Add demo trading analyst with plan validation and sizing
The analyst prompts the model with trading rules, published skills, account sizing inputs and prefetched market data, exposes read-only tools plus submit_trade_plan, and validates and sizes the submitted plan in pure functions covered by pytest. Manual analyses run as dry runs while trading is stopped; the trading page shows the latest plan and an analysis detail drawer.
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"""模拟盘分析器：为一个币的一个角度调用模型，产出经程序校验、可自动执行的交易计划。
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只开放只读工具和 submit_trade_plan；行情预先取好放进用户消息，减少工具往返。
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"""
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import asyncio
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import json
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import logging
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import time
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from dataclasses import dataclass
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from datetime import UTC, datetime
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from decimal import Decimal
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from pydantic import ValidationError
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from sqlalchemy import select
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from alphapulse.ai import llm
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from alphapulse.ai.prompt import data_overview, estimate_tokens, skills_section
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from alphapulse.ai.tools import TOOLS, dump, local, run_tool
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from alphapulse.core.db import SessionLocal
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from alphapulse.models import AiProvider, TradingAccount, TradingAnalysis, TradingEvent, TradingSymbol
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from alphapulse.trading import plan as rules
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from alphapulse.trading.client import public_get
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from alphapulse.trading.plan import PlanContext, PlanError, SymbolRules, TradePlan, size_plan
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from alphapulse.trading.snapshot import read_snapshot
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logger = logging.getLogger("alphapulse.trading.analyst")
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READ_TOOLS = ("search_news", "get_news", "search_symbols", "get_klines", "get_ticker", "get_derivatives")
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# 最后一轮只开放 submit_trade_plan，逼模型给出结论
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MAX_ROUNDS = 6
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RECENT_DECISIONS = 2
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EXCHANGE_INFO_SECONDS = 3600
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ANGLE_LABELS = {"short": "短线（24 小时内平仓）", "long": "长线（12–48 小时内平仓）"}
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DIRECTION_LABELS = {"long": "做多", "short": "做空"}
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CONFIDENCE_LABELS = {"high": "高", "medium": "中", "low": "低"}
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ACTION_LABELS = {"plan": "新计划", "keep": "维持当前计划", "exit": "结束当前计划", "wait": "观望"}
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# 与 README 第 1 节一致：定方向和找区的周期、衍生品参数
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KLINE_INTERVALS = {"short": ("4h", "1h", "15m"), "long": ("1d", "4h", "1h")}
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DERIVATIVES_PARAMS = {"short": ("1h", 24), "long": ("4h", 18)}
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def _pct(value: Decimal) -> str:
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return f"{(value * 100).normalize()}%"
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INSTRUCTIONS = f"""你是 AlphaPulse 的模拟盘交易分析器：为一个币种的一个角度给出由程序自动执行的交易计划。
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## 任务
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- 只分析用户消息指定的币种和角度（短线或长线），不给另一个角度。
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- 下方 Skills 是交易规范。以下几处按本节执行，与 Skills 冲突时以本节为准：
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- main/README 第 1 节「每次同时给出两个角度」不适用，只给指定角度。
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- main/README 第 8 节的输出表格不适用：用简短中文写出结论、用到的支撑区与压力区、依据（新闻、结构、衍生品）和风险；计划的数字只通过 submit_trade_plan 提交。
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- main/README 第 9 节不适用：没有 CONTEXT.md，不要写。之前的结论见用户消息里的「最近决策」，按 README 第 6 节做实时复核，不直接沿用旧点位。
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- main/README 5.4 的条件计划（突破回踩、事件落地后再入场等）不能直接挂单，改写成 reevaluate 条件：价格到达后程序会重新请你分析。
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- 数量由程序按交易资金计算，你只给价格、档位比例和置信度。
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- 用户消息里已预先取好实盘行情（ticker、K 线、衍生品），不要用相同参数重复调用；需要其他周期或其他币时再调用。
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- 新闻没有预取，必须按 main/README 第 3 节第 3 步用 search_news 检索相关新闻，并查清持仓时限内的重大数据及公布时间。
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- 最后必须调用一次 submit_trade_plan。校验不通过会返回原因，按原因修正后重新提交。
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## 程序如何执行计划
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- 入场：每档一张限价单；到 entry_valid_hours 仍未成交的档位撤销。买单价高于现价（卖单价低于现价）会立即成交。
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- 止损：与入场单同时在交易所挂只减仓止损单（按标记价触发），首档成交即受保护。
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- 止盈：短线止盈 1 平一半、止盈 2 平剩余；长线 3 个止盈依次平 50% / 30% / 20%。止盈 1 成交后撤销剩余入场单，止损移到保本价。
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- invalidation（失效条件）：短线用 1m 或 15m 收盘，触发后程序立即市价平仓；长线只能用 4h 收盘，触发后程序请你复核。
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- reevaluate：任一条件满足时程序重新请你分析（最多 {rules.MAX_REEVALUATE} 个）。用来表达另一侧方向、条件计划和需要复核的位置。
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- max_hold_hours 从首档成交起算，到时程序市价平仓。
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## 程序的硬性校验
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- 短线 1 档入场、2 个止盈；长线 1–3 档入场、3 个止盈；入场比例之和为 1。
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- 做多：止损 < 失效价 < 所有入场价 < 止盈 1 < 止盈 2 < 止盈 3，且止损低于现价、止盈 1 高于现价；做空反过来。
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- 盈亏比 ≥ {rules.MIN_REWARD_RISK}：短线按止盈 1，长线按 50/30/20 加权（全部档位成交时）。
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- entry_valid_hours：短线 ≤ {rules.ENTRY_VALID_HOURS['short']}，长线 ≤ {rules.ENTRY_VALID_HOURS['long']}；max_hold_hours：短线 ≤ {rules.MAX_HOLD_HOURS['short']}，长线 ≤ {rules.MAX_HOLD_HOURS['long']}。
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- 每档入场的名义价值不低于交易所最小下单金额，不足时合并或删档。
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- 单笔风险为交易资金的 {_pct(rules.RISK_PCT['short'])}（短线）或 {_pct(rules.RISK_PCT['long'])}（长线），置信度低时减半；名义仓位合计不超过 {rules.MAX_LEVERAGE} 倍交易资金。这两条由程序计算数量来保证。
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- 有持仓时新计划的方向必须与持仓一致；要反手先用 exit。
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"""
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SUBMIT_SPEC = llm.ToolSpec(
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"submit_trade_plan",
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"提交本次分析的结论和计划。程序会校验并计算仓位；不通过时返回原因，修正后重新提交。",
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TradePlan.model_json_schema(),
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)
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class AnalysisError(Exception):
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"""分析无法完成；message 写入分析记录和时间线"""
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@dataclass
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class Prepared:
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provider: AiProvider
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effort: str | None
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dry_run: bool
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angle: str
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system: str
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user_text: str
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context: PlanContext
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_exchange_info: tuple[float, dict[str, SymbolRules]] | None = None
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async def symbol_rules(symbol: str) -> SymbolRules:
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"""模拟盘的价格精度、数量精度、最小下单金额，按小时缓存"""
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global _exchange_info
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if _exchange_info is None or time.monotonic() - _exchange_info[0] > EXCHANGE_INFO_SECONDS:
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info = await public_get("/fapi/v1/exchangeInfo")
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parsed = {}
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for s in info["symbols"]:
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filters = {f["filterType"]: f for f in s["filters"]}
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parsed[s["symbol"]] = SymbolRules(
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tick_size=Decimal(filters["PRICE_FILTER"]["tickSize"]).normalize(),
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step_size=Decimal(filters["LOT_SIZE"]["stepSize"]).normalize(),
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min_notional=Decimal(filters["MIN_NOTIONAL"]["notional"]),
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)
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_exchange_info = (time.monotonic(), parsed)
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if symbol not in _exchange_info[1]:
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raise AnalysisError(f"模拟盘没有交易对 {symbol}")
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return _exchange_info[1][symbol]
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async def mark_price(symbol: str) -> Decimal:
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return Decimal((await public_get("/fapi/v1/premiumIndex", {"symbol": symbol}))["markPrice"])
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def plan_brief(decision: dict, sizing: dict | None) -> dict:
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"""给模型看的计划摘要：只留价格和状态，不带长段文字"""
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brief = {k: decision.get(k) for k in ("action", "summary", "direction", "confidence", "stop", "entry_valid_hours", "max_hold_hours")}
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brief["entries"] = [{"price": e["price"], "ratio": e["ratio"]} for e in decision.get("entries") or []]
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brief["take_profits"] = [t["price"] for t in decision.get("take_profits") or []]
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brief["invalidation"] = decision.get("invalidation")
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brief["reevaluate"] = [{k: c[k] for k in ("direction", "price", "timeframe")} for c in decision.get("reevaluate") or []]
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if sizing:
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brief["sizing"] = {k: sizing.get(k) for k in ("quantity", "notional", "risk_at_stop", "reward_risk")}
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return brief
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async def prefetch(symbol: str, angle: str) -> str:
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"""预取行情：工具出错时把错误原样放进消息，不中断分析"""
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period, limit = DERIVATIVES_PARAMS[angle]
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calls = [("get_ticker", {"symbol": symbol})]
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calls += [("get_klines", {"symbol": symbol, "interval": i, "limit": 200}) for i in KLINE_INTERVALS[angle]]
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calls.append(("get_derivatives", {"symbol": symbol, "period": period, "limit": limit}))
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outcomes = await asyncio.gather(*(run_tool(name, args) for name, args in calls))
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return "\n\n".join(f"### {name} {json.dumps(args, ensure_ascii=False)}\n{o.result}" for (name, args), o in zip(calls, outcomes, strict=True))
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async def prepare(symbol: str, reasons: list[str], analysis_id: int) -> Prepared:
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async with SessionLocal() as session:
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account = await session.get(TradingAccount, 1)
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target = await session.get(TradingSymbol, symbol)
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if target is None:
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raise AnalysisError(f"{symbol} 已不在交易列表中")
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if account is None or account.capital_usdt is None:
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raise AnalysisError("还没有设置交易资金")
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provider = await session.get(AiProvider, account.provider_id) if account.provider_id else await session.scalar(
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select(AiProvider).where(AiProvider.is_default)
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)
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if provider is None:
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raise AnalysisError("没有可用的模型配置，请先到「环境变量」页面添加")
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session.expunge(provider)
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symbols = list((await session.scalars(select(TradingSymbol.symbol))).all())
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recent = (await session.scalars(
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select(TradingAnalysis)
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.where(TradingAnalysis.symbol == symbol, TradingAnalysis.status == "done", TradingAnalysis.id != analysis_id)
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.order_by(TradingAnalysis.id.desc()).limit(RECENT_DECISIONS)
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)).all()
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current = await session.scalar(
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select(TradingAnalysis)
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.where(TradingAnalysis.symbol == symbol, TradingAnalysis.plan_status.in_(("pending", "open")), TradingAnalysis.dry_run.is_(False))
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.order_by(TradingAnalysis.id.desc()).limit(1)
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)
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system = "\n\n".join([INSTRUCTIONS, "## 数据概况", await data_overview(session), await skills_section(session)])
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angle, dry_run = target.angle, not account.running
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effort = account.reasoning_effort or provider.reasoning_effort
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snapshot = await read_snapshot()
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if not snapshot or not snapshot["account"]:
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raise AnalysisError("取不到模拟盘账户快照（引擎未同步到币安），无法计算仓位")
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equity = Decimal(snapshot["account"]["margin_balance"])
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capital = min(account.capital_usdt, equity)
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position = next((p for p in snapshot["positions"] if p["symbol"] == symbol), None)
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other_notional = sum((Decimal(p["notional"]) for p in snapshot["positions"] if p["symbol"] != symbol and p["symbol"] in symbols), Decimal(0))
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symbol_rules_ = await symbol_rules(symbol)
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mark = await mark_price(symbol)
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context = PlanContext(
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angle=angle, mark_price=mark, capital=capital, other_notional=other_notional, rules=symbol_rules_,
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has_plan=current is not None, position_side=("long" if position["side"] == "LONG" else "short") if position else None,
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)
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orders = [o for o in snapshot["orders"] + snapshot["algo_orders"] if o["symbol"] == symbol]
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risk = capital * rules.RISK_PCT[angle]
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lines = [
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"## 任务",
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f"- 币种：{symbol}（币安 U 本位永续）",
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f"- 角度：{ANGLE_LABELS[angle]}",
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f"- 触发原因：{'；'.join(reasons)}",
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f"- 运行模式：{'试运行（引擎已停止，计划只记录、不下单）' if dry_run else '交易中（计划会在模拟盘执行）'}",
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"",
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"## 账户（程序据此计算数量）",
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f"- 交易资金：{capital:.2f} USDT（设定 {account.capital_usdt} USDT，账户权益 {equity:.2f} USDT，取较小者）",
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f"- 本次单笔风险金额：{risk:.2f} USDT（{_pct(rules.RISK_PCT[angle])}；置信度低时 {risk * rules.LOW_CONFIDENCE_FACTOR:.2f}）",
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f"- 可用名义仓位：{capital * rules.MAX_LEVERAGE - other_notional:.2f} USDT（{rules.MAX_LEVERAGE} 倍交易资金减去交易列表其他币的持仓 {other_notional:.2f}）",
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f"- 模拟盘交易规则：价格精度 {symbol_rules_.tick_size}，数量精度 {symbol_rules_.step_size}，最小下单金额 {symbol_rules_.min_notional} USDT",
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f"- 模拟盘标记价：{mark}（校验以此为准；行情数据来自实盘，两者通常相差不到 0.1%）",
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"",
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"## 当前持仓与挂单（模拟盘）",
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f"- 持仓：{dump(position) if position else '无'}",
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f"- 挂单：{dump(orders) if orders else '无'}",
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"",
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"## 当前计划",
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dump({"id": current.id, "plan_status": current.plan_status, **plan_brief(current.decision, current.sizing)}) if current else "无",
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"",
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"## 最近决策（新的在前）",
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*([f"- {local(a.started_at)}{'（试运行）' if a.dry_run else ''}：{dump(plan_brief(a.decision, a.sizing))}" for a in recent] or ["无"]),
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"",
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"## 预取的行情（实盘数据，已取，不要用相同参数重复调用）",
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await prefetch(symbol, angle),
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]
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return Prepared(provider=provider, effort=effort, dry_run=dry_run, angle=angle, system=system, user_text="\n".join(lines), context=context)
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async def complete(prepared: Prepared, messages: list[dict], tools: list[llm.ToolSpec]) -> llm.Completion:
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async for item in llm.stream(prepared.provider, prepared.system, messages, tools, prepared.effort):
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if isinstance(item, llm.Completion):
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return item
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raise AssertionError("llm.stream 没有产出 Completion")
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async def add_event(symbol: str, analysis_id: int, kind: str, message: str, level: str = "info", data: dict | None = None) -> None:
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async with SessionLocal() as session:
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session.add(TradingEvent(symbol=symbol, analysis_id=analysis_id, kind=kind, level=level, message=message, data=data))
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await session.commit()
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def describe(plan: TradePlan, sizing: dict, dry_run: bool) -> str:
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prefix = "试运行" if dry_run else ""
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if plan.action != "plan":
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return f"{prefix}{ACTION_LABELS[plan.action]}：{plan.summary}"
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entries = "、".join(f"{e['price']}×{e['quantity']}" for e in sizing["entries"])
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targets = " / ".join(t["price"] for t in sizing["take_profits"])
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return (
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f"{prefix}计划：{DIRECTION_LABELS[plan.direction]}，置信度{CONFIDENCE_LABELS[plan.confidence]}；入场 {entries}，"
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f"止损 {sizing['stop']}，止盈 {targets}；止损亏损 {sizing['risk_at_stop']} USDT（交易资金的 {sizing['risk_pct']}%），"
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f"盈亏比 {sizing['reward_risk']}（仅首档 {sizing['reward_risk_first_only']}）。{plan.summary}"
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)
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async def submit(prepared: Prepared, symbol: str, analysis_id: int, arguments: dict) -> tuple[str, tuple[TradePlan, dict] | None]:
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try:
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plan = TradePlan.model_validate(arguments)
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except ValidationError as e:
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return dump({"error": "参数无效", "details": e.errors(include_url=False)}), None
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# 分析可能持续几分钟，按提交时的标记价校验
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prepared.context.mark_price = await mark_price(symbol)
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try:
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sizing = size_plan(plan, prepared.context)
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except PlanError as e:
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await add_event(symbol, analysis_id, "plan_rejected", f"计划未通过校验，已退回模型修正：{'；'.join(e.errors)}", "warn")
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return dump({"error": "计划未通过校验", "details": e.errors, "mark_price": str(prepared.context.mark_price)}), None
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return dump({"status": "已接受", "sizing": sizing}), (plan, sizing)
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async def run_analysis(symbol: str, reasons: list[str]) -> None:
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"""一次完整的分析；结果和失败原因都写进 trading_analyses 和时间线，不向外抛出"""
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async with SessionLocal() as session:
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account = await session.get(TradingAccount, 1)
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target = await session.get(TradingSymbol, symbol)
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analysis = TradingAnalysis(
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symbol=symbol, angle=target.angle if target else "long", reasons=reasons,
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dry_run=not (account and account.running), status="running",
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)
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session.add(analysis)
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await session.commit()
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analysis_id, dry_run = analysis.id, analysis.dry_run
|
||||
await add_event(symbol, analysis_id, "analysis_started", f"开始{'试运行' if dry_run else ''}分析：{'；'.join(reasons)}")
|
||||
|
||||
messages: list[dict] = []
|
||||
texts: list[str] = []
|
||||
usage = {"input_tokens": 0, "output_tokens": 0}
|
||||
accepted: tuple[TradePlan, dict] | None = None
|
||||
prepared: Prepared | None = None
|
||||
try:
|
||||
prepared = await prepare(symbol, reasons, analysis_id)
|
||||
estimated = estimate_tokens(prepared.system) + estimate_tokens(prepared.user_text)
|
||||
if estimated + prepared.provider.max_tokens > prepared.provider.context_window:
|
||||
raise AnalysisError(f"提示词约 {estimated} token，加上最大输出超过模型上下文上限 {prepared.provider.context_window}")
|
||||
messages.append({"role": "user", "text": prepared.user_text})
|
||||
specs = [llm.ToolSpec(name, TOOLS[name].description, TOOLS[name].args_model.model_json_schema()) for name in READ_TOOLS]
|
||||
for round_no in range(MAX_ROUNDS):
|
||||
last = round_no == MAX_ROUNDS - 1
|
||||
completion = await complete(prepared, messages, [SUBMIT_SPEC] if last else [*specs, SUBMIT_SPEC])
|
||||
for key in usage:
|
||||
usage[key] += completion.usage.get(key, 0)
|
||||
if completion.text.strip():
|
||||
texts.append(completion.text.strip())
|
||||
assistant = {"role": "assistant", "text": completion.text, "tool_calls": completion.tool_calls}
|
||||
if completion.thinking_blocks:
|
||||
assistant["thinking_blocks"] = completion.thinking_blocks
|
||||
messages.append(assistant)
|
||||
if not completion.tool_calls:
|
||||
if completion.truncated:
|
||||
raise AnalysisError(f"模型输出达到最大输出上限（{prepared.provider.max_tokens} token）被截断")
|
||||
messages.append({"role": "user", "text": "请调用 submit_trade_plan 提交结论。"})
|
||||
continue
|
||||
for call in completion.tool_calls:
|
||||
if completion.truncated:
|
||||
result = llm.TRUNCATED_RESULT
|
||||
elif call["name"] == "submit_trade_plan":
|
||||
result, outcome = await submit(prepared, symbol, analysis_id, call["arguments"])
|
||||
accepted = accepted or outcome
|
||||
elif call["name"] in READ_TOOLS:
|
||||
result = (await run_tool(call["name"], call["arguments"])).result
|
||||
else:
|
||||
result = dump({"error": f"分析时不能使用工具 {call['name']}"})
|
||||
messages.append({"role": "tool", "tool_call_id": call["id"], "name": call["name"], "result": result})
|
||||
if accepted:
|
||||
break
|
||||
if accepted is None:
|
||||
raise AnalysisError(f"模型在 {MAX_ROUNDS} 轮内没有提交通过校验的结论")
|
||||
except (AnalysisError, llm.LlmError, PlanError) as e:
|
||||
error = str(e)
|
||||
except Exception as e:
|
||||
logger.exception("%s 分析出错", symbol)
|
||||
error = f"程序出错：{e!r}"
|
||||
else:
|
||||
error = None
|
||||
|
||||
async with SessionLocal() as session:
|
||||
analysis = await session.get(TradingAnalysis, analysis_id)
|
||||
analysis.status = "failed" if error else "done"
|
||||
analysis.error = error
|
||||
analysis.text = "\n\n".join(texts) or None
|
||||
# thinking 块只在同一轮工具循环内需要回传，存档时去掉，减小体积
|
||||
analysis.transcript = [{k: v for k, v in m.items() if k != "thinking_blocks"} for m in messages] or None
|
||||
analysis.usage = usage
|
||||
analysis.finished_at = datetime.now(UTC)
|
||||
if prepared:
|
||||
analysis.provider_id, analysis.model, analysis.reasoning_effort = prepared.provider.id, prepared.provider.model, prepared.effort
|
||||
if accepted:
|
||||
plan, sizing = accepted
|
||||
analysis.action = plan.action
|
||||
analysis.decision = plan.model_dump(mode="json")
|
||||
analysis.sizing = sizing or None
|
||||
await session.commit()
|
||||
if error:
|
||||
await add_event(symbol, analysis_id, "analysis_failed", f"分析失败：{error}", "error")
|
||||
else:
|
||||
plan, sizing = accepted
|
||||
await add_event(symbol, analysis_id, "analysis_done", describe(plan, sizing, dry_run), data={"action": plan.action})
|
||||
Reference in new issue
Block a user