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195 dokumentov
The article explains option value through an everyday example: the right to use a truck. It identifies three drivers of that choice’s value: how useful the truck would be now, how uncertain the holder’s future need is, and how long the choice remains…
This tutorial demonstrates a basic feed-forward neural network workflow for classifying the direction of hourly foreign exchange price changes. It constructs features from hourly changes in closing, high, and low prices, along with distances among those…
The article argues that a stop loss is useful only when losses carry information about likely future returns. For a signal based on a factor such as sentiment, a falling position value does not by itself show that the signal has weakened. Exiting solely…
The document outlines using Google Compute Engine virtual machines to run trading software, with R and Zorro as examples, and connecting the system to a broker through Interactive Brokers Gateway. It frames cloud hosting as a way to avoid maintaining local…
The article presents a framework for judging whether an observed market feature is likely to persist: consider its economic rationale, inspect historical evidence, check consistency across time, and compare across markets. It illustrates the process with…
The document explains how log returns differ from simple returns using an asset that doubles in price. A simple return measures the gain against the starting price; the log return describes the constant rate that, applied across arbitrarily small intervals,…
This note applies lessons from gambling to strategy selection. It recommends looking for comparatively tractable opportunities, including harvesting risk premia and predicting relative returns across assets rather than forecasting the absolute direction of…
This brief research note explains why asset prices are difficult to analyze directly: a broad equity index can drift over time, making price levels from distant periods poorly comparable. It distinguishes a predictive question from a contemporaneous…
This course description presents a practical framework for evaluating trading ideas with spreadsheet analysis and freely available market data. Its proposed research process is to formulate a hypothesis, collect and clean relevant observations, explore the…
This guide introduces the perceptron, a basic neural network model for binary classification. It outlines activation functions and learning, then demonstrates how weights and a bias can be updated from classification errors. Examples use iris flower…
This installment proposes converting signals from overlapping pairs into security-level signals. For each spread, its z-score becomes two opposing votes: the relatively rich ticker receives a positive signal and the relatively cheap ticker a negative one.…
This beginner guide demonstrates an R workflow for managing stock price data with DuckDB. It explains why a database can help organize and query growing datasets, while noting tradeoffs such as setup, SQL knowledge, resource use, and reduced readability.…
The article treats trading as an operating business that must allocate limited capital, time, and skills across strategy research, infrastructure, reporting, accounting, and ongoing learning. Its guiding question is how to improve the trading setup in ways…
The workshop description outlines a mechanism-first approach to researching trades. It argues that potential returns may come from bearing risk premia or trading against participants whose constraints require them to transact, rather than from forecasting…
The Hurst exponent is presented as a way to characterize whether a time series tends to behave like a random walk, persist in its direction, or revert toward an average. The article connects this classification to the search for mean-reverting financial…
The document introduces a webinar about examining a simple seasonality effect with Excel. Its central research lesson is that an upward-sloping equity curve alone may not tell the whole story; researchers should investigate the market behavior behind the…
This excerpt presents a quantitative perspective on drawdowns as an expected part of trading. Its suggested response combines understanding market behavior, using a sound systematic research process, and keeping a measured perspective during losing periods.…
The article outlines a framework that groups daily candle patterns with k-means, then tests whether particular clusters support long or short trades. Its sample features are the day’s high, low, and close relative to its open. Historical observations are…
This guide explains how a Python application communicates with Interactive Brokers through Trader Workstation or Gateway. It covers the requirement that one of those desktop applications remain running, restart and reauthentication behavior, native API…
The article examines whether EUR/USD shows a repeatable return pattern around the US non-farm payroll release, scheduled for the first Friday of each month. It describes plotting average cumulative returns across the morning window from 6:00 to 11:00 Eastern…
This tutorial shows how to export a factor measured at trade entry from a Zorro simulation and compare it with subsequent trade returns in R. The example records rolling volatility before entry, attaches it to closed trades, and writes asset, entry date,…
This tutorial demonstrates a workflow for bringing nested JSON market data into R and shaping it into a data frame for analysis. It uses an HTTP request to retrieve an options-chain response, checks the response type and request status, and parses the JSON…
This article uses hypothetical investment paths to illustrate how compounding and randomness could shape an investor’s experience in Renaissance Technologies’ Medallion Fund. It describes a return and volatility scenario, then contrasts outcomes associated…
The article questions assumptions traders make about time, using a counting example to introduce the idea that familiar time units are conventions. It then points to the group, summarize, and analyze process commonly used with market data: observations are…