নলেজ লাইব্রেরি

This report compares AdaBoost, gradient boosting decision trees, and XGBoost as tools for selecting Chinese equities from factor data. Its workflow covers feature and label preparation, preprocessing, in-sample fitting, cross-validation, and out-of-sample…

The document briefly introduces r_Gator as an analogue of the Alligator technical indicator, with the stated aim of reducing signal lag. It notes that the indicator was first implemented in MQL4 and published in 2008. No formula, parameter settings, chart,…

This note shows how to construct a simple stock relative-return factor using a precomputed Chinese equity dataset. It identifies one field as each stock’s daily return and another as the CSI 300 index return, then defines relative return as the stock return…

This Chinese A-share screening idea combines three filters: daily price amplitude above a threshold, a minimum time since listing, and a MACD condition above the indicator’s zero axis. The article explains that amplitude is calculated from the day’s high and…

This product guide explains a redesigned strategy interface with two parts: a viewer for examining and comparing strategies, and a creator for configuring new tests. The viewer presents performance metrics, behavior over time, and individual trade details.…

This post outlines a Chinese stock screen for companies classified in the metaverse sector. It limits eligible stocks to those with circulating shares at or below 5.5 billion and excludes stocks that reached the upper price limit on the prior trading day. It…

This note describes a Chinese A-share screening rule for Shenzhen Main Board stocks. It combines daily amplitude above 1%, a reversal or engulfing-style signal, and positive price-to-earnings and price-to-book ratios below 29.01 and 3.11, respectively. The…

This troubleshooting note addresses errors that arise after adding a rolling-training module to an AI strategy for convertible bonds. It points to a revised notebook and identifies two implementation changes associated with the fix. First, the rolling…

This indicator description combines the Williams %R oscillator with Bollinger Band boundaries to define overbought and oversold areas. The band thresholds are intended to adapt to the oscillator’s behavior, rather than relying only on fixed levels. The…

This live-robot dashboard records a crypto spot arbitrage system trading BTC/USDT across Gate and KuCoin, alongside a wider set of monitored assets. Its logs show paired buys and sells on the two venues, with the direction varying by trade. The displayed…

This study applies a factor-timing framework from a 2019 paper to China A-share data. It parameterizes factor weights using external variables and compares two approaches based on where the information enters: a time-series model and a cross-sectional model.…

This post describes a Chinese equity selection screen with three filters: price amplitude above 1%, a ten-day price gain greater than zero but below 35%, and circulating market capitalization between 5 billion and 10 billion yuan. Its accompanying Python…

This short forum post asks how to export a backtest’s daily returns to a CSV file and where to place a suggested calculation in the BigQuant workflow. The proposed approach reads a performance DataFrame, adds one to the cumulative algorithm return, computes…

This post describes a stock screen requiring a daily price range greater than 1%, a trading date in 2021, and float market capitalization above 10 billion yuan. It presents these conditions as a way to identify higher-volatility shares while limiting the…

This Chinese-language post describes an equity screening rule using RSI below 65, a three-candle pattern described as three consecutive down candles, and prior-day turnover above 8%. It frames RSI and the candle sequence as signs of weakness, while high…

This note presents a third-generation moving average attributed to Dr. Manfred G. Dürschner. It calculates a short-period average, then averages that result over a longer period, and combines the two with a coefficient derived from their relative lengths.…

This Chinese equity selection rule filters stocks by trading activity, prior-day leaderboard appearance, and the latest daily price change. It seeks names with turnover between 3% and 12%, a positive leaderboard flag on the preceding day, and a daily return…

This Expert Advisor uses the AFIRMA autoregressive finite impulse response moving average indicator to generate trade signals. It checks for a change in the indicator’s direction when a bar closes, then forms a signal. The document notes that the indicator…

This short forum exchange addresses a Python import error that appeared after upgrading VeighNa. A user reports that strategies which previously worked now fail because `AccountData` cannot be imported from the CTA strategy package. The reply identifies the…

This report evaluates the Shanghai 180 index and the Hua An Shanghai 180 ETF. It describes an index weighted heavily toward financial companies and food and beverage firms, with large, liquid constituents. The report characterizes its recent one-, three-,…

This Expert Advisor combines ADX, CCI, RVI, and Williams %R indicators with a martingale sizing approach. Its suggested tuning sequence first optimizes the main signal and ADX settings with the WPR strategy disabled, then tunes WPR separately, and finally…

This indicator description defines alert conditions using Bollinger Bands and price envelopes together. A buy alert is triggered when a bullish candle opens and closes below both lower bands. A sell alert requires a bearish candle to open and close above…

This MetaTrader 5 Expert Advisor description outlines a breakout scalping system that trades only when a set of filters align. It uses fast and slow exponential moving averages to define direction, an ATR-based threshold for trend strength, price location…

This Chinese stock-screening post describes a three-part selection rule: rank stocks by volume ratio and keep the top 100, require weekly MACD to be above zero, and exclude stocks whose gain at 9:25 is 6% or more. The author interprets the filters as…