cpz-ai 3.1.1

CPZAI Python SDK — strategy framework, backtest engine, risk guards, execution algorithms, and multi-broker trading

CPZ Python SDK

Strategy Framework, Backtesting Engine, Risk Guards, Execution Algorithms, and Multi-Broker Trading

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What's New in v3.1.0

• Quantum-Inspired HRP (QHRP) — 1QBit's algorithm for superior hierarchical risk parity via QUBO-optimised asset permutation
• QUBO Portfolio Selection — Cardinality-constrained binary asset selection with quantum-ready formulation
• Amazon Braket Integration — Submit QUBOs to real quantum hardware (IonQ, Rigetti, IQM, QuEra) or AWS simulators
• Multi-tier quantum backends — Classical heuristic (default), simulated annealing, or real QPUs via Braket
• Auth-gated quantum compute — Braket access requires CPZ API keys; costs tracked per-account

v3.0.0

• Strategy Framework — Professional-grade Strategy base class with lifecycle hooks and backtest-live code parity
• Backtest Engine — Historical replay with fill simulation, slippage/commission models, and full analytics
• Pre-Trade Risk Guard — Intercepts every order before broker submission with 10 configurable rules
• Execution Algorithms — TWAP, VWAP, and Iceberg order splitting
• Typed Domain Model — Precision-safe Price, Quantity, Money types that reject NaN/Infinity
• Event Bus — Thread-safe pub/sub with wildcard topic matching

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Overview

The CPZ Python SDK is the unified interface for the CPZ quantitative trading platform. Write strategies once and run them in backtest or live mode with zero code changes.

Module	Description
Strategy	Professional-grade strategy framework with lifecycle hooks, backtest-live code parity
StrategyRunner	Run strategies live or backtest with historical replay and fill simulation
RiskGuard	Pre-trade risk validation — max order value, position limits, daily loss halts
cpz.portfolio	Portfolio optimization — MVO, Black-Litterman, HRP, CVaR, risk parity, QHRP, QUBO selection, and 10+ methods
cpz.portfolio.quantum	Quantum-inspired optimization — QHRP, QUBO solvers, Amazon Braket integration (IonQ, Rigetti QPUs)
client.risk	Portfolio risk analytics — VaR, Sharpe inference, Monte Carlo, stress testing
client.execution	Multi-broker order management (Alpaca, IBKR, SnapTrade, Polymarket) with TWAP/VWAP/Iceberg algos
client.engine	Low-latency HFT engine deployment (Rust) for autonomous execution
client.data	Stocks, crypto, options, 800K+ FRED series, SEC filings, social sentiment, 100+ indicators
client.simons	Simons — quantitative trading strategist for analysis, code generation, and strategy review

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Installation

pip install cpz-ai                    # core SDK (includes classical quantum-inspired optimizers)
pip install cpz-ai[risk]              # + statsmodels for advanced risk analytics
pip install cpz-ai[quantum]           # + dwave-neal for simulated annealing
pip install cpz-ai[quantum-braket]    # + Amazon Braket for real quantum hardware (IonQ, Rigetti)
pip install cpz-ai[all]               # + all optional dependencies

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Quantum-Inspired Portfolio Optimization

CPZAI is the first systematic trading SDK with integrated quantum computing. The cpz.portfolio module includes quantum-inspired algorithms that run classically by default and can execute on real quantum hardware via Amazon Braket.

Quantum-Inspired HRP (QHRP)

Based on the 1QBit white paper (Alipour et al., 2016). Outperformed standard HRP, IVP, and Minimum Variance on CTA futures benchmarks.

from cpz.portfolio import quantum_inspired_hrp, hierarchical_risk_parity

# Classical heuristic (default — no extra deps needed)
result = quantum_inspired_hrp(returns, backend="heuristic")
print(result.weights)  # {'SPY': 0.15, 'TLT': 0.23, 'GLD': 0.26, ...}

# Simulated annealing (pip install cpz-ai[quantum])
result = quantum_inspired_hrp(returns, backend="simulated_annealing")

# Real quantum hardware via Amazon Braket (pip install cpz-ai[quantum-braket])
from cpz.portfolio import BraketSolver
solver = BraketSolver(device_arn="ionq", s3_folder=("your-bucket", "braket-results"))
result = quantum_inspired_hrp(returns, solver=solver)

QUBO Portfolio Selection

Cardinality-constrained asset selection — pick the best K assets from a larger universe.

from cpz.portfolio import qubo_portfolio_selection

result = qubo_portfolio_selection(returns, target_k=5, risk_aversion=0.5)
print(result.selected_assets)  # ['AAPL', 'MSFT', 'GOOGL', 'AMZN', 'TSLA']

Available Quantum Backends

Backend	Install	Hardware	Cost
"heuristic"	pip install cpz-ai	Classical CPU	Free
"brute_force"	pip install cpz-ai	Classical CPU	Free
"simulated_annealing"	pip install cpz-ai[quantum]	Classical CPU	Free
"braket" (IonQ)	pip install cpz-ai[quantum-braket]	IonQ Forte-1 QPU	~$1.30/task
"braket" (Rigetti)	pip install cpz-ai[quantum-braket]	Rigetti Ankaa-3 QPU	~$0.34/task

Braket access requires authenticated CPZ API keys. Quantum compute costs are tracked per-account.

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Strategy Framework

Write your strategy once as a Strategy subclass. The same code runs against the backtest engine or live broker with zero changes.

Define a Strategy

from cpz import Strategy, StrategyConfig, StrategyRunner, CPZClient

class MomentumConfig(StrategyConfig):
    fast_period: int = 10
    slow_period: int = 30
    trade_size: float = 100

class MomentumStrategy(Strategy):
    def on_start(self):
        self.subscribe_bars(self.config.instruments[0], "1D")

    def on_bar(self, bar):
        prices = self.cache.bars(bar.symbol)
        if len(prices) < self.config.slow_period:
            return
        fast_ma = sum(b.close for b in prices[-self.config.fast_period:]) / self.config.fast_period
        slow_ma = sum(b.close for b in prices[-self.config.slow_period:]) / self.config.slow_period

        if fast_ma > slow_ma and not self.portfolio.is_long(bar.symbol):
            self.buy(bar.symbol, qty=self.config.trade_size)
        elif fast_ma < slow_ma and self.portfolio.is_long(bar.symbol):
            self.flatten(bar.symbol)

    def on_order_filled(self, event):
        self.log.info(f"Filled {event.symbol} @ {event.fill_price}")

    def on_position_closed(self, event):
        self.log.info(f"Closed {event.symbol}, realized P&L: ${event.realized_pnl:,.2f}")

Backtest

client = CPZClient()
runner = StrategyRunner(client, mode="backtest")
runner.add_strategy(MomentumStrategy(MomentumConfig(instruments=["AAPL"])))

result = runner.backtest(
    start="2024-01-01",
    end="2024-12-31",
    initial_cash=100_000,
    commission_pct=0.0003,    # 3 bps
    slippage_pct=0.0001,      # 1 bp
)
print(result.summary())

Output:

============================================================
  BACKTEST RESULTS
============================================================
  Period:            2024-01-01 → 2024-12-31
  Initial Cash:      $100,000.00
  Final Equity:      $112,450.00
  Total Return:      +12.45%
------------------------------------------------------------
  Sharpe Ratio:      1.842
  Sortino Ratio:     2.315
  Max Drawdown:      -4.23%
------------------------------------------------------------
  Total Trades:      47
  Win Rate:          63.8%
  Profit Factor:     2.14
============================================================

Live Trading (Same Code!)

runner = StrategyRunner(client, mode="live")
runner.add_strategy(MomentumStrategy(MomentumConfig(instruments=["AAPL"])))
runner.run()  # Blocks until Ctrl+C

Strategy Lifecycle Hooks

Hook	When It Fires
on_start()	Strategy starts — subscribe to data here
on_stop()	Strategy stops — cleanup here
on_bar(bar)	New OHLCV bar received
on_quote(quote)	New bid/ask quote received
on_trade(trade)	New trade tick received
on_order_submitted(event)	Order sent to broker
on_order_filled(event)	Order fully filled
on_order_canceled(event)	Order canceled
on_position_opened(event)	New position opened
on_position_changed(event)	Position size changed
on_position_closed(event)	Position fully closed
on_timer(event)	Timer alert fired
on_signal(signal)	Custom signal received

Strategy Actions

self.buy("AAPL", qty=100)                    # Market buy
self.sell("AAPL", qty=50)                     # Market sell
self.buy("AAPL", qty=10, order_type="limit", limit_price=150.00)
self.flatten("AAPL")                          # Close position
self.flatten_all()                            # Close all positions
self.cancel_all_orders()                      # Cancel open orders
self.emit_signal("crossover", value=1.0)      # Emit custom signal
self.subscribe_bars("AAPL", "1Min")           # Subscribe to bars
self.subscribe_quotes("AAPL")                 # Subscribe to quotes

Strategy Context

Every strategy has access to:

self.cache.bars("AAPL")           # Cached bar history
self.cache.latest_bar("AAPL")     # Most recent bar
self.portfolio.equity()           # Current equity
self.portfolio.cash               # Available cash
self.portfolio.is_long("AAPL")    # Position check
self.portfolio.positions()        # All positions
self.clock.utc_now()              # Current time (real or simulated)
self.log.info("message")          # Structured logging
self.config.instruments           # Strategy config

---

Pre-Trade Risk Guard

Intercepts every order before it reaches the broker. Any violation raises RiskGuardViolation and the order is rejected.

from cpz import CPZClient

client = CPZClient()

# Attach risk guard to execution
client.execution.set_risk_guard(
    max_order_value=50_000,         # Reject orders > $50K
    max_position_pct=0.10,          # Max 10% of equity per position
    max_daily_loss=-5_000,          # Halt trading if daily P&L < -$5K
    max_open_orders=20,             # Max concurrent open orders
    blocked_symbols=["GME", "AMC"], # Blacklist
    cooldown_seconds=5.0,           # Min 5s between orders per symbol
    max_orders_per_minute=30,       # Rate limit
)

# Orders are now validated before submission
order = client.execution.order(symbol="AAPL", qty=10, side="buy", strategy_id="my-strat")

Or use directly in a strategy:

from cpz import RiskGuard

class SafeStrategy(Strategy):
    def on_start(self):
        guard = RiskGuard(max_order_value=25_000, max_daily_loss=-2_000)
        self._router.set_risk_guard(guard)
        self.subscribe_bars("AAPL", "1D")

---

Execution Algorithms

Split large orders into smaller slices for better execution.

from cpz.execution.algos import TWAPAlgorithm, VWAPAlgorithm, IcebergAlgorithm

# TWAP — equal slices over time
algo = TWAPAlgorithm()
slices = algo.generate_slices(order_request, {
    "duration_minutes": 30,
    "num_slices": 10,
})

# VWAP — volume-weighted slicing with intraday profile
algo = VWAPAlgorithm()
slices = algo.generate_slices(order_request, {
    "duration_minutes": 60,
    "num_slices": 13,
})

# Iceberg — hidden quantity
algo = IcebergAlgorithm()
slices = algo.generate_slices(order_request, {
    "display_qty": 100,  # Only show 100 shares at a time
})

---

Typed Domain Model

Precision-safe value types that reject NaN, Infinity, and invalid data at construction time.

from cpz import Price, Quantity

# Validated, immutable
price = Price("150.25")                # From string (exact)
price = Price.from_float(150.25, precision=2)  # From float
qty = Quantity.from_int(100)

# Type-safe arithmetic
total = price * qty                    # Returns Money("15025.00", "USD")
spread = Price("150.50") - Price("150.25")  # Returns Price("0.25")

# Fail-fast on invalid data
Price(float("nan"))    # ValueError: Price must be finite
Quantity(-1)           # ValueError: Quantity cannot be negative

---

Event Bus

Thread-safe pub/sub with wildcard topic matching.

from cpz import EventBus

bus = EventBus()

# Subscribe with wildcards
bus.subscribe("bar.AAPL.*", handle_aapl_bars)     # Any timeframe for AAPL
bus.subscribe("order.**", handle_all_orders)       # All order events

# Publish
bus.publish("bar.AAPL.1Min", bar_event)

---

Quick Start

from cpz import CPZClient

client = CPZClient()

# ── Risk Analytics ──────────────────────────────────────
sharpe = client.risk.sharpe(daily_returns)
snapshot = client.risk.compute(daily_returns, spy_returns, weights)
mc = client.risk.monte_carlo(daily_returns, num_simulations=10000)

# ── Trading ─────────────────────────────────────────────
client.execution.use_broker("alpaca", environment="paper")
order = client.execution.order(symbol="AAPL", qty=10, side="buy", strategy_id="my-strat")

# ── Market Data ─────────────────────────────────────────
bars = client.data.bars("AAPL", timeframe="1D", limit=100)
quotes = client.data.quotes(["AAPL", "MSFT", "GOOGL"])
gdp = client.data.economic("GDP")

# ── Simons (Quant Strategist) ───────────────────────────
response = client.simons.chat("Analyze AAPL for momentum trading")
print(response.content)

---

Risk Analytics

Comprehensive quantitative risk computation powered by numpy/scipy. Install with pip install cpz-ai[risk].

The risk-free rate is automatically fetched from FRED (3-Month Treasury) on first use. All methods accept daily returns as a list of floats.

Core Metrics

from cpz import CPZClient

client = CPZClient()

# Individual metrics
sharpe = client.risk.sharpe(daily_returns)               # Annualized Sharpe ratio
sortino = client.risk.sortino(daily_returns)              # Sortino (downside vol only)
vol = client.risk.volatility(daily_returns)               # Annualized volatility (%)
mdd = client.risk.max_drawdown(daily_returns)             # Maximum drawdown (%)
b = client.risk.beta(daily_returns, spy_returns)          # Beta vs benchmark
a = client.risk.alpha(daily_returns, spy_returns)         # Jensen's alpha (annualized)

# Full risk snapshot (all metrics at once)
snapshot = client.risk.compute(
    daily_returns=portfolio_returns,
    benchmark_returns=spy_returns,
    position_weights={"AAPL": 0.4, "NVDA": 0.35, "BTC/USD": 0.25},
    total_exposure=100000,
)

Sharpe Inference (Lopez de Prado Framework)

Statistical significance testing for the Sharpe ratio. Implements all 5 corrections from Lopez de Prado (2012, 2018):

1. Non-normality — SE adjusted for skewness and kurtosis
2. Serial correlation — Lo (2002) autocorrelation adjustment
3. Probabilistic Sharpe Ratio (PSR) — P(true Sharpe > benchmark)
4. Deflated Sharpe Ratio (DSR) — Multiple-testing correction
5. Minimum Track Record Length — How long before the Sharpe is credible?

inference = client.risk.sharpe_inference(daily_returns, num_trials=20)
print(f"PSR (prob true SR > 0): {inference.psr:.1%}")
print(f"DSR (adjusted for 20 trials): {inference.deflated_sharpe}")
print(f"Min track record needed: {inference.min_track_record_months} months")

Value at Risk

var_95 = client.risk.parametric_var(daily_returns, confidence=0.95)
hist_var = client.risk.historical_var(daily_returns, confidence=0.95)
mc = client.risk.monte_carlo(daily_returns, num_simulations=10000, horizon_days=5)

Advanced Analytics

dd = client.risk.drawdown_analysis(daily_returns)        # Drawdown decomposition
tail = client.risk.tail_risk(daily_returns)              # Skewness, kurtosis, Cornish-Fisher VaR
sizing = client.risk.position_size(100000, returns)      # Kelly criterion + vol targeting
exposure = client.risk.factor_exposure(returns, factors) # OLS factor decomposition
impacts = client.risk.stress_test_all(weights)           # 7 historical crisis scenarios
rolling = client.risk.rolling_metrics(returns, spy, 20)  # Rolling Sharpe, vol, beta

---

Trading

Broker Configuration

from cpz import CPZClient

client = CPZClient()

client.execution.use_broker("alpaca", environment="paper")
client.execution.use_broker("alpaca", environment="live")
client.execution.use_broker("ibkr", environment="paper")

Order Placement

# Simple order
order = client.execution.order(
    symbol="AAPL",
    qty=10,
    side="buy",
    strategy_id="my-strategy"
)

# Full control
from cpz import OrderSubmitRequest, OrderSide, OrderType, TimeInForce

request = OrderSubmitRequest(
    symbol="AAPL",
    side=OrderSide.BUY,
    qty=10,
    order_type=OrderType.LIMIT,
    time_in_force=TimeInForce.GTC,
    limit_price=150.00,
    strategy_id="my-strategy"
)
order = client.execution.submit_order(request)

Account and Positions

account = client.execution.get_account()
print(f"Buying Power: ${account.buying_power:,.2f}")

positions = client.execution.get_positions()
for pos in positions:
    print(f"{pos.symbol}: {pos.qty} shares @ ${pos.avg_entry_price}")

---

Quantitative Libraries (v2.5.0+)

Local Indicators

25+ indicators computed locally (no API calls):

from cpz.indicators import sma, ema, rsi, macd, bollinger, atr, vwap

fast = ema(prices, period=10)
slow = ema(prices, period=30)
rsi_val = rsi(prices, period=14)
bb = bollinger(prices, period=20)   # .upper, .middle, .lower
macd_val = macd(prices)             # .macd, .signal, .histogram

Portfolio Optimization

12 optimizers including HRP, Black-Litterman, and Mean-CVaR:

from cpz.portfolio import mean_variance, risk_parity, hierarchical_risk_parity

weights = risk_parity(returns_df)
weights = hierarchical_risk_parity(returns_df)

Signal Construction

from cpz.signals import vol_target, kelly, regime_filter, max_positions

sized = vol_target(raw_signals, returns, target_vol=0.15)
filtered = regime_filter(signals, returns, window=60)
limited = max_positions(signals, max_n=10)

Alpha Research

from cpz.alpha import information_coefficient, signal_decay, quantile_returns

ic = information_coefficient(signals, forward_returns)
decay = signal_decay(signals, returns, max_lag=20)

---

HFT Engine

Deploy strategies to the Rust HFT engine for autonomous microsecond-latency execution.

status = client.engine.status()
client.engine.deploy(
    strategy_id="my-strategy",
    symbols=["AAPL", "NVDA"],
    broker="alpaca",
    environment="paper",
)
client.engine.stop("my-strategy")

---

Data

# Stock/crypto bars
bars = client.data.bars("AAPL", timeframe="1D", limit=100)

# Multi-symbol history for backtesting
df = client.data.history(["AAPL", "MSFT", "NVDA"], timeframe="1D", limit=252)

# Quotes, news, options, economic data, filings, sentiment
quotes = client.data.quotes(["AAPL", "MSFT"])
news = client.data.news("TSLA", limit=10)
gdp = client.data.economic("GDP")
filings = client.data.filings("AAPL", form="10-K")
sentiment = client.data.sentiment("GME")

# 18 data providers: Alpaca, TwelveData, FRED, SEC EDGAR, Yahoo Finance,
# Databento, Polygon, CoinGecko, Finnhub, Alpha Vantage, and more

---

Simons — Quantitative Trading Strategist

response = client.simons.chat("Analyze AAPL for momentum trading")
print(response.content)

for chunk in client.simons.stream("Write a mean-reversion backtest"):
    if chunk.type == "text":
        print(chunk.content, end="", flush=True)

---

Architecture

CPZClient
├── Strategy Framework (NEW in v3.0.0)
│   ├── Strategy          Base class with 20+ lifecycle hooks
│   ├── StrategyConfig    Serializable configuration
│   ├── StrategyRunner    Live and backtest orchestration
│   ├── BacktestEngine    Historical replay with fill simulation
│   ├── RiskGuard         Pre-trade order validation (10 rules)
│   └── TWAP/VWAP/Iceberg Execution algorithms
│
├── Typed Domain Model (NEW in v3.0.0)
│   ├── Price             Decimal-backed, fail-fast on NaN/Inf
│   ├── Quantity           Non-negative, precision-safe
│   ├── Money              Currency-tracked arithmetic
│   └── Events            BarEvent, FillEvent, PositionEvent, ...
│
├── risk               Portfolio risk analytics (numpy/scipy)
│   ├── compute()      Full risk snapshot
│   ├── monte_carlo()  Monte Carlo VaR (Rust-accelerated)
│   ├── sharpe_inference() Lopez de Prado framework
│   ├── stress_test_all()  7 historical crisis scenarios
│   └── [20+ more methods]
│
├── execution          Multi-broker trading
│   ├── use_broker()   Configure broker (Alpaca, IBKR, SnapTrade, Polymarket)
│   ├── order()        Place orders with risk guard validation
│   ├── set_risk_guard() Attach pre-trade risk rules
│   └── get_positions() Current positions
│
├── data               Market and reference data (18 providers)
│   ├── bars()         OHLCV price data
│   ├── history()      Multi-symbol DataFrames
│   ├── economic()     FRED (800K+ series)
│   └── [indicators]   100+ technical indicators via TwelveData
│
├── indicators         25+ local indicators (no API calls)
├── signals            Position sizing and signal filters
├── portfolio          12 portfolio optimizers
├── alpha              IC analysis, signal decay, quantile returns
│
├── simons             Quantitative trading strategist AI
└── engine             Rust HFT engine for autonomous execution

---

Configuration

Variable	Description	Required
CPZ_AI_API_KEY	CPZ API key	Yes
CPZ_AI_SECRET_KEY	CPZ API secret	Yes
CPZ_AI_STRATEGY_ID	Strategy ID for orders	For trading

Get credentials at ai.cpz-lab.com/settings.

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Testing

pytest --cov=cpz --cov-report=term-missing

Python	Status
3.9	Supported
3.10	Supported
3.11	Supported
3.12	Supported

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Support

• Platform: ai.cpz-lab.com
• Repository: github.com/CPZ-Lab/cpz-py
• Email: [e-mail]

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_{Built by CPZ}