asset-allocation

name asset-allocation description Asset allocation theory and optimizer usage — MPT / Black-Litterman / risk budgeting / all-weather strategy, including guides for 5 optimizers and rebalancing rules. category asset-class Asset Allocation and Portfolio Optimization Overview From asset allocation theory to practical implementation, this skill covers classical frameworks (MPT, BL, risk budgeting, all-weather) and the usage of the four optimizers built into this system. The output can be written directly into config.json . Asset Allocation Theory 1. Modern Portfolio Theory (MPT, Markowitz) Core idea : maximize expected return for a given level of risk (the efficient frontier). Optimization problem: min w'Σw (portfolio variance) s.t. w'μ = target_return Σw = 1 w ≥ 0 (no shorting) Advantages Disadvantages Mathematically rigorous Extremely sensitive to inputs (garbage in, garbage out) Efficient frontier is visualizable Concentrated-allocation problem (often produces extreme weights) Foundational framework Assumes normality and ignores fat tails Practical advice : do not use raw MPT directly. Add constraints (upper/lower bounds, sector limits) or use a regularized version. 2. Black-Litterman Model Core idea : start from market equilibrium and incorporate investor views. Steps: 1. Reverse-imply market equilibrium returns: π = δΣw_mkt 2. Build the view matrices: P (selection matrix), Q (view returns), Ω (view uncertainty) 3. Blend the posterior: μ_BL = [(τΣ)^-1 + P'Ω^-1 P]^-1 [(τΣ)^-1 π + P'Ω^-1 Q] 4. Run Markowitz optimization using posterior μ_BL Example views : Absolute view: "China A-shares will return 10% over the next year" → P=[1,0,0], Q=[0.10] Relative view: "China A-shares will outperform US equities by 5%" → P=[1,-1,0], Q=[0.05] Parameter guidance : τ (uncertainty scaling): 0.025-0.05 Ω : set according to view confidence, where higher confidence = smaller variance 3. Risk Budgeting Core idea : allocate by risk contribution rather than by capital share. Risk contribution: RC_i = w_i × (Σw)_i / σ_p Target: RC_i / σ_p = budget_i (for all i) Strategy Risk Budget Best Use Case Equal risk contribution Each asset 1/N When you do not know which asset is best Equity-tilted risk budget Stocks 60%, bonds 30%, commodities 10% When you want equities to contribute more risk Dynamic risk budget Adjust dynamically by signal strength When you have market-timing ability 4. All-Weather Strategy Bridgewater framework : allocate risk equally across economic environments. Economic environment Asset allocation ───────── ───────── Growth rising Equities + commodities + corporate bonds Growth falling Government bonds + inflation-protected bonds Inflation rising Commodities + inflation-protected bonds + EM debt Inflation falling Equities + government bonds Simplified allocation example for China-focused portfolios: - 30% CSI 300 / CSI 500 - 40% government bonds / credit bonds - 15% gold - 15% commodities / REITs Guide to the 5 Optimizers Overview of the Built-In Optimizers Configure them in config.json through optimizer and optimizer_params : optimizer Display Name Core Idea Best Use Case equal_volatility Equal Volatility Allocate weights by inverse volatility Simple and effective baseline risk_parity Risk Parity Equalize risk contribution while accounting for correlation Long-term robust allocation mean_variance Mean-Variance Maximize Sharpe ratio or minimize variance When return forecasts are available max_diversification Maximum Diversification Maximize the diversification ratio When pursuing a low-correlation portfolio turnover_aware Turnover-Aware Mean-variance utility with an L1 penalty on weight changes vs the previous rebalance When trading costs matter; tune turnover_penalty to your data frequency 1. equal_volatility { "optimizer" : "equal_volatility" , "optimizer_params" : { "lookback" : 60 } } Principle : w_i = (1/σ_i) / Σ(1/σ_j) Parameter Default Description lookback 60 Volatility calculation window (trading days) Advantages : simple and fast, no return forecast required, no correlation matrix required. Disadvantages : ignores cross-asset correlation. 2. risk_parity { "optimizer" : "risk_parity" , "optimizer_params" : { "lookback" : 60 } } Principle : solve for weights such that each asset contributes the same amount of risk. Parameter Default Description lookback 60 Covariance-matrix estimation window Advantages : accounts for correlation, spreads risk more evenly, and is robust over long horizons. Disadvantages : requires iterative solving and is sensitive to covariance estimates. 3. mean_variance { "optimizer" : "mean_variance" , "optimizer_params" : { "lookback" : 60 , "risk_free" : 0.0 } } Principle : Markowitz optimization that maximizes the Sharpe ratio. Parameter Default Description lookback 60 Window for estimating means and covariances risk_free 0.0 Risk-free rate (annualized) Advantages : theoretically optimal (if inputs are accurate). Disadvantages : extremely sensitive to inputs, prone to extreme weights, and often performs poorly out of sample. Recommendation : do not make lookback too short ( <30 easily overfits), and add upper/lower weight constraints. 4. max_diversification { "optimizer" : "max_diversification" , "optimizer_params" : { "lookback" : 60 } } Principle : maximize DR = (w'σ) / σ_p (the diversification ratio). Parameter Default Description lookback 60 Calculation window Advantages : does not require return forecasts and seeks true diversification. Disadvantages : effectiveness is limited in highly correlated environments. 5. turnover_aware { "optimizer" : "turnover_aware" , "optimizer_params" : { "lookback" : 60 , "risk_aversion" : 1.0 , "turnover_penalty" : 0.5 } } Principle : minimize -w'μ + λ·w'Σw + γ·||w - w_prev||₁ subject to long-only, fully-invested weights — mean-variance utility with an L1 penalty on weight changes versus the previous rebalance, so the optimizer only trades when the expected improvement outweighs the (implicit) cost. Parameter Default Description lookback 60 Calculation window risk_aversion 1.0 Weight on the variance term (λ) turnover_penalty 0.0 Weight on the L1 turnover term (γ); 0 reduces to the mean-variance baseline Advantages : dampens rebalancing churn, which usually dominates realized costs; the first rebalance is unpenalized so the cold start is undistorted. Disadvantages : turnover_penalty is scale-sensitive to the return frequency of the input window — for daily returns even γ ≈ 0.5 strongly prefers holding still, so tune it per data frequency. Optimizer Selection Decision Tree Do you have return forecasts? ├── Yes → Do trading costs / churn matter? │ ├── Yes → turnover_aware (tune turnover_penalty to data frequency) │ └── No → mean_variance (remember to add constraints) └── No → Do you need to account for correlation? ├── Yes → risk_parity (recommended default) └── No → Are volatility differences across assets large? ├── Yes → equal_volatility └── No → max_diversification Rebalancing Strategy Three Rebalancing Triggers Method Trigger Condition Advantages Disadvantages Periodic rebalancing Fixed monthly / quarterly date Simple, predictable trading cost May miss or delay adjustments Threshold trigger Deviation from target weight > X% Trades only when needed Frequent trading in high-volatility markets Volatility trigger VIX / volatility breaks a threshold Adapts to market regime Parameter selection is difficult Suggested Rebalancing Frequency Asset Class Suggested Frequency Threshold Equity portfolio Monthly ±5% Stock-bond mix Quarterly ±10% Global macro Quarterly / semiannual ±10% Cryptocurrency Weekly / biweekly ±15% (high volatility) Rebalancing in Backtests Implement rebalancing logic in signal_engine.py : # Periodic rebalancing example (every 20 trading days) if bar_count % rebalance_freq == 0 : # Recompute weights new_weights = calculate_target_weights(data_map) for code, weight in new_weights.items(): signals[code].iloc[i] = weight Cross-Asset Correlation Analysis Typical Correlation Matrix (China-Focused Portfolio Example) CSI 300 CSI 500 Government Bonds Gold BTC CSI 300 1.00 0.85 -0.15 0.05 0.10 CSI 500 0.85 1.00 -0.10 0.03 0.12 Government Bonds -0.15 -0.10 1.00 0.20 -0.05 Gold 0.05 0.03 0.20 1.00 0.15 BTC 0.10 0.12 -0.05 0.15 1.00 Key patterns : Negative stock-bond correlation is the foundation of allocation (but it does not always hold; in 2022 both stocks and bonds sold off) Gold has low correlation with equities and serves as a hedge BTC's correlation with traditional assets is unstable and tends to become positive in crises Large-cap versus small-cap China A-shares have high correlation ( 0.85 ), so diversification benefits are limited Output Format ## Asset Allocation Recommendation ### Allocation Plan | Asset | Weight | Risk Contribution | Expected Return (Annualized) | |------|------|---------|--------------| | CSI 300 | 30% | 45% | 8% | | Government Bond ETF | 40% | 15% | 3% | | Gold | 15% | 20% | 5% | | BTC | 15% | 20% | 15% | ### Optimizer Configuration ```json { "optimizer": "risk_parity", "optimizer_params": {"lookback": 60} } Expected Risk / Return Metric Value Expected annualized return 7.2% Expected annualized volatility 8.5% Expected Sharpe 0.85 Expected maximum drawdown -12% Rebalancing Rules Frequency: quarterly (first trading day of March / June / September / December) Threshold: trigger when any asset deviates from target by ±10% Cost: estimated annual trading cost 0.15% ## Notes 1. **The optimizer needs enough instruments**: at least 3 instruments are needed for meaningful optimization; with 2 instruments, `equal_volatility` is usually enough 2. **`lookback` window**: too short (`<20`) is noisy, too long (`>120`) reacts slowly, and 60 is a reasonable default 3. **`mean_variance` trap**: it is the easiest to overfit, and out-of-sample Sharpe is often cut by half or more 4. **Rebalancing cost**: frequent rebalancing eats into returns; for China A-share portfolios, stamp duty of 0.05% plus commissions is material 5. **Cross-market allocation**: use `"source": "auto"` in `config.json`, and let `codes` mix instruments from different markets 6. **Leverage constraint**: the sum of weights must be ≤ 1.0, and leverage is not allowed unless explicitly specified 7. **Survivorship bias**: historical correlations may be distorted by delistings and new listings