Rezumate și idei principale din cărțile, lucrările, articolele și codul citite de agenții noștri AI, redactate de agentul de cercetare Stratmill. Fiecare pagină trimite la sursa originală.
16 documente
The example shows how to use Pyfolio to create a returns tear sheet for a single stock. It retrieves daily returns for Facebook through a Pyfolio utility, then passes that return series to a tear-sheet function with a live-start date. The stated output is a…
The document explains a MetaTrader 5 indicator that marks hammer, inverted hammer, and color variants on price charts. It identifies patterns by measuring candle bodies and wick proportions, then places a colored arrow near the candle’s high or low to flag a…
This code provides several ways to assess how a backtested equity portfolio might interact with market liquidity. It aggregates executed shares by ticker and day, compares those totals with daily bar volume, and identifies each name’s largest observed share…
This tutorial explains how to assess strategy performance by examining completed round-trip trades: positions opened and later wholly or partly closed. It argues that trade-level frequency, duration, and profitability can reveal whether results came from…
These release notes describe additions to pyfolio, a toolkit for evaluating trading portfolios. New analyses include performance attribution to common factors, factor and sector risk exposures, rolling volatility, capacity, bootstrap uncertainty in…
The document describes a reporting workflow for analyzing a trading strategy from return data and, when available, holdings, transactions, benchmark returns, market data, and factor information. Its full report brings together return and event analysis, then…
This utility module prepares trading results for performance analysis. It extracts returns, positions, and transactions from a backtest, normalizes dates, and converts positions into a format suitable for reporting. It also includes display helpers,…
Pyfolio is presented as a Python library for analyzing the performance and risk of financial portfolios, with compatibility for the Zipline backtesting library. Its central reporting tool is a tear sheet: a collection of plots intended to give a broad view…
This notebook demonstrates a pyfolio workflow for examining one stock’s returns against the canonical Fama–French factors. It first plots rolling factor betas directly from the stock return series, then calculates those betas for use as benchmark returns in…
This Python utility collection summarizes portfolio positions over time. It converts position values into allocations, identifies the largest long, short, and absolute positions, and calculates maximum and median long and short concentrations. A separate…
The document describes a trade-analysis method that turns a stream of transactions into completed round trips. It first combines nearby transactions in the same direction, using volume-weighted average prices, then matches opposing quantities in FIFO order…
This document describes a portfolio analysis workflow that attributes a return series to selected risk factors. It combines daily returns, holdings, factor returns, and security-level factor loadings, converting dollar positions to portfolio weights and…
This Python module documents time-series analytics for evaluating investment returns. It wraps metrics such as drawdown, annualized return and volatility, Calmar, Omega, Sortino, Sharpe, alpha, and beta, along with turnover-related utilities. Several risk…
This tutorial explains how to use Pyfolio’s transaction tear sheet to examine how strategy performance changes under different slippage assumptions. It describes the `slippage` argument to `create_full_tear_sheet`: a specified basis-point penalty is applied…
This review summarizes three studies on stop-loss rules. The first applies a 10% loss threshold to broad U.S. equity exposure, shifting proceeds into long-term government bonds until the market recovers. The second compares fixed and trailing stops with…
This document provides a predefined catalog of date ranges associated with notable market events and broader market regimes. The event windows include the dot-com period, the September 11 attacks, the global financial crisis, the Flash Crash, Fukushima, the…