Celes Renata
4ffde8cc06
- Database migration 018 with 13 tables for trading engine state - Trading engine service (services/trading/) with 12 pure computation modules: position sizer, stop-loss manager, reserve pool, circuit breaker, risk tier controller, correlation matrix, tax lots, trading window, gradual entry, notifications, micro-trading, backtester - Core TradingEngine with pre-trade evaluation pipeline and integration wiring - FastAPI HTTP service with 14 endpoints (health, config, decisions, metrics, backtest) - Performance tracker with Sharpe ratio, drawdown, profit factor computation - 194 Python tests (165 property-based + 29 integration) - Frontend: 13 TanStack Query hooks, 7 dashboard panels, tabbed Trading Engine page - Helm chart entry, network policy, nginx proxy, ingress for trading-engine - Shared infrastructure: enums, Redis keys, TradingConfig in AppConfig
82 lines
2.5 KiB
Python
82 lines
2.5 KiB
Python
"""Gradual entry logic for the autonomous trading engine.
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Pure computation module that determines whether an order should be split
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into multiple tranches and performs the splitting. The
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``GradualEntryManager`` class (which tracks pending tranches at runtime
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and re-evaluates conditions) is intentionally left as a thin wrapper
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here — the core logic is in the pure functions below.
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"""
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from __future__ import annotations
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from dataclasses import dataclass
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# ---------------------------------------------------------------------------
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# Tranche dataclass
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# ---------------------------------------------------------------------------
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@dataclass
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class Tranche:
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"""A single tranche within a gradual-entry order sequence."""
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tranche_index: int
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quantity: int
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parent_decision_id: str
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status: str = "pending"
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# ---------------------------------------------------------------------------
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# Pure computation helpers
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# ---------------------------------------------------------------------------
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def should_use_gradual_entry(
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position_size_dollars: float,
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active_pool: float,
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threshold_dollars: float = 30.0,
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) -> bool:
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"""Return True when the position size exceeds the gradual-entry threshold.
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The effective threshold is ``min(threshold_dollars, 5% of active_pool)``.
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"""
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effective_threshold = min(threshold_dollars, 0.05 * active_pool)
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return position_size_dollars > effective_threshold
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def split_into_tranches(total_quantity: int, num_tranches: int = 3) -> list[int]:
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"""Split *total_quantity* into *num_tranches* approximately equal parts.
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The remainder is distributed one unit at a time to the first tranches
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so that:
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* ``sum(result) == total_quantity``
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* All values differ by at most 1
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"""
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if num_tranches <= 0:
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return []
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if total_quantity <= 0:
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return [0] * num_tranches
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base, remainder = divmod(total_quantity, num_tranches)
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return [base + (1 if i < remainder else 0) for i in range(num_tranches)]
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def create_tranches(
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total_quantity: int,
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parent_decision_id: str,
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num_tranches: int = 3,
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) -> list[Tranche]:
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"""Create :class:`Tranche` objects linked to *parent_decision_id*.
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Uses :func:`split_into_tranches` for the quantity distribution.
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"""
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quantities = split_into_tranches(total_quantity, num_tranches)
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return [
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Tranche(
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tranche_index=i,
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quantity=q,
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parent_decision_id=parent_decision_id,
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)
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for i, q in enumerate(quantities)
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]
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