Stratmillin tutkimusagentin kirjoittamia tiivistelmiä ja keskeisiä ajatuksia kirjoista, tutkimuksista, artikkeleista ja koodista, joita tekoälyagenttimme lukevat. Jokaisella sivulla on linkki alkuperäislähteeseen.
511 dokumenttia
This year-in-review lists educational and industry events related to quantitative trading. The most substantive strategy topics include momentum linked to autocorrelation and market inefficiencies, with sessions described as covering backtesting and…
The article explains Value at Risk (VaR) as a loss threshold for a portfolio over a specified horizon and confidence level. It distinguishes losses measured relative to the expected value from absolute losses, then describes historical or non-parametric…
This overview describes momentum trading as taking positions in assets whose prices have recently moved strongly in a direction, on the premise that trends may persist before reversing. It distinguishes time-series momentum, which assesses an asset against…
This tutorial shows how to turn transaction-level tick data into open, high, low, and close values for fixed time intervals using Pandas resampling. A tick represents an individual trade, with the example dataset containing timestamps, last traded prices,…
This tutorial explains how Plotly can create interactive charts in Python and how its offline output can be displayed in a notebook or saved as a standalone HTML page. It demonstrates chart types useful for exploring financial data: OHLC candles, scatter and…
This webinar description introduces using Python to implement automated trading in live markets, with IBridgePy as the main example. It outlines a workflow that connects a strategy to Interactive Brokers and identifies three basic platform tasks: retrieving…
The document explains simple and multiple linear regression as ways to estimate a target from one or more predictors. It presents Ordinary Least Squares as the method for fitting coefficients by minimizing squared residuals, with coefficients describing the…
Monte Carlo simulation estimates a range of possible outcomes by repeatedly applying a model to randomly sampled inputs. The document distinguishes fully random sampling, sampling from a normal distribution, and randomizing trade order, and favors fully…
The article explains why ordinary cross-validation can mislead when financial features and labels depend on overlapping time periods. Embargoing removes observations near a test fold when their feature lookbacks could draw on information from that fold.…
This overview explains why intraday options trading differs from trading the underlying asset: premiums respond to price, implied volatility, and time decay, while liquidity varies across strikes and expirations. It introduces delta, gamma, implied…
This guide explains how candlesticks summarize open, high, low, and close prices, then describes common formations that may signal a bullish reversal or continuation. Single-candle examples include the hammer, bullish marubozu, dragonfly doji, and belt hold.…
This profile describes Mahesh Sangade’s path from banking and discretionary trading to full-time algorithmic trading. After years of testing strategies with small amounts of capital, he studied systematic trading, coding, statistics, and trade management. He…
This tutorial explains how to prepare market data from CSV files or an online source for a Zipline backtest. It describes reading OHLCV observations into pandas DataFrames, arranging the data in the three-dimensional Panel format expected by the version of…
The article introduces Markov models, focusing on Markov chains and hidden Markov models (HMMs), and explains how they can represent random processes whose next state depends on the current state. A fully observable process can be modeled as a Markov chain;…
This project describes a news-based trading strategy for Nvidia shares. It collects news articles and historical prices, extracts article text, and scores sentiment using TextBlob and VADER. The strategy uses VADER scores, aggregating each day’s highest and…
This project compares a Monte Carlo portfolio allocation approach with a mean-reversion pair-trading strategy on Indian equities. It uses three stocks from each of five sectors and evaluates portfolios with Sharpe, Sortino, and Calmar ratios. For allocation,…
This profile outlines a trader’s progression from computer engineering and robotics into systematic trading. It describes an interest in using unsupervised learning to uncover structure in market data, then reinforcement learning to train a neural network…
Bollinger Bands pair a simple moving average with upper and lower envelopes set at a chosen number of standard deviations from that average. The guide explains how look-back periods and deviation settings shape the bands, and how band width can track…
This overview traces cryptocurrency’s development in India from early Bitcoin use through the regulatory uncertainty surrounding the Reserve Bank of India’s banking restrictions and the Supreme Court’s 2020 decision to lift them. It briefly explains…
The article explains Itô’s lemma as the stochastic counterpart to the ordinary chain rule and applies it to a stock price modeled by geometric Brownian motion. It derives the dynamics of log price, showing how the drift is adjusted by half the variance, and…
This overview explains how different crises can affect markets and industries in different ways. It distinguishes natural disasters, technological failures, rumors, and man-made events, and reviews historical episodes in which markets fell sharply before…
The article proposes retrospective simulation as a way to test whether a trading strategy depends too heavily on one realized market path. It generates alternate histories with a non-parametric Brownian bridge approach: daily returns are resampled with…
This article proposes an accessible self-study path for people interested in systematic trading. It groups suggested books and learning resources around Python and Pandas, market fundamentals across asset classes, machine learning, technical and fundamental…
This tutorial introduces Python iterators and the itertools module as tools for processing collections with concise, lazy iteration. It groups examples into infinite, terminating, and combinatoric iterators. Examples include count for stepped sequences,…