Think Different

                                                     Directly taken from my Kaggle notebook The formula can be adapted into an arbitrage-trading decision model like below, but by itself it is not an arbitrage formula. R ( t ) = R max ⁡ m ( t ) = R max ⁡ 7 ∑ i = 1 7 s i ( t ) R(t)=R_{\max}m(t) =\frac{R_{\max}}{7}\sum_{i=1}^{7}s_i(t) where the 7 signals s i ( t ) s_i(t) are normalized signals and R ( t ) R(t) is a dynamically calculated risk/reward/allocation value. For arbitrage, I would use the same concept like this: A ( t ) = 1 7 ∑ i = 1 7 s i ( t ) A(t)=\frac{1}{7}\sum_{i=1}^{7}s_i(t) where the signals could be: Price spread Bid/ask spread Trading fees Slippage Market liquidity Transfer/settlement latency Historical spread reliability Then calculate the actual arbitrage profit: P arb = Q ( P sell − P buy ) − F buy − F sell − S buy − S sell − C trans...

                                                               generated by meta ai Yes. In fact, causal  modelling  is one of the most advanced topics in quantitative finance and is becoming increasingly important because traditional ML models (LSTM, XGBoost, Transformers) often learn  correlations , whereas causal models aim  to discover why prices move. Since I am  pursuing an  MSc in Financial Engineering  at  WQU , I'll teach it at that level, covering  theory, mathematics, and Python implementations. Learning Roadmap We'll build from scratch. Correlation vs Causation Structural Causal Models (SCM) Directed Acyclic Graphs (DAGs) Causal Discovery Algorithms Do-Calculus (Pearl) Counterfactual Prediction Causal Forecasting Applying causal models to stock prediction Building an end-t...

                                                                  generated by meta ai Portfolio management is a vital part of the whole financial management. Today, we will discuss a few primary concepts and their algorithms behind them.  Five topics we are going to explain below, build on each other beautifully, moving from the foundations of modern risk measurement to cutting-edge portfolio construction and, finally, to the psychological and mathematical edges of investing. Let's unpack each one systematically. Consider this a MasterClass lecture series, with the math explained conceptually first, then formalized. --- 1. Value at Risk (VaR) & Portfolio Theory Value at Risk is the answer to a simple, critical question: "How much can my portfolio lose in a given period, with a given pr...

                                                            Generated by meta ai As of late 2022 to mid-2023 data, India had an outstanding home loan (a common form of mortgage) portfolio valued at approximately ₹22.4 lakh crore (about USD 270 billion). The share of housing loans in total bank loans had risen to 14.2% by March 2023. Key Statistics on Indian Mortgage Loans Total Outstanding Value: The outstanding home loan portfolio was around ₹22.4 lakh crore at the end of the Financial Year 2021 (FY21), and the market has shown consistent growth since then. Number of Loans: In 2022 alone, 34 lakh (3.4 million) new home loans were disbursed. It's estimated that only about 10% of Indian households live in owned homes, with a significant potential for market growth. Market Growth: The Indian home loan market grew at a CAGR of 32% betwee...

                                                                        generated by meta.ai Here’s the quick, practical list — the stuff people actually watch to sense a crash before it hits. No guarantees, but these are the common warning lights: • Market Breadth Collapse Fewer stocks making new highs while many quietly start making new lows. (Think: the “engine” is losing power even if the car is still moving.) • Volatility Index (VIX) Spikes A sudden and persistent rise in the VIX usually signals fear building up. • Yield Curve Inversion When short-term rates become higher than long-term rates — a classic recession/correction predictor. • Credit Spreads Widening Corporate bond yields rising faster than government bonds → stress in the system. • Moving Average Crossovers Price falls below the 200-day...