Backtesting

Orders are where trading systems stop sounding intelligent and start spending money. A model can narrate the market beautifully. It can explain momentum, liquidity, volatility regimes, inventory pressure, and the great moral tragedy of being early. None of that matters if the final system places the wrong limit order, sizes too aggressively, fills only in a fantasy simulator, or wins a backtest because it tried enough variants to accidentally find one that looked divine. ...

Most trading libraries begin innocently. Someone wants an EMA. Then RSI. Then ATR. Then Bollinger Bands, pivots, candlestick patterns, rolling quantiles, regime flags, entry rules, exit rules, position sizing, stop-loss templates, and one mysterious helper called generate_alpha_final_v7() that nobody dares delete because it once worked during a bull market. This is how indicator libraries become strategy junk drawers. ...

TL;DR for operators HARLF is not a story about a large language model suddenly becoming a portfolio manager. Sensible readers may exhale. The language component is FinBERT sentiment scoring applied to financial news, then converted into monthly asset-level signals. The heavier claim is architectural: instead of throwing price metrics and sentiment into one flat reinforcement-learning model and hoping the neural soup tastes like alpha, the paper separates the decision process into three tiers. ...

TL;DR for operators Volatility is usually treated as a risk input: measure it, size positions around it, and try not to get mugged by it before lunch. This paper treats volatility differently. It uses mid-range volatility to select stocks that are neither comatose nor explosive, then applies a causal-inference stack to find which stocks appear to move before others. ...