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 article presents the five-point pivot system, calculating the central pivot from the previous period's high, low, and close, then deriving two support and two resistance levels from that pivot and the prior range. It describes using prior-day data for…
The article explains the triple-barrier method as a way to create supervised learning labels using an upper profit-taking threshold, a lower stop threshold, and a time limit. It frames these barriers as a risk-aware alternative to fixed thresholds and…
This career guide outlines the work of quantitative analysts, traders, data scientists, and developers, then describes skills that may be assessed in quant interviews. It emphasizes practical market knowledge, programming, statistical analysis, and…
The article introduces support vector machines as classifiers that separate labeled observations with a boundary chosen to maximize the margin around nearby points. It explains support vectors, hard-margin classification, and soft margins that permit some…
The article describes a bull call spread, formed by buying a call and selling another call on the same underlying with the same expiration but a higher strike. The net premium paid is the maximum loss, while the difference between strikes less that premium…
The article surveys nine factors that may influence exchange rates: political conditions, inflation, interest rates, government debt, terms of trade, speculation, capital-market performance, employment data, and economic planning. For each, it gives a…
The article explains cloud computing as internet-delivered access to computing resources and outlines service models—software, platform, and infrastructure—as well as private, hybrid, and public deployment. It describes possible trading uses, including…
The document explains how to use a spreadsheet model to price calls and puts with the Black–Scholes formulas and chart the payoff of a portfolio across underlying prices. Users enter each option’s expiry, type, strike, premium, contract count, volatility,…
This study compares two systematic futures trend approaches across indices, bonds, currencies, and commodities: a one-year time-series momentum rule and a continuous-forecast framework inspired by systematic trading methods. The first takes long or short…
This project report explains portfolio diversification through Modern Portfolio Theory and introduces measures including alpha, beta, standard deviation, R-squared, and the Sharpe ratio. It describes the Fama-French three-factor model as an extension of CAPM…
This interview follows an experienced IT professional who developed an interest in algorithmic trading and built a short-duration intraday system for futures alongside a full-time job. He describes drawing on engineering, programming, mathematics, and…
This introduction presents Julia as a language intended to combine high computational performance with the ease of a dynamic language, addressing the need to rewrite demanding scientific code in lower-level languages. It outlines Julia’s origins and compares…
The article introduces the Central Limit Theorem (CLT) through simulated samples from exponential and binomial populations. It explains that, under suitable conditions, sample means approach a normal sampling distribution as sample size grows; the…
The article demonstrates foundational analysis of stock price and volume data with Python and pandas. It describes loading OHLCV records, inspecting the date index, counting observations, finding the maximum closing price and its date, calculating daily…
The document outlines a session about private equity secondary markets: transactions in existing investments, with coverage of seller and buyer types, their motivations, and factors that may make a secondary investment attractive compared with a primary one.…
This project builds daily trading signals for the Nifty 50 using logistic and linear regression. It derives predictors from OHLC relationships, including whether the close is nearer the high or low and whether the current high or low exceeds the prior day’s…
This recap of an industry Q&A discusses learning quantitative and algorithmic trading, career paths, and how AI tools affect research. Its central advice is to focus on process and evidence rather than the excitement of profit and loss or the novelty of…
This tutorial presents a workflow for adapting Pyfolio, a Python library for portfolio performance and risk analysis. It outlines the library’s reporting features, including return and risk statistics, rolling measures, drawdowns, and tear sheets, then…
The article explains how OHLC bars can be built using different measures of market activity. Time bars group trades into fixed intervals, while tick bars form after a set number of transactions. The examples show that equal-length time bars can conceal…
This article introduces factor investing as portfolio construction based on characteristics associated with differences in investment returns and risks. It distinguishes macroeconomic factors, such as growth, interest rates, and inflation, from style…
This tutorial explains how a neural network adjusts its weights after forward propagation produces an incorrect prediction. It defines a squared-error loss for predicted class probabilities, then describes gradient descent as repeated weight updates based on…
This project compares ARIMA time-series modeling with an LSTM neural network for next-day Nifty index price forecasts. The author used five years of adjusted closing-price data, selected an ARIMA specification after stationarity checks and parameter…
This article introduces seven indicators commonly used in chart analysis and describes what each measures: moving averages smooth price changes; Bollinger Bands reflect volatility around a moving average; RSI and MFI assess momentum and possible overbought…
The article introduces machine learning as a way to learn patterns from data and use them to classify or predict outcomes. It outlines supervised learning with labeled examples, including classification and regression; unsupervised learning that groups…