feat: trend pullback preview TP per DCA leg with unified stop loss across exchanges
Co-authored-by: Cursor <cursoragent@cursor.com>
This commit is contained in:
dekun
@@ -191,3 +191,201 @@ def compute_trend_plan_core(
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"leg_amounts": leg_list,
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}
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return payload, None
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def calc_planned_reward_risk_ratio(
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direction: str, entry_price: float, stop_loss: float, take_profit: float
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) -> Optional[float]:
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"""盈亏比（reward/risk），与四所 calc_rr_ratio 口径一致。"""
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try:
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entry = float(entry_price)
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sl = float(stop_loss)
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tp = float(take_profit)
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if entry <= 0 or sl <= 0 or tp <= 0:
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return None
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direction = (direction or "long").strip().lower()
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if direction == "short":
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risk = sl - entry
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reward = entry - tp
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else:
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risk = entry - sl
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reward = tp - entry
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if risk <= 0 or reward <= 0:
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return None
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return round(reward / risk, 4)
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except (TypeError, ValueError):
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return None
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def calc_take_profit_for_rr(
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direction: str, entry_price: float, stop_loss: float, reward_risk_ratio: float
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) -> Optional[float]:
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"""按统一止损与目标 RR 反推止盈价。"""
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try:
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entry = float(entry_price)
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sl = float(stop_loss)
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rr = float(reward_risk_ratio)
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if entry <= 0 or sl <= 0 or rr <= 0:
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return None
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direction = (direction or "long").strip().lower()
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if direction == "short":
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risk = sl - entry
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if risk <= 0:
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return None
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return round(entry - rr * risk, 10)
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risk = entry - sl
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if risk <= 0:
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return None
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return round(entry + rr * risk, 10)
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except (TypeError, ValueError):
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return None
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def weighted_avg_entry(legs: list[tuple[float, float]]) -> Optional[float]:
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"""按 (成交价, 张数) 加权均价。"""
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total = 0.0
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cost = 0.0
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for price, amount in legs or []:
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try:
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p = float(price)
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a = float(amount)
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except (TypeError, ValueError):
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continue
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if a <= 0:
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continue
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total += a
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cost += p * a
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if total <= 0:
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return None
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return cost / total
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def build_trend_preview_level_rows(preview: dict) -> tuple[dict, list[dict]]:
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"""
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预览：参考价首仓止盈 + 每档补仓后止盈；止损统一为计划止损（加仓后最大止损）。
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返回 (增强后的 preview 字段, 表格行列表，含首仓行)。
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"""
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p = dict(preview or {})
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direction = (p.get("direction") or "long").strip().lower()
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try:
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ref = float(p.get("live_price_ref"))
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sl = float(p.get("stop_loss"))
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user_tp = float(p.get("take_profit"))
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first_amt = float(p.get("first_order_amount"))
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except (TypeError, ValueError):
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return p, []
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rr = calc_planned_reward_risk_ratio(direction, ref, sl, user_tp)
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if rr is None:
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return p, []
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first_tp = calc_take_profit_for_rr(direction, ref, sl, rr)
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p["preview_target_rr"] = rr
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p["preview_first_take_profit"] = first_tp
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p["preview_unified_stop_loss"] = sl
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try:
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grid = json.loads(p.get("grid_prices_json") or "[]")
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if not isinstance(grid, list):
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grid = []
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except Exception:
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grid = []
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try:
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leg_amounts = json.loads(p.get("leg_amounts_json") or "[]")
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if not isinstance(leg_amounts, list):
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leg_amounts = []
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except Exception:
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leg_amounts = []
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rows: list[dict] = [
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{
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"i": 0,
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"label": "首仓",
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"price": ref,
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"contracts": first_amt,
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"avg_entry": ref,
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"take_profit": first_tp,
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"stop_loss": sl,
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"is_first": True,
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}
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]
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accumulated: list[tuple[float, float]] = [(ref, first_amt)]
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for i, pair in enumerate(zip(grid, leg_amounts), 1):
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try:
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price = float(pair[0])
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contracts = float(pair[1])
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except (TypeError, ValueError):
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continue
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accumulated.append((price, contracts))
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avg = weighted_avg_entry(accumulated)
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tp_after = calc_take_profit_for_rr(direction, avg, sl, rr) if avg is not None else None
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rows.append(
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{
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"i": i,
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"label": f"补仓{i}",
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"price": price,
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"contracts": contracts,
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"avg_entry": avg,
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"take_profit": tp_after,
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"stop_loss": sl,
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"is_first": False,
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}
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)
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return p, rows
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def enrich_trend_dca_levels_with_tp(plan: dict, levels: list[dict]) -> list[dict]:
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"""运行中计划：为 dca_levels 补充加仓后均价、止盈、统一止损。"""
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if not levels:
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return levels
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p = plan or {}
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direction = (p.get("direction") or "long").strip().lower()
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try:
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sl = float(p.get("stop_loss"))
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user_tp = float(p.get("take_profit"))
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first_amt = float(p.get("first_order_amount"))
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except (TypeError, ValueError):
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return levels
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ref_raw = p.get("live_price_ref")
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if ref_raw in (None, ""):
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ref_raw = p.get("avg_entry_price")
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try:
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ref = float(ref_raw)
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except (TypeError, ValueError):
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return levels
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rr = calc_planned_reward_risk_ratio(direction, ref, sl, user_tp)
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if rr is None:
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return levels
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out: list[dict] = []
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accumulated: list[tuple[float, float]] = []
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for lv in levels:
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row = dict(lv)
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is_first = row.get("leg_key") == "first" or row.get("label") == "首仓" or row.get("i") == 0
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if is_first:
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amt = row.get("contracts")
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try:
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amt_f = float(amt if amt is not None else first_amt)
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except (TypeError, ValueError):
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amt_f = first_amt
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accumulated = [(ref, amt_f)]
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row["avg_entry"] = ref
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row["take_profit"] = calc_take_profit_for_rr(direction, ref, sl, rr)
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row["stop_loss"] = sl
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else:
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price = row.get("price")
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contracts = row.get("contracts")
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if price is not None and contracts is not None:
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try:
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accumulated.append((float(price), float(contracts)))
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avg = weighted_avg_entry(accumulated)
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if avg is not None:
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row["avg_entry"] = avg
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row["take_profit"] = calc_take_profit_for_rr(direction, avg, sl, rr)
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row["stop_loss"] = sl
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except (TypeError, ValueError):
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pass
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out.append(row)
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return out
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