Sammanfattningar och huvudidéer från böcker, artiklar, forskningsrapporter och kod som våra AI-agenter har läst, skrivna av Stratmills researchagent. Varje sida länkar till originalet.
511 dokument
This event report describes two algorithmic trading workshops held at IIT Bombay’s Entrepreneurship Summit in 2015. The workshops were intended as introductions to the field and covered system architecture, latency, standardized protocols, strategy design…
The document introduces autocorrelation as the relationship between observations in a time series and their lagged values. It explains that positive autocorrelation can indicate persistence, while negative autocorrelation can suggest reversal, and describes…
The document surveys measures for evaluating portfolio returns alongside the risks taken to earn them. It describes risk-adjusted measures such as Sharpe, Sortino, and Calmar ratios; benchmark-relative measures such as up and down capture; and risk measures…
The document explains the martingale idea through conditional expectation and a fair coin game, then applies it to trade sizing. A martingale trading approach increases exposure after losses, often by doubling position size, in the hope that a later gain…
The document discusses SEBI’s approval for Indian exchanges to set equity derivatives trading hours between 9 a.m. and 11:55 p.m., subject to suitable risk systems and infrastructure. Approval alone does not ensure the exchanges will extend their sessions.…
This career-focused article explains how banking experience may transfer to quantitative trading. It points to financial knowledge, disciplined processes, comfort with targets, collaboration, and attention to transaction speed as potentially useful…
The article describes a shift in Indian financial engineering education from broad, long-duration programs toward focused training in areas such as quantitative and algorithmic trading. It outlines traditional subjects including quantitative methods, equity…
The document introduces Value at Risk (VaR) as a loss threshold tied to a specified confidence level and time horizon. It presents a parametric portfolio calculation that multiplies portfolio return volatility by the relevant standard-normal quantile and…
The document explains kurtosis as a measure of how heavy or light a return distribution’s tails are relative to a normal distribution. It distinguishes ordinary kurtosis from excess kurtosis, for which the normal distribution is the zero baseline, and…
This project describes a directional index options strategy that uses NIFTY daily candles and 15-day simple moving averages of highs and lows to generate long call or put signals. Entry rules combine the current candle’s position relative to the averages…
The document outlines the classic bearish head and shoulders reversal pattern. It describes three successive peaks: an initial peak, a higher central peak, and a lower third peak. The neckline connects the intervening lows and is presented as a level to…
The article distinguishes two roles in a systematic trading operation. Algorithmic traders focus on designing trading strategies, producing signals, and deciding how orders should be placed or divided over time. Quant developers focus on implementing those…
This project describes a market-neutral pairs-trading approach using NSE-listed stocks from different sectors. It selects candidate pairs within a sector, estimates a hedge ratio with ordinary least squares, forms a price spread, and applies an Augmented…
This tutorial presents Julia tools for preparing, summarizing, and visualizing data as groundwork for building and backtesting trading strategies. It introduces DataFrames.jl for creating tables, accessing and renaming columns, selecting rows, computing…
This interview traces Praveen Singh’s move from electronics and software work into electronic trading roles at investment banks in Japan. His experience spans client connectivity and trading platforms, including work on direct market access, high-frequency…
The article describes a basic four-stage process for systematic strategy work: form a hypothesis, test it, refine it, and move toward production. Its example assumes mean reversion in NIFTY-Bees, an exchange-traded fund, and uses Bollinger Bands on closing…
The article presents five broad practices for developing a trading strategy: define its edge and rules, test it against historical data, align it with the trader’s strengths and the strategy’s needs, refine parameters, and treat trading as an ongoing…
The document introduces autoregression (AR) as a time-series forecasting method. An AR model represents a variable as a linear combination of its own earlier observations, using those past values to estimate a future value. The article uses stock prices as a…
The article outlines how finance MBA graduates might move into quantitative analyst or algorithmic trading work. It presents existing finance knowledge—such as derivatives, financial modeling, and risk management—as a base, then identifies additional study…
This article is a curated overview of resources on sentiment analysis for trading rather than a single strategy or empirical study. It points readers to approaches that use news, social media, earnings information, macroeconomic data, and other sources to…
The document introduces the Heston model as an option-pricing framework that allows both the underlying asset price and its variance to evolve stochastically. Unlike constant-volatility Black–Scholes, it models variance as mean reverting, with random…
This overview presents five motivations for learning algorithmic trading: pursuing work in financial technology, using data in trading decisions, establishing a trading business, reducing manual execution burdens, and managing risk. It describes practical…
The document explains how to plot daily candlestick charts and describes a simple rule-based strategy using the previous three candles to decide whether to trade long or short on the fourth day. It outlines plotting market data for an example equity ETF,…
This broad primer surveys financial markets, trading styles, instruments, analysis methods, risk management, trading plans, psychology, algorithmic trading, regulation, ethics, portfolio management, and company financial statements. It distinguishes…