dekun
0f552eb50e
Enable in settings (default off): after N consecutive loss days, double the effective risk_loss_pct up to a configurable max; blocked when base pct > 3%. Co-authored-by: Cursor <cursoragent@cursor.com>
245 lines
8.3 KiB
Python
245 lines
8.3 KiB
Python
"""以损定仓纯函数测试。"""
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from __future__ import annotations
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from app.strategy.risk_sizing import (
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BASE_EXIT_USDT,
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BASE_OPTION_ETH,
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BASE_PERP_ETH,
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compute_k,
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floor_k_1dp,
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normalize_risk_leverage_basis,
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resolve_sizing_option_ask,
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unit_cost,
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)
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def test_floor_k_1dp() -> None:
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assert floor_k_1dp(1.29) == 1.2
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assert floor_k_1dp(0.19) == 0.1
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assert floor_k_1dp(0.09) == 0.0
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assert floor_k_1dp(2.0) == 2.0
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def test_compute_k_scales_1_2_15() -> None:
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# I=2000, A=20, fee=0.0005 → unit = 2*20 + 2000*0.0005*3 = 40 + 3 = 43
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# budget=43 → k=1.0
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r = compute_k(budget=43.0, index_px=2000.0, option_ask=20.0, fee_rate=0.0005)
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assert r.ok
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assert r.k == 1.0
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assert r.perp_qty_eth == BASE_PERP_ETH
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assert r.option_qty_eth == BASE_OPTION_ETH
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assert r.net_profit_target == BASE_EXIT_USDT
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assert r.max_loss is not None and r.max_loss <= 43.0 + 1e-6
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assert r.budget == 43.0
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def test_compute_k_custom_units() -> None:
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# option_unit=4 → premium unit = 20*4=80; fee=3; cost=83; budget=83 → k=1
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r = compute_k(
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budget=83.0,
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index_px=2000.0,
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option_ask=20.0,
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fee_rate=0.0005,
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perp_unit=0.5,
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option_unit=4.0,
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exit_unit=30.0,
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)
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assert r.ok
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assert r.k == 1.0
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assert r.perp_qty_eth == 0.5
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assert r.option_qty_eth == 4.0
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assert r.net_profit_target == 30.0
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def test_money_rounds_2dp() -> None:
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r = compute_k(budget=50.123456, index_px=1900.0, option_ask=18.5, fee_rate=0.0005)
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assert r.ok
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assert r.budget == round(50.123456, 2)
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assert r.max_loss is not None
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assert abs(r.max_loss * 100 - round(r.max_loss * 100)) < 1e-9
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def test_compute_k_never_exceeds_budget() -> None:
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r = compute_k(budget=50.0, index_px=1900.0, option_ask=18.5, fee_rate=0.0005)
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assert r.ok
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assert r.k is not None
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assert abs(r.k * 10 - round(r.k * 10)) < 1e-9 # 一位小数
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assert r.max_loss is not None and r.max_loss <= 50.0 + 1e-6
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assert r.perp_qty_eth == round(1.0 * r.k, 4)
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assert r.option_qty_eth == round(2.0 * r.k, 4)
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assert r.net_profit_target == round(15.0 * r.k, 4)
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def test_compute_k_too_small() -> None:
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# unit≈43, budget=2 → k_raw≪0.1
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r = compute_k(budget=2.0, index_px=2000.0, option_ask=20.0, fee_rate=0.0005)
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assert not r.ok
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assert "最小仓" in r.detail or "k=" in r.detail
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def test_unit_cost() -> None:
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assert abs(unit_cost(index_px=2000, option_ask=20, fee_rate=0.0005) - 43.0) < 1e-9
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def test_normalize_risk_leverage_basis() -> None:
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assert normalize_risk_leverage_basis("actual") == "actual"
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assert normalize_risk_leverage_basis("selection") == "selection"
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assert normalize_risk_leverage_basis("min_option_leverage") == "selection"
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assert normalize_risk_leverage_basis("weird", default="selection") == "selection"
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def test_resolve_sizing_ask_selection_vs_actual() -> None:
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# 指数 2000、选约杠杆 100 → 隐含卖一 20；实际卖一更便宜 10
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sel_ask, basis = resolve_sizing_option_ask(
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index_px=2000.0,
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option_ask=10.0,
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leverage_basis="selection",
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min_option_leverage=100.0,
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)
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assert basis == "selection"
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assert abs(sel_ask - 20.0) < 1e-9
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act_ask, basis2 = resolve_sizing_option_ask(
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index_px=2000.0,
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option_ask=10.0,
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leverage_basis="actual",
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min_option_leverage=100.0,
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)
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assert basis2 == "actual"
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assert abs(act_ask - 10.0) < 1e-9
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def test_selection_basis_yields_smaller_k_when_ask_cheap() -> None:
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# 预算 43：选约隐含 ask=20 → k=1；若用实际 ask=10 → 单位成本更小 → k 更大
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r_sel = compute_k(budget=43.0, index_px=2000.0, option_ask=20.0, fee_rate=0.0005)
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r_act = compute_k(budget=43.0, index_px=2000.0, option_ask=10.0, fee_rate=0.0005)
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assert r_sel.ok and r_act.ok
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assert r_sel.k == 1.0
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assert r_act.k is not None and r_act.k > r_sel.k
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def test_compute_risk_sizing_respects_basis(tmp_path, monkeypatch) -> None:
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monkeypatch.setenv("MODE", "SIM")
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from app.models.db import Database
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from app.strategy.risk_sizing import compute_risk_sizing
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db = Database(tmp_path / "risk_basis.db")
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db.set_setting("sizing_mode", "risk_based")
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db.set_setting("risk_loss_mode", "absolute")
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db.set_setting("risk_loss_usdt", "43")
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db.set_setting("fee_rate", "0.0005")
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db.set_setting("min_option_leverage", "100")
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db.set_setting("risk_perp_unit", "1")
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db.set_setting("risk_option_unit", "2")
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db.set_setting("risk_exit_unit", "15")
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db.set_setting("risk_leverage_basis", "selection")
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r1 = compute_risk_sizing(index_px=2000.0, option_ask=10.0, db=db)
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assert r1.ok
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assert r1.leverage_basis == "selection"
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assert r1.k == 1.0
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assert r1.actual_option_ask == 10.0
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assert r1.option_ask == 20.0
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db.set_setting("risk_leverage_basis", "actual")
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r2 = compute_risk_sizing(index_px=2000.0, option_ask=10.0, db=db)
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assert r2.ok
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assert r2.leverage_basis == "actual"
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assert r2.k is not None and r2.k > 1.0
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assert r2.option_ask == 10.0
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db.close()
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def test_consecutive_loss_days_and_martingale(tmp_path, monkeypatch) -> None:
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monkeypatch.setenv("MODE", "SIM")
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from datetime import datetime, timezone
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from zoneinfo import ZoneInfo
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from app.models.db import Database
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from app.strategy.risk_sizing import consecutive_loss_days, resolve_martingale
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db = Database(tmp_path / "mg.db")
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sh = ZoneInfo("Asia/Shanghai")
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def day_ms(ymd: str, hour: int = 16) -> int:
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dt = datetime.strptime(ymd, "%Y-%m-%d").replace(
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hour=hour, tzinfo=sh
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)
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return int(dt.astimezone(timezone.utc).timestamp() * 1000)
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# 插入：盈利日打断后连亏 3 天（有成交日序列，跳过无成交日）
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rows = [
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("g1", day_ms("2026-07-28"), 10.0),
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("g2", day_ms("2026-07-29"), -5.0),
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("g3", day_ms("2026-07-30"), -3.0),
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("g4", day_ms("2026-07-31"), -1.0),
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]
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for gid, ms, pnl in rows:
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db.execute(
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"""INSERT INTO groups(
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group_id, status, realized_pnl, close_at_ms, open_at_ms
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) VALUES(?,?,?,?,?)""",
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(gid, "closed", pnl, ms, ms - 3600_000),
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)
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assert consecutive_loss_days(db) == 3
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db.set_setting("sizing_mode", "risk_based")
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db.set_setting("risk_loss_mode", "percent")
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db.set_setting("risk_loss_pct", "2")
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db.set_setting("martingale_enabled", "true")
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db.set_setting("martingale_start_after_loss_days", "2")
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db.set_setting("martingale_max_doubles", "3")
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mg = resolve_martingale(db, base_pct=2.0)
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assert mg["eligible"] is True
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assert mg["loss_days"] == 3
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# 连亏3天、start=2 → doubles = min(3-2+1, 3) = 2 → 2%*4 = 8%
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assert mg["doubles"] == 2
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assert abs(float(mg["effective_pct"]) - 8.0) < 1e-9
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db.set_setting("risk_loss_pct", "3.1")
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mg2 = resolve_martingale(db, base_pct=3.1)
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assert mg2["eligible"] is False
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assert mg2["doubles"] == 0
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assert abs(float(mg2["effective_pct"]) - 3.1) < 1e-9
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db.close()
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def test_martingale_doubles_capped(tmp_path, monkeypatch) -> None:
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monkeypatch.setenv("MODE", "SIM")
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from datetime import datetime, timezone
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from zoneinfo import ZoneInfo
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from app.models.db import Database
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from app.strategy.risk_sizing import resolve_martingale
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db = Database(tmp_path / "mg_cap.db")
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sh = ZoneInfo("Asia/Shanghai")
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def day_ms(ymd: str) -> int:
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dt = datetime.strptime(ymd, "%Y-%m-%d").replace(hour=12, tzinfo=sh)
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return int(dt.astimezone(timezone.utc).timestamp() * 1000)
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for i, ymd in enumerate(
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["2026-07-26", "2026-07-27", "2026-07-28", "2026-07-29", "2026-07-30"]
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):
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db.execute(
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"""INSERT INTO groups(
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group_id, status, realized_pnl, close_at_ms, open_at_ms
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) VALUES(?,?,?,?,?)""",
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(f"c{i}", "closed", -1.0, day_ms(ymd), day_ms(ymd) - 1000),
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)
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db.set_setting("sizing_mode", "risk_based")
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db.set_setting("risk_loss_mode", "percent")
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db.set_setting("martingale_enabled", "true")
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db.set_setting("martingale_start_after_loss_days", "2")
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db.set_setting("martingale_max_doubles", "3")
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mg = resolve_martingale(db, base_pct=2.0)
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# 连亏5、start2 → raw=4，cap=3 → 2%*8=16%
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assert mg["doubles"] == 3
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assert abs(float(mg["effective_pct"]) - 16.0) < 1e-9
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db.close()
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