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Training data generation.

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jarno
2026-07-26 21:38:14 +03:00
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.gitignore
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@@ -9,3 +9,4 @@ build/
*.egg-info/ *.egg-info/
data/ibkr/ data/ibkr/
data/training/
README.md
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@@ -43,6 +43,8 @@ Do not commit secrets such as IBKR credentials, account identifiers, API tokens,
See [docs/README.md](docs/README.md) for the initial architecture notes and decision log. See [docs/README.md](docs/README.md) for the initial architecture notes and decision log.
The first data collection design note is [docs/data-fetcher.md](docs/data-fetcher.md). The first data collection design note is [docs/data-fetcher.md](docs/data-fetcher.md), and the first training dataset contract is [docs/training-dataset.md](docs/training-dataset.md).
The first dataset generation notebook is [notebooks/spy_direction_dataset.ipynb](notebooks/spy_direction_dataset.ipynb).
Manual test instructions for the IBKR fetcher skeleton are in [docs/manual-test/README.md](docs/manual-test/README.md). Manual test instructions for the IBKR fetcher skeleton are in [docs/manual-test/README.md](docs/manual-test/README.md).
docs/README.md
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@@ -12,6 +12,8 @@ Responsible for acquiring and storing market, asset, and any future feature data
The first planned tool is an IBKR daily candle fetcher. It should fetch open, high, low, close, and volume data for a ticker and date range, then eventually persist that data to a ticker-named Parquet file. See [data-fetcher.md](data-fetcher.md). The first planned tool is an IBKR daily candle fetcher. It should fetch open, high, low, close, and volume data for a ticker and date range, then eventually persist that data to a ticker-named Parquet file. See [data-fetcher.md](data-fetcher.md).
The initial supervised training dataset is documented in [training-dataset.md](training-dataset.md). It derives market-regime features from `SPY`, `VIX`, `TLT`, and `USO`, then labels whether `SPY` closes higher five trading days later.
Parquet files are partitioned by ticker, not by date. Parquet files are partitioned by ticker, not by date.
Open decisions: Open decisions:
@@ -30,7 +32,6 @@ Responsible for building datasets, training models, evaluating candidates, and w
Open decisions: Open decisions:
- prediction target; - prediction target;
- feature set;
- model family; - model family;
- validation strategy; - validation strategy;
- evaluation metrics; - evaluation metrics;
docs/training-dataset.md
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# Training Dataset Design
## Initial Goal
Build a supervised learning dataset from stored IBKR daily candle data. The first dataset predicts whether `SPY` closes higher five trading days after the observation date.
The first implementation is [../notebooks/spy_direction_dataset.ipynb](../notebooks/spy_direction_dataset.ipynb), a Python notebook using pandas. This keeps the feature calculations inspectable while the dataset design is still changing. Once the feature contract settles, reusable loading and feature-building code can move into `src/trading_bot/models` or `src/trading_bot/data`.
## Raw Data
Raw daily candles are stored under:
```text
data/ibkr/daily
```
Storage expectations:
- one Parquet file per symbol;
- file name is the symbol, such as `SPY.parquet`;
- each row contains one daily candle for that symbol;
- expected columns are `date`, `symbol`, `open`, `high`, `low`, `close`, and `volume`.
The initial dataset requires at least these symbols:
- `SPY`;
- `VIX`;
- `TLT`;
- `USO`.
## Observation Rows
Each dataset row represents one trading date. Feature values are derived from data available on or before that date. The target is derived from `SPY` closing price five trading days after that date.
Rows should be dropped when any required feature or target value cannot be computed.
## Input Features
| Feature | Description |
| --- | --- |
| `SPY_ret_5` | `SPY` fractional return over the previous 5 trading days. |
| `SPY_ret_20` | `SPY` fractional return over the previous 20 trading days. |
| `SPY_dist_sma50` | `SPY` distance from its 50 trading day simple moving average, divided by that average. |
| `VIX_change_5` | `VIX` fractional change over the previous 5 trading days. |
| `VIX_rank_20` | `VIX` percentile rank within a rolling 20 trading day window. |
| `TLT_ret_10` | `TLT` fractional return over the previous 10 trading days. |
| `USO_ret_5` | `USO` fractional return over the previous 5 trading days. |
| `SPY_TLT_ratio_ret` | Fractional 5 trading day change in the `SPY` to `TLT` close-price ratio. |
Fractional percentage changes should follow pandas `pct_change` convention. For example, a five percent return is represented as `0.05`.
## Output Target
The target column is a binary indicator of `SPY` forward return over the next five trading days.
| Target | Description |
| --- | --- |
| `spy_up_5d` | `1.0` when `SPY` closes above today's close five trading days later; `0.0` when `SPY` closes below today's close five trading days later; `0.5` when the future close equals today's close. |
Using `0.5` for unchanged prices preserves the row while making the target explicitly neutral.
## Data Alignment
The dataset should use trading-day alignment rather than calendar-day alignment. A five day lookback or forecast horizon means five available trading sessions for the relevant symbol, not five calendar days.
`SPY` should define the primary observation calendar because the first target is an `SPY` forward-return label. Other symbols should be joined to the `SPY` observation dates after their own features are computed.
## Notebook Feature Sketch
The first notebook should use pandas transformations close to this shape:
```python
import numpy as np
import pandas as pd
df["SPY_ret_5"] = df["SPY_close"].pct_change(5)
df["SPY_ret_20"] = df["SPY_close"].pct_change(20)
sma_50 = df["SPY_close"].rolling(50).mean()
df["SPY_dist_sma50"] = (df["SPY_close"] - sma_50) / sma_50
df["VIX_change_5"] = df["VIX_close"].pct_change(5)
df["VIX_rank_20"] = df["VIX_close"].rolling(20).rank(pct=True)
df["TLT_ret_10"] = df["TLT_close"].pct_change(10)
df["USO_ret_5"] = df["USO_close"].pct_change(5)
df["SPY_TLT_ratio_ret"] = (df["SPY_close"] / df["TLT_close"]).pct_change(5)
spy_forward_close = df["SPY_close"].shift(-5)
df["spy_up_5d"] = np.select(
[
spy_forward_close > df["SPY_close"],
spy_forward_close < df["SPY_close"],
],
[1.0, 0.0],
default=0.5,
)
df_model = df.dropna().copy()
```
## Dataset Splits
Training, validation, and test splits should be chronological:
- oldest rows for training;
- newer rows for validation;
- newest rows for the final test set.
A split indicator column such as `split` is useful in the dataset artifact for auditability and reproducibility. It should be treated as metadata, not as a model input feature. The model training code should build `X` from the explicit feature column list and exclude metadata columns such as `date`, `split`, raw close prices, and the target.
## Open Decisions
- exact adjusted versus unadjusted close handling;
- whether same-day `VIX`, `TLT`, and `USO` values are acceptable for the intended trading decision timing;
- exact train, validation, and test date boundaries or split percentages;
- output dataset file location, schema metadata, and versioning.
notebooks/spy_direction_dataset.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# SPY Direction Training Dataset\n",
"\n",
"Build the first model-ready dataset from IBKR daily candle Parquets. The target is whether `SPY` closes above, below, or unchanged from today's close five trading days later."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Setup\n",
"\n",
"The notebook is deliberately plain pandas so feature logic remains easy to inspect. `VIX_INPUT_SYMBOL` defaults to `VIXY` because that is the VIX-related Parquet currently present locally; switch it to `VIX` after fetching a `VIX.parquet` file."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(PosixPath('/home/jarno/repos/trading-bot'),\n",
" PosixPath('/home/jarno/repos/trading-bot/data/ibkr/daily'),\n",
" PosixPath('/home/jarno/repos/trading-bot/data/training/spy_direction_5d.parquet'))"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from pathlib import Path\n",
"\n",
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"\n",
"def find_project_root(start: Path | None = None) -> Path:\n",
" current = (start or Path.cwd()).resolve()\n",
" for candidate in [current, *current.parents]:\n",
" if (candidate / \"pyproject.toml\").exists():\n",
" return candidate\n",
" raise RuntimeError(\"Could not find project root containing pyproject.toml\")\n",
"\n",
"\n",
"PROJECT_ROOT = find_project_root()\n",
"RAW_DATA_DIR = PROJECT_ROOT / \"data\" / \"ibkr\" / \"daily\"\n",
"OUTPUT_PATH = PROJECT_ROOT / \"data\" / \"training\" / \"spy_direction_5d.parquet\"\n",
"\n",
"SPY_SYMBOL = \"SPY\"\n",
"VIX_INPUT_SYMBOL = \"VIXY\"\n",
"TLT_SYMBOL = \"TLT\"\n",
"USO_SYMBOL = \"USO\"\n",
"\n",
"TRAIN_FRACTION = 0.70\n",
"VALIDATION_FRACTION = 0.15\n",
"TEST_FRACTION = 0.15\n",
"\n",
"PROJECT_ROOT, RAW_DATA_DIR, OUTPUT_PATH"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load Close Prices\n",
"\n",
"Each symbol file is read, sorted by trading date, and reduced to a single close-price column. `SPY` defines the observation calendar through the inner join."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.DataFrame'>\n",
"DatetimeIndex: 1251 entries, 2021-07-30 to 2026-07-24\n",
"Data columns (total 4 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 SPY_close 1251 non-null float64\n",
" 1 VIX_close 1251 non-null float64\n",
" 2 TLT_close 1251 non-null float64\n",
" 3 USO_close 1251 non-null float64\n",
"dtypes: float64(4)\n",
"memory usage: 48.9 KB\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>SPY_close</th>\n",
" <th>VIX_close</th>\n",
" <th>TLT_close</th>\n",
" <th>USO_close</th>\n",
" </tr>\n",
" <tr>\n",
" <th>date</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2021-07-30</th>\n",
" <td>438.51</td>\n",
" <td>495.4</td>\n",
" <td>149.52</td>\n",
" <td>50.66</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-08-02</th>\n",
" <td>437.59</td>\n",
" <td>513.6</td>\n",
" <td>150.67</td>\n",
" <td>49.18</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-08-03</th>\n",
" <td>441.15</td>\n",
" <td>488.0</td>\n",
" <td>150.75</td>\n",
" <td>48.85</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-08-04</th>\n",
" <td>438.98</td>\n",
" <td>487.6</td>\n",
" <td>151.06</td>\n",
" <td>47.20</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-08-05</th>\n",
" <td>441.76</td>\n",
" <td>474.8</td>\n",
" <td>150.29</td>\n",
" <td>48.10</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" SPY_close VIX_close TLT_close USO_close\n",
"date \n",
"2021-07-30 438.51 495.4 149.52 50.66\n",
"2021-08-02 437.59 513.6 150.67 49.18\n",
"2021-08-03 441.15 488.0 150.75 48.85\n",
"2021-08-04 438.98 487.6 151.06 47.20\n",
"2021-08-05 441.76 474.8 150.29 48.10"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def load_close(symbol: str, alias: str | None = None) -> pd.DataFrame:\n",
" alias = alias or symbol\n",
" path = RAW_DATA_DIR / f\"{symbol}.parquet\"\n",
" if not path.exists():\n",
" raise FileNotFoundError(f\"Missing raw data file: {path}\")\n",
"\n",
" frame = pd.read_parquet(path)\n",
" if \"date\" in frame.columns:\n",
" frame = frame.set_index(\"date\")\n",
" if \"close\" not in frame.columns:\n",
" raise ValueError(f\"{path} does not contain a close column\")\n",
"\n",
" frame = frame.copy()\n",
" frame.index = pd.to_datetime(frame.index)\n",
" frame.index.name = \"date\"\n",
" frame = frame.sort_index()\n",
" return frame[[\"close\"]].rename(columns={\"close\": f\"{alias}_close\"})\n",
"\n",
"\n",
"prices = pd.concat(\n",
" [\n",
" load_close(SPY_SYMBOL, \"SPY\"),\n",
" load_close(VIX_INPUT_SYMBOL, \"VIX\"),\n",
" load_close(TLT_SYMBOL, \"TLT\"),\n",
" load_close(USO_SYMBOL, \"USO\"),\n",
" ],\n",
" axis=1,\n",
" join=\"inner\",\n",
")\n",
"\n",
"prices.info()\n",
"prices.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Generate Features And Target\n",
"\n",
"Feature values use only current and prior rows. The target looks five trading rows ahead in `SPY_close`; unchanged future prices are encoded as `0.5`."
]
},
{
"cell_type": "code",
"execution_count": 3,
"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>SPY_ret_5</th>\n",
" <th>SPY_ret_20</th>\n",
" <th>SPY_dist_sma50</th>\n",
" <th>VIX_change_5</th>\n",
" <th>VIX_rank_20</th>\n",
" <th>TLT_ret_10</th>\n",
" <th>USO_ret_5</th>\n",
" <th>SPY_TLT_ratio_ret</th>\n",
" <th>spy_up_5d</th>\n",
" </tr>\n",
" <tr>\n",
" <th>date</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2021-10-08</th>\n",
" <td>0.008336</td>\n",
" <td>-0.017017</td>\n",
" <td>-0.011155</td>\n",
" <td>-0.075239</td>\n",
" <td>0.125</td>\n",
" <td>-0.034239</td>\n",
" <td>0.041495</td>\n",
" <td>0.032998</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-10-11</th>\n",
" <td>0.014114</td>\n",
" <td>-0.026625</td>\n",
" <td>-0.018145</td>\n",
" <td>-0.090869</td>\n",
" <td>0.200</td>\n",
" <td>-0.033135</td>\n",
" <td>0.031015</td>\n",
" <td>0.038908</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-10-12</th>\n",
" <td>0.001201</td>\n",
" <td>-0.023752</td>\n",
" <td>-0.020386</td>\n",
" <td>-0.074091</td>\n",
" <td>0.100</td>\n",
" <td>-0.001041</td>\n",
" <td>0.008628</td>\n",
" <td>-0.001303</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-10-13</th>\n",
" <td>0.000644</td>\n",
" <td>-0.028356</td>\n",
" <td>-0.016597</td>\n",
" <td>-0.081006</td>\n",
" <td>0.050</td>\n",
" <td>0.006928</td>\n",
" <td>0.036928</td>\n",
" <td>-0.005897</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-10-14</th>\n",
" <td>0.008754</td>\n",
" <td>-0.010443</td>\n",
" <td>-0.000214</td>\n",
" <td>-0.096759</td>\n",
" <td>0.050</td>\n",
" <td>0.010809</td>\n",
" <td>0.026192</td>\n",
" <td>-0.011991</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" SPY_ret_5 SPY_ret_20 SPY_dist_sma50 VIX_change_5 VIX_rank_20 \\\n",
"date \n",
"2021-10-08 0.008336 -0.017017 -0.011155 -0.075239 0.125 \n",
"2021-10-11 0.014114 -0.026625 -0.018145 -0.090869 0.200 \n",
"2021-10-12 0.001201 -0.023752 -0.020386 -0.074091 0.100 \n",
"2021-10-13 0.000644 -0.028356 -0.016597 -0.081006 0.050 \n",
"2021-10-14 0.008754 -0.010443 -0.000214 -0.096759 0.050 \n",
"\n",
" TLT_ret_10 USO_ret_5 SPY_TLT_ratio_ret spy_up_5d \n",
"date \n",
"2021-10-08 -0.034239 0.041495 0.032998 1.0 \n",
"2021-10-11 -0.033135 0.031015 0.038908 1.0 \n",
"2021-10-12 -0.001041 0.008628 -0.001303 1.0 \n",
"2021-10-13 0.006928 0.036928 -0.005897 1.0 \n",
"2021-10-14 0.010809 0.026192 -0.011991 1.0 "
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = prices.copy()\n",
"\n",
"df[\"SPY_ret_5\"] = df[\"SPY_close\"].pct_change(5)\n",
"df[\"SPY_ret_20\"] = df[\"SPY_close\"].pct_change(20)\n",
"\n",
"sma_50 = df[\"SPY_close\"].rolling(50).mean()\n",
"df[\"SPY_dist_sma50\"] = (df[\"SPY_close\"] - sma_50) / sma_50\n",
"\n",
"df[\"VIX_change_5\"] = df[\"VIX_close\"].pct_change(5)\n",
"df[\"VIX_rank_20\"] = df[\"VIX_close\"].rolling(20).rank(pct=True)\n",
"\n",
"df[\"TLT_ret_10\"] = df[\"TLT_close\"].pct_change(10)\n",
"df[\"USO_ret_5\"] = df[\"USO_close\"].pct_change(5)\n",
"df[\"SPY_TLT_ratio_ret\"] = (df[\"SPY_close\"] / df[\"TLT_close\"]).pct_change(5)\n",
"\n",
"spy_forward_close = df[\"SPY_close\"].shift(-5)\n",
"df[\"spy_up_5d\"] = np.nan\n",
"df.loc[spy_forward_close > df[\"SPY_close\"], \"spy_up_5d\"] = 1.0\n",
"df.loc[spy_forward_close < df[\"SPY_close\"], \"spy_up_5d\"] = 0.0\n",
"df.loc[spy_forward_close == df[\"SPY_close\"], \"spy_up_5d\"] = 0.5\n",
"\n",
"FEATURE_COLUMNS = [\n",
" \"SPY_ret_5\",\n",
" \"SPY_ret_20\",\n",
" \"SPY_dist_sma50\",\n",
" \"VIX_change_5\",\n",
" \"VIX_rank_20\",\n",
" \"TLT_ret_10\",\n",
" \"USO_ret_5\",\n",
" \"SPY_TLT_ratio_ret\",\n",
"]\n",
"TARGET_COLUMN = \"spy_up_5d\"\n",
"\n",
"df_model = df[FEATURE_COLUMNS + [TARGET_COLUMN]].dropna().copy()\n",
"df_model.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Chronological Splits\n",
"\n",
"Splits are assigned by row order after feature/target cleanup: oldest rows for training, newer rows for validation, newest rows for test. The `split` column is metadata and should not be used as a model input."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>rows</th>\n",
" <th>start_date</th>\n",
" <th>end_date</th>\n",
" <th>target_mean</th>\n",
" </tr>\n",
" <tr>\n",
" <th>split</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>train</th>\n",
" <td>837</td>\n",
" <td>2021-10-08</td>\n",
" <td>2025-02-07</td>\n",
" <td>0.583035</td>\n",
" </tr>\n",
" <tr>\n",
" <th>validation</th>\n",
" <td>179</td>\n",
" <td>2025-02-10</td>\n",
" <td>2025-10-24</td>\n",
" <td>0.653631</td>\n",
" </tr>\n",
" <tr>\n",
" <th>test</th>\n",
" <td>181</td>\n",
" <td>2025-10-27</td>\n",
" <td>2026-07-17</td>\n",
" <td>0.558011</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" rows start_date end_date target_mean\n",
"split \n",
"train 837 2021-10-08 2025-02-07 0.583035\n",
"validation 179 2025-02-10 2025-10-24 0.653631\n",
"test 181 2025-10-27 2026-07-17 0.558011"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def assign_chronological_splits(\n",
" frame: pd.DataFrame,\n",
" train_fraction: float,\n",
" validation_fraction: float,\n",
" test_fraction: float,\n",
") -> pd.Series:\n",
" total_fraction = train_fraction + validation_fraction + test_fraction\n",
" if not np.isclose(total_fraction, 1.0):\n",
" raise ValueError(f\"Split fractions must sum to 1.0, got {total_fraction}\")\n",
"\n",
" n_rows = len(frame)\n",
" train_end = int(n_rows * train_fraction)\n",
" validation_end = train_end + int(n_rows * validation_fraction)\n",
"\n",
" split = pd.Series(index=frame.index, dtype=\"object\")\n",
" split.iloc[:train_end] = \"train\"\n",
" split.iloc[train_end:validation_end] = \"validation\"\n",
" split.iloc[validation_end:] = \"test\"\n",
" return split\n",
"\n",
"\n",
"df_model[\"split\"] = assign_chronological_splits(\n",
" df_model,\n",
" TRAIN_FRACTION,\n",
" VALIDATION_FRACTION,\n",
" TEST_FRACTION,\n",
")\n",
"\n",
"split_summary = df_model.groupby(\"split\", sort=False).agg(\n",
" rows=(TARGET_COLUMN, \"size\"),\n",
" start_date=(TARGET_COLUMN, lambda values: values.index.min().date()),\n",
" end_date=(TARGET_COLUMN, lambda values: values.index.max().date()),\n",
" target_mean=(TARGET_COLUMN, \"mean\"),\n",
")\n",
"split_summary"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Inspect And Save\n",
"\n",
"`model_input_columns` is the source of truth for columns that are allowed into the model. The saved Parquet includes `date`, features, target, and split metadata."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Rows: 1,197\n",
"Feature columns: ['SPY_ret_5', 'SPY_ret_20', 'SPY_dist_sma50', 'VIX_change_5', 'VIX_rank_20', 'TLT_ret_10', 'USO_ret_5', 'SPY_TLT_ratio_ret']\n",
"Target column: spy_up_5d\n",
"Training matrix shape: (837, 8)\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>SPY_ret_5</th>\n",
" <th>SPY_ret_20</th>\n",
" <th>SPY_dist_sma50</th>\n",
" <th>VIX_change_5</th>\n",
" <th>VIX_rank_20</th>\n",
" <th>TLT_ret_10</th>\n",
" <th>USO_ret_5</th>\n",
" <th>SPY_TLT_ratio_ret</th>\n",
" <th>spy_up_5d</th>\n",
" <th>split</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>1197.000000</td>\n",
" <td>1197.000000</td>\n",
" <td>1197.000000</td>\n",
" <td>1197.000000</td>\n",
" <td>1197.000000</td>\n",
" <td>1197.000000</td>\n",
" <td>1197.000000</td>\n",
" <td>1197.000000</td>\n",
" <td>1197.000000</td>\n",
" <td>1197</td>\n",
" </tr>\n",
" <tr>\n",
" <th>unique</th>\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>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>top</th>\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>train</td>\n",
" </tr>\n",
" <tr>\n",
" <th>freq</th>\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>837</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>0.002556</td>\n",
" <td>0.009841</td>\n",
" <td>0.011067</td>\n",
" <td>-0.008646</td>\n",
" <td>0.387009</td>\n",
" <td>-0.004083</td>\n",
" <td>0.004662</td>\n",
" <td>0.004927</td>\n",
" <td>0.589808</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>0.023081</td>\n",
" <td>0.043634</td>\n",
" <td>0.037371</td>\n",
" <td>0.092413</td>\n",
" <td>0.333697</td>\n",
" <td>0.028557</td>\n",
" <td>0.052319</td>\n",
" <td>0.027656</td>\n",
" <td>0.492074</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>-0.114962</td>\n",
" <td>-0.123975</td>\n",
" <td>-0.141995</td>\n",
" <td>-0.387709</td>\n",
" <td>0.050000</td>\n",
" <td>-0.092880</td>\n",
" <td>-0.196652</td>\n",
" <td>-0.117208</td>\n",
" <td>0.000000</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>-0.009535</td>\n",
" <td>-0.017017</td>\n",
" <td>-0.008847</td>\n",
" <td>-0.058376</td>\n",
" <td>0.075000</td>\n",
" <td>-0.023517</td>\n",
" <td>-0.026200</td>\n",
" <td>-0.010013</td>\n",
" <td>0.000000</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>0.003869</td>\n",
" <td>0.015797</td>\n",
" <td>0.017906</td>\n",
" <td>-0.020655</td>\n",
" <td>0.250000</td>\n",
" <td>-0.004339</td>\n",
" <td>0.004817</td>\n",
" <td>0.005710</td>\n",
" <td>1.000000</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>0.015957</td>\n",
" <td>0.038385</td>\n",
" <td>0.038332</td>\n",
" <td>0.027778</td>\n",
" <td>0.700000</td>\n",
" <td>0.014272</td>\n",
" <td>0.031883</td>\n",
" <td>0.021406</td>\n",
" <td>1.000000</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>0.082843</td>\n",
" <td>0.157566</td>\n",
" <td>0.088103</td>\n",
" <td>0.937828</td>\n",
" <td>1.000000</td>\n",
" <td>0.091322</td>\n",
" <td>0.327273</td>\n",
" <td>0.129204</td>\n",
" <td>1.000000</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" SPY_ret_5 SPY_ret_20 SPY_dist_sma50 VIX_change_5 VIX_rank_20 \\\n",
"count 1197.000000 1197.000000 1197.000000 1197.000000 1197.000000 \n",
"unique NaN NaN NaN NaN NaN \n",
"top NaN NaN NaN NaN NaN \n",
"freq NaN NaN NaN NaN NaN \n",
"mean 0.002556 0.009841 0.011067 -0.008646 0.387009 \n",
"std 0.023081 0.043634 0.037371 0.092413 0.333697 \n",
"min -0.114962 -0.123975 -0.141995 -0.387709 0.050000 \n",
"25% -0.009535 -0.017017 -0.008847 -0.058376 0.075000 \n",
"50% 0.003869 0.015797 0.017906 -0.020655 0.250000 \n",
"75% 0.015957 0.038385 0.038332 0.027778 0.700000 \n",
"max 0.082843 0.157566 0.088103 0.937828 1.000000 \n",
"\n",
" TLT_ret_10 USO_ret_5 SPY_TLT_ratio_ret spy_up_5d split \n",
"count 1197.000000 1197.000000 1197.000000 1197.000000 1197 \n",
"unique NaN NaN NaN NaN 3 \n",
"top NaN NaN NaN NaN train \n",
"freq NaN NaN NaN NaN 837 \n",
"mean -0.004083 0.004662 0.004927 0.589808 NaN \n",
"std 0.028557 0.052319 0.027656 0.492074 NaN \n",
"min -0.092880 -0.196652 -0.117208 0.000000 NaN \n",
"25% -0.023517 -0.026200 -0.010013 0.000000 NaN \n",
"50% -0.004339 0.004817 0.005710 1.000000 NaN \n",
"75% 0.014272 0.031883 0.021406 1.000000 NaN \n",
"max 0.091322 0.327273 0.129204 1.000000 NaN "
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model_input_columns = FEATURE_COLUMNS\n",
"model_target_column = TARGET_COLUMN\n",
"\n",
"X_train = df_model.loc[df_model[\"split\"] == \"train\", model_input_columns]\n",
"y_train = df_model.loc[df_model[\"split\"] == \"train\", model_target_column]\n",
"\n",
"print(f\"Rows: {len(df_model):,}\")\n",
"print(f\"Feature columns: {model_input_columns}\")\n",
"print(f\"Target column: {model_target_column}\")\n",
"print(f\"Training matrix shape: {X_train.shape}\")\n",
"\n",
"df_model.describe(include=\"all\")"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(PosixPath('/home/jarno/repos/trading-bot/data/training/spy_direction_5d.parquet'),\n",
" (1197, 11),\n",
" date SPY_ret_5 SPY_ret_20 SPY_dist_sma50 VIX_change_5 \\\n",
" 0 2021-10-08 0.008336 -0.017017 -0.011155 -0.075239 \n",
" 1 2021-10-11 0.014114 -0.026625 -0.018145 -0.090869 \n",
" 2 2021-10-12 0.001201 -0.023752 -0.020386 -0.074091 \n",
" 3 2021-10-13 0.000644 -0.028356 -0.016597 -0.081006 \n",
" 4 2021-10-14 0.008754 -0.010443 -0.000214 -0.096759 \n",
" \n",
" VIX_rank_20 TLT_ret_10 USO_ret_5 SPY_TLT_ratio_ret spy_up_5d split \n",
" 0 0.125 -0.034239 0.041495 0.032998 1.0 train \n",
" 1 0.200 -0.033135 0.031015 0.038908 1.0 train \n",
" 2 0.100 -0.001041 0.008628 -0.001303 1.0 train \n",
" 3 0.050 0.006928 0.036928 -0.005897 1.0 train \n",
" 4 0.050 0.010809 0.026192 -0.011991 1.0 train )"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"OUTPUT_PATH.parent.mkdir(parents=True, exist_ok=True)\n",
"dataset_to_save = df_model.reset_index()\n",
"dataset_to_save.to_parquet(OUTPUT_PATH, index=False)\n",
"\n",
"OUTPUT_PATH, dataset_to_save.shape, dataset_to_save.head()"
]
}
],
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"kernelspec": {
"display_name": "Python 3",
"language": "python",
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pyproject.toml
+3 -1
View File
@@ -11,7 +11,9 @@ dependencies = [
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[dependency-groups] [dependency-groups]
dev = [] dev = [
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[tool.uv] [tool.uv]
package = true package = true
uv.lock
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