I. System Architecture
The Hybrid Cloud Topology: Optimizing for both Latency and Capacity.
The Hybrid Model
We cannot run everything in the Cloud due to latency, nor everything On-Prem due to cost. The architecture is split into two distinct zones connected by a secure leased line.
Zone A: Co-location (On-Prem)
NY4 / NJ2 Data Centers. Directly cross-connected to exchanges.
Zone B: The Cloud (AWS/GCP)
Elastic compute for research, massive data storage, and non-latency sensitive tasks.
Data
II. The Data Foundation
The lifeblood of the fund. Ensuring Point-in-Time correctness and cleanliness.
The 3-Tier Storage Model
We optimize storage for access patterns. Trading engines need nanoseconds; researchers need terabytes.
In-memory. Last 24 hours of ticks. Used for live signals and real-time dashboards.
NVMe SSDs. Recent history (5 years). Used for daily retraining of models.
Object Store. Full history (20+ years). Used for deep-dive research.
# Data Movement Logic
def migrate_data():
# 1. Capture realtime feed
ticks = kdb.query("select from quote where time > -5m")
# 2. End of Day: Flush to Delta Lake
if market_status == 'CLOSED':
df = kdb.query("select from quote where date=.z.d")
df.write.format("delta").save("/mnt/lake/quotes")
# 3. Optimistic Z-Ordering for fast retrieval
spark.sql("OPTIMIZE quotes ZORDER BY (symbol, time)")III. Machine Learning Design
From feature engineering to meta-labeling: The Deep Dive.
State-of-the-Art Architectures
Modern quants moved beyond simple Linear Regression years ago. We now utilize a hybrid approach, combining the interpretability of tree-based models with the feature-extraction power of deep learning.
TabNet & ResNets
Neural networks adapted for tabular data. They use attention mechanisms to select relevant features instance-wise, offering interpretability similar to decision trees.
Graph Neural Networks (GNNs)
Used for supply chain analysis. Stocks are nodes, and supplier/customer relationships are edges. Shocks to one node propagate through the graph to predict impact on connected entities.
Transformer Encoders
Applied to time-series (replacing LSTMs). We use "Time2Vec" positional encodings to capture periodicities in market microstructure.
Model_Pipeline_Config.json
IV. Backtesting & Simulation
The Time Machine: From Vectorized Prototypes to Event-Driven Reality.
Vectorized vs. Event-Driven
Retail backtests often use "Vectorized" calculations (pandas), which are fast but prone to look-ahead bias. Professional backtests use an Event-Driven loop that processes data one tick at a time, exactly mimicking the live execution environment.
V. Risk & Convex Optimization
The Final Step: Turning Alpha into a Portfolio.
Convex Optimization (MVO)
We don't pick stocks; we pick weights. The optimizer solves for the optimal weights $w$ that maximize expected return minus a risk penalty, subject to constraints.
The Objective Function
- $\mu$ = Expected Returns (Alpha)
- $\Sigma$ = Covariance Matrix (Risk)
- $\lambda$ = Risk Aversion Parameter
*Because the problem is Convex, we are guaranteed to find the global maximum efficiently.
import cvxpy as cp
# 1. Variables
w = cp.Variable(n_assets)
# 2. Risk Model (Factor)
# Sigma = B*Cov_f*B.T + D
risk = cp.quad_form(w, Sigma)
# 3. Objective (Utility)
# Maximize Alpha - Risk - TransactionCosts
objective = cp.Maximize(mu @ w - gamma * risk)
# 4. Constraints
constraints = [
cp.sum(w) == 0, # Dollar Neutral
cp.sum(cp.abs(w)) <= 2.0, # 2x Leverage
w <= 0.05, # Max Pos Size
w >= -0.05
]
# 5. Solve
prob = cp.Problem(objective, constraints)
prob.solve(solver=cp.ECOS)