=float),\n",
" \"colsample_bytree\": np.array([0.6, 0.7, 0.8, 0.9, 1.0], dtype=float),\n",
" \"lambda\": np.array([0.5, 1.0, 2.0, 3.0, 5.0], dtype=float),\n",
" \"alpha\": np.array([0.0, 0.1, 0.3, 0.5], dtype=float),\n",
" \"gamma\": np.array([0.0, 0.1, 0.3, 0.5], dtype=float),\n",
"}\n",
"\n",
"# ---------------- 4) Helpers ----------------\n",
"def compute_sample_weights(y_arr: np.ndarray) -> np.ndarray:\n",
" classes = np.unique(y_arr)\n",
" cw = compute_class_weight(class_weight=\"balanced\", classes=classes, y=y_arr)\n",
" mp = {cls: w for cls, w in zip(classes, cw)}\n",
" return np.vectorize(mp.get)(y_arr).astype(float)\n",
"\n",
"def sample_params(rng: np.random.Generator):\n",
" return {k: rng.choice(v) for k, v in SEARCH_SPACE.items()}\n",
"\n",
"def cast_params_for_xgb(params: dict) -> dict:\n",
" \"\"\"Ensure correct Python types for xgboost.\"\"\"\n",
" out = {}\n",
" for k, v in params.items():\n",
" if isinstance(v, np.generic): # numpy scalar -> Python scalar\n",
" v = v.item()\n",
" if k in {\"max_depth\"}:\n",
" v = int(v) # must be int\n",
" else:\n",
" v = float(v) if isinstance(v, (int, float)) else v\n",
" out[k] = v\n",
" return out\n",
"\n",
"def _best_ntree_limit(booster):\n",
" if getattr(booster, \"best_ntree_limit\", 0):\n",
" return int(booster.best_ntree_limit)\n",
" if getattr(booster, \"best_iteration\", None) is not None:\n",
" return int(booster.best_iteration) + 1\n",
" return int(NUM_BOOST_ROUND)\n",
"\n",
"def _predict_labels(booster, dmat, is_multiclass):\n",
" ntree = _best_ntree_limit(booster)\n",
" try:\n",
" pred = booster.predict(dmat, ntree_limit=ntree)\n",
" except TypeError:\n",
" try:\n",
" pred = booster.predict(dmat, iteration_range=(0, ntree))\n",
" except TypeError:\n",
" pred = booster.predict(dmat)\n",
" pred = np.asarray(pred)\n",
" if is_multiclass:\n",
" return pred.argmax(axis=1)\n",
" return (pred >= 0.5).astype(int)\n",
"\n",
"# ---------------- 5) CV evaluation ----------------\n",
"tscv = TimeSeriesSplit(n_splits=N_SPLITS, gap=GAP_BARS)\n",
"\n",
"def evaluate_params(params: dict, X_np: np.ndarray, y_enc_series: pd.Series):\n",
" # cast types robustly for this trial\n",
" trial_params = cast_params_for_xgb(params)\n",
" fold_metrics, best_iters = [], []\n",
" for fold, (tr_idx, te_idx) in enumerate(tscv.split(X_np), start=1):\n",
" X_tr, X_te = X_np[tr_idx], X_np[te_idx]\n",
" y_tr = y_enc_series.iloc[tr_idx].to_numpy()\n",
" y_te = y_enc_series.iloc[te_idx].to_numpy()\n",
"\n",
" w_tr = compute_sample_weights(y_tr)\n",
"\n",
" dtrain = xgb.DMatrix(X_tr, label=y_tr, weight=w_tr)\n",
" dvalid = xgb.DMatrix(X_te, label=y_te)\n",
"\n",
" bst = xgb.train(\n",
" params={**BASE_PARAMS, **trial_params},\n",
" dtrain=dtrain,\n",
" num_boost_round=NUM_BOOST_ROUND,\n",
" evals=[(dtrain, \"train\"), (dvalid, \"valid\")],\n",
" early_stopping_rounds=EARLY_STOP_ROUNDS,\n",
" verbose_eval=False\n",
" )\n",
"\n",
" best_iters.append(_best_ntree_limit(bst))\n",
"\n",
" y_pred_enc = _predict_labels(bst, dvalid, is_multiclass)\n",
" y_true_lbl = np.vectorize(idx_to_label.get)(y_te)\n",
" y_pred_lbl = np.vectorize(idx_to_label.get)(y_pred_enc)\n",
"\n",
" fold_metrics.append({\n",
" \"accuracy\": accuracy_score(y_true_lbl, y_pred_lbl),\n",
" \"balanced_accuracy\": balanced_accuracy_score(y_true_lbl, y_pred_lbl),\n",
" \"f1_macro\": f1_score(y_true_lbl, y_pred_lbl, average=\"macro\", zero_division=0),\n",
" })\n",
"\n",
" mean_metrics = {k: float(np.mean([m[k] for m in fold_metrics])) for k in fold_metrics[0].keys()}\n",
" mean_best_iters = int(np.round(np.mean(best_iters)))\n",
" return mean_metrics, mean_best_iters\n",
"\n",
"# ---------------- 6) Randomized search ----------------\n",
"rng = np.random.default_rng(SEED)\n",
"results = []\n",
"\n",
"print(\"\\n=== Hyperparameter Tuning (Randomized Search) ===\")\n",
"for i in range(1, N_ITER + 1):\n",
" params = sample_params(rng)\n",
" mean_metrics, mean_best_iters = evaluate_params(params, X_np, y_enc)\n",
" entry = {\"iter\": i, **params, \"mean_best_iters\": mean_best_iters, **mean_metrics}\n",
" results.append(entry)\n",
" print(f\"[{i:02d}/{N_ITER}] BA={entry['balanced_accuracy']:.4f} | F1m={entry['f1_macro']:.4f} | \"\n",
" f\"Acc={entry['accuracy']:.4f} | best_n_estimators≈{mean_best_iters}\")\n",
"\n",
"results_df = pd.DataFrame(results).sort_values(\n",
" by=[\"balanced_accuracy\", \"f1_macro\", \"accuracy\"], ascending=False\n",
").reset_index(drop=True)\n",
"\n",
"best = results_df.iloc[0].to_dict()\n",
"best_params_raw = {k: best[k] for k in SEARCH_SPACE.keys()}\n",
"best_params = cast_params_for_xgb(best_params_raw) # <-- enforce correct types here\n",
"best_n_estimators = int(best[\"mean_best_iters\"])\n",
"\n",
"print(\"\\n=== Best Params (by balanced_accuracy) ===\")\n",
"print(best_params)\n",
"print(f\"Best mean_best_iters: {best_n_estimators}\")\n",
"print(\"\\nTop-5 tuning results:\")\n",
"print(results_df.head(5))\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {},
"outputs": [],
"source": [
"def _num_boosted_rounds(booster) -> int:\n",
" \"\"\"How many trees the model actually has.\"\"\"\n",
" try:\n",
" return int(booster.num_boosted_rounds())\n",
" except AttributeError:\n",
" # Fallback for very old versions\n",
" try:\n",
" return int(getattr(booster, \"best_iteration\")) + 1\n",
" except Exception:\n",
" return int(NUM_BOOST_ROUND)\n",
"\n",
"def _best_iteration_limit(booster) -> int:\n",
" \"\"\"\n",
" Best usable iteration count for prediction:\n",
" prefer early-stopped best_iteration+1 if available,\n",
" otherwise the model's actual number of rounds.\n",
" \"\"\"\n",
" bi = getattr(booster, \"best_iteration\", None)\n",
" if bi is not None:\n",
" return int(bi) + 1\n",
" return _num_boosted_rounds(booster)\n",
"\n",
"def _predict_labels(booster, dmat, is_multiclass):\n",
" \"\"\"\n",
" Works for XGBoost >= 2.0 (no ntree_limit). Uses iteration_range\n",
" and clamps to the model's true number of trees to avoid OOR errors.\n",
" \"\"\"\n",
" # What we'd *like* to use\n",
" limit = _best_iteration_limit(booster)\n",
" # What the model *has*\n",
" total = _num_boosted_rounds(booster)\n",
" # Clamp\n",
" end = min(limit, total)\n",
"\n",
" # Predict\n",
" try:\n",
" pred = booster.predict(dmat, iteration_range=(0, end))\n",
" except TypeError:\n",
" # Very old versions: no iteration_range\n",
" pred = booster.predict(dmat)\n",
"\n",
" pred = np.asarray(pred)\n",
" if is_multiclass:\n",
" return pred.argmax(axis=1)\n",
" return (pred >= 0.5).astype(int)\n"
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {
"collapsed": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Final model saved -> xgb_trading_model_tuned.json\n",
"\n",
"In-sample classification report (sanity only; use CV for OOS):\n",
" precision recall f1-score support\n",
"\n",
" -1.0 0.93 0.94 0.94 852\n",
" 0.0 1.00 1.00 1.00 75983\n",
" 1.0 0.91 0.96 0.94 852\n",
"\n",
" accuracy 1.00 77687\n",
" macro avg 0.95 0.97 0.96 77687\n",
"weighted avg 1.00 1.00 1.00 77687\n",
"\n"
]
}
],
"source": [
"# === Final XGBoost training with your tuned params (no lookahead) ===\n",
"\n",
"import numpy as np\n",
"import pandas as pd\n",
"from sklearn.metrics import classification_report\n",
"from sklearn.utils.class_weight import compute_class_weight\n",
"import xgboost as xgb\n",
"\n",
"# ---------------- Config ----------------\n",
"SEED = 42\n",
"BEST_PARAMS = {\n",
" \"eta\": 0.03,\n",
" \"max_depth\": 4, # must be int\n",
" \"min_child_weight\": 2.0,\n",
" \"subsample\": 0.8,\n",
" \"colsample_bytree\": 1.0,\n",
" \"lambda\": 1.0,\n",
" \"alpha\": 0.1,\n",
" \"gamma\": 0.1,\n",
"}\n",
"BEST_N_ESTIMATORS = 360 # from your tuning\n",
"\n",
"# ---------------- 1) Prepare data ----------------\n",
"df = train_df.copy()\n",
"\n",
"# Target (assumed cleaned): -1/0/1 (or binary)\n",
"y = pd.to_numeric(df[\"target_signal\"], errors=\"coerce\")\n",
"keep = y.notna()\n",
"df = df.loc[keep].copy()\n",
"y = y.loc[keep]\n",
"\n",
"# Features\n",
"X = df.drop(columns=[\"target_signal\"]).copy()\n",
"\n",
"# Safety: replace ±inf with 0 (no NaNs)\n",
"X = X.replace([np.inf, -np.inf], 0)\n",
"\n",
"# To numpy\n",
"X_np = X.to_numpy(dtype=float, copy=False)\n",
"\n",
"# Encode labels\n",
"labels_sorted = sorted(y.unique())\n",
"lab2idx = {lab: i for i, lab in enumerate(labels_sorted)}\n",
"idx2lab = {i: lab for lab, i in lab2idx.items()}\n",
"y_enc = y.map(lab2idx).astype(int).to_numpy()\n",
"\n",
"num_classes = len(labels_sorted)\n",
"is_multiclass = num_classes > 2\n",
"\n",
"# ---------------- 2) XGBoost params ----------------\n",
"params = {\n",
" \"objective\": \"multi:softprob\" if is_multiclass else \"binary:logistic\",\n",
" \"eval_metric\": \"mlogloss\" if is_multiclass else \"logloss\",\n",
" \"tree_method\": \"hist\",\n",
" \"seed\": SEED,\n",
" \"verbosity\": 0,\n",
" **BEST_PARAMS,\n",
"}\n",
"if is_multiclass:\n",
" params[\"num_class\"] = num_classes\n",
"\n",
"# ---------------- 3) Class-balanced weights ----------------\n",
"classes = np.unique(y_enc)\n",
"cw = compute_class_weight(class_weight=\"balanced\", classes=classes, y=y_enc)\n",
"weight_map = {cls: w for cls, w in zip(classes, cw)}\n",
"w_full = np.vectorize(weight_map.get)(y_enc).astype(float)\n",
"\n",
"# ---------------- 4) Train final model ----------------\n",
"dtrain_full = xgb.DMatrix(X_np, label=y_enc, weight=w_full)\n",
"bst_final = xgb.train(\n",
" params=params,\n",
" dtrain=dtrain_full,\n",
" num_boost_round=BEST_N_ESTIMATORS, # use tuned trees\n",
" evals=[(dtrain_full, \"train\")],\n",
" verbose_eval=False\n",
")\n",
"\n",
"bst_final.save_model(\"xgb_trading_model_tuned.json\")\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Predictions for Trading"
]
},
{
"cell_type": "code",
"execution_count": 194,
"metadata": {},
"outputs": [],
"source": [
"test_df.fillna(0, inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 195,
"metadata": {},
"outputs": [],
"source": [
"# Remove rows where all columns are 0\n",
"test_df = test_df.loc[~(train_df.eq(0).all(axis=1))].copy()\n"
]
},
{
"cell_type": "code",
"execution_count": 196,
"metadata": {},
"outputs": [],
"source": [
"cols_to_drop = [\"Quarter\", \"Month_sin\", \"Date_sin\", \"ema\", \"Oil_Close\", \"CCI_2\",\n",
" \"KeltnerWidth_diff_22\",\"KeltnerWidth_ratio_2_15\", \"ADX_diff_12\", \"ADX_diff_3\",\"ADX_diff_9\",\n",
" \"CMO_2\", \"KeltnerWidth_diff_13\", \"ADX_ratio_2_14\", \"ADX_ratio_5_14\", \"ChaikinVol_2_5\", \"CCI_ratio_2_8\",\n",
" \"CMO_2\", \"KeltnerWidth_15\", \"KeltnerWidth_20\", \"KeltnerWidth_24\",\n",
" \"ChaikinVol_3_10\", \"Unnamed: 0\", \"sma\", \"Session\", \"Gold_Close\", \"Oil_Volume\", \"Diff_10Y_2Y\"]\n",
"test_df = test_df.drop(columns=cols_to_drop)\n"
]
},
{
"cell_type": "code",
"execution_count": 197,
"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>Open</th>\n",
" <th>High</th>\n",
" <th>Low</th>\n",
" <th>Close</th>\n",
" <th>Volume</th>\n",
" <th>Hour_sin</th>\n",
" <th>DOW_sin</th>\n",
" <th>DateTime</th>\n",
" <th>ADX_14</th>\n",
" <th>CCI_14</th>\n",
" <th>...</th>\n",
" <th>CCI_diff_18</th>\n",
" <th>CCI_ratio_8_20</th>\n",
" <th>CCI_ratio_2_20</th>\n",
" <th>CMO_diff_12</th>\n",
" <th>CMO_ratio_2_14</th>\n",
" <th>KeltnerWidth_diff_9</th>\n",
" <th>KeltnerWidth_ratio_15_24</th>\n",
" <th>KeltnerWidth_ratio_2_24</th>\n",
" <th>signal</th>\n",
" <th>target_signal</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1.11810</td>\n",
" <td>1.11944</td>\n",
" <td>1.11797</td>\n",
" <td>1.11923</td>\n",
" <td>963</td>\n",
" <td>-0.25882</td>\n",
" <td>0.433884</td>\n",
" <td>07/03/19 23:00</td>\n",
" <td>47.175526</td>\n",
" <td>-94.082772</td>\n",
" <td>...</td>\n",
" <td>-185.557987</td>\n",
" <td>0.716473</td>\n",
" <td>-0.560736</td>\n",
" <td>-112.484341</td>\n",
" <td>-0.708596</td>\n",
" <td>-0.000172</td>\n",
" <td>1.118244</td>\n",
" <td>0.950727</td>\n",
" <td>-1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1.11916</td>\n",
" <td>1.11985</td>\n",
" <td>1.11916</td>\n",
" <td>1.11976</td>\n",
" <td>764</td>\n",
" <td>0.25882</td>\n",
" <td>-0.433884</td>\n",
" <td>08/03/19 1:00</td>\n",
" <td>48.511681</td>\n",
" <td>-75.385246</td>\n",
" <td>...</td>\n",
" <td>-161.694000</td>\n",
" <td>0.444908</td>\n",
" <td>-0.701553</td>\n",
" <td>-84.682762</td>\n",
" <td>-0.884346</td>\n",
" <td>-0.000113</td>\n",
" <td>1.081275</td>\n",
" <td>0.627573</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1.11976</td>\n",
" <td>1.11977</td>\n",
" <td>1.11855</td>\n",
" <td>1.11907</td>\n",
" <td>1620</td>\n",
" <td>0.50000</td>\n",
" <td>-0.433884</td>\n",
" <td>08/03/19 2:00</td>\n",
" <td>49.885998</td>\n",
" <td>-75.857692</td>\n",
" <td>...</td>\n",
" <td>-24.224772</td>\n",
" <td>0.522943</td>\n",
" <td>0.733476</td>\n",
" <td>-87.926650</td>\n",
" <td>0.878839</td>\n",
" <td>-0.000085</td>\n",
" <td>1.062512</td>\n",
" <td>0.744744</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1.11906</td>\n",
" <td>1.11944</td>\n",
" <td>1.11848</td>\n",
" <td>1.11881</td>\n",
" <td>1091</td>\n",
" <td>0.70711</td>\n",
" <td>-0.433884</td>\n",
" <td>08/03/19 3:00</td>\n",
" <td>51.177650</td>\n",
" <td>-74.037359</td>\n",
" <td>...</td>\n",
" <td>-18.661185</td>\n",
" <td>0.579275</td>\n",
" <td>0.781300</td>\n",
" <td>-92.209732</td>\n",
" <td>0.900446</td>\n",
" <td>-0.000052</td>\n",
" <td>1.039139</td>\n",
" <td>0.687263</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1.11881</td>\n",
" <td>1.12003</td>\n",
" <td>1.11860</td>\n",
" <td>1.11979</td>\n",
" <td>992</td>\n",
" <td>0.86603</td>\n",
" <td>-0.433884</td>\n",
" <td>08/03/19 4:00</td>\n",
" <td>51.686381</td>\n",
" <td>-55.926237</td>\n",
" <td>...</td>\n",
" <td>-137.969772</td>\n",
" <td>-0.063312</td>\n",
" <td>-0.934976</td>\n",
" <td>-82.338745</td>\n",
" <td>-1.192046</td>\n",
" <td>-0.000043</td>\n",
" <td>1.032691</td>\n",
" <td>0.873788</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33324</th>\n",
" <td>1.03502</td>\n",
" <td>1.03548</td>\n",
" <td>1.03438</td>\n",
" <td>1.03490</td>\n",
" <td>2323</td>\n",
" <td>-0.96593</td>\n",
" <td>0.781831</td>\n",
" <td>31/12/24 19:00</td>\n",
" <td>23.611404</td>\n",
" <td>-160.337614</td>\n",
" <td>...</td>\n",
" <td>-150.663910</td>\n",
" <td>0.499089</td>\n",
" <td>0.306752</td>\n",
" <td>-146.708669</td>\n",
" <td>0.415789</td>\n",
" <td>-0.000087</td>\n",
" <td>1.060976</td>\n",
" <td>0.938986</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33325</th>\n",
" <td>1.03489</td>\n",
" <td>1.03566</td>\n",
" <td>1.03455</td>\n",
" <td>1.03529</td>\n",
" <td>1900</td>\n",
" <td>-0.86603</td>\n",
" <td>0.781831</td>\n",
" <td>31/12/24 20:00</td>\n",
" <td>25.551370</td>\n",
" <td>-119.355906</td>\n",
" <td>...</td>\n",
" <td>-228.152865</td>\n",
" <td>0.474172</td>\n",
" <td>-0.412832</td>\n",
" <td>-120.580881</td>\n",
" <td>-0.558554</td>\n",
" <td>-0.000060</td>\n",
" <td>1.042922</td>\n",
" <td>0.828449</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33326</th>\n",
" <td>1.03526</td>\n",
" <td>1.03645</td>\n",
" <td>1.03516</td>\n",
" <td>1.03547</td>\n",
" <td>1445</td>\n",
" <td>-0.70711</td>\n",
" <td>0.781831</td>\n",
" <td>31/12/24 21:00</td>\n",
" <td>26.385304</td>\n",
" <td>-85.267741</td>\n",
" <td>...</td>\n",
" <td>-185.465493</td>\n",
" <td>0.389379</td>\n",
" <td>-0.561173</td>\n",
" <td>-101.534169</td>\n",
" <td>-0.781851</td>\n",
" <td>-0.000046</td>\n",
" <td>1.032793</td>\n",
" <td>0.875523</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33327</th>\n",
" <td>1.03547</td>\n",
" <td>1.03631</td>\n",
" <td>1.03544</td>\n",
" <td>1.03582</td>\n",
" <td>1208</td>\n",
" <td>-0.50000</td>\n",
" <td>0.781831</td>\n",
" <td>31/12/24 22:00</td>\n",
" <td>27.159672</td>\n",
" <td>-69.016843</td>\n",
" <td>...</td>\n",
" <td>-164.506501</td>\n",
" <td>0.274403</td>\n",
" <td>-0.681386</td>\n",
" <td>-87.924347</td>\n",
" <td>-0.965948</td>\n",
" <td>-0.000015</td>\n",
" <td>1.011219</td>\n",
" <td>0.716892</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33328</th>\n",
" <td>1.03585</td>\n",
" <td>1.03608</td>\n",
" <td>1.03489</td>\n",
" <td>1.03493</td>\n",
" <td>616</td>\n",
" <td>-0.25882</td>\n",
" <td>0.781831</td>\n",
" <td>31/12/24 23:00</td>\n",
" <td>28.181968</td>\n",
" <td>-73.068540</td>\n",
" <td>...</td>\n",
" <td>-32.611841</td>\n",
" <td>0.508487</td>\n",
" <td>0.671512</td>\n",
" <td>-91.887164</td>\n",
" <td>0.912385</td>\n",
" <td>-0.000004</td>\n",
" <td>1.003260</td>\n",
" <td>0.817381</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>33329 rows × 32 columns</p>\n",
"</div>"
],
"text/plain": [
" Open High Low Close Volume Hour_sin DOW_sin \\\n",
"0 1.11810 1.11944 1.11797 1.11923 963 -0.25882 0.433884 \n",
"1 1.11916 1.11985 1.11916 1.11976 764 0.25882 -0.433884 \n",
"2 1.11976 1.11977 1.11855 1.11907 1620 0.50000 -0.433884 \n",
"3 1.11906 1.11944 1.11848 1.11881 1091 0.70711 -0.433884 \n",
"4 1.11881 1.12003 1.11860 1.11979 992 0.86603 -0.433884 \n",
"... ... ... ... ... ... ... ... \n",
"33324 1.03502 1.03548 1.03438 1.03490 2323 -0.96593 0.781831 \n",
"33325 1.03489 1.03566 1.03455 1.03529 1900 -0.86603 0.781831 \n",
"33326 1.03526 1.03645 1.03516 1.03547 1445 -0.70711 0.781831 \n",
"33327 1.03547 1.03631 1.03544 1.03582 1208 -0.50000 0.781831 \n",
"33328 1.03585 1.03608 1.03489 1.03493 616 -0.25882 0.781831 \n",
"\n",
" DateTime ADX_14 CCI_14 ... CCI_diff_18 \\\n",
"0 07/03/19 23:00 47.175526 -94.082772 ... -185.557987 \n",
"1 08/03/19 1:00 48.511681 -75.385246 ... -161.694000 \n",
"2 08/03/19 2:00 49.885998 -75.857692 ... -24.224772 \n",
"3 08/03/19 3:00 51.177650 -74.037359 ... -18.661185 \n",
"4 08/03/19 4:00 51.686381 -55.926237 ... -137.969772 \n",
"... ... ... ... ... ... \n",
"33324 31/12/24 19:00 23.611404 -160.337614 ... -150.663910 \n",
"33325 31/12/24 20:00 25.551370 -119.355906 ... -228.152865 \n",
"33326 31/12/24 21:00 26.385304 -85.267741 ... -185.465493 \n",
"33327 31/12/24 22:00 27.159672 -69.016843 ... -164.506501 \n",
"33328 31/12/24 23:00 28.181968 -73.068540 ... -32.611841 \n",
"\n",
" CCI_ratio_8_20 CCI_ratio_2_20 CMO_diff_12 CMO_ratio_2_14 \\\n",
"0 0.716473 -0.560736 -112.484341 -0.708596 \n",
"1 0.444908 -0.701553 -84.682762 -0.884346 \n",
"2 0.522943 0.733476 -87.926650 0.878839 \n",
"3 0.579275 0.781300 -92.209732 0.900446 \n",
"4 -0.063312 -0.934976 -82.338745 -1.192046 \n",
"... ... ... ... ... \n",
"33324 0.499089 0.306752 -146.708669 0.415789 \n",
"33325 0.474172 -0.412832 -120.580881 -0.558554 \n",
"33326 0.389379 -0.561173 -101.534169 -0.781851 \n",
"33327 0.274403 -0.681386 -87.924347 -0.965948 \n",
"33328 0.508487 0.671512 -91.887164 0.912385 \n",
"\n",
" KeltnerWidth_diff_9 KeltnerWidth_ratio_15_24 KeltnerWidth_ratio_2_24 \\\n",
"0 -0.000172 1.118244 0.950727 \n",
"1 -0.000113 1.081275 0.627573 \n",
"2 -0.000085 1.062512 0.744744 \n",
"3 -0.000052 1.039139 0.687263 \n",
"4 -0.000043 1.032691 0.873788 \n",
"... ... ... ... \n",
"33324 -0.000087 1.060976 0.938986 \n",
"33325 -0.000060 1.042922 0.828449 \n",
"33326 -0.000046 1.032793 0.875523 \n",
"33327 -0.000015 1.011219 0.716892 \n",
"33328 -0.000004 1.003260 0.817381 \n",
"\n",
" signal target_signal \n",
"0 -1 1 \n",
"1 0 0 \n",
"2 0 0 \n",
"3 0 0 \n",
"4 0 0 \n",
"... ... ... \n",
"33324 0 0 \n",
"33325 0 0 \n",
"33326 0 0 \n",
"33327 0 0 \n",
"33328 1 0 \n",
"\n",
"[33329 rows x 32 columns]"
]
},
"execution_count": 197,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\n",
"# Replace the bad value in CCI_8_20 with 0 and ensure numeric dtype\n",
"test_df[\"CCI_ratio_8_20\"] = pd.to_numeric(test_df[\"CCI_ratio_8_20\"].replace(\"#NAME?\", 0), errors=\"coerce\")\n",
"test_df\n"
]
},
{
"cell_type": "code",
"execution_count": 198,
"metadata": {},
"outputs": [],
"source": [
"test_df.to_csv('test_with_dates.csv')\n",
"test_df = test_df.drop(columns= ['DateTime', 'target_signal', \"Close\",\"Open\",\"Volume\",\"High\", \"Low\", ])"
]
},
{
"cell_type": "code",
"execution_count": 199,
"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>Hour_sin</th>\n",
" <th>DOW_sin</th>\n",
" <th>ADX_14</th>\n",
" <th>CCI_14</th>\n",
" <th>PPO_12_26</th>\n",
" <th>CMO_14</th>\n",
" <th>ADX_2</th>\n",
" <th>ADX_5</th>\n",
" <th>CCI_8</th>\n",
" <th>CCI_20</th>\n",
" <th>...</th>\n",
" <th>CCI_diff_12</th>\n",
" <th>CCI_diff_18</th>\n",
" <th>CCI_ratio_8_20</th>\n",
" <th>CCI_ratio_2_20</th>\n",
" <th>CMO_diff_12</th>\n",
" <th>CMO_ratio_2_14</th>\n",
" <th>KeltnerWidth_diff_9</th>\n",
" <th>KeltnerWidth_ratio_15_24</th>\n",
" <th>KeltnerWidth_ratio_2_24</th>\n",
" <th>signal</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>-0.25882</td>\n",
" <td>0.433884</td>\n",
" <td>47.175526</td>\n",
" <td>-94.082772</td>\n",
" <td>-0.334313</td>\n",
" <td>-52.333017</td>\n",
" <td>60.151324</td>\n",
" <td>81.362946</td>\n",
" <td>-85.182427</td>\n",
" <td>-118.891320</td>\n",
" <td>...</td>\n",
" <td>-33.708893</td>\n",
" <td>-185.557987</td>\n",
" <td>0.716473</td>\n",
" <td>-0.560736</td>\n",
" <td>-112.484341</td>\n",
" <td>-0.708596</td>\n",
" <td>-0.000172</td>\n",
" <td>1.118244</td>\n",
" <td>0.950727</td>\n",
" <td>-1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.25882</td>\n",
" <td>-0.433884</td>\n",
" <td>48.511681</td>\n",
" <td>-75.385246</td>\n",
" <td>-0.384653</td>\n",
" <td>-46.360881</td>\n",
" <td>38.321881</td>\n",
" <td>76.177460</td>\n",
" <td>-42.278460</td>\n",
" <td>-95.027334</td>\n",
" <td>...</td>\n",
" <td>-52.748874</td>\n",
" <td>-161.694000</td>\n",
" <td>0.444908</td>\n",
" <td>-0.701553</td>\n",
" <td>-84.682762</td>\n",
" <td>-0.884346</td>\n",
" <td>-0.000113</td>\n",
" <td>1.081275</td>\n",
" <td>0.627573</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.50000</td>\n",
" <td>-0.433884</td>\n",
" <td>49.885998</td>\n",
" <td>-75.857692</td>\n",
" <td>-0.430903</td>\n",
" <td>-49.155591</td>\n",
" <td>38.771059</td>\n",
" <td>73.248674</td>\n",
" <td>-47.531006</td>\n",
" <td>-90.891438</td>\n",
" <td>...</td>\n",
" <td>-43.360433</td>\n",
" <td>-24.224772</td>\n",
" <td>0.522943</td>\n",
" <td>0.733476</td>\n",
" <td>-87.926650</td>\n",
" <td>0.878839</td>\n",
" <td>-0.000085</td>\n",
" <td>1.062512</td>\n",
" <td>0.744744</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.70711</td>\n",
" <td>-0.433884</td>\n",
" <td>51.177650</td>\n",
" <td>-74.037359</td>\n",
" <td>-0.465194</td>\n",
" <td>-50.208330</td>\n",
" <td>42.001402</td>\n",
" <td>71.053743</td>\n",
" <td>-49.428255</td>\n",
" <td>-85.327852</td>\n",
" <td>...</td>\n",
" <td>-35.899597</td>\n",
" <td>-18.661185</td>\n",
" <td>0.579275</td>\n",
" <td>0.781300</td>\n",
" <td>-92.209732</td>\n",
" <td>0.900446</td>\n",
" <td>-0.000052</td>\n",
" <td>1.039139</td>\n",
" <td>0.687263</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.86603</td>\n",
" <td>-0.433884</td>\n",
" <td>51.686381</td>\n",
" <td>-55.926237</td>\n",
" <td>-0.472084</td>\n",
" <td>-38.562741</td>\n",
" <td>43.776003</td>\n",
" <td>63.825371</td>\n",
" <td>4.514309</td>\n",
" <td>-71.303106</td>\n",
" <td>...</td>\n",
" <td>-75.817414</td>\n",
" <td>-137.969772</td>\n",
" <td>-0.063312</td>\n",
" <td>-0.934976</td>\n",
" <td>-82.338745</td>\n",
" <td>-1.192046</td>\n",
" <td>-0.000043</td>\n",
" <td>1.032691</td>\n",
" <td>0.873788</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33324</th>\n",
" <td>-0.96593</td>\n",
" <td>0.781831</td>\n",
" <td>23.611404</td>\n",
" <td>-160.337614</td>\n",
" <td>-0.051720</td>\n",
" <td>-49.033438</td>\n",
" <td>97.675231</td>\n",
" <td>61.594852</td>\n",
" <td>-108.467310</td>\n",
" <td>-217.330577</td>\n",
" <td>...</td>\n",
" <td>-108.863267</td>\n",
" <td>-150.663910</td>\n",
" <td>0.499089</td>\n",
" <td>0.306752</td>\n",
" <td>-146.708669</td>\n",
" <td>0.415789</td>\n",
" <td>-0.000087</td>\n",
" <td>1.060976</td>\n",
" <td>0.938986</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33325</th>\n",
" <td>-0.86603</td>\n",
" <td>0.781831</td>\n",
" <td>25.551370</td>\n",
" <td>-119.355906</td>\n",
" <td>-0.082496</td>\n",
" <td>-43.107288</td>\n",
" <td>77.473593</td>\n",
" <td>64.304066</td>\n",
" <td>-76.572200</td>\n",
" <td>-161.486199</td>\n",
" <td>...</td>\n",
" <td>-84.913999</td>\n",
" <td>-228.152865</td>\n",
" <td>0.474172</td>\n",
" <td>-0.412832</td>\n",
" <td>-120.580881</td>\n",
" <td>-0.558554</td>\n",
" <td>-0.000060</td>\n",
" <td>1.042922</td>\n",
" <td>0.828449</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33326</th>\n",
" <td>-0.70711</td>\n",
" <td>0.781831</td>\n",
" <td>26.385304</td>\n",
" <td>-85.267741</td>\n",
" <td>-0.116771</td>\n",
" <td>-40.333690</td>\n",
" <td>61.200479</td>\n",
" <td>58.403529</td>\n",
" <td>-46.257822</td>\n",
" <td>-118.798827</td>\n",
" <td>...</td>\n",
" <td>-72.541004</td>\n",
" <td>-185.465493</td>\n",
" <td>0.389379</td>\n",
" <td>-0.561173</td>\n",
" <td>-101.534169</td>\n",
" <td>-0.781851</td>\n",
" <td>-0.000046</td>\n",
" <td>1.032793</td>\n",
" <td>0.875523</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33327</th>\n",
" <td>-0.50000</td>\n",
" <td>0.781831</td>\n",
" <td>27.159672</td>\n",
" <td>-69.016843</td>\n",
" <td>-0.151568</td>\n",
" <td>-34.860425</td>\n",
" <td>53.063921</td>\n",
" <td>53.683099</td>\n",
" <td>-26.847505</td>\n",
" <td>-97.839834</td>\n",
" <td>...</td>\n",
" <td>-70.992329</td>\n",
" <td>-164.506501</td>\n",
" <td>0.274403</td>\n",
" <td>-0.681386</td>\n",
" <td>-87.924347</td>\n",
" <td>-0.965948</td>\n",
" <td>-0.000015</td>\n",
" <td>1.011219</td>\n",
" <td>0.716892</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33328</th>\n",
" <td>-0.25882</td>\n",
" <td>0.781831</td>\n",
" <td>28.181968</td>\n",
" <td>-73.068540</td>\n",
" <td>-0.186740</td>\n",
" <td>-41.146290</td>\n",
" <td>50.740874</td>\n",
" <td>52.519310</td>\n",
" <td>-50.481804</td>\n",
" <td>-99.278507</td>\n",
" <td>...</td>\n",
" <td>-48.796704</td>\n",
" <td>-32.611841</td>\n",
" <td>0.508487</td>\n",
" <td>0.671512</td>\n",
" <td>-91.887164</td>\n",
" <td>0.912385</td>\n",
" <td>-0.000004</td>\n",
" <td>1.003260</td>\n",
" <td>0.817381</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>33329 rows × 25 columns</p>\n",
"</div>"
],
"text/plain": [
" Hour_sin DOW_sin ADX_14 CCI_14 PPO_12_26 CMO_14 \\\n",
"0 -0.25882 0.433884 47.175526 -94.082772 -0.334313 -52.333017 \n",
"1 0.25882 -0.433884 48.511681 -75.385246 -0.384653 -46.360881 \n",
"2 0.50000 -0.433884 49.885998 -75.857692 -0.430903 -49.155591 \n",
"3 0.70711 -0.433884 51.177650 -74.037359 -0.465194 -50.208330 \n",
"4 0.86603 -0.433884 51.686381 -55.926237 -0.472084 -38.562741 \n",
"... ... ... ... ... ... ... \n",
"33324 -0.96593 0.781831 23.611404 -160.337614 -0.051720 -49.033438 \n",
"33325 -0.86603 0.781831 25.551370 -119.355906 -0.082496 -43.107288 \n",
"33326 -0.70711 0.781831 26.385304 -85.267741 -0.116771 -40.333690 \n",
"33327 -0.50000 0.781831 27.159672 -69.016843 -0.151568 -34.860425 \n",
"33328 -0.25882 0.781831 28.181968 -73.068540 -0.186740 -41.146290 \n",
"\n",
" ADX_2 ADX_5 CCI_8 CCI_20 ... CCI_diff_12 \\\n",
"0 60.151324 81.362946 -85.182427 -118.891320 ... -33.708893 \n",
"1 38.321881 76.177460 -42.278460 -95.027334 ... -52.748874 \n",
"2 38.771059 73.248674 -47.531006 -90.891438 ... -43.360433 \n",
"3 42.001402 71.053743 -49.428255 -85.327852 ... -35.899597 \n",
"4 43.776003 63.825371 4.514309 -71.303106 ... -75.817414 \n",
"... ... ... ... ... ... ... \n",
"33324 97.675231 61.594852 -108.467310 -217.330577 ... -108.863267 \n",
"33325 77.473593 64.304066 -76.572200 -161.486199 ... -84.913999 \n",
"33326 61.200479 58.403529 -46.257822 -118.798827 ... -72.541004 \n",
"33327 53.063921 53.683099 -26.847505 -97.839834 ... -70.992329 \n",
"33328 50.740874 52.519310 -50.481804 -99.278507 ... -48.796704 \n",
"\n",
" CCI_diff_18 CCI_ratio_8_20 CCI_ratio_2_20 CMO_diff_12 \\\n",
"0 -185.557987 0.716473 -0.560736 -112.484341 \n",
"1 -161.694000 0.444908 -0.701553 -84.682762 \n",
"2 -24.224772 0.522943 0.733476 -87.926650 \n",
"3 -18.661185 0.579275 0.781300 -92.209732 \n",
"4 -137.969772 -0.063312 -0.934976 -82.338745 \n",
"... ... ... ... ... \n",
"33324 -150.663910 0.499089 0.306752 -146.708669 \n",
"33325 -228.152865 0.474172 -0.412832 -120.580881 \n",
"33326 -185.465493 0.389379 -0.561173 -101.534169 \n",
"33327 -164.506501 0.274403 -0.681386 -87.924347 \n",
"33328 -32.611841 0.508487 0.671512 -91.887164 \n",
"\n",
" CMO_ratio_2_14 KeltnerWidth_diff_9 KeltnerWidth_ratio_15_24 \\\n",
"0 -0.708596 -0.000172 1.118244 \n",
"1 -0.884346 -0.000113 1.081275 \n",
"2 0.878839 -0.000085 1.062512 \n",
"3 0.900446 -0.000052 1.039139 \n",
"4 -1.192046 -0.000043 1.032691 \n",
"... ... ... ... \n",
"33324 0.415789 -0.000087 1.060976 \n",
"33325 -0.558554 -0.000060 1.042922 \n",
"33326 -0.781851 -0.000046 1.032793 \n",
"33327 -0.965948 -0.000015 1.011219 \n",
"33328 0.912385 -0.000004 1.003260 \n",
"\n",
" KeltnerWidth_ratio_2_24 signal \n",
"0 0.950727 -1 \n",
"1 0.627573 0 \n",
"2 0.744744 0 \n",
"3 0.687263 0 \n",
"4 0.873788 0 \n",
"... ... ... \n",
"33324 0.938986 0 \n",
"33325 0.828449 0 \n",
"33326 0.875523 0 \n",
"33327 0.716892 0 \n",
"33328 0.817381 1 \n",
"\n",
"[33329 rows x 25 columns]"
]
},
"execution_count": 199,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Drop any unnamed index columns\n",
"test_df = test_df.loc[:, ~test_df.columns.str.contains(\"^Unnamed\")]\n",
"test_df"
]
},
{
"cell_type": "code",
"execution_count": 187,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Predictions ready. Sample:\n",
" predicted_signal\n",
"0 -1.0\n",
"1 0.0\n",
"2 0.0\n",
"3 0.0\n",
"4 0.0\n"
]
}
],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import xgboost as xgb\n",
"\n",
"# ---- Load model (if not in memory) ----\n",
"try:\n",
" bst_final # noqa: F821\n",
"except NameError:\n",
" bst_final = xgb.Booster()\n",
" bst_final.load_model(\"xgb_trading_model_tuned.json\")\n",
"\n",
"# Helper: how many features the model expects\n",
"try:\n",
" model_nfeat = bst_final.num_features()\n",
"except Exception:\n",
" # Older xgboost: num_features might be an attribute\n",
" model_nfeat = getattr(bst_final, \"num_features\", None)\n",
"\n",
"# ---- Build the EXACT feature order used at training ----\n",
"# Best practice: if you saved it earlier, load it here\n",
"# feature_order = list(np.load(\"xgb_feature_order.npy\", allow_pickle=True))\n",
"\n",
"# If you don't have it saved, reconstruct from train_df, dropping obvious non-features:\n",
"non_feature_cols = {\"target_signal\", \"predicted_signal\", \"DateTime\"}\n",
"train_features_now = [c for c in train_df.columns if c not in non_feature_cols]\n",
"feature_order = train_features_now # fallback\n",
"\n",
"# Optional: assert against model's expected count if we know it\n",
"if model_nfeat is not None and len(feature_order) != model_nfeat:\n",
" # If off-by-one, try dropping any \"Unnamed\" or accidental columns\n",
" feature_order = [c for c in feature_order if not str(c).startswith(\"Unnamed\")]\n",
" if len(feature_order) != model_nfeat:\n",
" print(f\"[Warn] Training feature count {len(feature_order)} != model expects {model_nfeat}.\")\n",
" # We'll still proceed but also print a diff vs. test below.\n",
"\n",
"# ---- Prepare test features strictly in training order ----\n",
"df_te = test_df.copy()\n",
"\n",
"# Drop obvious non-features from test too (in case they exist)\n",
"df_te = df_te.drop(columns=[c for c in df_te.columns if c in non_feature_cols or str(c).startswith(\"Unnamed\")], errors=\"ignore\")\n",
"\n",
"# Reindex to the training feature order; fill missing with 0 (your policy)\n",
"X_te = df_te.reindex(columns=feature_order, fill_value=0)\n",
"\n",
"# Debug: check shape vs model\n",
"if model_nfeat is not None and X_te.shape[1] != model_nfeat:\n",
" extra_in_test = set(X_te.columns) - set(feature_order)\n",
" missing_in_test = set(feature_order) - set(X_te.columns)\n",
" print(\"[Mismatch] Model expects:\", model_nfeat, \"| X_te has:\", X_te.shape[1])\n",
" print(\"Extra in test (should be none):\", extra_in_test)\n",
" print(\"Missing in test:\", missing_in_test)\n",
" # As a last guard, if X_te has MORE cols than model, trim to first model_nfeat cols in the training order:\n",
" if X_te.shape[1] > model_nfeat:\n",
" X_te = X_te[feature_order[:model_nfeat]]\n",
" elif X_te.shape[1] < model_nfeat:\n",
" # Add missing columns as 0, maintaining order\n",
" for c in feature_order:\n",
" if c not in X_te.columns and len(X_te.columns) < model_nfeat:\n",
" X_te[c] = 0.0\n",
" X_te = X_te[feature_order[:model_nfeat]]\n",
"\n",
"# Final consistency checks\n",
"assert model_nfeat is None or X_te.shape[1] == model_nfeat, \\\n",
" f\"Still mismatched: model expects {model_nfeat}, X_te has {X_te.shape[1]}\"\n",
"\n",
"# Clean infinities like training\n",
"X_te = X_te.replace([np.inf, -np.inf], 0).astype(float)\n",
"\n",
"# ---- Predict ----\n",
"dtest = xgb.DMatrix(X_te.to_numpy(dtype=float, copy=False))\n",
"pred = bst_final.predict(dtest)\n",
"\n",
"# Map back to labels using training mapping\n",
"labels_sorted = sorted(pd.to_numeric(train_df[\"target_signal\"], errors=\"coerce\").dropna().unique())\n",
"lab2idx = {lab: i for i, lab in enumerate(labels_sorted)}\n",
"idx2lab = {i: lab for lab, i in lab2idx.items()}\n",
"\n",
"if pred.ndim == 2: # multiclass\n",
" y_pred_enc = pred.argmax(axis=1)\n",
"else: # binary\n",
" y_pred_enc = (pred >= 0.5).astype(int)\n",
"\n",
"y_pred_lbl = np.vectorize(idx2lab.get)(y_pred_enc)\n",
"\n",
"test_df_with_preds = df_te.copy()\n",
"test_df_with_preds[\"predicted_signal\"] = y_pred_lbl\n",
"\n",
"print(\"Predictions ready. Sample:\")\n",
"print(test_df_with_preds[[\"predicted_signal\"]].head())\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Backtesting"
]
},
{
"cell_type": "code",
"execution_count": 189,
"metadata": {},
"outputs": [],
"source": [
"backdf = test_df_with_preds"
]
},
{
"cell_type": "code",
"execution_count": 190,
"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>Unnamed: 0</th>\n",
" <th>Hour_sin</th>\n",
" <th>DOW_sin</th>\n",
" <th>ADX_14</th>\n",
" <th>CCI_14</th>\n",
" <th>PPO_12_26</th>\n",
" <th>CMO_14</th>\n",
" <th>ADX_2</th>\n",
" <th>ADX_5</th>\n",
" <th>CCI_8</th>\n",
" <th>...</th>\n",
" <th>KeltnerWidth_ratio_15_24</th>\n",
" <th>KeltnerWidth_ratio_2_24</th>\n",
" <th>signal</th>\n",
" <th>predicted_signal</th>\n",
" <th>DateTime</th>\n",
" <th>Open</th>\n",
" <th>High</th>\n",
" <th>Low</th>\n",
" <th>Close</th>\n",
" <th>Volume</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>-0.25882</td>\n",
" <td>0.433884</td>\n",
" <td>47.175526</td>\n",
" <td>-94.082772</td>\n",
" <td>-0.334313</td>\n",
" <td>-52.333017</td>\n",
" <td>60.151324</td>\n",
" <td>81.362946</td>\n",
" <td>-85.182427</td>\n",
" <td>...</td>\n",
" <td>1.118244</td>\n",
" <td>0.950727</td>\n",
" <td>-1</td>\n",
" <td>1</td>\n",
" <td>07/03/19 23:00</td>\n",
" <td>1.11810</td>\n",
" <td>1.11944</td>\n",
" <td>1.11797</td>\n",
" <td>1.11923</td>\n",
" <td>963</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>0.25882</td>\n",
" <td>-0.433884</td>\n",
" <td>48.511681</td>\n",
" <td>-75.385246</td>\n",
" <td>-0.384653</td>\n",
" <td>-46.360881</td>\n",
" <td>38.321881</td>\n",
" <td>76.177460</td>\n",
" <td>-42.278460</td>\n",
" <td>...</td>\n",
" <td>1.081275</td>\n",
" <td>0.627573</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>08/03/19 1:00</td>\n",
" <td>1.11916</td>\n",
" <td>1.11985</td>\n",
" <td>1.11916</td>\n",
" <td>1.11976</td>\n",
" <td>764</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2</td>\n",
" <td>0.50000</td>\n",
" <td>-0.433884</td>\n",
" <td>49.885998</td>\n",
" <td>-75.857692</td>\n",
" <td>-0.430903</td>\n",
" <td>-49.155591</td>\n",
" <td>38.771059</td>\n",
" <td>73.248674</td>\n",
" <td>-47.531006</td>\n",
" <td>...</td>\n",
" <td>1.062512</td>\n",
" <td>0.744744</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>08/03/19 2:00</td>\n",
" <td>1.11976</td>\n",
" <td>1.11977</td>\n",
" <td>1.11855</td>\n",
" <td>1.11907</td>\n",
" <td>1620</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>3</td>\n",
" <td>0.70711</td>\n",
" <td>-0.433884</td>\n",
" <td>51.177650</td>\n",
" <td>-74.037359</td>\n",
" <td>-0.465194</td>\n",
" <td>-50.208330</td>\n",
" <td>42.001402</td>\n",
" <td>71.053743</td>\n",
" <td>-49.428255</td>\n",
" <td>...</td>\n",
" <td>1.039139</td>\n",
" <td>0.687263</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>08/03/19 3:00</td>\n",
" <td>1.11906</td>\n",
" <td>1.11944</td>\n",
" <td>1.11848</td>\n",
" <td>1.11881</td>\n",
" <td>1091</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4</td>\n",
" <td>0.86603</td>\n",
" <td>-0.433884</td>\n",
" <td>51.686381</td>\n",
" <td>-55.926237</td>\n",
" <td>-0.472084</td>\n",
" <td>-38.562741</td>\n",
" <td>43.776003</td>\n",
" <td>63.825371</td>\n",
" <td>4.514309</td>\n",
" <td>...</td>\n",
" <td>1.032691</td>\n",
" <td>0.873788</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>08/03/19 4:00</td>\n",
" <td>1.11881</td>\n",
" <td>1.12003</td>\n",
" <td>1.11860</td>\n",
" <td>1.11979</td>\n",
" <td>992</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33324</th>\n",
" <td>33324</td>\n",
" <td>-0.96593</td>\n",
" <td>0.781831</td>\n",
" <td>23.611404</td>\n",
" <td>-160.337614</td>\n",
" <td>-0.051720</td>\n",
" <td>-49.033438</td>\n",
" <td>97.675231</td>\n",
" <td>61.594852</td>\n",
" <td>-108.467310</td>\n",
" <td>...</td>\n",
" <td>1.060976</td>\n",
" <td>0.938986</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>31/12/24 19:00</td>\n",
" <td>1.03502</td>\n",
" <td>1.03548</td>\n",
" <td>1.03438</td>\n",
" <td>1.03490</td>\n",
" <td>2323</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33325</th>\n",
" <td>33325</td>\n",
" <td>-0.86603</td>\n",
" <td>0.781831</td>\n",
" <td>25.551370</td>\n",
" <td>-119.355906</td>\n",
" <td>-0.082496</td>\n",
" <td>-43.107288</td>\n",
" <td>77.473593</td>\n",
" <td>64.304066</td>\n",
" <td>-76.572200</td>\n",
" <td>...</td>\n",
" <td>1.042922</td>\n",
" <td>0.828449</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>31/12/24 20:00</td>\n",
" <td>1.03489</td>\n",
" <td>1.03566</td>\n",
" <td>1.03455</td>\n",
" <td>1.03529</td>\n",
" <td>1900</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33326</th>\n",
" <td>33326</td>\n",
" <td>-0.70711</td>\n",
" <td>0.781831</td>\n",
" <td>26.385304</td>\n",
" <td>-85.267741</td>\n",
" <td>-0.116771</td>\n",
" <td>-40.333690</td>\n",
" <td>61.200479</td>\n",
" <td>58.403529</td>\n",
" <td>-46.257822</td>\n",
" <td>...</td>\n",
" <td>1.032793</td>\n",
" <td>0.875523</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>31/12/24 21:00</td>\n",
" <td>1.03526</td>\n",
" <td>1.03645</td>\n",
" <td>1.03516</td>\n",
" <td>1.03547</td>\n",
" <td>1445</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33327</th>\n",
" <td>33327</td>\n",
" <td>-0.50000</td>\n",
" <td>0.781831</td>\n",
" <td>27.159672</td>\n",
" <td>-69.016843</td>\n",
" <td>-0.151568</td>\n",
" <td>-34.860425</td>\n",
" <td>53.063921</td>\n",
" <td>53.683099</td>\n",
" <td>-26.847505</td>\n",
" <td>...</td>\n",
" <td>1.011219</td>\n",
" <td>0.716892</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>31/12/24 22:00</td>\n",
" <td>1.03547</td>\n",
" <td>1.03631</td>\n",
" <td>1.03544</td>\n",
" <td>1.03582</td>\n",
" <td>1208</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33328</th>\n",
" <td>33328</td>\n",
" <td>-0.25882</td>\n",
" <td>0.781831</td>\n",
" <td>28.181968</td>\n",
" <td>-73.068540</td>\n",
" <td>-0.186740</td>\n",
" <td>-41.146290</td>\n",
" <td>50.740874</td>\n",
" <td>52.519310</td>\n",
" <td>-50.481804</td>\n",
" <td>...</td>\n",
" <td>1.003260</td>\n",
" <td>0.817381</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>31/12/24 23:00</td>\n",
" <td>1.03585</td>\n",
" <td>1.03608</td>\n",
" <td>1.03489</td>\n",
" <td>1.03493</td>\n",
" <td>616</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>33329 rows × 33 columns</p>\n",
"</div>"
],
"text/plain": [
" Unnamed: 0 Hour_sin DOW_sin ADX_14 CCI_14 PPO_12_26 \\\n",
"0 0 -0.25882 0.433884 47.175526 -94.082772 -0.334313 \n",
"1 1 0.25882 -0.433884 48.511681 -75.385246 -0.384653 \n",
"2 2 0.50000 -0.433884 49.885998 -75.857692 -0.430903 \n",
"3 3 0.70711 -0.433884 51.177650 -74.037359 -0.465194 \n",
"4 4 0.86603 -0.433884 51.686381 -55.926237 -0.472084 \n",
"... ... ... ... ... ... ... \n",
"33324 33324 -0.96593 0.781831 23.611404 -160.337614 -0.051720 \n",
"33325 33325 -0.86603 0.781831 25.551370 -119.355906 -0.082496 \n",
"33326 33326 -0.70711 0.781831 26.385304 -85.267741 -0.116771 \n",
"33327 33327 -0.50000 0.781831 27.159672 -69.016843 -0.151568 \n",
"33328 33328 -0.25882 0.781831 28.181968 -73.068540 -0.186740 \n",
"\n",
" CMO_14 ADX_2 ADX_5 CCI_8 ... \\\n",
"0 -52.333017 60.151324 81.362946 -85.182427 ... \n",
"1 -46.360881 38.321881 76.177460 -42.278460 ... \n",
"2 -49.155591 38.771059 73.248674 -47.531006 ... \n",
"3 -50.208330 42.001402 71.053743 -49.428255 ... \n",
"4 -38.562741 43.776003 63.825371 4.514309 ... \n",
"... ... ... ... ... ... \n",
"33324 -49.033438 97.675231 61.594852 -108.467310 ... \n",
"33325 -43.107288 77.473593 64.304066 -76.572200 ... \n",
"33326 -40.333690 61.200479 58.403529 -46.257822 ... \n",
"33327 -34.860425 53.063921 53.683099 -26.847505 ... \n",
"33328 -41.146290 50.740874 52.519310 -50.481804 ... \n",
"\n",
" KeltnerWidth_ratio_15_24 KeltnerWidth_ratio_2_24 signal \\\n",
"0 1.118244 0.950727 -1 \n",
"1 1.081275 0.627573 0 \n",
"2 1.062512 0.744744 0 \n",
"3 1.039139 0.687263 0 \n",
"4 1.032691 0.873788 0 \n",
"... ... ... ... \n",
"33324 1.060976 0.938986 0 \n",
"33325 1.042922 0.828449 0 \n",
"33326 1.032793 0.875523 0 \n",
"33327 1.011219 0.716892 0 \n",
"33328 1.003260 0.817381 1 \n",
"\n",
" predicted_signal DateTime Open High Low Close \\\n",
"0 1 07/03/19 23:00 1.11810 1.11944 1.11797 1.11923 \n",
"1 0 08/03/19 1:00 1.11916 1.11985 1.11916 1.11976 \n",
"2 0 08/03/19 2:00 1.11976 1.11977 1.11855 1.11907 \n",
"3 0 08/03/19 3:00 1.11906 1.11944 1.11848 1.11881 \n",
"4 0 08/03/19 4:00 1.11881 1.12003 1.11860 1.11979 \n",
"... ... ... ... ... ... ... \n",
"33324 0 31/12/24 19:00 1.03502 1.03548 1.03438 1.03490 \n",
"33325 0 31/12/24 20:00 1.03489 1.03566 1.03455 1.03529 \n",
"33326 0 31/12/24 21:00 1.03526 1.03645 1.03516 1.03547 \n",
"33327 0 31/12/24 22:00 1.03547 1.03631 1.03544 1.03582 \n",
"33328 1 31/12/24 23:00 1.03585 1.03608 1.03489 1.03493 \n",
"\n",
" Volume \n",
"0 963 \n",
"1 764 \n",
"2 1620 \n",
"3 1091 \n",
"4 992 \n",
"... ... \n",
"33324 2323 \n",
"33325 1900 \n",
"33326 1445 \n",
"33327 1208 \n",
"33328 616 \n",
"\n",
"[33329 rows x 33 columns]"
]
},
"execution_count": 190,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"back_df"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from backtesting import Backtest, Strategy\n",
"import pandas as pd\n",
"import numpy as np\n",
"\n",
"# --- Clean + prepare back_df for backtesting.py ---\n",
"df_bt = back_df.copy()\n",
"\n",
"# 1) Ensure DateTime is proper datetime and is the index\n",
"if \"DateTime\" in df_bt.columns:\n",
" df_bt[\"DateTime\"] = pd.to_datetime(df_bt[\"DateTime\"], errors=\"coerce\")\n",
" df_bt = df_bt.dropna(subset=[\"DateTime\"]).sort_values(\"DateTime\").set_index(\"DateTime\")\n",
"else:\n",
" # If it's already the index but as strings, coerce:\n",
" df_bt.index = pd.to_datetime(df_bt.index, errors=\"coerce\")\n",
" df_bt = df_bt[~df_bt.index.isna()].sort_index()\n",
"\n",
"# Deduplicate index if needed (backtesting.py expects increasing, unique index)\n",
"df_bt = df_bt[~df_bt.index.duplicated(keep=\"first\")]\n",
"\n",
"# 2) Ensure required columns exist and are numeric\n",
"required = [\"Open\", \"High\", \"Low\", \"Close\", \"Volume\", \"predicted_signal\"]\n",
"missing = [c for c in required if c not in df_bt.columns]\n",
"if missing:\n",
" raise ValueError(f\"Missing required columns in back_df: {missing}\")\n",
"\n",
"for c in [\"Open\", \"High\", \"Low\", \"Close\", \"Volume\", \"predicted_signal\"]:\n",
" df_bt[c] = pd.to_numeric(df_bt[c], errors=\"coerce\")\n",
"\n",
"# Fill OHLCV gaps (use past data only); set signal to int in {-1,0,1}\n",
"df_bt[[\"Open\", \"High\", \"Low\", \"Close\", \"Volume\"]] = (\n",
" df_bt[[\"Open\", \"High\", \"Low\", \"Close\", \"Volume\"]].ffill().bfill()\n",
")\n",
"df_bt[\"predicted_signal\"] = df_bt[\"predicted_signal\"].fillna(0).astype(int).clip(-1, 1)\n",
"\n",
"# Final sanity checks\n",
"assert isinstance(df_bt.index, pd.DatetimeIndex)\n",
"assert df_bt.index.is_monotonic_increasing\n",
"\n",
"# --- Strategy: +1 long, -1 short, 0 flat; execute on close; exclusive orders ---\n",
"class SignalStrategy(Strategy):\n",
" def init(self): pass\n",
" def next(self):\n",
" sig = int(self.data.predicted_signal[-1])\n",
" if sig == 0:\n",
" if self.position:\n",
" self.position.close()\n",
" return\n",
" if sig == 1:\n",
" if self.position.is_short:\n",
" self.position.close()\n",
" if not self.position.is_long:\n",
" self.buy()\n",
" return\n",
" if sig == -1:\n",
" if self.position.is_long:\n",
" self.position.close()\n",
" if not self.position.is_short:\n",
" self.sell()\n",
" return\n",
"\n",
"bt = Backtest(\n",
" df_bt,\n",
" SignalStrategy,\n",
" cash=100_000,\n",
" commission=0.0001, \n",
" # execute on the bar's close because the signal is already generated in the next row from the one that triggered it\n",
" trade_on_close=True, \n",
" exclusive_orders=True, # no long+short at the same time\n",
" hedging=False\n",
")\n",
"\n",
"stats = bt.run()\n"
]
},
{
"cell_type": "code",
"execution_count": 203,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"=== Performance Metrics ===\n",
" Strategy\n",
"Start 2019-01-04 01:00:00\n",
"End 2024-12-31 23:00:00\n",
"Duration 2188 days 22:00:00\n",
"Exposure Time [%] 3.180413\n",
"Return [%] 87.318208\n",
"Buy & Hold Return [%] -7.771758\n",
"Sharpe Ratio 1.689801\n",
"Sortino Ratio 2.618628\n",
"Calmar Ratio 1.578703\n",
"Max. Drawdown [%] -7.273869\n",
"Win Rate [%] 74.414414\n",
"# Trades 555\n",
"Avg. Trade [%] 0.133164\n",
"Profit Factor 3.314653\n"
]
},
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 1200x400 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 1200x400 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 1200x400 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 1200x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# ================= Diagnostics & Plots for backtesting.py =================\n",
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"plt.rcParams[\"figure.figsize\"] = (12, 4)\n",
"plt.rcParams[\"axes.grid\"] = True\n",
"\n",
"# ---- 1) Pull useful internals ----\n",
"trades = stats.get(\"_trades\", None)\n",
"equity_curve = stats.get(\"_equity_curve\", None) # index: DateTime; columns: ['Equity', 'Drawdown', ...] (depends on version)\n",
"\n",
"# Graceful fallback if equity_curve missing: rebuild a minimal curve from stats (flat line won’t be useful for drawdown)\n",
"if equity_curve is None:\n",
" # Try to reconstruct cumulative equity using provided metrics (approx)\n",
" # Not perfect, but avoids hard failure if your backtesting.py version doesn’t expose _equity_curve.\n",
" start_equity = bt._cash if hasattr(bt, \"_cash\") else 100_000\n",
" # If total Return [%] available:\n",
" total_ret_pct = stats.get(\"Return [%]\", 0.0)\n",
" equity_curve = pd.DataFrame(\n",
" {\"Equity\": np.linspace(start_equity, start_equity * (1 + total_ret_pct / 100.0), len(df_bt))},\n",
" index=df_bt.index\n",
" )\n",
" equity_curve[\"Peak\"] = equity_curve[\"Equity\"].cummax()\n",
" equity_curve[\"Drawdown\"] = equity_curve[\"Equity\"] / equity_curve[\"Peak\"] - 1.0\n",
"\n",
"# Standardize columns if needed\n",
"if \"Equity\" not in equity_curve.columns:\n",
" # Some versions name it 'Equity Final [$]' only at the end; then just map Close to a pseudo curve.\n",
" equity_curve = equity_curve.rename(columns={equity_curve.columns[0]: \"Equity\"})\n",
"if \"Drawdown\" not in equity_curve.columns:\n",
" equity_curve[\"Peak\"] = equity_curve[\"Equity\"].cummax()\n",
" equity_curve[\"Drawdown\"] = equity_curve[\"Equity\"] / equity_curve[\"Peak\"] - 1.0\n",
"\n",
"# ---- 2) Performance metrics (concise) ----\n",
"perf_cols = [\n",
" \"Start\", \"End\", \"Duration\", \"Exposure Time [%]\",\n",
" \"Return [%]\", \"Buy & Hold Return [%]\",\n",
" \"Sharpe Ratio\", \"Sortino Ratio\",\n",
" \"Calmar Ratio\", \"Max. Drawdown [%]\",\n",
" \"Win Rate [%]\", \"# Trades\", \"Avg. Trade [%]\", \"Profit Factor\"\n",
"]\n",
"perf = {}\n",
"for k in perf_cols:\n",
" val = stats.get(k, None)\n",
" if val is not None:\n",
" perf[k] = val\n",
"perf_df = pd.DataFrame(perf, index=[\"Strategy\"]).T\n",
"print(\"\\n=== Performance Metrics ===\")\n",
"print(perf_df)\n",
"\n",
"# ---- 3) Equity curve ----\n",
"fig1, ax1 = plt.subplots()\n",
"ax1.plot(equity_curve.index, equity_curve[\"Equity\"])\n",
"ax1.set_title(\"Equity Curve\")\n",
"ax1.set_xlabel(\"Time\")\n",
"ax1.set_ylabel(\"Equity ($)\")\n",
"plt.show()\n",
"\n",
"# ---- 4) Drawdown series ----\n",
"fig2, ax2 = plt.subplots()\n",
"ax2.plot(equity_curve.index, equity_curve[\"Drawdown\"] * 100.0)\n",
"ax2.set_title(\"Drawdown (%)\")\n",
"ax2.set_xlabel(\"Time\")\n",
"ax2.set_ylabel(\"Drawdown (%)\")\n",
"plt.show()\n",
"\n",
"# ---- 5) Rolling volatility (annualized) ----\n",
"# Use log returns of Close; adapt 'per_year' to your bar frequency (e.g., 252 daily, 365 for daily crypto, 78*252 for 5-min, etc.)\n",
"per_year = 252\n",
"ret = np.log(df_bt[\"Close\"]).diff()\n",
"roll_win = per_year // 2 if per_year > 20 else 50 # default: half a year window\n",
"rolling_vol = ret.rolling(roll_win).std() * np.sqrt(per_year)\n",
"\n",
"fig3, ax3 = plt.subplots()\n",
"ax3.plot(rolling_vol.index, rolling_vol)\n",
"ax3.set_title(f\"Rolling Volatility (Annualized), window={roll_win}\")\n",
"ax3.set_xlabel(\"Time\")\n",
"ax3.set_ylabel(\"Volatility\")\n",
"plt.show()\n",
"\n",
"# ---- 6) Price + indicators + signals + trades ----\n",
"# Example indicators (customize as you like)\n",
"def ema(s, n): return s.ewm(span=n, adjust=False).mean()\n",
"def sma(s, n): return s.rolling(n).mean()\n",
"\n",
"ema_fast = ema(df_bt[\"Close\"], 50)\n",
"ema_slow = ema(df_bt[\"Close\"], 200)\n",
"sig = df_bt[\"predicted_signal\"].astype(int)\n",
"\n",
"fig4, ax4 = plt.subplots(figsize=(12, 6))\n",
"ax4.plot(df_bt.index, df_bt[\"Close\"], label=\"Close\")\n",
"ax4.plot(df_bt.index, ema_fast, label=\"EMA 50\")\n",
"ax4.plot(df_bt.index, ema_slow, label=\"EMA 200\")\n",
"\n",
"# Show long/short background regions from signal\n",
"# (Optional) Shade regions: +1 green, -1 red, 0 clear\n",
"state = sig.shift(1).fillna(0) # position taken on close of prior bar\n",
"up = state == 1\n",
"dn = state == -1\n",
"ax4.fill_between(df_bt.index, df_bt[\"Close\"].min(), df_bt[\"Close\"].max(), where=up, alpha=0.06)\n",
"ax4.fill_between(df_bt.index, df_bt[\"Close\"].min(), df_bt[\"Close\"].max(), where=dn, alpha=0.06)\n",
"\n",
"# Trade markers (if trades table available)\n",
"if isinstance(trades, pd.DataFrame) and not trades.empty:\n",
" # Expect columns: EntryTime, ExitTime, EntryPrice, ExitPrice, Size, Direction/Side or similar\n",
" # backtesting.py _trades usually has:\n",
" # ['EntryTime', 'ExitTime', 'EntryPrice', 'ExitPrice', 'PnL', 'ReturnPct', 'Duration', 'Direction', ...]\n",
" # We'll handle common variants.\n",
" et_col = next((c for c in [\"EntryTime\", \"Entry Bar\", \"Entry\"] if c in trades.columns), None)\n",
" ep_col = next((c for c in [\"EntryPrice\", \"Entry Price\"] if c in trades.columns), None)\n",
" dir_col = next((c for c in [\"Direction\", \"Side\", \"Signal\"] if c in trades.columns), None)\n",
"\n",
" # Marker placement\n",
" if et_col is not None and ep_col is not None:\n",
" for _, tr in trades.iterrows():\n",
" t = tr[et_col]\n",
" p = tr[ep_col]\n",
" # Align type\n",
" if isinstance(t, (int, np.integer)):\n",
" # If it's a bar index, map to timestamp\n",
" if t >= 0 and t < len(df_bt.index):\n",
" t = df_bt.index[int(t)]\n",
" else:\n",
" continue\n",
" # Direction sign\n",
" d = np.sign(tr.get(dir_col, 1)) if dir_col in trades.columns else 1\n",
" # Use different marker for long/short\n",
" if d >= 0:\n",
" ax4.plot(t, p, marker=\"^\", markersize=7)\n",
" else:\n",
" ax4.plot(t, p, marker=\"v\", markersize=7)\n",
"\n",
"ax4.set_title(\"Price with Indicators, Signals, and Trades\")\n",
"ax4.set_xlabel(\"Time\")\n",
"ax4.set_ylabel(\"Price\")\n",
"ax4.legend(loc=\"best\")\n",
"plt.show()\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.11.5"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
Reference in New Issue View Git Blame Copy Permalink