flashcards how to

.gitignore

@@ -0,0 +1,6 @@
+.env
+__pycache__/
+*.pyc
+*.pyo
+cache/
+*.parquet

.python-version

@@ -0,0 +1 @@
+3.14

README.md

@@ -4,3 +4,73 @@ My roadmap for becoming competent quant and capable of creating automated tradin
 
 Current Goal: **Learn Quantitative Foundations**
 
+## Study
+
+Current reading [Introduction to Probability, Statistics, and Random Processes - Hossein Pishro-Nik](./textbooks/reading/20260622012450_Introduction%20to%20Probability,%20Statistics,%20and%20Random%20Processes/Introduction%20to%20Probability,%20Statistics,%20and%20Random%20Processes%20-%20Hossein%20Pishro-Nik.pdf) and [Algorithmic Trading](./textbooks/reading/20260622012451_Algorithmic%20Trading%20Winning%20Strategies%20and%20their%20rationale/Algorithmic%20Trading%20Winning%20Strategies%20and%20their%20rationale.pdf)
+
+## Phases
+
+Recommended path, generated by CLAUDE
+
+### Phase 1 — Math Foundation (*)
+
+1. [ ] Pishro-Nik (*)
+2. [x] Quantitative Trading
+3. [ ] Elementary Stochastic Calculus
+4. [ ] A guide to Brownian motion
+
+### Phase 2 — Get Practical Early
+
+1. [ ] Algorithmic Trading — Chan ← read this soon; short, orients everything else
+2. [ ] Analysis of Financial Time Series — Tsay ← essential for price/return modeling
+
+### Phase 3 — Core Quant ML
+
+1. [ ] Data-Driven Science and Engineering — Brunton & Kutz
+2. [ ] Advances in Financial Machine Learning — Lopez de Prado ← most important book in your backlog
+3. [ ] Stochastic Calculus: An Introduction with Applications ← now the theory lands better
+4. [ ] Detecting Regime Change in Computational Finance
+
+### Phase 4 — Portfolio, Risk & Systems
+
+1. [ ] Active Portfolio Management — Grinold & Kahn
+2. [ ] The Microstructure of Financial Markets — de Jong & Rindi
+3. [ ] Trading Systems and Methods — Kaufman (use as reference)
+4. [ ] The Mathematics of Money Management — Vince
+5. [ ] The Leverage Space Trading Model — Vince
+6. [ ]Testing and Tuning Market Trading Systems
+
+### Phase 5 — Specialized / Optional
+
+1. [ ] Assessing and Improving Prediction and Classification (C++ heavy, niche)
+2. [ ] Trading on Sentiment (alt data / NLP, very specialized)
+3. [ ] Numerical Recipes (reference only, don't read cover to cover)
+4. [ ] Measure Theory (only if you want pure math depth — lowest ROI for bots)
+
+### Missing textbooks
+
+- Systematic Trading: A unique new method for designing trading and investing systems
+- Permutation and Randomization Tests for Trading System Development: Algorithms in C++
+
+## Textbooks
+
+- *Introduction to Probability, Statistics, and Random Processes* - Hossein Pishro-Nik
+- *Quantitative Trading* - Ernest P. Chan
+- *Algorithmic Trading: Winning Strategies and Their Rationale* - Ernest P. Chan
+- *Elementary Stochastic Calculus* - Thomas Mikosch
+- *A Guide to Brownian Motion and Related Stochastic Processes* - Jim Pitman & Marc Yor
+- *Stochastic Calculus: An Introduction with Applications* - Bernt Øksendal
+- *Active Portfolio Management* - Richard Grinold & Ronald Kahn
+- *Probability and Statistics: The Science of Uncertainty* - Michael J. Evans & Jeffrey S. Rosenthal
+- *Analysis of Financial Time Series* - Ruey S. Tsay
+- *Data-Driven Science and Engineering* - Steven L. Brunton & J. Nathan Kutz
+- *Advances in Financial Machine Learning* - Marcos Lopez de Prado
+- *Detecting Regime Change in Computational Finance* - Timothy Masters
+- *Trading Systems and Methods* - Perry J. Kaufman
+- *The Mathematics of Money Management* - Ralph Vince
+- *The Leverage Space Trading Model* - Ralph Vince
+- *Testing and Tuning Market Trading Systems* - Timothy Masters
+- *Assessing and Improving Prediction and Classification* - Timothy Masters
+- *Trading on Sentiment* - Richard L. Peterson
+- *Numerical Recipes: The Art of Scientific Computing* - Press, Teukolsky, Vetterling & Flannery
+- *An Introduction to Measure Theory* - Terence Tao

pyproject.toml

@@ -0,0 +1,34 @@
+[project]
+name = "study"
+version = "0.1.0"
+description = "Add your description here"
+readme = "README.md"
+requires-python = ">=3.14"
+dependencies = [
+    "aiohttp>=3.14.1",
+    "altair>=6.2.1",
+    "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",
+    "vegafusion>=2.0.3",
+    "vl-convert-python>=1.9.0.post1",
+]
+
+[[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'" },
+]

textbooks/backlog/010_Elementary Stochastic Caculus/README.md

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+# ELEMENTARY STOCHASTIC CACULUS with Finance in View
+
+total pages=217
+
+**Currently reading:** page 1

textbooks/backlog/020_A guide to Brownian motion and related stochastic processes/README.md

@@ -0,0 +1,5 @@
+# A guide to Brownian motion and related stochastic processes
+
+total pages=101
+
+**Currently reading:** page 1

textbooks/library/20260622012452_Quantitative_Trading_Ernest_P_Chan/README.md

@@ -0,0 +1,11 @@
+# Quantitative Trading
+
+[pdf](./Quantitative_Trading_Ernest_P_Chan.pdf)
+
+I will be loosey reading through this book. Many of the concepts will be gone into more rigour in other textbooks.
+
+## Post read notes
+
+Not alot of math, but math that did show up was hand wavy.(Seems like the book assumes you understand certain concepts)
+
+Main takeaway is use Kelly formula for sizing positions.

textbooks/library/20260622012452_Quantitative_Trading_Ernest_P_Chan/notes.ipynb

@@ -0,0 +1,460 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "ec753176",
+   "metadata": {},
+   "source": [
+    "# Notes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b13688b9",
+   "metadata": {},
+   "source": [
+    "When loss of money occurs, rationality is often the first victim.\n",
+    "\n",
+    "As long as financial markets demand instant liquidity, however, there will always be a profitable\n",
+    "niche for quantitative trading."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5df7046f",
+   "metadata": {},
+   "source": [
+    "## Daily routine"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5a622bb3",
+   "metadata": {},
+   "source": [
+    "\"The largest block of time I need to spend is in the morning\n",
+    "before the market opens: I typically need to run various programs to\n",
+    "download and process the latest historical data, read company news\n",
+    "that comes up on my alert screen, run programs to generate the orders for the day, and then launch a few baskets of orders before\n",
+    "the market opens and start a program that will launch orders automatically throughout the day. I would also update my spreadsheet\n",
+    "to record the previous day’s profit and loss (P&L) of the different\n",
+    "strategies I run based on the brokerages’ statements. All of this takes\n",
+    "about two hours.\" - Quant trader morning routine"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f0855890",
+   "metadata": {},
+   "source": [
+    "## Definitions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8ec7516e",
+   "metadata": {},
+   "source": [
+    "**Defn** Information Ratio (Sharpe ratio):\n",
+    "\n",
+    "$$\\text{Information Ratio} = \\frac{\\text{Average of Excess Returns}}{\\text{Standard Deviation of Excess Returns}}$$\n",
+    "\n",
+    "$$\\text{Excess Returns} = \\text{Portfolio Returns} - \\text{Benchmark Returns}$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7aab1f0d",
+   "metadata": {},
+   "source": [
+    "- ***directional trades*** -  long or short only\n",
+    "- ***dollar-neutral trades*** - hedged or pair trades\n",
+    "- ***dollar neutral portfolio*** - The market value of the long positions equals the market value of the short positions\n",
+    "- ***market neutral portfolio*** - The beta of the portfolio with respect to a market index is close to zero, where beta measures the ratio between the expected returns of the portfolio and the expected returns of the market index\n",
+    "- ***Leverage*** - Borrowing funds to buy an investment\n",
+    "- ***slippage*** - The difference between the price that triggers the trading signal and the average execution price of the entire order"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "865b04b1",
+   "metadata": {},
+   "source": [
+    "- ***equaity curve***: A line chart of your portfolio's value over time\n",
+    "- ***drawdown***: The decline from a peak to a subsequent trough, expressed as a percentage. It's a measure of loss from a prior high, not from your starting point.\n",
+    "- ***maximum drawdown***: The largest peak-to-trough decline over the entire history of a strategy. It answers: \"What's the worst loss someone could have experienced if they invested at the worst possible time?\" It's one of the most common risk metrics used to evaluate strategies.\n",
+    "- ***high watermark***: The highest portfolio value ever reached\n",
+    "- ***maximum drawdown duration***: The longest amount of time spent below the high watermark.\n",
+    "- ***basis points***: 1 basis point is 0.01%\n",
+    "- ***regime shift*** - Situation when the financial market structure or the macroeconomic environment undergoes a drastic change so much so that trading strategies that were profitable before may not be profitable now"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "076ad86c",
+   "metadata": {},
+   "source": [
+    "- ***Risk-Adjusted returns*** - "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a845580c",
+   "metadata": {},
+   "source": [
+    "## Ruling out bad strategies"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "303f8dc1",
+   "metadata": {},
+   "source": [
+    "\n",
+    "- If a strategy trades only a few times a year, chances are its\n",
+    "Sharpe ratio won’t be high. This does not prevent it from being\n",
+    "part of your multistrategy trading business, but it does disqualify\n",
+    "the strategy from being your main profit center.\n",
+    "- If a strategy has deep (e.g., more than 10 percent) or lengthy\n",
+    "(e.g., four or more months) drawdowns, it is unlikely that it will\n",
+    "have a high Sharpe ratio. I will explain the concept of drawdown\n",
+    "in the next section, but you can just visually inspect the equity\n",
+    "curve (which is also the cumulative profit-and-loss curve, assuming no redemption or cash infusion) to see if it is very bumpy\n",
+    "or not. Any peak-to-trough of that curve is a drawdown. (See\n",
+    "Figure 2.1 for an example.)\n",
+    "\n",
+    "As a rule of thumb, any strategy that has a Sharpe ratio of less\n",
+    "than 1 is not suitable as a stand-alone strategy\n",
+    "\n",
+    "For a given strategy, its important to ask the following:\n",
+    "\n",
+    "- Does it outperform a benchmark?\n",
+    "- Does it have a high enough Sharpe ratio?\n",
+    "- Does it have a small enough drawdown and short enough drawdown duration?\n",
+    "- Does the backtest suffer from survivorship bias?\n",
+    "- Does the strategy lose steam in recent years compared to its earlier years?\n",
+    "- Does the Strategy Suffer from Data-Snooping Bias?\n",
+    "- Does the strategy have its own “niche” that protects it from intense competition from large institutional money managers?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ecc17253",
+   "metadata": {},
+   "source": [
+    "## Backtesting\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "98567746",
+   "metadata": {},
+   "source": [
+    "- When gathering data for backtesting, ensure data is split and dividend adjusted.\n",
+    "- A backtest that relies on high and low data is less reliable than one that relies on the open and close\n",
+    "- Typically, an extreme return should be accompanied by a news announcement, or should occur on a day when the market index also experienced extreme returns. If not, then your data is suspect."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0dbd8630",
+   "metadata": {},
+   "source": [
+    "$$\\text{Annualized Sharpe Ratio} = \\sqrt{N_T} \\times \\text{Sharpe Ratio Based on }T$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "319f28df",
+   "metadata": {},
+   "source": [
+    "Incorporate transaction costs into backtests. Transaction costs include:\n",
+    "\n",
+    "- Commission\n",
+    "- Liquidity cost\n",
+    "- Opportunity cost\n",
+    "- Market Impact\n",
+    "- Slippage\n",
+    "\n",
+    "Try to combine all of these into a \"one way transaction cost\" (onewaytcost)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1cbf1e0a",
+   "metadata": {},
+   "source": [
+    "Potential backtest performance issues:\n",
+    "\n",
+    "- Data: Split/dividend adjustments, noise in daily high/low, and\n",
+    "survivorship bias.\n",
+    "- Performance measurement: Annualized Sharpe ratio and maximum drawdown.\n",
+    "- Look-ahead bias: Using unobtainable future information for past\n",
+    "trading decisions.\n",
+    "- Data-snooping bias: Using too many parameters to fit historical\n",
+    "data, and avoiding it using large enough sample, out-of-sample\n",
+    "testing, and sensitivity analysis.\n",
+    "- Transaction cost: Impact of transaction costs on performance.\n",
+    "- Strategy refinement: Common ways to make small variations on\n",
+    "the strategy to optimize performance."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8047f4f0",
+   "metadata": {},
+   "source": [
+    "## Execution Systems: Why does actual performance diverge from expectations?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "30a1531a",
+   "metadata": {},
+   "source": [
+    "- Do you have bugs in your ATS software?\n",
+    "- Do the trades generated by your ATS match the ones generated\n",
+    "by your backtest program?\n",
+    "- Are the execution costs much higher than what you expected?\n",
+    "- Are you trading illiquid stocks that caused a lot of market impact?\n",
+    "- Strategy may have suffered from data-snooping bias or regime shift"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d5ea890d",
+   "metadata": {},
+   "source": [
+    "## Money and Risk Management"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "41ddff80",
+   "metadata": {},
+   "source": [
+    "### The Kelly Formula"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "169dc996",
+   "metadata": {},
+   "source": [
+    "Let $F^*$ be the optimal fractions of your equity that you should allocate to each of your $n$ strategies by a column vector $F^* = (f_1^*, f_2^*, \\dots, f_n^*)^T$\n",
+    "\n",
+    "Let $C$ be the covariance matrix such that matrix element $C_{ij}$ is the covariance of the returns of the $i^\\text{th}$ and $j^\\text{th}$ strategies.\n",
+    "\n",
+    "Let $M = (m_1, m_2, \\dots, m_n)^T$ be the column vector of mean returns of the strategies, where $m_i$ is a one-period, simple(uncompounded), unlevered return.\n",
+    "\n",
+    "Given our optimization objective and the Gaussian assumption, Dr. Thorp has shown that the optimal allocation is given by\n",
+    "\n",
+    "$$F^* = C^{-1}M$$\n",
+    "\n",
+    "If we assume that the strategies are all statistically independent, the covariance matrix becomes a diagonal matrix, with the diagonal elements equal to the variance of the individual strategies. This leads to an especially simple formula\n",
+    "\n",
+    "$$f_i = \\frac{m_i}{s_i^2}$$\n",
+    "\n",
+    "This is the famous Kelly formula as applied to continuous finance as opposed to gambling with discrete outcomes, and it gives the optimal leverage one should employ for a particular trading strategy.\n",
+    "\n",
+    "As a practical procedure, this continuous updating of the capital allocation should occur at least once at the end of each trading\n",
+    "day. In addition to updating the capital allocation, one should also\n",
+    "periodically update F* itself by recalculating the most recent trailing mean return and standard deviation. What should the lookback\n",
+    "period be and how often do you need to update these inputs to the\n",
+    "Kelly formula? These depend on the average holding period of your\n",
+    "strategy. If you hold your positions for only one day or so, then as a\n",
+    "rule of thumb, I would advise using a lookback period of six months.\n",
+    "Using a relatively short lookback period has the advantage of allowing you to gradually reduce your exposure to strategies that have\n",
+    "been losing their performance. As for the frequency of update, it\n",
+    "should not be a burden to update F* daily once you have written a\n",
+    "program to do so."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "270f0944",
+   "metadata": {},
+   "source": [
+    "Model risk simply refers to the possibility that trading losses are\n",
+    "not due to the statistical vagaries of the market, but to the fact that\n",
+    "the trading model is wrong\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b6709290",
+   "metadata": {},
+   "source": [
+    "the one golden rule in risk management is to keep the size of your portfolio under control at all times\n",
+    "\n",
+    "Do not succumb to either despair or greed"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a6edc9d9",
+   "metadata": {},
+   "source": [
+    "# Mean-reverting versus Momentum strategies"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "63f1028e",
+   "metadata": {},
+   "source": [
+    "Security prices are either mean reverting or trending. Otherwise they are random walking and trading will be futile. \n",
+    "\n",
+    "- ***Mean reverting***: Prices tend to return to an average (mean) over time\n",
+    "- ***Trending***: Prices move persistently in one direction. Momentum builds and continues.\n",
+    "\n",
+    "Sometimes (usually) a security is both mean reverting and trending."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "084a3a0b",
+   "metadata": {},
+   "source": [
+    "## Stationarity and Cointegration\n",
+    "\n",
+    "- ***cointegrated***: Most stock price series are not stationary—they exhibit a geometric random walk that gets them farther and farther away from their starting (i.e., initial public offering) values. However, you can often find a pair of stocks such that if you long one and short the other, the market value of the pair is stationary, then the pair of stocks are cointegrated\n",
+    "\n",
+    "If a price series (of a stock, a pair of stocks, or, in general, a portfolio of stocks) is stationary, then a mean-reverting strategy is guaranteed to be profitable, as long as the stationarity persists into the future (which is by no means guaranteed)\n",
+    "\n",
+    "**Cointegration Is Not Correlation**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a60d882c",
+   "metadata": {},
+   "source": [
+    "## Factor Models"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2d21e3c0",
+   "metadata": {},
+   "source": [
+    "- ***Factor returns***: The common drivers of stock returns\n",
+    "- ***Factor exposures***: The sensitivities to each of these common drivers\n",
+    "- ***Specific return***: Any part of a stock’s return that cannot be explained by these common factor returns is deemed a specific return\n",
+    "\n",
+    "Each stock’s specific return is assumed to be uncorrelated to another stock’s."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6e48e218",
+   "metadata": {},
+   "source": [
+    "## Exit Strategy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e61edcb1",
+   "metadata": {},
+   "source": [
+    "- A fixed holding period\n",
+    "- A target price or profit cap\n",
+    "- The latest entry signals\n",
+    "- A stop price\n",
+    "\n",
+    "The mean reversion of a time series can be modeled by an equation called the Ornstein-Uhlenbeck formula. See page 163 for more info.\n",
+    "\n",
+    "The properties of the Ornstein-Uhlenbeck formula can inform us about the exit strategies.\n",
+    "\n",
+    "If you believe that your security is mean reverting, then you also have a ready-made target price—the mean value of the historical prices of the security, or µ in the Ornstein-Uhlenbeck formula.\n",
+    "\n",
+    "Target prices can also be used in the case of momentum models if you have a fundamental valuation model of a company. But as fundamental valuation is at best an inexact science, target prices are not as easily justified in momentum models as in mean-reverting models.\n",
+    "\n",
+    "Exiting a position based on running an entry model also tells us whether a stop-loss strategy is recommended"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eb884fe5",
+   "metadata": {},
+   "source": [
+    "## High Frequency Trading Strategies"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "22ad0ba8",
+   "metadata": {},
+   "source": [
+    "Requires low level programming and alot of reasources to consider viable"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9c8eeee5",
+   "metadata": {},
+   "source": [
+    "## Other topics / notes\n",
+    "\n",
+    "- Markov regime switching / hidden Markov models\n",
+    "- Kalman filter\n",
+    "- neural networks\n",
+    "\n",
+    "Empirical studies have found that a portfolio that consists of low-beta stocks generally has lower risk and thus a higher Sharpe ratio"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bdd23956",
+   "metadata": {},
+   "source": [
+    "- Mean-reverting regimes are more prevalent than trending regimes.\n",
+    "- There are some tricky data issues involved with backtesting mean-reversion strategies: Outlier quotes and survivorship bias are among them.\n",
+    "- Trending regimes are usually triggered by the diffusion of new\n",
+    "information, the execution of a large institutional order, or\n",
+    "“herding” behavior.\n",
+    "- Competition between traders tends to reduce the number of\n",
+    "mean-reverting trading opportunities.\n",
+    "- Competition between traders tends to reduce the optimal holding period of a momentum trade.\n",
+    "- Regime switching can sometimes be detected using a dataminingx approach with numerous input features.\n",
+    "- A stationary price series is ideal for a mean-reversion trade.\n",
+    "- Two or more nonstationary price series can be combined to form a stationary one if they are “cointegrating.”\n",
+    "- Cointegration and correlation are different things: Cointegration\n",
+    "is about the long-term behavior of the prices of two or more\n",
+    "stocks, while correlation is about the short-term behavior of\n",
+    "their returns.\n",
+    "- Factor models, or arbitrage pricing theory, are commonly used\n",
+    "for modeling how fundamental factors affect stock returns linearly.\n",
+    "- One of the most well-known factor models is the Fama-French\n",
+    "Three-Factor model, which postulates that stock returns are\n",
+    "proportional to their beta and book-to-price ratio, and negatively\n",
+    "to their market capitalizations.\n",
+    "- Factor models typically have a relatively long holding period and\n",
+    "long drawdowns due to regime switches.\n",
+    "- Exit signals should be created differently for mean-reversion versus momentum strategies.\n",
+    "- Estimation of the optimal holding period of a mean-reverting strategy can be quite robust, due to the Ornstein-Uhlenbeck formula.\n",
+    "- Estimation of the optimal holding period of a momentum strategy can be error prone due to the small number of signals.\n",
+    "- Stop loss can be suitable for momentum strategies but not reversal strategies.\n",
+    "- Seasonal trading strategies for stocks (i.e., calendar effect) have\n",
+    "become unprofitable in recent years.\n",
+    "- Seasonal trading strategies for commodity futures continue to\n",
+    "be profitable.\n",
+    "- High-frequency trading strategies rely on the “law of large numbers” for their high Sharpe ratios.\n",
+    "- High-frequency trading strategies typically generate the highest\n",
+    "long-term compounded growth due to their high Sharpe ratios.\n",
+    "- High-frequency trading strategies are very difficult to backtest\n",
+    "and very technology reliant for their execution.\n",
+    "- Holding a highly leveraged portfolio of low-beta stocks should\n",
+    "generate higher long-term compounded growth than holding unleveraged portfolio of high-beta stocks."
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

textbooks/reading/20260622012450_Introduction to Probability, Statistics, and Random Processes/.ipynb_checkpoints/ch1-checkpoint.ipynb

@@ -0,0 +1,203 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "a6732353-51d5-4478-9cf8-5834e57e5a4e",
+   "metadata": {},
+   "source": [
+    "# Chapter 1 Notes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "48d9ec9e-83da-40ca-ae79-3c45f8af137c",
+   "metadata": {},
+   "source": [
+    "## Main Concepts\n",
+    "\n",
+    "Outcome: A result of a random experiment.\n",
+    "\n",
+    "Sample Space: The set of all possible outcomes.\n",
+    "\n",
+    "Event: A subset of the sample space.\n",
+    "\n",
+    "Inclusion-exclusion principle holds for probability\n",
+    "\n",
+    "Consider a sample space S. If S is a countable set, this refers to a discrete probability\n",
+    "mode\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7ac122be-50b2-423c-b88f-e4b3327b21bd",
+   "metadata": {},
+   "source": [
+    "## Example Problems"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7fb87a35-a470-4d98-935f-80c814e3f95d",
+   "metadata": {},
+   "source": [
+    "Example 1.5 - soln\n",
+    "\n",
+    "- there are 10 people with white shirts and 8 people with red shirts;\n",
+    "- 4 people have black shoes and white shirts\n",
+    "- 3 people have black shoes and red shirts\n",
+    "- the total number of people with white or red shirts or black shoes is 21\n",
+    "\n",
+    "Let A be the set of people with white shirts, B be the set of people with red shirts and let C be the set of people with black shoes.\n",
+    "\n",
+    "\\begin{align*}\n",
+    "|A|=10 \\\\\n",
+    "|B|=8 \\\\\n",
+    "|A \\cap C| = 4 \\\\\n",
+    "|B \\cap C| = 3 \\\\\n",
+    "|A \\cup B \\cup C| = 21\n",
+    "\\end{align*}\n",
+    "\n",
+    "Now we solve for $|C|$:\n",
+    "\n",
+    "\\begin{align*}\n",
+    "|A| + |B| + |C| - |A \\cap B| - |A \\cap C| - |B \\cap C| + |A \\cap B \\cap C| = 21 \\\\\n",
+    "10 + 8 + |C| - 0 - 4 - 3 - 0 = 21 \\\\\n",
+    "18 + |C| - 7 = 21 \\\\\n",
+    "|C| + 11 = 21 \\\\\n",
+    "|C| = 10\n",
+    "\\end{align*}\n",
+    "\n",
+    "$\\therefore$ number of people with black shoes is 10\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "72b40733-531a-48f3-9879-75601684afc2",
+   "metadata": {},
+   "source": [
+    "Example 1.11 - soln\n",
+    "\n",
+    "Suppose we have the following information:\n",
+    "1. There is a 60 percent chance that it will rain today.\n",
+    "2. There is a 50 percent chance that it will rain tomorrow.\n",
+    "3. There is a 30 percent chance that it does not rain either day.\n",
+    "\n",
+    "T = rains\n",
+    "F = no rain\n",
+    "\n",
+    "$S = \\{(F, F), (F, T), (T, F), (T, T)\\}$\n",
+    "\n",
+    "$P((T, F) \\cup (T, T)) = 0.6$\n",
+    "\n",
+    "$P((F, T) \\cup (T, T)) = 0.5$\n",
+    "\n",
+    "$P((F, F)) = 0.3$\n",
+    "\n",
+    "\\begin{align*}\n",
+    "P(S) = 1 \\\\\n",
+    "P(\\{(F, F)\\} \\cup \\{(F, T)\\} \\cup \\{(T, F)\\} \\cup \\{(T, T)\\}) = 1 \\\\\n",
+    "P((F,F)) + P(\\{(F, T)\\} \\cup \\{(T, F)\\} \\cup \\{(T, T)\\}) = 1 \\\\\n",
+    "0.3 + P(\\{(F, T)\\} \\cup \\{(T, F)\\} \\cup \\{(T, T)\\}) = 1 \\\\\n",
+    "P(\\{(F, T)\\} \\cup \\{(T, F)\\} \\cup \\{(T, T)\\}) = 0.7 \\\\\n",
+    "P(\\{(F, T)\\} \\cup \\{(T, T)\\}) + P((T, F)) = 0.7 \\\\\n",
+    "0.5 + P((T, F)) = 0.7 \\\\\n",
+    "P((T, F)) = 0.2 \\\\\n",
+    "P(\\{(T, F)\\} \\cup \\{(T, T)\\}) + P((F, T)) = 0.7 \\\\\n",
+    "P((F, T)) = 0.1\n",
+    "\\end{align*}\n",
+    "\n",
+    "Find the following probabilities:\n",
+    "\n",
+    "a. The probability that it will rain today or tomorrow.\n",
+    "\n",
+    "\\begin{align*}\n",
+    "P((T, F) \\cup (F, T) \\cup (T, T)) = 0.7\n",
+    "\\end{align*}\n",
+    "\n",
+    "b. The probability that it will rain today and tomorrow.\n",
+    "\n",
+    "\\begin{align*}\n",
+    "P((T, T)) = 1 - 0.3 - 0.2 - 0.1 = 0.4\n",
+    "\\end{align*}\n",
+    "\n",
+    "c. The probability that it will rain today but not tomorrow.\n",
+    "\n",
+    "\\begin{align*}\n",
+    "P((T, F)) = 0.2\n",
+    "\\end{align*}\n",
+    "\n",
+    "d. The probability that it either will rain today or tomorrow, but not both.\n",
+    "\n",
+    "\\begin{align*} \n",
+    "P(\\{(T, F)\\} \\cup \\{(F, T)\\}) = P((T, F)) + P((F, T)) = 0.2 + 0.1 = 0.3\n",
+    "\\end{align*}\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8b5131dd-5ebd-4156-b808-f8df273317fb",
+   "metadata": {},
+   "source": [
+    "Example 1.12 - soln\n",
+    "\n",
+    "$S = \\{ -1, 0, 1, 2, 3, ... \\}$\n",
+    "\n",
+    "$\\forall x \\in S, P(x) = \\frac{1}{2^{x + 2}}$\n",
+    "\n",
+    "What is the probability that I win more than or equal to 1 dollar and less than 4 dollars?\n",
+    "\n",
+    "\\begin{align*} \n",
+    "P({1, 2, 3}) = P(1) + P(2) + P(3) \\\\\n",
+    "= 1/8 + 1/16 + 1/32\n",
+    "\\end{align*}\n",
+    "\n",
+    "What is the probability that I win more than 2 dollars?\n",
+    "\n",
+    "\\begin{align*} \n",
+    "\\sum_{i=3}^{\\infty} P(i) = P(3) + P(4) + P(5) + P(6) + ... \\\\\n",
+    "= 1/32 + 1/64 + 1/128 + 1/256 + ... \\\\\n",
+    "=\\frac{\\frac{1}{32}}{1 - \\frac{1}{2}}\n",
+    "=\\frac{1}{16}\n",
+    "\\end{align*}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "69962960-18a6-436b-b9eb-9a6dfae7559a",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "11163495-a6cc-43b9-bf41-02748b13d210",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "roadmap (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
+}

textbooks/reading/20260622012450_Introduction to Probability, Statistics, and Random Processes/README.md

@@ -0,0 +1,28 @@
+# Introduction to Probability, Statistics, and Random Processes - Hossein Pishro-Nik
+
+[pdf](./Introduction%20to%20Probability,%20Statistics,%20and%20Random%20Processes%20-%20Hossein%20Pishro-Nik.pdf)
+
+total pages=1007
+
+**Currently reading:** chapter 3, page 236
+
+TODO:
+
+- 3.2.5 problems
+- ch3 end of chapter problems
+- 4.1.4 problems
+- 4.2.6 problems
+- 4.3.3 problems
+- ch4 end of chapter problems
+
+## Flashcards
+
+Flashcards can be found for each chapter under `chX/flashcards.md`
+
+Flashcard usage:
+
+1. Open `mochi`
+2. Click `Import`
+3. Import by `Markdown`
+4. Select `Multiple cards per .md file`
+5. Set string delimiter as `===`

textbooks/reading/20260622012450_Introduction to Probability, Statistics, and Random Processes/ch1/flashcards/definitions.md

@@ -0,0 +1,222 @@
+State De Morgan's Law for $n$ sets
+---
+
+For any sets $A_1, A_2, \dots, A_n$:
+
+$$\left(\bigcup_{i=1}^n A_i\right)^c = \bigcap_{i=1}^n A_i^c$$
+
+$$\left(\bigcap_{i=1}^n A_i\right)^c = \bigcup_{i=1}^n A_i^c$$
+
+The complement of a union is the intersection of complements, and vice versa.
+
+===
+
+State the Distributive Law for sets $A$, $B$, and $C$
+---
+
+$$A \cap (B \cup C) = (A \cap B) \cup (A \cap C)$$
+
+$$A \cup (B \cap C) = (A \cup B) \cap (A \cup C)$$
+
+===
+
+State the inclusion-exclusion principle for a finite collection of sets $A_1, A_2, A_3, \dots A_n$ where $n=2$
+
+---
+
+$n = 2$ case:
+
+$|A \cup B| = |A| + |B| - |A \cap B|$
+
+---
+
+$n = 3$ case:
+
+$|A \cup B \cup C| = |A| + |B| + |C| - |A \cap B| - |A \cap C| - |B \cap C| + |A \cap B \cap C|$
+
+---
+
+For a finite collection of sets $A_1, A_2, A_3, \dots A_n$, we have
+
+$\left| \bigcup_{i=1}^n A_i \right| = \sum_{i=1}^n |A_i| - \sum_{i < j} |A_i \cap A_j| + \sum_{i < j < k} |A_i \cap A_j \cap A_k| - \dots + (-1)^{n-1} |A_1 \cap A_2 \cap \dots \cap A_n|$
+
+===
+
+For the function
+
+$$f: A \to B$$
+
+State the following of $f$:
+
+1. Domain
+2. Co-domain
+3. Range
+
+---
+
+1. $A$
+2. $B$
+3. Set of all possible outputs of $f$ (not necessarily $B$)
+
+===
+
+State definition for each the following:
+
+1. Random experiment
+2. Outcome
+3. Sample space
+4. Event
+
+---
+
+1. A **random experiment** is a process by which we observe something uncertain
+2. An **outcome** is a result of a random experiment
+3. The **sample space** $S$ is the set of all possible outcomes
+4. An **event** is a subset of the sample space
+
+===
+
+State the Axioms of Probability
+
+---
+**Axioms of Probability**
+
+1. For any event $A$, $P(A) \geq 0$
+2. $P(S) = 1$
+3. If $A_1, A_2, A_3, \dots$ are disjoint events, then $P(A_1 \cup A_2 \cup A_3 \cup \dots) = P(A_1) + P(A_2) + P(A_3) + \dots$
+
+===
+
+In a finite sample space $S$, where all outcomes are equally likely, what is the probability of any event $A$?
+
+---
+
+$P(A) = \frac{|A|}{|S|}$
+
+===
+
+What is $P(A^c)$ in terms of $P(A)$?
+---
+
+$$P(A^c) = 1 - P(A)$$
+
+Follows from the axioms: $A$ and $A^c$ are disjoint, and $A \cup A^c = S$, so $P(A) + P(A^c) = P(S) = 1$.
+
+===
+
+Define conditional probability $P(A|B)$
+---
+
+The probability of $A$ given $B$ (when $P(B) > 0$):
+
+$$P(A|B) = \frac{P(A \cap B)}{P(B)}$$
+
+In the equally-likely case:
+
+$$P(A|B) = \frac{|A \cap B|}{|B|}$$
+
+===
+
+State the chain rule of probability for $n$ events
+---
+
+$$P(A_1 \cap A_2 \cap \cdots \cap A_n) = P(A_1)\,P(A_2|A_1)\,P(A_3|A_1,A_2)\cdots P(A_n|A_1,A_2,\dots,A_{n-1})$$
+
+Each factor conditions on all previously listed events.
+
+===
+
+What conditions must hold for three events $A$, $B$, $C$ to be independent?
+---
+
+All four conditions must hold:
+
+1. $P(A \cap B) = P(A)P(B)$
+2. $P(A \cap C) = P(A)P(C)$
+3. $P(B \cap C) = P(B)P(C)$
+4. $P(A \cap B \cap C) = P(A)P(B)P(C)$
+
+Pairwise independence alone is **not** sufficient.
+
+===
+
+What does it mean for $n$ events $A_1, A_2, \dots, A_n$ to be independent?
+---
+
+Every subset of the events must satisfy the product rule. That is, for all $i < j < k < \dots$:
+
+$$P(A_i \cap A_j) = P(A_i)P(A_j)$$
+$$P(A_i \cap A_j \cap A_k) = P(A_i)P(A_j)P(A_k)$$
+$$\vdots$$
+$$P(A_1 \cap A_2 \cap \cdots \cap A_n) = \prod_{i=1}^n P(A_i)$$
+
+===
+
+If $A_1, A_2, \dots, A_n$ are independent, what is $P(A_1 \cup A_2 \cup \cdots \cup A_n)$?
+---
+
+$$P(A_1 \cup A_2 \cup \cdots \cup A_n) = 1 - \prod_{i=1}^n (1 - P(A_i))$$
+
+Derived by taking the complement (none of the events occur) and using independence.
+
+===
+
+What is the difference between disjoint and independent?
+
+---
+
+- Disjoint: $A$ and $B$ cannot occur at the same time. $A \cap B = \empty$
+- Independent: $A$ does not give any information about $B$
+
+===
+
+State the Law of Total Probability using event $B$ and its complement
+---
+
+$$P(A) = P(A|B)\,P(B) + P(A|B^c)\,P(B^c)$$
+
+===
+
+State the general Law of Total Probability for a partition of the sample space
+---
+
+If $B_1, B_2, B_3, \dots$ is a partition of $S$, then for any event $A$:
+
+$$P(A) = \sum_i P(A \cap B_i) = \sum_i P(A|B_i)\,P(B_i)$$
+
+===
+
+State Bayes' Rule for two events $A$ and $B$
+---
+
+For $P(A) \neq 0$:
+
+$$P(B|A) = \frac{P(A|B)\,P(B)}{P(A)}$$
+
+===
+
+State Bayes' Rule when $B_1, B_2, \dots$ form a partition of $S$
+---
+
+For any event $A$ with $P(A) \neq 0$:
+
+$$P(B_j|A) = \frac{P(A|B_j)\,P(B_j)}{\displaystyle\sum_i P(A|B_i)\,P(B_i)}$$
+
+The denominator expands $P(A)$ via the Law of Total Probability.
+
+===
+
+State the meaning of conditionally independent between events $A$ and $B$ given event $C$. Note $P(C) \gt 0$.
+
+---
+
+$$P(A \cap B \mid C) = P(A \mid C)P(B \mid C)$$
+
+===
+
+State the multiplication rule for $P(A \cap B)$
+---
+
+$$P(A \cap B) = P(A|B)\,P(B) = P(B|A)\,P(A)$$
+
+Rearrangement of the definition of conditional probability.

textbooks/reading/20260622012450_Introduction to Probability, Statistics, and Random Processes/ch1/flashcards/problems.md

@@ -0,0 +1,17 @@
+For any event $A$, prove $P(A^c) = 1 - P(A)$
+
+---
+
+$$
+1 = P(S)
+= P(A \cup A^c)
+= P(A) + P(A^c)
+$$
+
+===
+
+Prove $P(A \setminus B) = P(A) - P(A \cap B)$
+
+---
+
+TODO

textbooks/reading/20260622012450_Introduction to Probability, Statistics, and Random Processes/ch1/notes.ipynb

@@ -0,0 +1,216 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "206bf674",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import sys\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "\n",
+    "sns.set_theme(style=\"whitegrid\", context=\"notebook\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "612bd02c",
+   "metadata": {},
+   "source": [
+    "# Chapter 1 Summary Notes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "be70f5df",
+   "metadata": {},
+   "source": [
+    "## Intro (1.0.0 - 1.3.1)\n",
+    "\n",
+    "**Theorem 1.1: De Morgan's law** \\\n",
+    "For any sets $A_1, A_2, A_3, \\dots A_n$, we have\n",
+    "- $(A_1 \\cup A_2 \\cup A_3 \\cup \\dots A_n)^c = A_1^c \\cap A_2^c \\cap A_3^c \\cap \\dots A_n^c$\n",
+    "- $(A_1 \\cap A_2 \\cap A_3 \\cap \\dots A_n)^c = A_1^c \\cup A_2^c \\cup A_3^c \\cup \\dots A_n^c$\n",
+    "\n",
+    "**Theorem 1.2: Distributive law** \\\n",
+    "For any sets $A, B,$ and $C$ we have\n",
+    "- $A \\cap (B \\cup C) = (A \\cap B)\\cup(A \\cap C)$\n",
+    "- $A \\cup (B \\cap C) = (A \\cup B)\\cap(A \\cup C)$\n",
+    "\n",
+    "**Inclusion-exclusion principle** \\\n",
+    "For a finite collection of sets $A_1, A_2, A_3, \\dots A_n$, we have\n",
+    "\n",
+    "$\\left| \\bigcup_{i=1}^n A_i \\right| = \\sum_{i=1}^n |A_i| - \\sum_{i < j} |A_i \\cap A_j| + \\sum_{i < j < k} |A_i \\cap A_j \\cap A_k| - \\dots + (-1)^{n-1} |A_1 \\cap A_2 \\cap \\dots \\cap A_n|$\n",
+    "\n",
+    "$n = 2$ case:\n",
+    "\n",
+    "$|A \\cup B| = |A| + |B| - |A \\cap B|$\n",
+    "\n",
+    "$n = 3$ case:\n",
+    "\n",
+    "$|A \\cup B \\cup C| = |A| + |B| + |C| - |A \\cap B| - |A \\cap C| - |B \\cap C| + |A \\cap B \\cap C|$\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c7475d4f",
+   "metadata": {},
+   "source": [
+    "## Random experiments (1.3.1 - 1.4)\n",
+    "\n",
+    "- A **random experiment** is a process by which we observe something uncertain\n",
+    "- An **outcome** is a result of a random experiment\n",
+    "- The **sample space** $S$ is the set of all possible outcomes\n",
+    "- An **event** $A$ is any subset of $S$\n",
+    "\n",
+    "> In the context of a random experiment, the sample space is our *universal set*\n",
+    "\n",
+    "**Axioms of Probability**\n",
+    "\n",
+    "1. For any event $A$, $P(A) \\geq 0$\n",
+    "2. $P(S) = 1$\n",
+    "3. If $A_1, A_2, A_3, \\dots$ are disjoint events, then $P(A_1 \\cup A_2 \\cup A_3 \\cup \\dots) = P(A_1) + P(A_2) + P(A_3) + \\dots$\n",
+    "\n",
+    "**Some notation**\n",
+    "\n",
+    "- $P(A \\cap B) = P(A$ and $B) = P(A,B)$\n",
+    "- $P(A \\cup B) = P(A$ or $B)$\n",
+    "\n",
+    "In a finite sample space $S$, where all outcomes are equally likely, the probability of any event $A$ can be found by\n",
+    "\n",
+    "\\begin{align*}\n",
+    "P(A) = \\frac{|A|}{|S|}\n",
+    "\\end{align*}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b705ef32",
+   "metadata": {},
+   "source": [
+    "## Conditional probability (1.4.0)\n",
+    "\n",
+    "If $A$ and $B$ are twos events in sample space $S$, then the **conditional probability of $A$ given $B$** is defined as\n",
+    "\n",
+    "\\begin{align*}\n",
+    "P(A|B) = \\frac{P(A \\cap B)}{P(B)}, \\text{when } P(B) > 0\n",
+    "\\end{align*}\n",
+    "\n",
+    "or\n",
+    "\n",
+    "\\begin{align*}\n",
+    "P(A|B) = \\frac{|A \\cap B|}{|B|}, \\text{when } P(B) > 0\n",
+    "\\end{align*}\n",
+    "\n",
+    "For events $A, B,$ and $C$, with $P(C) \\gt 0$, we have\n",
+    "\n",
+    "- $P(A^c|C) = 1 - P(A|C)$\n",
+    "- $P(\\empty|C) = 0$\n",
+    "- $P(A|C) \\leq 1$\n",
+    "- $P(A \\setminus B|C) = P(A|C) - P(A \\cap B|C)$\n",
+    "- $P(A \\cup B|C) = P(A|C) + P(B|C) - P(A \\cap B|C)$\n",
+    "- if $A \\subset B$ then $P(A|C) \\leq P(B|C)$\n",
+    "\n",
+    "![](../public/conditional_prob_tree.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "188a8fc2",
+   "metadata": {},
+   "source": [
+    "## Independence (1.4.1)\n",
+    "\n",
+    "**Definition.** Two events $A$ and $B$ are *independent* if $P(A \\cap B) = P(A)P(B)$. AKA $P(A|B) = P(A)$\n",
+    "\n",
+    "**Definition.** Three events $A, B,$ and $C$ are independent if **all** of the following conditions hold:\n",
+    "- $P(A \\cap B) = P(A)P(B)$\n",
+    "- $P(A \\cap C) = P(A)P(C)$\n",
+    "- $P(B \\cap C) = P(B)P(C)$\n",
+    "- $P(A \\cap B \\cap C) = P(A)P(B)P(C)$\n",
+    "\n",
+    "**Definition.** $N$ events $A_1, A_2, A_3, \\dots, A_n$ are independent if **all** the following conditions holds:\n",
+    "- $P(A_i \\cap B_j) = P(A_i)P(A_j)$\n",
+    "- $P(A_i \\cap A_j \\cap A_k) = P(A_i)P(A_j)P(A_k)$ where $i \\in [1:n+1]$, $j \\in [i:n+1]$, $k \\in [j:n+1]$\n",
+    "- $\\dots$\n",
+    "- $P(A_1 \\cap A_2 \\cap A_3 \\cap \\dots \\cap A_n) = \\prod_{i=1}^nP(A_i)$\n",
+    "\n",
+    "**Lemma.** \\\n",
+    "If $A$ and $B$ are independent then\n",
+    "- $A$ and $B^c$ are independent\n",
+    "- $A^c$ and $B$ are independent\n",
+    "- $A^c$ and $B^c$ are independent\n",
+    "\n",
+    "**Definition.** If $A_1, A_2, \\dots, A_n$ are independent then\n",
+    "$$P(A_1 \\cup A_2 \\cup \\dots \\cup A_n) = 1 - (1 - P(A_1))(1 - P(A_2))\\dots(1 - P(A_n))$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "90cf5b00",
+   "metadata": {},
+   "source": [
+    "## Law of Total Probability (1.4.2)\n",
+    "\n",
+    "$$P(A) = P(A|B)P(B) + P(A|B^c)P(B^c)$$\n",
+    "\n",
+    "**Definition.** Law of Total Probability: \\\n",
+    "If $B_1, B_2, B_3, \\dots $ is a partition of the sample space $S$, then for any event $A$ we have\n",
+    "\n",
+    "$$P(A) = \\sum_i P(A \\cap B_i) = \\sum_i P(A|B_i)P(B_i)$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c33df800",
+   "metadata": {},
+   "source": [
+    "## Bayes' Rule (1.4.3)\n",
+    "\n",
+    "**Definition.** Bayes' Rule\n",
+    "\n",
+    "- For any two events $A$ and $B$, where $P(A) \\neq 0$, we have\n",
+    "\n",
+    "$$P(B|A) = \\frac{P(A|B)P(B)}{P(A)}$$\n",
+    "\n",
+    "- If $B_1, B_2, B_3, \\dots$ form a partition of the sample space $S$, and $A$ is any event with $P(A) \\neq 0$, we have\n",
+    "\n",
+    "$$P(B_j|A) = \\frac{P(A|B_j)P(B_j)}{\\sum_i P(A|B_i)P(B_i)}$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "849d7c99",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "roadmap (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
+}

textbooks/reading/20260622012450_Introduction to Probability, Statistics, and Random Processes/ch2/notes.ipynb

@@ -0,0 +1,139 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "c58309b2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import sys\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "\n",
+    "sns.set_theme(style=\"whitegrid\", context=\"notebook\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a6732353-51d5-4478-9cf8-5834e57e5a4e",
+   "metadata": {},
+   "source": [
+    "# Chapter 2 Notes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9f0046c2",
+   "metadata": {},
+   "source": [
+    "## Counting (2.0.0 - 2.1.5)\n",
+    "\n",
+    "***Definition.*** Multiplication Principle:  \\\n",
+    "Suppose that we perform $r$ experiments such that the $k\\text{th}$ experiment has $n_k$ possible outcomes, for $k=1,2,\\dots,r$. Then there are a total of $n_1 \\times n_2 \\times n_3 \\times \\dots \\times n_r$ possible outcomes for the sequence of $r$ experiments\n",
+    "\n",
+    "### Terminology\n",
+    "\n",
+    "- **Sampling**: Sampling from a set means choosing an element from that set. We\n",
+    "often **draw** a sample at random from a given set in which each element of the\n",
+    "set has equal chance of being chosen\n",
+    "\n",
+    "- **With or without replacement**: Usually we draw multiple samples from a set. If\n",
+    "we put each object back after each draw, we call this sampling with\n",
+    "replacement. In this case a single object can be possibly chosen multiple times.\n",
+    "For example, if A = {a1, a2, a3, a4} and we pick 3 elements with replacement, a\n",
+    "possible choice might be (a3, a1, a3). Thus \"with replacement\" means \"repetition\n",
+    "is allowed.\" On the other hand, if repetition is not allowed, we call it sampling\n",
+    "without replacement\n",
+    "\n",
+    "- **Ordered or unordered**: If ordering matters (i.e.: a1, a2, a3 ≠ a2, a3, a1), this is\n",
+    "called ordered sampling. Otherwise, it is called unordered\n",
+    "\n",
+    "### Counting Formulas\n",
+    "\n",
+    "- **ordered sampling with replacement:** $n^k$\n",
+    "\n",
+    "- **ordered sampling without replacement:** $n$ permute $k$ $\\quad$ ie $P^n_k = \\frac{n!}{(n - k)!}$\n",
+    "\n",
+    "- **unordered sampling without replacement:** $n$ choose $k$ $\\quad$ ie $\\binom{n}{k} = \\frac{n!}{k!(n-k)!}$\n",
+    "\n",
+    "- **unordered sampling with replacement:** $\\binom{n + k - 1}{k}$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2e93e0fe",
+   "metadata": {},
+   "source": [
+    "## Problem Solving Principles"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "87279e59",
+   "metadata": {},
+   "source": [
+    "When solving a combinatorics problem, consider:\n",
+    "1. Does order matter?\n",
+    "    - Yes → Permutations\n",
+    "    - No → Combinations\n",
+    "\n",
+    "    - \"Are HHHTT and THHHT the same outcome to me?\"\n",
+    "\n",
+    "2. Are we sampling with of without replacement?\n",
+    "    - Without replacement → Hypergeometric (phone problem)\n",
+    "    - With replacement → Binomial (coin flips)\n",
+    "    \n",
+    "    \"Can the same item be chosen twice?\"\n",
+    "3. Are the \"groups\" labeled or unlabeled?\n",
+    "    - Labeled/distinguishable → Just multiply combinations\n",
+    "    - Unlabeled/interchangeable → Divide by k!\n",
+    "\n",
+    "    \"Does it matter which group is called group 1?\"\n",
+    "4. Are the items distinguishable?\n",
+    "    - Distinguishable → Each item is unique, classical probability applies\n",
+    "    - Indistinguishable → Outcomes are not equally likely, be careful\n",
+    "\n",
+    "      \"Could I label these items 1 to n?\"\n",
+    "5. Is complement of inclusion-exclusion easier?\n",
+    "    - Complement → When \"at least\" or \"at most\" language appears\n",
+    "    - Inclusion-Exclusion → When events overlap\n",
+    "\n",
+    "    \"Is the opposite event simpler to count?\"\n",
+    "6. Am I counting each outcome exactly once? \n",
+    "  - If yes, done. Otherwhise we are overcounting or undercounting"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9c5783dc",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "roadmap (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
+}

textbooks/reading/20260622012450_Introduction to Probability, Statistics, and Random Processes/ch4/example-problems.ipynb

@@ -0,0 +1,101 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "206bf674",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import sys\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "\n",
+    "sns.set_theme(style=\"whitegrid\", context=\"notebook\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "612bd02c",
+   "metadata": {},
+   "source": [
+    "# Chapter 4 Example Problems"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5ce47b88",
+   "metadata": {},
+   "source": [
+    "4.2 \n",
+    "\n",
+    "a. \n",
+    "\n",
+    "$\\int_{-\\infty}^{\\infty}f_X(u)du = 1$\n",
+    "\n",
+    "ie\n",
+    "\n",
+    "\\begin{align*}\n",
+    "\\int_{-\\infty}^{0^+}f_X(u)du + \\int_{0}^{\\infty}f_X(x)dx = 1 \\\\\n",
+    "0 + \\int_{0}^{\\infty}ce^{-x}dx = 1 \\\\\n",
+    "c\\cdot\\lim_{t \\to \\infty }\\int_{0}^{t}e^{-x}dx = 1 \\\\\n",
+    "c\\cdot\\lim_{t \\to \\infty } [-e^{-x}]_0^t = 1 \\\\\n",
+    "c\\cdot\\lim_{t \\to \\infty } ((-e^{-t}) - (-e^{-0})) = 1 \\\\\n",
+    "c\\cdot\\lim_{t \\to \\infty } (-e^{-t} + 1) = 1 \\\\\n",
+    "c\\cdot 1 = 1 \\\\\n",
+    "c = 1\n",
+    "\\end{align*}\n",
+    "\n",
+    "b.\n",
+    "\n",
+    "Note that\n",
+    "\n",
+    "$$F_X(x) = \\int_{-\\infty}^x f_X(u)du$$\n",
+    "\n",
+    "and $c=1$\n",
+    "\n",
+    "so\n",
+    "\n",
+    "\\begin{align*}\n",
+    "F_X(x) = \\begin{cases} \n",
+    "1-e^{-x}     & \\text{for } x \\geq 0 \\\\  \n",
+    "0     & \\text{otherwise} \n",
+    "\\end{cases}\n",
+    "\\end{align*}\n",
+    "\n",
+    "c. $F_X(3) - F_X(1) = $"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "95a3b21c",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "roadmap (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
+}

textbooks/reading/20260622012450_Introduction to Probability, Statistics, and Random Processes/ch4/notes.ipynb

@@ -0,0 +1,176 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "c58309b2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import sys\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "\n",
+    "sns.set_theme(style=\"whitegrid\", context=\"notebook\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a6732353-51d5-4478-9cf8-5834e57e5a4e",
+   "metadata": {},
+   "source": [
+    "# Chapter 4 Notes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9f0046c2",
+   "metadata": {},
+   "source": [
+    "## Continuous Random Variables and their Distributions (4.1.0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ea9b1f96",
+   "metadata": {},
+   "source": [
+    "***Definition*** A random variable $X$ with CDF $F_X(x)$ is said to be continuous if $F_X(x)$ is a continuous for all $x \\in \\mathbb{R}$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a96da4d3",
+   "metadata": {},
+   "source": [
+    "## Probability Density Function (PDF) (4.1.1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c62129a5",
+   "metadata": {},
+   "source": [
+    "***Definition*** Consider a continuous random variable $X$ with an absolutely continuous CDF $F_X(x)$. The function $f_X(x)$ defined by\n",
+    "\n",
+    "$$f_X(x) = \\frac{dF_X(x)}{dx} = F'_X(x) \\quad \\text{if } F_X(x) \\text{ is differentiable at } x$$\n",
+    " \n",
+    "is called the probability density function (PDF) of $X$."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "df411869",
+   "metadata": {},
+   "source": [
+    "NOTE: The PDF being constant implies uniformity\n",
+    "\n",
+    "NOTE: For small values of $\\delta$,\n",
+    "\n",
+    "$$P(x \\lt X \\leq x + \\delta) \\approx  f_X(x)\\delta$$\n",
+    "\n",
+    "Thus if $f_X(x_1) \\gt f_X(x_2)$, we can say $P(x_1 \\lt X \\leq x_1 + \\delta) \\gt P(x_2 \\lt X \\leq x_2 + \\delta)$, ie the value of $X$ is more likely to be around $x_1$ then $x_2$\n",
+    "\n",
+    "NOTE: The CDF can be obtained from the PDF via (assuming absolute continuity)\n",
+    "\n",
+    "$$F_X(x) = \\int_{-\\infty}^x f_X(u)du$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f29a3bfb",
+   "metadata": {},
+   "source": [
+    "***Properties*** Consider a continuous random variable $X$ with PDF $f_X(x)$. We have\n",
+    "- $f_X(x) \\geq 0, \\forall x \\in \\mathbb{R}$\n",
+    "- $\\int_{-\\infty}^{\\infty}f_X(u)du = 1$\n",
+    "- $P(a \\lt X \\leq b) = F_X(b) - F_X(a) = \\int_a^bf_X(u)du$\n",
+    "- For a set $A$, $P(X \\in A) = \\int_Af_X(u)du$. However, set $A$ must satisfy:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6f546fed",
+   "metadata": {},
+   "source": [
+    "***Definition*** If $X$ is a continuous random variable, we can write the range of $X$ as\n",
+    "\n",
+    "$$R_X = \\{ x \\mid f_X(x) \\gt 0 \\}$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "170db3a0",
+   "metadata": {},
+   "source": [
+    "***Property*** The expected value if a continuous random variable $X$ is\n",
+    "\n",
+    "$$E[X] = \\int_{-\\infty}^{\\infty}xf_X(x)dx$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "deccea51",
+   "metadata": {},
+   "source": [
+    "***Property*** Law of the unconscious statistician (LOTUS) for continuous random variables\n",
+    "\n",
+    "$$E[g(X)] = \\int_{-\\infty}^{\\infty}g(x)f_X(x)dx$$\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c0a20195",
+   "metadata": {},
+   "source": [
+    "***Property*** Variance for a continuous random variable $X$, we can write\n",
+    "\n",
+    "\\begin{align*}\n",
+    "\\text{Var}(X) &= E[(X - E[x])^2] = \\int_{-\\infty}^{\\infty}(x - E[X])^2f_X(x)dx \\\\\n",
+    "&= E[X^2] - E[X]^2 = \\int_{-\\infty}^{\\infty}x^2f_X(x)dx - E[X]^2\n",
+    "\\end{align*}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6f2f6b72",
+   "metadata": {},
+   "source": [
+    "## Functions of Continuous Random Variables"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f4a95738",
+   "metadata": {},
+   "source": [
+    "***Theorem***"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "roadmap (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
+}

textbooks/reading/20260622012451_Algorithmic Trading Winning Strategies and their rationale/README.md

@@ -0,0 +1,7 @@
+# Algorithmic Trading - Winning Strategies and their rationale
+
+[pdf](./Algorithmic%20Trading%20Winning%20Strategies%20and%20their%20rationale.pdf)
+
+total pages=225
+
+**Currently reading:** chapter 1, page 19

textbooks/reading/20260622012451_Algorithmic Trading Winning Strategies and their rationale/ch1/notes.ipynb

@@ -0,0 +1,51 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "93b4302b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import sys\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "\n",
+    "sns.set_theme(style=\"whitegrid\", context=\"notebook\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bdd46cba",
+   "metadata": {},
+   "source": [
+    "# Chapter 1 Notes"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "roadmap (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
+}

textbooks/reading/20260622012451_Algorithmic Trading Winning Strategies and their rationale/notes.ipynb

@@ -0,0 +1,51 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "93b4302b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import sys\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "\n",
+    "sns.set_theme(style=\"whitegrid\", context=\"notebook\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bdd46cba",
+   "metadata": {},
+   "source": [
+    "# Notes"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "roadmap (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
+}

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
+
+<https://www.youtube.com/playlist?list=PLdsqLas3-Kg3fM8VgykXBJ2gRIFBpKHjY>

tutorials/20260622164035_Let's Build a Quant Trading Strategy, MemLabs/binance.py

@@ -0,0 +1,148 @@
+from typing import List
+import requests
+import zipfile
+from pathlib import Path
+import polars as pl
+from datetime import datetime, timedelta
+from tqdm import tqdm
+
+import research
+
+MAKER_FEE = 0.000450
+TAKER_FEE = 0.000450
+
+def download_and_unzip(symbol: str, date: str | datetime,
+                       download_dir: str = "data", cache_dir: str = "cache") -> pl.DataFrame:
+    """
+    Download and unzip Binance futures trade data for a given symbol and date.
+    Caches results as parquet files to avoid repeated downloads.
+    """
+    # Normalize date to string
+    date_str = date.strftime('%Y-%m-%d') if isinstance(date, datetime) else date
+
+    cache_dir = Path(cache_dir)
+    cache_dir.mkdir(exist_ok=True)
+    cache_path = cache_dir / f"{symbol}-trades-{date_str}.parquet"
+
+    if cache_path.exists():
+        return pl.read_parquet(cache_path)
+
+    url = f"https://data.binance.vision/data/futures/um/daily/trades/{symbol}/{symbol}-trades-{date_str}.zip"
+
+    download_dir = Path(download_dir)
+    download_dir.mkdir(exist_ok=True)
+    zip_path = download_dir / f"{symbol}-trades-{date_str}.zip"
+
+    # Download zip
+    response = requests.get(url, stream=True)
+    response.raise_for_status()
+    with open(zip_path, 'wb') as f:
+        for chunk in response.iter_content(chunk_size=8192):
+            f.write(chunk)
+
+    # Extract
+    with zipfile.ZipFile(zip_path, 'r') as zf:
+        zf.extractall(download_dir)
+
+    csv_path = download_dir / f"{symbol}-trades-{date_str}.csv"
+
+    # Load into Polars
+    df = pl.read_csv(
+        csv_path,
+        schema={
+            "id": pl.Int64,
+            "price": pl.Float64,
+            "qty": pl.Float64,
+            "quoteQty": pl.Float64,
+            "time": pl.Int64,
+            "isBuyerMaker": pl.Boolean,
+        }
+    ).with_columns(
+        pl.from_epoch("time", time_unit="ms").alias("datetime")
+    )
+
+    # Cache and clean
+    df.write_parquet(cache_path)
+    zip_path.unlink(missing_ok=True)
+    csv_path.unlink(missing_ok=True)
+
+    return df
+
+
+def download_date_range(symbol: str, start_date: str | datetime, end_date: str | datetime,
+                        download_dir: str = "data", cache_dir: str = "cache") -> list[pl.DataFrame]:
+    """
+    Download trade data for a range of dates with a progress bar.
+    """
+    if isinstance(start_date, str):
+        start_date = datetime.strptime(start_date, '%Y-%m-%d')
+    if isinstance(end_date, str):
+        end_date = datetime.strptime(end_date, '%Y-%m-%d')
+
+    num_days = (end_date - start_date).days + 1
+
+    for i in tqdm(range(num_days), desc=f"Downloading {symbol}"):
+        current_date = start_date + timedelta(days=i)
+        try:
+            download_and_unzip(symbol, current_date, download_dir, cache_dir)
+        except Exception as e:
+            tqdm.write(f"[ERROR] {symbol} {current_date.date()}: {e}")
+
+
+
+def download_trades(symbol: str, no_days: int,
+                    download_dir: str = "data", cache_dir: str = "cache", return_trades=False) -> pl.DataFrame:
+    """
+    Download trades for the last N days up to yesterday with a progress bar.
+    """
+    yesterday = datetime.now() - timedelta(days=1)
+    start_date = yesterday - timedelta(days=no_days - 1)
+
+    dfs = []
+    for i in tqdm(range(no_days), desc=f"Downloading {symbol}"):
+        current_date = start_date + timedelta(days=i)
+        try:
+            if return_trades:
+                dfs.append(download_and_unzip(symbol, current_date, download_dir, cache_dir))
+            else:
+                download_and_unzip(symbol, current_date, download_dir, cache_dir)
+        except Exception as e:
+            tqdm.write(f"[ERROR] {symbol} {current_date.date()}: {e}")
+    
+    return pl.concat(dfs) if return_trades else None
+
+
+def download_ohlc_timeseries(symbol: str, no_days: int, time_interval: str, download_dir: str = "data", cache_dir: str = "cache") -> pl.DataFrame:
+    """
+    Download trades for the last N days up to yesterday with a progress bar.
+    """
+    yesterday = datetime.now() - timedelta(days=1)
+    start_date = yesterday - timedelta(days=no_days - 1)
+
+    time_series = []
+    for i in tqdm(range(no_days), desc=f"Downloading {symbol}"):
+        current_date = start_date + timedelta(days=i)
+        try:
+            trades = download_and_unzip(symbol, current_date, download_dir, cache_dir)
+            time_series.append(research.timeseries(trades, time_interval, research.OHLC_AGGS))
+        except Exception as e:
+            tqdm.write(f"[ERROR] {symbol} {current_date.date()}: {e}")
+    return pl.concat(time_series)
+
+
+def download_timeseries(symbol: str, no_days: int, time_interval: str, aggs: List[pl.Expr], download_dir: str = "data", cache_dir: str = "cache") -> pl.DataFrame:
+    """
+    Download trades for the last N days up to yesterday with a progress bar.
+    """
+    yesterday = datetime.now() - timedelta(days=1)
+    start_date = yesterday - timedelta(days=no_days - 1)
+
+    time_series = []
+    for i in tqdm(range(no_days), desc=f"Downloading {symbol}"):
+        current_date = start_date + timedelta(days=i)
+        try:
+            trades = download_and_unzip(symbol, current_date, download_dir, cache_dir)
+            time_series.append(research.timeseries(trades, time_interval, aggs))
+        except Exception as e:
+            tqdm.write(f"[ERROR] {symbol} {current_date.date()}: {e}")
+    return pl.concat(time_series)