From 669271901deced919a6eddcde81ebdf59ceec9fb Mon Sep 17 00:00:00 2001 From: Caleb Burke Date: Mon, 22 Jun 2026 17:13:57 -0700 Subject: [PATCH] Started new tutorial. Added a bunch of python packages such as pytorch --- .gitignore | 3 + pyproject.toml | 24 +- .../ch1/example-problems.ipynb | 13 +- .../README.md | 3 + .../part1.ipynb | 79 ++ .../research.py | 1112 +++++++++++++++++ uv.lock | 801 +++++++++++- 7 files changed, 1984 insertions(+), 51 deletions(-) create mode 100644 tutorials/20260622164035_Let's Build a Quant Trading Strategy, MemLabs/README.md create mode 100644 tutorials/20260622164035_Let's Build a Quant Trading Strategy, MemLabs/part1.ipynb create mode 100644 tutorials/20260622164035_Let's Build a Quant Trading Strategy, MemLabs/research.py diff --git a/.gitignore b/.gitignore index 4c49bd7..f7a830c 100644 --- a/.gitignore +++ b/.gitignore @@ -1 +1,4 @@ .env +__pycache__/ +*.pyc +*.pyo diff --git a/pyproject.toml b/pyproject.toml index 66f011d..796c5b9 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,13 +1,33 @@ [project] -name = "roadmap" +name = "study" version = "0.1.0" description = "Add your description here" readme = "README.md" requires-python = ">=3.14" dependencies = [ - "dotenv>=0.9.9", + "aiohttp>=3.14.1", + "altair>=6.2.1", + "binance>=0.3.110", + "cryptography>=49.0.0", "jupyter>=1.1.1", "matplotlib>=3.10.9", "numpy>=2.4.6", + "polars>=1.41.2", + "python-dotenv>=1.2.2", "seaborn>=0.13.2", + "torch>=2.12.1", + "tqdm>=4.68.3", +] + +[[tool.uv.index]] +name = "pytorch-cu132" +url = "https://download.pytorch.org/whl/cu132" +explicit = true + +[tool.uv.sources] +torch = [ + { index = "pytorch-cu132", marker = "sys_platform == 'linux' or sys_platform == 'win32'" }, +] +torchvision = [ + { index = "pytorch-cu132", marker = "sys_platform == 'linux' or sys_platform == 'win32'" }, ] diff --git a/textbooks/reading/20260622012450_Introduction to Probability, Statistics, and Random Processes/ch1/example-problems.ipynb b/textbooks/reading/20260622012450_Introduction to Probability, Statistics, and Random Processes/ch1/example-problems.ipynb index 8d9b4dd..ce72ace 100644 --- a/textbooks/reading/20260622012450_Introduction to Probability, Statistics, and Random Processes/ch1/example-problems.ipynb +++ b/textbooks/reading/20260622012450_Introduction to Probability, Statistics, and Random Processes/ch1/example-problems.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "id": "c58309b2", "metadata": {}, "outputs": [], @@ -299,7 +299,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "e589ecc1", "metadata": {}, "outputs": [ @@ -312,13 +312,6 @@ }, "metadata": {}, "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2.25091334378745\n" - ] } ], "source": [ @@ -769,7 +762,7 @@ ], "metadata": { "kernelspec": { - "display_name": "roadmap (3.14.5)", + "display_name": "study (3.14.5)", "language": "python", "name": "python3" }, diff --git a/tutorials/20260622164035_Let's Build a Quant Trading Strategy, MemLabs/README.md b/tutorials/20260622164035_Let's Build a Quant Trading Strategy, MemLabs/README.md new file mode 100644 index 0000000..46e5372 --- /dev/null +++ b/tutorials/20260622164035_Let's Build a Quant Trading Strategy, MemLabs/README.md @@ -0,0 +1,3 @@ +# Youtube Playlist - Let's Build a Quant Trading Strategy + + diff --git a/tutorials/20260622164035_Let's Build a Quant Trading Strategy, MemLabs/part1.ipynb b/tutorials/20260622164035_Let's Build a Quant Trading Strategy, MemLabs/part1.ipynb new file mode 100644 index 0000000..1906437 --- /dev/null +++ b/tutorials/20260622164035_Let's Build a Quant Trading Strategy, MemLabs/part1.ipynb @@ -0,0 +1,79 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 7, + "id": "0ba75c87", + "metadata": {}, + "outputs": [], + "source": [ + "# Data and analysis libraries\n", + "import polars as pl # Fast dataframes for financial data\n", + "import numpy as np # Numerical computing library\n", + "from datetime import datetime, timedelta # Date and time operations\n", + "import random\n", + "\n", + "# Machine learning libraries \n", + "import torch # PyTorch framework\n", + "import torch.nn as nn # Neural network modules\n", + "import torch.optim as optim # Optimization algorithms\n", + "import research # Model building and training utilities\n", + "\n", + "# Visualization and \n", + "import altair as alt # Interactive visualization library\n", + "\n", + "# data sources\n", + "import binance # Binance market data utilities" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "ee4d8884", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CUDA Available: True\n", + "CUDA Version: 13.2\n" + ] + } + ], + "source": [ + "print('CUDA Available:', torch.cuda.is_available())\n", + "print('CUDA Version:', torch.version.cuda)" + ] + }, + { + "cell_type": "markdown", + "id": "e0f64bf2", + "metadata": {}, + "source": [ + "# Part 1: ML Model in PyTorch" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "study (3.14.5)", + "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": "3.14.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/tutorials/20260622164035_Let's Build a Quant Trading Strategy, MemLabs/research.py b/tutorials/20260622164035_Let's Build a Quant Trading Strategy, MemLabs/research.py new file mode 100644 index 0000000..68e8f47 --- /dev/null +++ b/tutorials/20260622164035_Let's Build a Quant Trading Strategy, MemLabs/research.py @@ -0,0 +1,1112 @@ +""" +research.py - Model Research & Development Utilities +==================================================== +Reusable boilerplate code for machine learning research and model development. +This module eliminates copy-paste by centralizing common functions for: + +- Data preprocessing and time series aggregation +- Feature engineering for financial data +- Model architecture inspection and debugging +- Visualization and exploratory data analysis +- Training utilities + +Import this in all your research notebooks to save time and maintain consistency. + +Author: MemLabs +Course: Build a Quant Trading System +""" + +# ============================================================================ +# IMPORTS +# ============================================================================ + +# Data manipulation and analysis +import polars as pl # Fast dataframes for financial data +from typing import Dict, List, Tuple, Union # Type hints for function signatures + +# Machine learning framework +import torch # PyTorch for neural networks +import torch.nn as nn # Neural network modules +import torch.optim as optim # Optimization algorithms + +# Numerical computing and datetime +import numpy as np # Numerical operations +import numpy.typing as npt +from datetime import datetime, timedelta # Date and time handling + +# Visualization +import altair # Interactive plotting library +import matplotlib.pyplot as plt + +import random +import re +import itertools +from pathlib import Path +from tqdm import tqdm +import os + +SEED = 42 + +# ============================================================================ +# TIME SERIES AGGREGATION +# ============================================================================ +OHLC_AGGS = [ + # Price statistics (core OHLC data) + pl.col("price").first().alias("open"), # Opening price + pl.col("price").max().alias("high"), # Highest price + pl.col("price").min().alias("low"), # Lowest price + pl.col("price").last().alias("close"), # Closing price (most important) +] + + +def get_trade_files(directory: str, sym: str) -> List[Path]: + """ + Get all files in directory that start with '{sym}-trades'. + + Args: + directory: Path to directory to search + sym: Symbol prefix (e.g., 'BTCUSDT') + + Returns: + List of Path objects matching the pattern + + Example: + >>> files = get_trade_files('./data', 'BTCUSDT') + >>> # Returns: ['BTCUSDT-trades-2024.csv', 'BTCUSDT-trades-raw.parquet', ...] + """ + dir_path = Path(directory) + pattern = f"{sym}-trades*" + return sorted(dir_path.glob(pattern)) + + +from pathlib import Path +from typing import List, Optional + +def load_ohlc_timeseries(sym: str, time_interval: str): + return load_timeseries(sym, time_interval, OHLC_AGGS) + +def load_timeseries( + sym: str, + time_interval: str, + aggs: List[pl.Expr], + data_path: Optional[str] = None +) -> pl.DataFrame: + """ + Load trade CSV files one by one, aggregate to time series, and concatenate. + + Args: + sym: Symbol prefix (e.g., 'BTCUSDT') + time_interval: Time interval for aggregation (e.g., '1h', '5m') + aggs: List of aggregation expressions + data_path: Optional directory path. Defaults to './cache' if not provided + + Returns: + Concatenated time series DataFrame + + Example: + >>> # Use default './cache' directory + >>> ts = load_ohlc_ts('BTCUSDT', '1h', ohlc_aggs) + + >>> # Specify custom directory + >>> ts = load_ohlc_ts('BTCUSDT', '1h', ohlc_aggs, data_path='./my_data') + """ + # Default to './cache' if not provided + if data_path is None: + data_path = './cache' + + files = get_trade_files(data_path, sym) + + if not files: + raise FileNotFoundError(f"No files found for {sym} in {data_path}") + + # Process each file and collect results + ts_list = [] + + # Add progress bar + for file in tqdm(files, desc=f"Loading {sym}", unit="file"): + + # Load trades from parquet + trades = pl.read_parquet(file) + + # Ensure datetime column exists and is correct type + if "datetime" not in trades.columns: + raise ValueError(f"Column 'datetime' not found in {file.name}") + + trades = trades.with_columns( + pl.col("datetime").cast(pl.Datetime) + ).sort("datetime") + + # Aggregate to time series + ts = trades.group_by_dynamic( + "datetime", + every=time_interval, + offset="0m" + ).agg(aggs) + + ts_list.append(ts) + + # Concatenate all time series + result = pl.concat(ts_list) + + # Sort by datetime and remove duplicates if any + result = result.sort("datetime").unique(subset=["datetime"]) + + return result + + +def load_timeseries_range( + sym: str, + time_interval: str, + start_date: datetime, + end_date: datetime, + agg_cols: Union[pl.Expr,List[pl.Expr]], + data_path: Optional[str] = None +) -> pl.DataFrame: + """ + Load and aggregate trade data for a symbol between start_date and end_date + into OHLC time series using the given time interval. + + Expects daily files named like: + {symbol}-trades-YYYY-MM-DD.parquet + + Example filename: + BTCUSDT-trades-2025-09-22.parquet + + Args: + sym: Symbol prefix (e.g., 'BTCUSDT') + time_interval: Aggregation interval (e.g., '1h', '5m') + start_date: Start datetime (inclusive) + end_date: End datetime (inclusive) + data_path: Directory containing cached trade parquet files (default: './cache') + + Returns: + Polars DataFrame with aggregated OHLC time series for the given range. + """ + if data_path is None: + data_path = "./cache" + + if start_date > end_date: + raise ValueError("start_date must be before or equal to end_date") + + ts_list = [] + total_days = (end_date - start_date).days + 1 + + for i in tqdm(range(total_days), desc=f"Loading {sym}", unit="day"): + current_date = start_date + timedelta(days=i) + file_name = f"{sym}-trades-{current_date.strftime('%Y-%m-%d')}.parquet" + file_path = os.path.join(data_path, file_name) + + if not os.path.exists(file_path): + tqdm.write(f"[WARNING] Missing file: {file_name}") + continue + + try: + trades = pl.read_parquet(file_path) + + if "datetime" not in trades.columns: + raise ValueError(f"Column 'datetime' not found in {file_name}") + + trades = trades.with_columns(pl.col("datetime").cast(pl.Datetime)) + + ts = trades.group_by_dynamic("datetime", every=time_interval, offset="0m").agg(agg_cols) + ts_list.append(ts) + + except Exception as e: + tqdm.write(f"[ERROR] {file_name}: {e}") + + if not ts_list: + raise ValueError(f"No trade data found for {sym} in range {start_date} to {end_date}") + + result = pl.concat(ts_list).sort("datetime").unique(subset=["datetime"]) + return result + +def load_ohlc_timeseries_range( + sym: str, + time_interval: str, + start_date: datetime, + end_date: datetime, + data_path: Optional[str] = None +) -> pl.DataFrame: + """ + Load and aggregate trade data for a symbol between start_date and end_date + into OHLC time series using the given time interval. + + Expects daily files named like: + {symbol}-trades-YYYY-MM-DD.parquet + + Example filename: + BTCUSDT-trades-2025-09-22.parquet + + Args: + sym: Symbol prefix (e.g., 'BTCUSDT') + time_interval: Aggregation interval (e.g., '1h', '5m') + start_date: Start datetime (inclusive) + end_date: End datetime (inclusive) + data_path: Directory containing cached trade parquet files (default: './cache') + + Returns: + Polars DataFrame with aggregated OHLC time series for the given range. + """ + if data_path is None: + data_path = "./cache" + + if start_date > end_date: + raise ValueError("start_date must be before or equal to end_date") + + ts_list = [] + total_days = (end_date - start_date).days + 1 + + for i in tqdm(range(total_days), desc=f"Loading {sym}", unit="day"): + current_date = start_date + timedelta(days=i) + file_name = f"{sym}-trades-{current_date.strftime('%Y-%m-%d')}.parquet" + file_path = os.path.join(data_path, file_name) + + if not os.path.exists(file_path): + tqdm.write(f"[WARNING] Missing file: {file_name}") + continue + + try: + trades = pl.read_parquet(file_path) + + if "datetime" not in trades.columns: + raise ValueError(f"Column 'datetime' not found in {file_name}") + + trades = trades.with_columns(pl.col("datetime").cast(pl.Datetime)) + + ts = trades.group_by_dynamic("datetime", every=time_interval, offset="0m").agg(OHLC_AGGS) + ts_list.append(ts) + + except Exception as e: + tqdm.write(f"[ERROR] {file_name}: {e}") + + if not ts_list: + raise ValueError(f"No trade data found for {sym} in range {start_date} to {end_date}") + + result = pl.concat(ts_list).sort("datetime").unique(subset=["datetime"]) + return result + + +def sharpe_annualization_factor(interval: str, + trading_days_per_year: int = 365, + trading_hours_per_day: float = 24) -> float: + """ + Compute annualization factor (sqrt of periods per year) given a return interval. + + interval : str + Frequency string like '1d', '1h', '30m', '15s'. + trading_days_per_year : int + Number of trading days in a year (default 252). + trading_hours_per_day : float + Number of trading hours in a trading day (default 6.5). + + Returns + ------- + float : annualization factor + """ + match = re.match(r"(\d+)([dhms])", interval.lower()) + if not match: + raise ValueError("Interval must be like '1d', '2h', '15m', '30s'") + + value, unit = int(match.group(1)), match.group(2) + + # periods per year + if unit == 'd': + periods = trading_days_per_year / value + elif unit == 'h': + periods = trading_days_per_year * (trading_hours_per_day / value) + elif unit == 'm': + periods = trading_days_per_year * (trading_hours_per_day * 60 / value) + elif unit == 's': + periods = trading_days_per_year * (trading_hours_per_day * 3600 / value) + else: + raise ValueError(f"Unsupported unit: {unit}") + + return np.sqrt(periods) + + +def ohlc_timeseries(df: pl.DataFrame, time_interval: str) -> pl.DataFrame: + """ + Convert tick-level trade data into OHLC (Open, High, Low, Close) bars. + + This function aggregates raw trade data into standardized price bars + with basic volume and trade statistics. If you want to extend this then call regular_timeseries + + Args: + df: DataFrame containing trade data with columns: + - datetime: Timestamp of each trade + - price: Execution price + - quote_qty: Trade size in quote currency (e.g., USDT) + - is_short: Boolean indicating if trade was a short sale + time_interval: Aggregation period (e.g., '1m', '5m', '15m', '1h', '1d') + + Returns: + DataFrame with OHLC bars containing: + - datetime: Bar timestamp + - open: First price in interval + - high: Highest price in interval + - low: Lowest price in interval + - close: Last price in interval (most important for ML) + - volume: Total trading volume in quote currency + - trade_count: Number of individual trades + - short_ratio: Percentage of trades that were short sales + - mean_price: Average price (volume-weighted alternative) + + Example: + >>> # Create 15-minute OHLC bars + >>> bars_15m = ohlc_timeseries(trades_df, '15m') + >>> + >>> # Create hourly bars for longer-term analysis + >>> bars_1h = ohlc_timeseries(trades_df, '1h') + """ + # Define aggregation expressions for OHLC calculation + + + # Use the generic time series aggregation function + return timeseries(df, time_interval, OHLC_AGGS) + +def lag_col_names(col: str, n: int) -> List[str]: + return [f'{col}_lag_{i}' for i in range(1, n+1)] + +def auto_reg_corr_matrx(df, target, max_no_lags) -> pl.DataFrame: + return df.drop_nulls().select([target]+lag_col_names(target, max_no_lags)).corr() + +def log_returns_col(name: str, step_size = 1) -> pl.Expr: + return (pl.col(name)/pl.col(name).shift(step_size)).log().alias(f'{name}_log_return') + +def timeseries( + df: pl.DataFrame, + time_interval: str, + aggs: Union[List[pl.Expr],pl.Expr] +) -> pl.DataFrame: + """ + Generic function for aggregating data into regular time intervals. + + This is a flexible time series aggregation framework that can handle + any custom aggregation expressions. Used as the foundation for OHLC + bars and other time-based features. + + Args: + df: DataFrame with a 'datetime' column + time_interval: Aggregation period (Polars duration string) + Examples: '1m', '5m', '15m', '1h', '4h', '1d' + aggs: List of Polars expressions defining aggregations to compute + + Returns: + DataFrame with time-aggregated data + + Technical Details: + - Uses left-closed intervals: [start_time, end_time) + - Bars start at round times (e.g., 09:00, 09:15, 09:30) + - Missing bars (no trades) are automatically excluded + + Example: + >>> # Custom aggregation for volatility analysis + >>> custom_aggs = [ + ... pl.col("price").std().alias("price_volatility"), + ... pl.col("volume").sum().alias("total_volume"), + ... ] + >>> df_volatility = regular_timeseries(df, '1h', custom_aggs) + """ + return df.group_by_dynamic( + "datetime", # Column to group by (must be datetime type) + every=time_interval, # Aggregation frequencyß + offset="0m" # No offset (bars align to round times) + ).agg(aggs) + + +# ============================================================================ +# VISUALIZATION +# ============================================================================ + +def plot(df: pl.DataFrame, col: str, title: str = "") -> altair.Chart: + """ + Create a smooth density plot for analyzing feature distributions. + + Useful for: + - Understanding data distributions before modeling + - Detecting outliers and skewness + - Comparing feature distributions across different time periods + - Validating data preprocessing steps + + Args: + df: DataFrame containing the column to plot + col: Name of the column to visualize + title: Optional chart title (defaults to None) + + Returns: + Altair Chart object (displays automatically in Jupyter) + + Example: + >>> # Plot distribution of returns + >>> plot(df, 'returns', title='Return Distribution') + >>> + >>> # Plot price changes + >>> plot(df, 'price_change', title='Price Change Distribution') + + Note: + The density estimation uses kernel density estimation (KDE) + with basis interpolation for smooth curves. + """ + return altair.Chart(df).mark_area( + opacity=0.7, # Semi-transparent fill + interpolate='basis' # Smooth curve interpolation + ).transform_density( + col, # Column to compute density for + as_=[col, 'density'] # Output column names + ).encode( + x=altair.X(f'{col}:Q', title=col), # X-axis: feature values + y=altair.Y('density:Q', title='Density') # Y-axis: probability density + ).properties( + width=600, + height=400, + title=title if title else f'Distribution of {col}' + ) + +def plot_distribution(data: pl.DataFrame, col: str, label = None, no_bins = 100): + return altair.Chart(data).mark_bar().encode( + altair.X(f'{col}:Q', bin=altair.Bin(maxbins=no_bins)), + y='count()' + ).properties( + width=600, + height=400, + title=f'Distribution of {label if label else col}' + ).configure_scale(zero=False).add_params( + altair.selection_interval(bind='scales') +) + +def plot_static_timeseries(ts: pl.DataFrame, sym: str, col: str, interval_size: str): + plt.figure(figsize=(12, 6)) + plt.plot(ts['datetime'], ts[col], label=col) # or whatever column you want + plt.title(f'{sym} {interval_size} Bars') + plt.xlabel('time') + plt.ylabel(col) + plt.legend() + plt.xticks(rotation=45) + plt.tight_layout() + plt.show() + + +def plot_multiple_lines( + df: pl.DataFrame, + cols_to_plot: List[str], + sym: str, + width: int = 15, + height: int = 6, + xlabel_unit: str = "Time Step" +): + import matplotlib.pyplot as plt + """ + Plots multiple columns from a Polars DataFrame on the same axes using Matplotlib. + The x-axis uses a simple numerical index (since no datetime column is present). + + Parameters: + ----------- + df : polars.DataFrame + The Polars DataFrame containing the columns to plot. + cols_to_plot : list[str] + A list of column names to plot (e.g., ['log_return', 'mean']). + sym : str + A symbol or identifier for the series (used in the title). + width : int, default 15 + Width of the plot in inches. + height : int, default 6 + Height of the plot in inches. + xlabel_unit : str, default 'Time Step' + Label for the X-axis (the numerical index). + """ + + # 1. Create the numerical index for the x-axis + x_index = np.arange(len(df)) + + # 2. Set the figure size (controls the width/height) + plt.figure(figsize=(width, height)) + + # 3. Loop through the list of columns and plot each one + for col in cols_to_plot: + if col in df.columns: + # Extract column data as a NumPy array (efficient) + y_values = df[col].to_numpy() + + # Plot the line, using the column name for the label + plt.plot(x_index, y_values, label=col) + else: + print(f"Warning: Column '{col}' not found in DataFrame.") + + # 4. Finalize the plot + + # Dynamically generate the title based on the symbol and columns + title_cols = ', '.join(cols_to_plot) + plt.title(f'{sym} Series: {title_cols}') + + plt.xlabel(xlabel_unit) + plt.ylabel('Value') # Generic Y-label since multiple series are plotted + plt.legend(loc='best') + plt.grid(True, linestyle=':', alpha=0.6) + + # Adjust layout to prevent labels from being cut off + plt.tight_layout() + plt.show() + +def plot_dyn_timeseries(ts: pl.DataFrame, sym: str, col: str, time_interval: str ): + return altair.Chart(ts).mark_line(tooltip=True).encode( + x="datetime", + y=col + ).properties( + width=800, + height=400, + title=f"{sym} {time_interval} {col}" + ).configure_scale(zero=False).add_selection( + altair.selection_interval(bind='scales', encodings=['x']), # Only zoom x-axis + altair.selection_interval(bind='scales', encodings=['y']) # Only zoom y-axis + ) + +def to_tensor(x, dtype=None) -> torch.Tensor: + return torch.tensor(x.to_numpy(), dtype=torch.float32 if dtype is None else dtype) + +# ============================================================================ +# MODEL ANALYSIS +# ============================================================================ + +def print_model_complexity_ratio(m1, m1_name, m2, m2_name): + m1_params = total_model_params(m1) + m2_params = total_model_params(m2) + complexity_ratio = m2_params / m1_params + + print(f"Complexity Comparsion:") + print(f"\t{m2_name} has {complexity_ratio:.1f}x more parameters than {m1_name}") + print(f"\tParametric difference: {m2_params - m1_params:,} additional parameters") + +def total_model_params(model: nn.Module) -> int: + return sum(p.numel() for p in model.parameters()) + +def print_model_info(model: torch.nn.Module, model_name: str) -> None: + """ + Print detailed information about a PyTorch model's architecture and parameters. + + This function helps you understand: + - Model complexity (number of parameters) + - Which parameters are trainable vs frozen + - Overall model architecture + + Useful for: + - Comparing different model architectures + - Debugging training issues + - Estimating memory requirements + - Understanding model capacity + + Args: + model: PyTorch model (nn.Module) + model_name: Descriptive name for the model (e.g., 'LSTM Predictor') + + Returns: + None (prints to console) + + Example: + >>> model = MyTradingModel(input_size=10, hidden_size=64) + >>> print_model_info(model, 'Trading LSTM v1') + + Output: + Trading LSTM v1: + Architecture: MyTradingModel(...) + Total parameters: 15,234 + Trainable parameters: 15,234 + + Note: + Total parameters includes both trainable and frozen parameters. + For transfer learning, trainable params may be less than total. + """ + # Count all parameters in the model + total_params = sum(p.numel() for p in model.parameters()) + + # Count only parameters that will be updated during training + trainable_params = sum( + p.numel() for p in model.parameters() if p.requires_grad + ) + + # Print formatted model information + print(f"\n{'='*60}") + print(f"{model_name}") + print(f"{'='*60}") + print(f"\nArchitecture:") + print(f" {model}") + print(f"\nParameter Count:") + print(f" Total parameters: {total_params:,}") + print(f" Trainable parameters: {trainable_params:,}") + + # Warn if some parameters are frozen + if total_params != trainable_params: + frozen_params = total_params - trainable_params + print(f" Frozen parameters: {frozen_params:,}") + print(f"\n ⚠️ Note: {frozen_params:,} parameters are frozen") + + print(f"{'='*60}\n") + +def _prefix_cols(df, prefix): + return df.rename({col: f"{prefix}_{col}" for col in df.columns}) + +def _prefix_close_ts(trades, time_interval, prefix): + return _prefix_cols(ohlc_timeseries(trades, time_interval), prefix) + +def compare_ts_corr(x_df, x_prefix, y_df, y_prefix, time_interval, col = 'close'): + x_col, y_col = f'{x_prefix}_{col}',f'{y_prefix}_{col}' + joined_ts = pl.concat([ + _prefix_close_ts(x_df, time_interval, x_prefix), + _prefix_close_ts(y_df, time_interval, y_prefix) + ], how="horizontal") + return joined_ts.select(pl.corr(x_col, y_col)).item() + +def log_return_col(col: str) -> str: + return f"{col}_log_return" + +def log_return(col: str, shift_size: int = 1) -> pl.Expr: + return (pl.col(col)/pl.col(col).shift(shift_size)).log().alias(log_return_col(col)) + +def lag_cols(col: str, forecast_horizon: str, no_lags: int) -> List[pl.Expr]: + return [pl.col(col).shift(forecast_horizon * i).alias(f'{col}_lag_{i}') for i in range(1, no_lags + 1)] + +def add_lags(df: pl.DataFrame, col: str, max_no_lags: int, forecast_step: int) -> pl.DataFrame: + return df.with_columns([pl.col(col).shift(i * forecast_step).alias(f'{col}_lag_{i}') for i in range(1, max_no_lags + 1)]) + +def batch_train_reg( + model: nn.Module, + X_train, + X_test, + y_train, + y_test, + no_epochs: int, + criterion=None, + optimizer=None, + logging=True, + lr=None +): + if criterion is None: + criterion = nn.L1Loss() + + if lr is None: + lr = 0.0002 + + # Default optimizer + if optimizer is None: + # Use strong_wolfe line search (more stable) + optimizer = optim.LBFGS( + model.parameters(), + lr=1, + line_search_fn='strong_wolfe', + tolerance_grad=1e-7, + tolerance_change=1e-9 + ) + + # Logging model info + if logging: + print(f"\nModel parameters: {sum(p.numel() for p in model.parameters())}") + print("Model architecture:") + for name, param in model.named_parameters(): + print(f" {name}: {param.shape} ({param.numel()} params)") + print("\nTraining model...") + + train_loss = None + log_tick_size = max(no_epochs // 10, 1) # avoid zero division + + # Training loop + if isinstance(optimizer, torch.optim.LBFGS): + # LBFGS requires a closure + for epoch in range(no_epochs): + def closure(): + optimizer.zero_grad() + predictions = model(X_train) + loss = criterion(predictions, y_train) + loss.backward() + return loss + + optimizer.step(closure) + + with torch.no_grad(): + train_loss = criterion(model(X_train), y_train).item() + + if logging and (epoch + 1) % log_tick_size == 0: + print(f"Epoch [{epoch+1}/{no_epochs}], Loss: {train_loss:.6f}") + + else: + # SGD/Adam loop + for epoch in range(no_epochs): + optimizer.zero_grad() + predictions = model(X_train) + loss = criterion(predictions, y_train) + loss.backward() + optimizer.step() + train_loss = loss.item() + + if logging and (epoch + 1) % log_tick_size == 0: + print(f"Epoch [{epoch+1}/{no_epochs}], Loss: {loss.item():.6f}") + + # After training + if logging: + print("\nLearned parameters:") + for name, param in model.named_parameters(): + if param.requires_grad: + print(f"{name}:\n{param.data.numpy()}") + + # Evaluation + model.eval() + with torch.no_grad(): + y_hat = model(X_test) + test_loss = criterion(y_hat, y_test) + if logging: + print(f'\nTest Loss: {test_loss.item():.6f}, Train Loss: {train_loss:.6f}') + + return y_hat + + +def timeseries_train_test_split(df: pl.DataFrame, features, target, test_size=0.25) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + df = df.drop_nulls() + X = to_tensor(df[features]) + y = to_tensor(df[target]).reshape(-1, 1) + X_train, X_test = timeseries_split(X, test_size) + y_train, y_test = timeseries_split(y, test_size) + return X_train, X_test, y_train, y_test + +def timeseries_split(t, test_size=0.25): + """ + Split a tensor or array into train/test sets based on a proportion. + + Parameters + ---------- + t : torch.Tensor or np.ndarray + Time series data. + test_size : float, default 0.25 + Proportion of data to use for testing. Must be between 0 and 1. + + Returns + ------- + train, test : same type as t + Train and test splits. + + Raises + ------ + ValueError + If test_size is not strictly between 0 and 1. + """ + if not (0 < test_size < 1): + raise ValueError(f"test_size must be between 0 and 1 (got {test_size})") + + split_idx = int(len(t) * (1 - test_size)) + return t[:split_idx], t[split_idx:] + + +def plot_column(df, col_name, figsize=(15, 6), title=None, xlabel='Index'): + """ + Plot a column from a Polars DataFrame using matplotlib. + + Parameters: + ----------- + df : polars.DataFrame + The Polars DataFrame + column_name : str + Name of the column to plot + figsize : tuple, default (15, 6) + Figure size as (width, height) in inches + title : str, optional + Plot title. If None, uses column name + xlabel : str, default 'Index' + X-axis label + ylabel : str, optional + Y-axis label. If None, uses column name + """ + + if title is None: + title = col_name + + chart = df[col_name].plot.line() + return chart.properties( + width=800, + height=400, + title=title + ) + + +def plot_columns(df, col_name, figsize=(15, 6), title=None, xlabel='Index'): + """ + Plot a columns from a Polars DataFrame using matplotlib. + + Parameters: + ----------- + df : polars.DataFrame + The Polars DataFrame + column_name : str + Name of the column to plot + figsize : tuple, default (15, 6) + Figure size as (width, height) in inches + title : str, optional + Plot title. If None, uses column name + xlabel : str, default 'Index' + X-axis label + ylabel : str, optional + Y-axis label. If None, uses column name + """ + if title is None: + title = col_name + + chart = df[col_name].plot.line() + return chart.properties( + width=800, + height=400, + title=title + ) + + +def model_trade_results(y_true, y_pred) -> pl.DataFrame: + """Generate trade-level results from model predictions.""" + + trade_results = pl.DataFrame({ + 'y_pred': y_pred.squeeze(), + 'y_true': y_true.squeeze() + }).with_columns([ + (pl.col('y_pred').sign() == pl.col('y_true').sign()).alias('is_won'), + pl.col('y_pred').sign().alias('position') + ]).with_columns([ + (pl.col('position') * pl.col('y_true')).alias('trade_log_return') + ]).with_columns([ + pl.col('trade_log_return').cum_sum().alias('equity_curve') + ]).with_columns( + (pl.col('equity_curve')-pl.col('equity_curve').cum_max()).alias('drawdown_log_return'), + ) + return trade_results + + +def add_tx_fee(trades: pl.DataFrame, tx_fee: float, name: str): + tx_fee_col = (pl.col('exit_trade_value') * tx_fee + pl.col('entry_trade_value') * tx_fee).alias(f"tx_fee_{name}") + return trades.with_columns(tx_fee_col) + + +def add_tx_fees(trades: pl.DataFrame, maker_fee: float, taker_fee: float): + trades = add_tx_fee(trades, maker_fee, 'maker') + trades = add_tx_fee(trades, taker_fee, 'taker') + return trades + +def add_tx_fees_log(trades: pl.DataFrame, maker_fee, taker_fee): + return trades.with_columns( + (pl.col('trade_log_return') + np.log(maker_fee)).alias('trade_log_return_net_maker'), + (pl.col('trade_log_return') + np.log(taker_fee)).alias('trade_log_return_net_taker'), + ).with_columns( + pl.col('trade_log_return_net_maker').cum_sum().alias('equity_curve_net_maker'), + pl.col('trade_log_return_net_taker').cum_sum().alias('equity_curve_net_taker'), + ) + +def eval_model_performance(y_actual, y_pred, feature_names: List[str], target_name: str, annualized_rate: float) -> Dict[str, any]: + """Calculate performance metrics for the trading model.""" + trade_results = model_trade_results(y_actual, y_pred) + + accuracy = trade_results['is_won'].mean() + avg_win = trade_results.filter(pl.col('is_won'))['trade_log_return'].mean() + avg_loss = trade_results.filter(~pl.col('is_won'))['trade_log_return'].mean() + expected_value = accuracy * avg_win + (1 - accuracy) * avg_loss + drawdown = (trade_results['equity_curve'] - trade_results['equity_curve'].cum_max()) + max_drawdown = drawdown.min() + sharpe = trade_results['trade_log_return'].mean() / trade_results['trade_log_return'].std() if trade_results['trade_log_return'].std() > 0 else 0 + annualized_sharpe = sharpe * annualized_rate + equity_trough = trade_results['equity_curve'].min() + equity_peak = trade_results['equity_curve'].max() + total_log_return = trade_results['trade_log_return'].sum() + std = trade_results['trade_log_return'].std() + return { + 'features': ','.join(list(feature_names)), + 'target': target_name, + 'no_trades': len(trade_results), + 'win_rate': accuracy, + 'avg_win': avg_win, + 'avg_loss': avg_loss, + 'best_trade': trade_results['trade_log_return'].max(), + 'worst_trade': trade_results['trade_log_return'].min(), + 'ev': expected_value, + 'std': std, + 'total_log_return': total_log_return, + 'compound_return': np.exp(total_log_return), + 'max_drawdown': max_drawdown, + 'equity_trough': equity_trough, + 'equity_peak': equity_peak, + 'sharpe': annualized_sharpe, + } + +def train_reg_model(df: pl.DataFrame, features: List[str], target: str, model: nn.Module, annualized_rate, test_size=0.25, loss = None, optimizer = None, no_epochs = None, log = False, lr = None): + df_train, df_test = timeseries_split(df, test_size=test_size) + if no_epochs is None: + no_epochs = 6000 + X_train, y_train = torch.tensor(df_train[features].to_numpy(), dtype=torch.float32), torch.tensor(df_train[target].to_numpy(), dtype=torch.float32).reshape(-1, 1) + X_test, y_test = torch.tensor(df_test[features].to_numpy(), dtype=torch.float32), torch.tensor(df_test[target].to_numpy(),dtype=torch.float32).reshape(-1, 1) + + y_hat = batch_train_reg(model, X_train, X_test, y_train, y_test, no_epochs, loss, optimizer, lr = lr, logging = log) + + + +def benchmark_reg_model(df: pl.DataFrame, features: List[str], target: str, model: nn.Module, annualized_rate, test_size=0.25, loss = None, optimizer = None, no_epochs = None, log = False, lr = None): + df_train, df_test = timeseries_split(df, test_size=test_size) + if no_epochs is None: + no_epochs = 6000 + X_train, y_train = torch.tensor(df_train[features].to_numpy(), dtype=torch.float32), torch.tensor(df_train[target].to_numpy(), dtype=torch.float32).reshape(-1, 1) + X_test, y_test = torch.tensor(df_test[features].to_numpy(), dtype=torch.float32), torch.tensor(df_test[target].to_numpy(),dtype=torch.float32).reshape(-1, 1) + + y_hat = batch_train_reg(model, X_train, X_test, y_train, y_test, no_epochs, loss, optimizer, lr = lr, logging = log) + + perf = eval_model_performance(y_test, y_hat, features, target, annualized_rate) + + weights, biases = get_linear_params(model) + perf['weights'] = str(weights) + perf['biases'] = str(biases) + + return perf + + +def learn_model_trades(df: pl.DataFrame, features: List[str], target: str, model: nn.Module, test_size=0.25, loss = None, optimizer = None, no_epochs = None, log = False, lr = None): + df = df.drop_nulls() + df_train, df_test = timeseries_split(df, test_size=test_size) + if no_epochs is None: + no_epochs = 6000 + + X_train, y_train = torch.tensor(df_train[features].to_numpy(), dtype=torch.float32), torch.tensor(df_train[target].to_numpy(), dtype=torch.float32).reshape(-1, 1) + X_test, y_test = torch.tensor(df_test[features].to_numpy(), dtype=torch.float32), torch.tensor(df_test[target].to_numpy(),dtype=torch.float32).reshape(-1, 1) + + y_hat = batch_train_reg(model, X_train, X_test, y_train, y_test, no_epochs, criterion=loss, optimizer=optimizer, lr = lr, logging = log) + + return model_trade_results(y_test, y_hat) + +def learn_model_trade_pnl(df: pl.DataFrame, features: List[str], target: str, model: nn.Module, test_size=0.25, loss = None, optimizer = None, no_epochs = None, log = False, lr = None): + df_train, df_test = timeseries_split(df, test_size=test_size) + if no_epochs is None: + no_epochs = 6000 + + X_train, y_train = torch.tensor(df_train[features].to_numpy(), dtype=torch.float32), torch.tensor(df_train[target].to_numpy(), dtype=torch.float32).reshape(-1, 1) + X_test, y_test = torch.tensor(df_test[features].to_numpy(), dtype=torch.float32), torch.tensor(df_test[target].to_numpy(),dtype=torch.float32).reshape(-1, 1) + + y_hat = batch_train_reg(model, X_train, X_test, y_train, y_test, no_epochs, loss, optimizer, lr = lr, logging = log) + + trade_results = model_trade_results(y_test, y_hat) + + + +def set_seed(seed): + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + torch.cuda.manual_seed_all(seed) + torch.backends.cudnn.deterministic = True + torch.backends.cudnn.benchmark = False + torch.use_deterministic_algorithms(True) + +def init_weights(m): + if isinstance(m, nn.Linear): + torch.manual_seed(42) # ensures same init every time + nn.init.xavier_uniform_(m.weight) + if m.bias is not None: + nn.init.zeros_(m.bias) + +def get_linear_params(model: nn.Module) -> tuple[np.ndarray, float]: + """Extract weights and bias from LinearModel as (w, b).""" + weight = model.linear.weight.detach().cpu().numpy().flatten() + bias = model.linear.bias.detach().cpu().numpy().item() + return weight, bias + +def add_log_return_features(df: pl.DataFrame, col: str, forecast_horizon: int, max_no_lags = None): + if max_no_lags is None: + max_no_lags = 0 + df = df.with_columns(log_return(col, forecast_horizon)) + if max_no_lags > 0: + df = add_lags(df, log_return_col('close'), max_no_lags, forecast_horizon) + return df + +def benchmark_linear_models(ts: pl.DataFrame, target: str, feature_pool: List[str], annualized_rate: int, max_no_features: int = 1, no_epochs = 200, loss = None, test_size=0.25) -> pl.DataFrame: + import models + + ts = ts.drop_nulls() + + benchmarks = [] + fs = [] + for i in range(1, max_no_features+1): + fs += list(itertools.combinations(feature_pool, i)) + + for features in fs: + m = models.LinearModel(len(features)) + m.apply(init_weights) + benchmarks.append(benchmark_reg_model(ts, list(features), target, m, annualized_rate, no_epochs=no_epochs, loss=loss, test_size=test_size)) + + benchmark = pl.DataFrame(benchmarks) + return benchmark.sort('sharpe', descending=True) + + +# print out our learned params +def print_model_params(model: nn.Module): + for name, param in model.named_parameters(): + if param.requires_grad: + print(f"{name}:\n{param.data.numpy()}") + + +def add_model_predictions(test_trades: pl.DataFrame, model: nn.Module, features: Union[str, List[str]]) -> pl.DataFrame: + if type(features) != list: + features = [features] + X_test = torch.tensor(test_trades[features].to_numpy(), dtype=torch.float32) + y_hat = model(X_test) + s = pl.Series('y_hat', model(X_test).detach().cpu().numpy().squeeze()) + return test_trades.with_columns(s) + + +def add_trade_log_returns(trades: pl.DataFrame, pre_trade_values: Union[List[float],npt.NDArray[np.float32]], tx_fee: float, initial_capital: float) -> pl.DataFrame: + # add directional signal to indicate if we're going long or short + trades = trades.with_columns(pl.col('y_hat').sign().alias('dir_signal')) + # calculate trade log return + trades = trades.with_columns((pl.col('close_log_return') * pl.col('dir_signal')).alias('trade_log_return')) + # calculate the cumulative sum of the trade log returns - this is the equity curves in log space + trades = trades.with_columns(pl.col('trade_log_return').cum_sum().alias('cum_trade_log_return')) + trades = trades.with_columns( + # add pre trade values + pre_trade_values.alias('pre_trade_value'), + # add post trade values + (pre_trade_values * pl.col('trade_log_return').exp()).alias('post_trade_value'), + # add trade qty + (pre_trade_values / pl.col('open')).alias('trade_qty'), + ) + + trades = trades.with_columns( + # add signed trade quantities (the main output of our strategy) + (pl.col('trade_qty') * pl.col('dir_signal')).alias('signed_trade_qty'), + # add trade gross pnl + (pl.col('post_trade_value') - pl.col('pre_trade_value')).alias('trade_gross_pnl') + # add tx fees + (pl.col('pre_trade_value') * tx_fee + pl.col('post_trade_value') * tx_fee).alias('tx_fees') + ) + trades = trades.with_columns( + # calculate each trade's profit after fees (net) + (pl.col('trade_gross_pnl')-pl.col('tx_fees')).alias('trade_net_pnl') + ) + trades = trades.with_columns( + # calculate equity curve for gross profit + (initial_capital + pl.col('trade_gross_pnl').cum_sum()).alias('equity_curve_gross') + # calculate equity curve for net profit + (initial_capital + pl.col('trade_net_pnl').cum_sum()).alias('equity_curve_net') + ) + +def add_equity_curve(trades: pl.DataFrame, initial_capital: float, col_name: str, suffix: str) -> pl.DataFrame: + return trades.with_columns( + (initial_capital + pl.col(col_name).cum_sum()).alias(f'equity_curve_{suffix}') + ) + +def add_compounding_trades(trades, capital, leverage, maker_fee, taker_fee): + lev_capital = capital * leverage + # calculate entry and exit trade value and size + trades = trades.with_columns( + ((pl.col('cum_trade_log_return').exp()) * lev_capital).shift().fill_null(lev_capital).alias('entry_trade_value'), + ((pl.col('cum_trade_log_return').exp()) * lev_capital).alias('exit_trade_value'), + ).with_columns( + (pl.col('entry_trade_value') / pl.col('open') * pl.col('dir_signal')).alias('signed_trade_qty'), + (pl.col('exit_trade_value')-pl.col('entry_trade_value')).alias('trade_gross_pnl'), + ) + # add transaction fee + trades = add_tx_fees(trades, maker_fee, taker_fee) + # add net trade pnl + trades = trades.with_columns( + (pl.col('trade_gross_pnl') - pl.col('tx_fee_taker')).alias('trade_net_taker_pnl'), + (pl.col('trade_gross_pnl') - pl.col('tx_fee_maker')).alias('trade_net_maker_pnl'), + ) + trades = 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