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功能细节优化

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Lukas
2026-06-15 01:26:39 +08:00
parent e524a3589a
commit 964c17c200
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"""持仓成本可视化增强"""
import datetime as dt
from typing import Dict, List, Optional
from collections import defaultdict
from sqlalchemy import select, func
from db import get_session
from models import Trade, DailyQuote, StockMetric
# A股交易成本配置
COST_CONFIG = {
"stamp_tax": 0.001, # 印花税 0.1%（仅卖出）
"commission_rate": 0.0003, # 佣金费率 0.03%
"commission_min": 5.0, # 最低佣金 5元
"transfer_fee": 0.00001, # 过户费 0.001%（沪市）
}
def calculate_trade_cost(price: float, qty: int, side: str, is_sh: bool = True) -> Dict:
"""精确计算交易成本
Args:
price: 成交价格
qty: 成交数量
side: buy/sell
is_sh: 是否沪市（影响过户费）
Returns:
成本明细字典
"""
amount = price * qty
# 佣金（买卖都有）
commission = max(amount * COST_CONFIG["commission_rate"], COST_CONFIG["commission_min"])
# 印花税（仅卖出）
stamp_tax = amount * COST_CONFIG["stamp_tax"] if side == "sell" else 0.0
# 过户费（沪市买卖都有，深市无）
transfer_fee = amount * COST_CONFIG["transfer_fee"] if is_sh else 0.0
total_cost = commission + stamp_tax + transfer_fee
return {
"amount": round(amount, 2),
"commission": round(commission, 2),
"stamp_tax": round(stamp_tax, 2),
"transfer_fee": round(transfer_fee, 2),
"total_cost": round(total_cost, 2),
"cost_rate": round(total_cost / amount * 100, 4) if amount > 0 else 0.0
}
def get_position_cost_lines(code: str) -> Dict:
"""获取个股的持仓成本线数据（用于K线图标注）
Returns:
{
"code": "600519",
"name": "贵州茅台",
"current_position": {
"qty": 100,
"avg_cost": 1680.5,
"total_cost": 168050.0,
"trades_count": 3
},
"cost_history": [
{"date": "2024-01-15", "cost": 1650.0, "qty": 100, "action": "买入"},
{"date": "2024-02-10", "cost": 1680.5, "qty": 100, "action": "补仓"}
]
}
"""
with get_session() as s:
trades = s.execute(
select(Trade).where(Trade.code == code)
.order_by(Trade.date, Trade.id)
).scalars().all()
if not trades:
return {"ok": False, "msg": "该股票无交易记录"}
# 计算持仓成本变化
qty = 0
cost = 0.0
cost_history = []
for t in trades:
is_sh = t.code.startswith("6")
if t.side == "buy":
# 买入：加权平均成本
old_qty = qty
old_cost = cost
qty += t.qty
cost += t.price * t.qty + t.fee
avg_cost = cost / qty if qty > 0 else 0
action = "补仓" if old_qty > 0 else "买入"
cost_history.append({
"date": t.date.isoformat(),
"cost": round(avg_cost, 2),
"qty": qty,
"action": action,
"trade_price": t.price,
"trade_qty": t.qty
})
else: # sell
if qty <= 0:
continue
avg_cost = cost / qty
sell_qty = min(t.qty, qty)
# 卖出：减少持仓
cost -= avg_cost * sell_qty
qty -= sell_qty
action = "清仓" if qty == 0 else "减仓"
cost_history.append({
"date": t.date.isoformat(),
"cost": round(cost / qty, 2) if qty > 0 else 0,
"qty": qty,
"action": action,
"trade_price": t.price,
"trade_qty": sell_qty,
"pnl": round((t.price - avg_cost) * sell_qty - t.fee, 2)
})
# 当前持仓
current_position = None
if qty > 0:
avg_cost = cost / qty
# 获取当前价格
metric = s.execute(
select(StockMetric).where(StockMetric.code == code)
).scalar_one_or_none()
current_price = metric.close if metric else avg_cost
current_position = {
"qty": qty,
"avg_cost": round(avg_cost, 2),
"total_cost": round(cost, 2),
"current_price": round(current_price, 2),
"market_value": round(current_price * qty, 2),
"unrealized_pnl": round((current_price - avg_cost) * qty, 2),
"unrealized_pct": round((current_price / avg_cost - 1) * 100, 2) if avg_cost > 0 else 0,
"trades_count": len([t for t in trades if t.side == "buy"])
}
return {
"ok": True,
"code": code,
"name": trades[0].name,
"current_position": current_position,
"cost_history": cost_history
}
def get_position_cost_distribution() -> Dict:
"""获取所有持仓的成本分布（盈亏区间图）
Returns:
{
"profitable": [...], # 盈利持仓
"unprofitable": [...], # 亏损持仓
"breakeven": [...] # 持平持仓
}
"""
with get_session() as s:
trades = s.execute(
select(Trade).order_by(Trade.date, Trade.id)
).scalars().all()
# 计算当前持仓
pos = defaultdict(lambda: {"qty": 0, "cost": 0.0, "name": ""})
for t in trades:
p = pos[t.code]
p["name"] = t.name or p["name"]
if t.side == "buy":
p["cost"] += t.price * t.qty + t.fee
p["qty"] += t.qty
else:
if p["qty"] > 0:
avg = p["cost"] / p["qty"]
qty = min(t.qty, p["qty"])
p["cost"] -= avg * qty
p["qty"] -= qty
# 获取当前价格
codes = [c for c, v in pos.items() if v["qty"] > 0]
if not codes:
return {"ok": True, "profitable": [], "unprofitable": [], "breakeven": []}
metrics = s.execute(
select(StockMetric).where(StockMetric.code.in_(codes))
).scalars().all()
price_map = {m.code: m.close for m in metrics}
# 分类统计
profitable = []
unprofitable = []
breakeven = []
for code, p in pos.items():
if p["qty"] <= 0:
continue
avg_cost = p["cost"] / p["qty"]
current_price = price_map.get(code, avg_cost)
unrealized = (current_price - avg_cost) * p["qty"]
unrealized_pct = (current_price / avg_cost - 1) * 100 if avg_cost > 0 else 0
item = {
"code": code,
"name": p["name"],
"qty": p["qty"],
"avg_cost": round(avg_cost, 2),
"current_price": round(current_price, 2),
"market_value": round(current_price * p["qty"], 2),
"cost_value": round(p["cost"], 2),
"unrealized": round(unrealized, 2),
"unrealized_pct": round(unrealized_pct, 2)
}
if unrealized_pct > 0.5:
profitable.append(item)
elif unrealized_pct < -0.5:
unprofitable.append(item)
else:
breakeven.append(item)
# 排序
profitable.sort(key=lambda x: x["unrealized"], reverse=True)
unprofitable.sort(key=lambda x: x["unrealized"])
return {
"ok": True,
"profitable": profitable,
"unprofitable": unprofitable,
"breakeven": breakeven,
"summary": {
"total_positions": len(codes),
"profitable_count": len(profitable),
"unprofitable_count": len(unprofitable),
"breakeven_count": len(breakeven),
"win_rate": round(len(profitable) / len(codes) * 100, 1) if codes else 0
}
}
def estimate_trade_cost(code: str, price: float, qty: int, side: str) -> Dict:
"""估算交易成本（下单前预估）
Args:
code: 股票代码
price: 预计成交价
qty: 交易数量
side: buy/sell
Returns:
成本明细和净值
"""
is_sh = code.startswith("6")
cost_detail = calculate_trade_cost(price, qty, side, is_sh)
if side == "buy":
net_amount = cost_detail["amount"] + cost_detail["total_cost"]
msg = f"买入需支付: {round(net_amount, 2)} 元（含交易成本 {cost_detail['total_cost']} 元）"
else:
net_amount = cost_detail["amount"] - cost_detail["total_cost"]
msg = f"卖出可获得: {round(net_amount, 2)} 元（扣除交易成本 {cost_detail['total_cost']} 元）"
return {
"ok": True,
"code": code,
"price": price,
"qty": qty,
"side": side,
"cost_detail": cost_detail,
"net_amount": round(net_amount, 2),
"message": msg
}
def get_cost_breakdown_for_position(code: str) -> Dict:
"""获取持仓的详细成本拆解
Returns:
{
"total_cost": 168500.0,
"purchase_amount": 168050.0, # 实际买入金额
"commission": 350.0, # 累计佣金
"stamp_tax": 0.0, # 累计印花税（买入无）
"transfer_fee": 100.0, # 累计过户费
"trades": [...] # 每笔交易明细
}
"""
with get_session() as s:
trades = s.execute(
select(Trade).where(Trade.code == code, Trade.side == "buy")
.order_by(Trade.date)
).scalars().all()
if not trades:
return {"ok": False, "msg": "该股票无买入记录"}
is_sh = code.startswith("6")
total_purchase = 0.0
total_commission = 0.0
total_stamp = 0.0
total_transfer = 0.0
trade_details = []
for t in trades:
cost = calculate_trade_cost(t.price, t.qty, "buy", is_sh)
total_purchase += cost["amount"]
total_commission += cost["commission"]
total_stamp += cost["stamp_tax"]
total_transfer += cost["transfer_fee"]
trade_details.append({
"date": t.date.isoformat(),
"price": t.price,
"qty": t.qty,
"amount": cost["amount"],
"cost_detail": cost
})
total_cost = total_purchase + total_commission + total_stamp + total_transfer
return {
"ok": True,
"code": code,
"name": trades[0].name,
"total_cost": round(total_cost, 2),
"purchase_amount": round(total_purchase, 2),
"commission": round(total_commission, 2),
"stamp_tax": round(total_stamp, 2),
"transfer_fee": round(total_transfer, 2),
"cost_rate": round((total_cost - total_purchase) / total_purchase * 100, 4) if total_purchase > 0 else 0,
"trades": trade_details
}