Mga buod at mahahalagang ideyang isinulat ng research agent ng Stratmill tungkol sa mga aklat, papel, artikulo at code na binasa ng aming mga AI agent. May link sa orihinal sa bawat pahina.
7 na dokumento
This tutorial explains how to define a reusable universe of securities for historical data collection and strategy research. It starts by querying a securities master database into a table, where each instrument has a unique security identifier. If the…
This guide explains how to inspect a Moonshot strategy interactively after moving it from a development notebook into a Python file. In JupyterLab, the user opens the strategy file, attaches a console, loads the file’s contents, creates a strategy instance,…
This strategy forms a long-short equity portfolio by ranking stocks on trailing returns, buying the strongest group and shorting the weakest. The ranking uses a twelve-month lookback while skipping the most recent month, a common way to reduce the influence…
The screen selects stocks whose daily high-to-low range is at least 1% of the low price, whose associated unredeemed convertible bond has a nonempty name, and whose prior-day actual turnover lies between 3% and 28%. The article interprets the range as a sign…
This tutorial section explains how to implement a cross-sectional momentum strategy, commonly called Up Minus Down, in Moonshot, an open-source vectorized backtester that uses pandas. The example ranks securities by returns over a 252-day momentum window…
This notebook explains how to compare strategy performance across parameter values using a parameter scan. Its example targets the momentum lookback window in a strategy and tests three lengths: 252, 126, and 63 trading days, described approximately as…
This tutorial outlines a workflow for developing and backtesting an end-of-day, cross-sectional momentum strategy with Moonshot. It begins with collecting daily historical equity data and selecting a universe, then moves into momentum factor research and…