diff --git "a/troubleshoot_day_model.ipynb" "b/troubleshoot_day_model.ipynb"
--- "a/troubleshoot_day_model.ipynb"
+++ "b/troubleshoot_day_model.ipynb"
@@ -2,45 +2,39 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": 61,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
- "from model_day import get_data, walk_forward_validation_seq"
+ "from model_intra import get_data, walk_forward_validation_seq"
]
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 62,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
- "getting econ tickers: 100%|██████████| 3/3 [00:00<00:00, 6.56it/s]\n",
- "Getting release dates: 100%|██████████| 8/8 [00:01<00:00, 5.08it/s]\n",
- "Making indicators: 100%|██████████| 8/8 [00:00<00:00, 2664.11it/s]\n",
- "Merging econ data: 100%|██████████| 8/8 [00:00<00:00, 1010.95it/s]\n"
+ "getting econ tickers: 100%|██████████| 3/3 [00:00<00:00, 6.07it/s]\n",
+ "Getting release dates: 100%|██████████| 8/8 [00:01<00:00, 4.53it/s]\n",
+ "Making indicators: 100%|██████████| 8/8 [00:00<00:00, 3997.43it/s]\n",
+ "Found cached dataset text (C:/Users/WINSTON-ITX/.cache/huggingface/datasets/boomsss___text/boomsss--spx_intra-e0e5e7af8fd43022/0.0.0/cb1e9bd71a82ad27976be3b12b407850fe2837d80c22c5e03a28949843a8ace2)\n",
+ "Merging econ data: 100%|██████████| 8/8 [00:00<00:00, 999.12it/s]\n"
]
}
],
"source": [
- "data, df_final, final_row = get_data()"
+ "data, df_final, final_row = get_data(2)"
]
},
{
"cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": 4,
+ "execution_count": 63,
"metadata": {},
"outputs": [],
"source": [
@@ -52,21 +46,23 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 64,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
- "LR Model: 100%|██████████| 1195/1195 [00:03<00:00, 347.11it/s]\n",
- "d:\\Projects\\gamedayspx\\model_day.py:88: SettingWithCopyWarning: \n",
+ "LR Model: 100%|██████████| 1126/1126 [00:03<00:00, 332.50it/s]\n",
+ "d:\\Projects\\gamedayspx\\model_intra.py:97: SettingWithCopyWarning:\n",
+ "\n",
+ "\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",
- " for_merge['RegrModelOut'] = for_merge['RegrModelOut'] > 0\n",
- "CLF Model: 100%|██████████| 1095/1095 [00:08<00:00, 129.89it/s]\n"
+ "\n",
+ "CLF Model: 100%|██████████| 1026/1026 [00:07<00:00, 141.65it/s]\n"
]
}
],
@@ -76,7 +72,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 65,
"metadata": {},
"outputs": [],
"source": [
@@ -105,6 +101,7 @@
"hi_thres = 0.6 # res1.loc[middle_quantiles, 'Predicted'].max()\n",
"\n",
"roc_auc_score_all = roc_auc_score(res1['True'].astype(int), res1['Predicted'].values)\n",
+ "roc_auc_score_calib = roc_auc_score(res1.dropna(subset='CalibGreenProba')['True'].astype(int), res1.dropna(subset='CalibGreenProba')['CalibGreenProba'].values)\n",
"precision_score_all = precision_score(res1['True'].astype(int), res1['Predicted'] > 0.5)\n",
"recall_score_all = recall_score(res1['True'].astype(int), res1['Predicted'] > 0.5)\n",
"len_all = len(res1)\n",
@@ -112,6 +109,7 @@
"res2_filtered = res1.loc[(res1['Predicted'] > hi_thres) | (res1['Predicted'] <= lo_thres)]\n",
"\n",
"roc_auc_score_hi = roc_auc_score(res2_filtered['True'].astype(int), res2_filtered['Predicted'].values)\n",
+ "roc_auc_score_hi_calib = roc_auc_score(res2_filtered.dropna(subset='CalibGreenProba')['True'].astype(int), res2_filtered.dropna(subset='CalibGreenProba')['CalibGreenProba'].values)\n",
"precision_score_hi = precision_score(res2_filtered['True'].astype(int), res2_filtered['Predicted'] > 0.5)\n",
"recall_score_hi = recall_score(res2_filtered['True'].astype(int), res2_filtered['Predicted'] > 0.5)\n",
"len_hi = len(res2_filtered)\n",
@@ -120,6 +118,7 @@
" index=[\n",
" 'N',\n",
" 'ROC AUC',\n",
+ " 'Calib. AUC',\n",
" 'Precision',\n",
" 'Recall'\n",
" ],\n",
@@ -130,6 +129,7 @@
" data = [\n",
" [len_all, len_hi],\n",
" [roc_auc_score_all, roc_auc_score_hi],\n",
+ " [roc_auc_score_calib, roc_auc_score_hi_calib],\n",
" [precision_score_all, precision_score_hi],\n",
" [recall_score_all, recall_score_hi]\n",
" ]\n",
@@ -167,7 +167,7 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 66,
"metadata": {},
"outputs": [
{
@@ -193,188 +193,244 @@
"
| \n",
" True | \n",
" Predicted | \n",
+ " CalibPredicted | \n",
+ " CalibGreenProba | \n",
" Accuracy | \n",
" HTML | \n",
" \n",
- " \n",
- " index | \n",
- " | \n",
- " | \n",
- " | \n",
- " | \n",
- "
\n",
" \n",
" \n",
" \n",
- " 2023-09-08 | \n",
- " True | \n",
- " 0.757584 | \n",
- " ✅ | \n",
- " ✅ | \n",
- "
\n",
- " \n",
" 2023-09-11 | \n",
" False | \n",
- " 0.300308 | \n",
+ " 0.207403 | \n",
+ " 0.2350 | \n",
+ " 0.120567 | \n",
" ✅ | \n",
" ✅ | \n",
"
\n",
" \n",
" 2023-09-12 | \n",
" True | \n",
- " 0.542126 | \n",
+ " 0.568866 | \n",
+ " 0.6140 | \n",
+ " 0.556391 | \n",
" 🟨 | \n",
" ⬜ | \n",
"
\n",
" \n",
" 2023-09-13 | \n",
" True | \n",
- " 0.749427 | \n",
+ " 0.766335 | \n",
+ " 0.8030 | \n",
+ " 0.870690 | \n",
" ✅ | \n",
" ✅ | \n",
"
\n",
" \n",
" 2023-09-14 | \n",
" False | \n",
- " 0.385973 | \n",
+ " 0.252581 | \n",
+ " 0.2350 | \n",
+ " 0.119718 | \n",
" ✅ | \n",
" ✅ | \n",
"
\n",
" \n",
" 2023-09-15 | \n",
" True | \n",
- " 0.326088 | \n",
- " ❌ | \n",
- " ❌ | \n",
+ " 0.587229 | \n",
+ " 0.6140 | \n",
+ " 0.559701 | \n",
+ " 🟨 | \n",
+ " ⬜ | \n",
"
\n",
" \n",
" 2023-09-18 | \n",
" False | \n",
- " 0.341849 | \n",
+ " 0.223258 | \n",
+ " 0.2350 | \n",
+ " 0.118881 | \n",
" ✅ | \n",
" ✅ | \n",
"
\n",
" \n",
" 2023-09-19 | \n",
" False | \n",
- " 0.687576 | \n",
+ " 0.606953 | \n",
+ " 0.6140 | \n",
+ " 0.562963 | \n",
" ❌ | \n",
" ❌ | \n",
"
\n",
" \n",
" 2023-09-20 | \n",
" False | \n",
- " 0.209737 | \n",
+ " 0.196109 | \n",
+ " 0.2350 | \n",
+ " 0.118056 | \n",
" ✅ | \n",
" ✅ | \n",
"
\n",
" \n",
" 2023-09-21 | \n",
" False | \n",
- " 0.477048 | \n",
+ " 0.431239 | \n",
+ " 0.4245 | \n",
+ " 0.420168 | \n",
" 🟨 | \n",
" ⬜ | \n",
"
\n",
" \n",
" 2023-09-22 | \n",
" True | \n",
- " 0.381210 | \n",
- " ❌ | \n",
- " ❌ | \n",
+ " 0.411964 | \n",
+ " 0.4245 | \n",
+ " 0.416667 | \n",
+ " 🟨 | \n",
+ " ⬜ | \n",
"
\n",
" \n",
" 2023-09-25 | \n",
" False | \n",
- " 0.306969 | \n",
+ " 0.199644 | \n",
+ " 0.2350 | \n",
+ " 0.117241 | \n",
" ✅ | \n",
" ✅ | \n",
"
\n",
" \n",
" 2023-09-26 | \n",
" True | \n",
- " 0.437918 | \n",
+ " 0.446124 | \n",
+ " 0.4245 | \n",
+ " 0.421488 | \n",
" 🟨 | \n",
" ⬜ | \n",
"
\n",
" \n",
" 2023-09-27 | \n",
" True | \n",
- " 0.502720 | \n",
- " 🟨 | \n",
- " ⬜ | \n",
+ " 0.637091 | \n",
+ " 0.6140 | \n",
+ " 0.558824 | \n",
+ " ✅ | \n",
+ " ✅ | \n",
"
\n",
" \n",
" 2023-09-28 | \n",
" False | \n",
- " 0.772822 | \n",
+ " 0.776567 | \n",
+ " 0.8030 | \n",
+ " 0.871245 | \n",
" ❌ | \n",
" ❌ | \n",
"
\n",
" \n",
" 2023-09-29 | \n",
" True | \n",
- " 0.456547 | \n",
+ " 0.502801 | \n",
+ " 0.5195 | \n",
+ " 0.557377 | \n",
" 🟨 | \n",
" ⬜ | \n",
"
\n",
" \n",
" 2023-10-02 | \n",
" False | \n",
- " 0.278969 | \n",
+ " 0.198343 | \n",
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+ " 0.116438 | \n",
" ✅ | \n",
" ✅ | \n",
"
\n",
" \n",
" 2023-10-03 | \n",
" True | \n",
- " 0.597911 | \n",
- " 🟨 | \n",
- " ⬜ | \n",
+ " 0.610792 | \n",
+ " 0.6140 | \n",
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+ " ✅ | \n",
+ " ✅ | \n",
"
\n",
" \n",
" 2023-10-04 | \n",
" False | \n",
- " 0.504107 | \n",
- " 🟨 | \n",
- " ⬜ | \n",
+ " 0.250420 | \n",
+ " 0.2350 | \n",
+ " 0.115646 | \n",
+ " ✅ | \n",
+ " ✅ | \n",
"
\n",
" \n",
" 2023-10-05 | \n",
" True | \n",
- " 0.399995 | \n",
+ " 0.303259 | \n",
+ " 0.3300 | \n",
+ " 0.204724 | \n",
" ❌ | \n",
" ❌ | \n",
"
\n",
+ " \n",
+ " 2023-10-06 | \n",
+ " True | \n",
+ " 0.628482 | \n",
+ " 0.6140 | \n",
+ " 0.565217 | \n",
+ " ✅ | \n",
+ " ✅ | \n",
+ "
\n",
" \n",
"\n",
""
],
"text/plain": [
- " True Predicted Accuracy HTML\n",
- "index \n",
- "2023-09-08 True 0.757584 ✅ ✅\n",
- "2023-09-11 False 0.300308 ✅ ✅\n",
- "2023-09-12 True 0.542126 🟨 ⬜\n",
- "2023-09-13 True 0.749427 ✅ ✅\n",
- "2023-09-14 False 0.385973 ✅ ✅\n",
- "2023-09-15 True 0.326088 ❌ ❌\n",
- "2023-09-18 False 0.341849 ✅ ✅\n",
- "2023-09-19 False 0.687576 ❌ ❌\n",
- "2023-09-20 False 0.209737 ✅ ✅\n",
- "2023-09-21 False 0.477048 🟨 ⬜\n",
- "2023-09-22 True 0.381210 ❌ ❌\n",
- "2023-09-25 False 0.306969 ✅ ✅\n",
- "2023-09-26 True 0.437918 🟨 ⬜\n",
- "2023-09-27 True 0.502720 🟨 ⬜\n",
- "2023-09-28 False 0.772822 ❌ ❌\n",
- "2023-09-29 True 0.456547 🟨 ⬜\n",
- "2023-10-02 False 0.278969 ✅ ✅\n",
- "2023-10-03 True 0.597911 🟨 ⬜\n",
- "2023-10-04 False 0.504107 🟨 ⬜\n",
- "2023-10-05 True 0.399995 ❌ ❌"
+ " True Predicted CalibPredicted CalibGreenProba Accuracy \\\n",
+ "2023-09-11 False 0.207403 0.2350 0.120567 ✅ \n",
+ "2023-09-12 True 0.568866 0.6140 0.556391 🟨 \n",
+ "2023-09-13 True 0.766335 0.8030 0.870690 ✅ \n",
+ "2023-09-14 False 0.252581 0.2350 0.119718 ✅ \n",
+ "2023-09-15 True 0.587229 0.6140 0.559701 🟨 \n",
+ "2023-09-18 False 0.223258 0.2350 0.118881 ✅ \n",
+ "2023-09-19 False 0.606953 0.6140 0.562963 ❌ \n",
+ "2023-09-20 False 0.196109 0.2350 0.118056 ✅ \n",
+ "2023-09-21 False 0.431239 0.4245 0.420168 🟨 \n",
+ "2023-09-22 True 0.411964 0.4245 0.416667 🟨 \n",
+ "2023-09-25 False 0.199644 0.2350 0.117241 ✅ \n",
+ "2023-09-26 True 0.446124 0.4245 0.421488 🟨 \n",
+ "2023-09-27 True 0.637091 0.6140 0.558824 ✅ \n",
+ "2023-09-28 False 0.776567 0.8030 0.871245 ❌ \n",
+ "2023-09-29 True 0.502801 0.5195 0.557377 🟨 \n",
+ "2023-10-02 False 0.198343 0.2350 0.116438 ✅ \n",
+ "2023-10-03 True 0.610792 0.6140 0.562044 ✅ \n",
+ "2023-10-04 False 0.250420 0.2350 0.115646 ✅ \n",
+ "2023-10-05 True 0.303259 0.3300 0.204724 ❌ \n",
+ "2023-10-06 True 0.628482 0.6140 0.565217 ✅ \n",
+ "\n",
+ " HTML \n",
+ "2023-09-11 ✅ \n",
+ "2023-09-12 ⬜ \n",
+ "2023-09-13 ✅ \n",
+ "2023-09-14 ✅ \n",
+ "2023-09-15 ⬜ \n",
+ "2023-09-18 ✅ \n",
+ "2023-09-19 ❌ \n",
+ "2023-09-20 ✅ \n",
+ "2023-09-21 ⬜ \n",
+ "2023-09-22 ⬜ \n",
+ "2023-09-25 ✅ \n",
+ "2023-09-26 ⬜ \n",
+ "2023-09-27 ✅ \n",
+ "2023-09-28 ❌ \n",
+ "2023-09-29 ⬜ \n",
+ "2023-10-02 ✅ \n",
+ "2023-10-03 ✅ \n",
+ "2023-10-04 ✅ \n",
+ "2023-10-05 ❌ \n",
+ "2023-10-06 ✅ "
]
},
- "execution_count": 7,
+ "execution_count": 66,
"metadata": {},
"output_type": "execute_result"
}
@@ -385,16 +441,16 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 67,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "157"
+ "147"
]
},
- "execution_count": 8,
+ "execution_count": 67,
"metadata": {},
"output_type": "execute_result"
}
@@ -403,14 +459,9 @@
"df_probas.loc[df_probas.index[0], 'NumObs']"
]
},
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": []
- },
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 68,
"metadata": {},
"outputs": [
{
@@ -441,37 +492,43 @@
" \n",
" \n",
" N | \n",
- " 1095.00 | \n",
- " 743.00 | \n",
+ " 1026.00 | \n",
+ " 761.00 | \n",
"
\n",
" \n",
" ROC AUC | \n",
- " 0.74 | \n",
+ " 0.80 | \n",
+ " 0.85 | \n",
+ "
\n",
+ " \n",
+ " Calib. AUC | \n",
" 0.77 | \n",
+ " 0.82 | \n",
"
\n",
" \n",
" Precision | \n",
- " 0.68 | \n",
- " 0.74 | \n",
+ " 0.72 | \n",
+ " 0.76 | \n",
"
\n",
" \n",
" Recall | \n",
- " 0.75 | \n",
- " 0.83 | \n",
+ " 0.79 | \n",
+ " 0.86 | \n",
"
\n",
" \n",
"\n",
""
],
"text/plain": [
- " All High Confidence\n",
- "N 1095.00 743.00\n",
- "ROC AUC 0.74 0.77\n",
- "Precision 0.68 0.74\n",
- "Recall 0.75 0.83"
+ " All High Confidence\n",
+ "N 1026.00 761.00\n",
+ "ROC AUC 0.80 0.85\n",
+ "Calib. AUC 0.77 0.82\n",
+ "Precision 0.72 0.76\n",
+ "Recall 0.79 0.86"
]
},
- "execution_count": 9,
+ "execution_count": 68,
"metadata": {},
"output_type": "execute_result"
}
@@ -482,9 +539,9131 @@
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+ },
+ "yaxis": {
+ "backgroundcolor": "rgb(17,17,17)",
+ "gridcolor": "#506784",
+ "gridwidth": 2,
+ "linecolor": "#506784",
+ "showbackground": true,
+ "ticks": "",
+ "zerolinecolor": "#C8D4E3"
+ },
+ "zaxis": {
+ "backgroundcolor": "rgb(17,17,17)",
+ "gridcolor": "#506784",
+ "gridwidth": 2,
+ "linecolor": "#506784",
+ "showbackground": true,
+ "ticks": "",
+ "zerolinecolor": "#C8D4E3"
+ }
+ },
+ "shapedefaults": {
+ "line": {
+ "color": "#f2f5fa"
+ }
+ },
+ "sliderdefaults": {
+ "bgcolor": "#C8D4E3",
+ "bordercolor": "rgb(17,17,17)",
+ "borderwidth": 1,
+ "tickwidth": 0
+ },
+ "ternary": {
+ "aaxis": {
+ "gridcolor": "#506784",
+ "linecolor": "#506784",
+ "ticks": ""
+ },
+ "baxis": {
+ "gridcolor": "#506784",
+ "linecolor": "#506784",
+ "ticks": ""
+ },
+ "bgcolor": "rgb(17,17,17)",
+ "caxis": {
+ "gridcolor": "#506784",
+ "linecolor": "#506784",
+ "ticks": ""
+ }
+ },
+ "title": {
+ "x": 0.05
+ },
+ "updatemenudefaults": {
+ "bgcolor": "#506784",
+ "borderwidth": 0
+ },
+ "xaxis": {
+ "automargin": true,
+ "gridcolor": "#283442",
+ "linecolor": "#506784",
+ "ticks": "",
+ "title": {
+ "standoff": 15
+ },
+ "zerolinecolor": "#283442",
+ "zerolinewidth": 2
+ },
+ "yaxis": {
+ "automargin": true,
+ "gridcolor": "#283442",
+ "linecolor": "#506784",
+ "ticks": "",
+ "title": {
+ "standoff": 15
+ },
+ "zerolinecolor": "#283442",
+ "zerolinewidth": 2
+ }
+ }
+ },
+ "title": {
+ "text": "OHLC Chart with Predictions (Right/Wrong)"
+ },
+ "xaxis": {
+ "range": [
+ "2023-07-28T00:00:00",
+ "2023-10-06T00:00:00"
+ ],
+ "rangebreaks": [
+ {
+ "bounds": [
+ "sat",
+ "mon"
+ ]
+ }
+ ],
+ "rangeslider": {
+ "visible": false
+ },
+ "title": {
+ "text": "Date"
+ }
+ },
+ "yaxis": {
+ "range": [
+ 4166.4501953125,
+ 4594.22021484375
+ ],
+ "title": {
+ "text": "Price"
+ }
+ }
+ }
+ }
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
"source": [
"import pandas as pd\n",
"import plotly.graph_objs as go\n",
@@ -550,11 +9729,28 @@
"fig.show()\n"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": []
+ },
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 70,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "ename": "NameError",
+ "evalue": "name 'XXX' is not defined",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
+ "\u001b[1;32md:\\Projects\\gamedayspx\\troubleshoot_day_model.ipynb Cell 11\u001b[0m line \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[0m XXX\n",
+ "\u001b[1;31mNameError\u001b[0m: name 'XXX' is not defined"
+ ]
+ }
+ ],
"source": [
"XXX"
]
|