Stratmill tyrimų agento parengtos knygų, straipsnių, mokslinių darbų ir kodo, kuriuos skaito mūsų DI agentai, santraukos ir pagrindinės mintys. Kiekviename puslapyje pateikiama nuoroda į originalą.
511 dokumentų
This article introduces Nasdaq Data Link as a source of traditional financial, ESG, and alternative datasets, then explains how to retrieve data through the Quandl API in Python. It describes dataset categories and subscription access, and outlines the…
The article walks through a simple rule-based strategy implemented with the Quantiacs Python toolbox. It describes configuring a backtest, loading stock or futures data, setting parameters such as the lookback, capital, and slippage, and examining results…
The article explains the conditions commonly used to justify ordinary least squares regression and why they matter for estimation and inference. It covers linearity in the model parameters, lack of perfect multicollinearity, independent errors, constant…
This profile recounts how Debdutta Bhattacharya moved from directional trading toward searching for opportunities with a statistical edge. He describes learning to think about trades in probabilities, validating methods over time, and using analysis tools,…
A mechanical engineering professor describes developing an interest in quantitative finance through mathematical study of options models, earlier programming in Fortran, and later adoption of Python for algorithmic trading. After joining a formal trading…
This profile traces a trader’s move from business analytics to US equities trading and then quantitative investing in digital assets. A practical motivation for automation was the difficulty of manually monitoring many potential tickers at once. He describes…
A retail trader describes moving from options volatility trading toward a broader systematic approach after the 2018 bear market exposed limits in relying on one strategy. He is refining his earlier short volatility system and exploring a floor-and-ceiling…
This project describes a framework for classifying market conditions with a Random Forest and adapting capital allocation to the detected regime. It uses historical Nifty 500 data and market breadth features intended to capture cross-stock momentum, trend…
This tutorial outlines a TensorFlow multilayer perceptron that predicts whether Tata Motors’ next daily close will rise. It derives eight inputs from daily OHLC data: price spreads, moving averages, short-period volatility, RSI, and Williams %R. The target…
The document presents an adaptive Bitcoin strategy that first infers market regimes from daily returns with a Hidden Markov Model, then uses a regime-specific Random Forest classifier to predict the next day’s direction. Within a rolling historical window,…
The article explains algorithmic trading as using defined instructions to generate signals and place or manage orders, then argues that learning it requires programming, market knowledge, analysis and backtesting. Its practical example describes reading…
This overview introduces several methods for modeling financial data when a straight-line relationship is inadequate or the target is not a continuous average. It describes logistic regression for binary outcomes, including interpreting its output as a…
The document introduces decision trees as supervised models for classifying a stock’s next daily move as up or down. It outlines a workflow using historical OHLCV data, technical indicators such as RSI, moving averages and ADX, and a target class derived…
The article describes how MBA graduates might move into algorithmic trading and identifies skills that can transfer, including business judgment and awareness of ethics and compliance. It recommends building stronger knowledge of markets and trading…
This project tests active management of a Big Tech stock portfolio against equal-weight buy-and-hold and monthly rebalancing baselines. It uses online linear regression, principal component features, and a Kalman filter to estimate each stock’s value…
The document explains Donchian Channels, which mark the highest high and lowest low over a chosen lookback window, with a middle line derived from the two bands. It describes three breakout strategies: long-short, long-only, and long-only entries filtered by…
The article explains proprietary trading as a firm’s use of its own capital, then surveys strategies including merger arbitrage, index arbitrage, global macro trading, and volatility arbitrage. Its index example illustrates buying an ETF while shorting its…
This project describes a statistical arbitrage strategy for Chinese futures. It screens contract pairs with an Augmented Dickey-Fuller test for stationary spreads, estimates a dynamic hedge ratio with a Kalman filter, and uses the spread’s half-life to set a…
This guide introduces index futures as standardized contracts linked to stock indexes, generally settled in cash rather than through delivery of constituent shares. It illustrates settlement by multiplying the change between the agreed index level and the…
Angela Zhao’s career profile includes several practical observations about quantitative trading. She describes moving from finance and discretionary investing into data analytics and machine learning, with algorithmic trading appealing as a way to make…
This introductory guide explains descriptive statistics and probability concepts using daily Apple stock data. It defines mean, mode, and median, then introduces range and standard deviation as ways to describe price levels and dispersion. It distinguishes…
This project describes a machine-learning system that uses a decision tree to generate binary signals for trading individual stocks. Indicator buy triggers are used as inputs, while indicator sell rules are omitted to keep the model focused; a zero signal…
This article surveys an end-to-end approach to applying machine learning and artificial intelligence to trading. It advocates starting with a trading objective, selecting a model only when it adds value, and interpreting model outputs in the context of…
The article presents value investing as buying shares below an estimate of their intrinsic worth, with the gap between estimated value and purchase price serving as a margin of safety. It describes selling when price approaches or exceeds estimated value and…