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Load semiconductor rotation income from UsEquityStrategies
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‎README.md‎

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Quant system on LongPort OpenAPI and Google Cloud Run.
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This strategy repo now depends on `QuantPlatformKit` for LongPort token handling, context bootstrap, account snapshot access, market-data access, and order submission. Cloud Run still deploys this strategy repo only.
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The current `semiconductor_rotation_income` allocation logic is loaded from `UsEquityStrategies`. LongBridgePlatform keeps the LongPort runtime, token refresh, execution, and notification flow.
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**Layers**
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基于 LongPort OpenAPI 和 Google Cloud Run 的量化交易系统。
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这个策略仓库现在通过 `QuantPlatformKit` 复用 LongPort token 处理、上下文初始化、账户快照、行情读取和下单逻辑。Cloud Run 仍然只部署这个策略仓库本身。
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当前 `semiconductor_rotation_income` 的仓位与调仓计算逻辑已经改为从 `UsEquityStrategies` 加载；LongBridgePlatform 继续保留 LongPort 运行时、token 刷新、执行和通知流程。
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**层级**
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‎requirements.txt‎

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flask
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gunicorn
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quant-platform-kit @ git+https://github.com/QuantStrategyLab/QuantPlatformKit.git@v0.5.0
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us-equity-strategies @ git+https://github.com/QuantStrategyLab/UsEquityStrategies.git@v0.4.0
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pandas
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requests
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pytz

‎strategy/allocation.py‎

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"""Allocation and plan helpers for LongBridgePlatform."""
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from __future__ import annotations
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import numpy as np
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def get_dynamic_allocation(
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total_equity_usd,
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*,
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small_account_deploy_ratio,
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mid_account_deploy_ratio,
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large_account_deploy_ratio,
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trade_layer_decay_coeff,
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):
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if total_equity_usd <= 10000:
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return small_account_deploy_ratio
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if total_equity_usd <= 80000:
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return float(
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np.interp(
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total_equity_usd,
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[10000, 80000],
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[small_account_deploy_ratio, mid_account_deploy_ratio],
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)
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)
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if total_equity_usd <= 180000:
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return float(
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np.interp(
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total_equity_usd,
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[80000, 180000],
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[mid_account_deploy_ratio, large_account_deploy_ratio],
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)
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)
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decayed_ratio = large_account_deploy_ratio - (
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trade_layer_decay_coeff * np.log10(total_equity_usd / 180000)
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)
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return max(0.0, decayed_ratio)
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def get_income_layer_ratio(
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total_equity_usd,
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*,
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income_layer_start_usd,
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income_layer_max_ratio,
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):
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if total_equity_usd <= income_layer_start_usd:
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return 0.0
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if total_equity_usd <= (income_layer_start_usd * 2):
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return float(
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np.interp(
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total_equity_usd,
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[income_layer_start_usd, income_layer_start_usd * 2],
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[0.0, income_layer_max_ratio],
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)
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)
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return income_layer_max_ratio
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def build_rebalance_plan(
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indicators,
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account_state,
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*,
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trend_ma_window,
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translator,
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cash_reserve_ratio,
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min_trade_ratio,
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min_trade_floor,
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rebalance_threshold_ratio,
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small_account_deploy_ratio,
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mid_account_deploy_ratio,
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large_account_deploy_ratio,
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trade_layer_decay_coeff,
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income_layer_start_usd,
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income_layer_max_ratio,
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income_layer_qqqi_weight,
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income_layer_spyi_weight,
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):
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strategy_assets = ["SOXL", "SOXX", "BOXX", "QQQI", "SPYI"]
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available_cash = account_state["available_cash"]
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market_values = account_state["market_values"]
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quantities = account_state["quantities"]
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sellable_quantities = account_state["sellable_quantities"]
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total_strategy_equity = account_state["total_strategy_equity"]
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current_min_trade = max(min_trade_floor, total_strategy_equity * min_trade_ratio)
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current_income_layer_value = market_values["QQQI"] + market_values["SPYI"]
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income_layer_ratio = get_income_layer_ratio(
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total_strategy_equity,
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income_layer_start_usd=income_layer_start_usd,
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income_layer_max_ratio=income_layer_max_ratio,
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)
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desired_income_layer_value = total_strategy_equity * income_layer_ratio
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locked_income_layer_value = max(current_income_layer_value, desired_income_layer_value)
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income_layer_add_value = max(0.0, locked_income_layer_value - current_income_layer_value)
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core_equity = max(0.0, total_strategy_equity - locked_income_layer_value)
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deploy_ratio = get_dynamic_allocation(
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core_equity,
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small_account_deploy_ratio=small_account_deploy_ratio,
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mid_account_deploy_ratio=mid_account_deploy_ratio,
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large_account_deploy_ratio=large_account_deploy_ratio,
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trade_layer_decay_coeff=trade_layer_decay_coeff,
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)
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deployed_capital = core_equity * deploy_ratio
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deploy_ratio_text = f"{deploy_ratio * 100:.1f}%"
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income_ratio_text = f"{income_layer_ratio * 100:.1f}%"
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income_locked_ratio_text = (
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f"{(locked_income_layer_value / total_strategy_equity) * 100:.1f}%"
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if total_strategy_equity > 0
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else "0.0%"
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)
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soxl_price = indicators["soxl"]["price"]
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soxl_ma_trend = indicators["soxl"]["ma_trend"]
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active_risk_asset = "SOXL" if soxl_price > soxl_ma_trend else "SOXX"
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market_status = (
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f"🚀 RISK-ON ({active_risk_asset})"
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if active_risk_asset == "SOXL"
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else "🛡️ DE-LEVER (SOXX)"
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)
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signal_message = (
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translator("signal_risk_on", window=trend_ma_window, ratio=deploy_ratio_text)
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if active_risk_asset == "SOXL"
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else translator("signal_delever", window=trend_ma_window, ratio=deploy_ratio_text)
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)
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targets = {
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"SOXL": deployed_capital if active_risk_asset == "SOXL" else 0.0,
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"SOXX": deployed_capital if active_risk_asset == "SOXX" else 0.0,
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"QQQI": market_values["QQQI"] + (income_layer_add_value * income_layer_qqqi_weight),
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"SPYI": market_values["SPYI"] + (income_layer_add_value * income_layer_spyi_weight),
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"BOXX": max(0.0, core_equity - deployed_capital),
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}
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return {
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"strategy_assets": strategy_assets,
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"available_cash": available_cash,
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"market_values": market_values,
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"quantities": quantities,
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"sellable_quantities": sellable_quantities,
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"total_strategy_equity": total_strategy_equity,
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"current_min_trade": current_min_trade,
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"targets": targets,
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"market_status": market_status,
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"signal_message": signal_message,
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"deploy_ratio_text": deploy_ratio_text,
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"income_ratio_text": income_ratio_text,
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"income_locked_ratio_text": income_locked_ratio_text,
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"active_risk_asset": active_risk_asset,
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"investable_cash": max(0, available_cash - (total_strategy_equity * cash_reserve_ratio)),
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"threshold_value": total_strategy_equity * rebalance_threshold_ratio,
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}
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from us_equity_strategies.strategies.semiconductor_rotation_income import (
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build_rebalance_plan,
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get_dynamic_allocation,
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get_income_layer_ratio,
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)
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__all__ = [
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"build_rebalance_plan",
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"get_dynamic_allocation",
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"get_income_layer_ratio",
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]

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