commit 56f0db7844d7de75ae5dfde13a067b60f2516ae9 Author: Lxy Date: Mon Mar 9 01:09:26 2026 +0800 feat: 初始化，并增加分析 diff --git a/README.md b/README.md new file mode 100644 index 0000000..96c0b2c --- /dev/null +++ b/README.md @@ -0,0 +1,14 @@ +# claw skills合集 + +## futures-technical-analysis + +获取多周期数据并进行分析 + +### 使用示例 + +- 分析一下Au2604 -- 期货 +- 分析一下000001 -- 股票 +- 上传数据（json），分析一下xxxx + + + diff --git a/futures-technical-analysis/SKILL.md b/futures-technical-analysis/SKILL.md new file mode 100644 index 0000000..dab429d --- /dev/null +++ b/futures-technical-analysis/SKILL.md @@ -0,0 +1,307 @@ +--- +name: futures-technical-analysis +description: 期货和股票技术分析实战分析工具。提供支撑阻力、止跌反弹、多周期共振、滞涨回调、多空分歧五大核心主题的判断方法与实操Checklist。支持两种数据输入模式：(1)自动获取 - 当用户提供合约代码(如SN2504)或股票代码(如000001)时，自动调用数据获取脚本获取多周期K线数据；(2)手动提供 - 用户直接提供JSON格式K线数据。自动分析各周期多空情况、关键点位、交易建议及预案。适用于期货日内/波段交易分析、股票技术分析、趋势识别、关键位验证、交易决策支持。当用户需要期货/股票技术分析指导、交易策略制定、市场结构判断、多周期共振验证、支撑阻力有效性确认时使用本Skill。 +--- + +# 期货技术分析实战分析 + +本Skill提供完整的期货交易技术分析框架，涵盖五大核心主题： + +## 核心功能 + +1. **支撑/阻力有效性判断** - 五维度验证法（测试次数、成交量、周期共振、基本面、突破后表现） +2. **止跌与反弹鉴别** - 四大维度确认止跌，区分下跌中继与反转趋势 +3. **多周期共振交易法** - 60分钟定趋势→30分钟找拐点→15分钟择入场 +4. **滞涨与回调性质区分** - 洗盘vs反转的多周期共振判断 +5. **多空分歧识别** - 五维度识别分歧强度，预判趋势转折 +6. **JSON数据自动分析** - 接收多周期K线JSON数据，输出完整分析报告 + +## 使用方法 + +### 分析流程 + +当用户需要进行期货或股票技术分析时，按以下步骤执行： + +1. **判断数据来源** - 检查用户是否提供了JSON格式的K线数据 + - **情况A：未提供JSON数据** → 执行[数据获取流程](#数据获取流程) + - **情况B：已提供JSON数据** → 直接执行[JSON数据分析](#json数据输入分析) +2. **明确分析目标** - 确定是判断支撑阻力、分析止跌反弹、还是识别多空分歧 +3. **查阅参考手册** - 阅读 `references/期货交易技术分析实战手册（整合版）.md` 获取完整方法论 +4. **使用Checklist** - 根据具体场景选用对应的Checklist进行逐项核查 +5. **给出判断结论** - 基于核查结果给出明确的交易建议 + +### 数据获取流程（当用户未提供JSON数据时） + +当用户询问"分析一下XX合约"或"分析一下XX股票"但未附带JSON数据时： + +#### 步骤1：识别代码类型 +从用户输入中提取合约/股票代码，判断类型： +- **期货合约**：格式为品种代码+年份+月份，如 `SN2504`(沪锡)、`AG2506`(沪银)、`LC2505`(碳酸锂) +- **股票代码**：6位数字，如 `000001`(平安银行)、`600000`(浦发银行) + +#### 步骤2：调用数据获取脚本 +使用 skill 目录下的 `futures_data_collector.py` 脚本获取数据： + +```bash +# 期货数据获取 +python futures_data_collector.py --symbol <合约代码> --type futures + +# 股票数据获取 +python futures_data_collector.py --symbol <股票代码> --type stock +``` + +#### 步骤3：检查数据获取结果 +- 如果脚本执行成功，会返回多周期K线数据（5min/15min/30min/60min/daily） +- 如果执行失败（如合约代码错误、数据源异常），向用户说明情况并请求提供JSON数据 + +#### 步骤4：将获取的数据传入分析流程 +脚本输出的JSON数据格式与[JSON数据输入分析](#json数据输入分析)章节定义的格式一致，直接传入后续分析步骤。 + +### 场景指导 + +| 用户场景 | 分析方法 | 关键Checklist | 数据来源 | +|---------|---------|--------------|---------| +| "分析一下SN2504"（无JSON） | 先调用脚本获取数据 → 多周期自动分析 | 完整分析报告输出 | 自动获取 | +| "分析一下000001股票"（无JSON） | 先调用脚本获取数据 → 多周期自动分析 | 完整分析报告输出 | 自动获取 | +| "这个位置是支撑还是阻力？" | 支撑阻力五维度判断法 | 支撑/阻力有效性Checklist | 用户提供 | +| "可以抄底了吗？" | 止跌四大维度判断+反弹性质鉴别 | 止跌确认Checklist + 反弹性质判断Checklist | 用户提供 | +| "现在可以入场吗？" | 多周期共振交易法 | 多周期共振入场Checklist | 用户提供 | +| "这是洗盘还是真跌？" | 滞涨回调多周期共振判断 | 滞涨判断Checklist | 用户提供 | +| "多空分歧大吗？" | 多空分歧五维度识别 | 多空分歧识别Checklist | 用户提供 | +| **提供JSON数据进行分析** | **多周期自动分析+关键位计算+预案生成** | **完整分析报告输出** | **用户提供** | + +--- + +## JSON数据输入分析 + +当用户未提供JSON数据时，先调用 `futures_data_collector.py` 脚本获取数据；当用户已提供JSON格式的多周期K线数据时，直接执行完整的技术分析流程。 + +### 脚本自动获取数据说明 + +数据获取脚本 `futures_data_collector.py` 支持： +- **期货数据**：支持上海期货交易所、大连商品交易所、郑州商品交易所、广州期货交易所等主流品种 +- **股票数据**：支持A股所有个股 +- **多周期覆盖**：自动获取5min/15min/30min/60min/daily五个周期的K线数据 +- **技术指标计算**：每根K线自动计算MA10、MA20、MACD(DIF/DEA/Histogram) + +**常见期货合约代码示例**： +- `SN2504` - 沪锡2504合约（上海期货交易所） +- `AG2506` - 沪银2506合约（上海期货交易所） +- `LC2505` - 碳酸锂2505合约（广州期货交易所） +- `NI2505` - 沪镍2505合约（上海期货交易所） + +**常见股票代码示例**： +- `000001` - 平安银行 +- `600000` - 浦发银行 +- `000858` - 五粮液 +- `600519` - 贵州茅台 + +### 输入数据格式 + +用户提供或通过脚本获取的JSON数据结构： + +```json +{ + "symbol": "合约代码", + "current_price": 当前价格, + "timeframes": { + "60min": { + "trend": "up|down|sideways", + "ma10": 数值, + "ma20": 数值, + "macd_signal": "bullish|bearish|neutral", + "volume_trend": "increasing|decreasing|stable", + "support": 支撑位, + "resistance": 阻力位, + "candles": [ + {"time": "", "open": 0, "high": 0, "low": 0, "close": 0, "volume": 0} + ] + }, + "30min": { ... }, + "15min": { ... } + }, + "fundamentals": { + "recent_news": "近期消息", + "inventory_change": "库存变化", + "cost_support": 成本支撑价 + } +} +``` + +参考示例：`references/kline-data-example.json` + +### 分析输出格式 + +必须包含以下五个部分： + +#### 1. 各周期多空情况分析 + +| 周期 | 趋势方向 | 多空强度 | 关键信号 | 一致性检查 | +|------|---------|---------|---------|-----------| +| 60分钟 | 多/空/震荡 | 强/中/弱 | MA排列、MACD位置、量价关系 | - | +| 30分钟 | 多/空/震荡 | 强/中/弱 | 拐点信号、突破情况 | - | +| 15分钟 | 多/空/震荡 | 强/中/弱 | 入场信号、量能配合 | - | +| **周期共振** | **共振方向** | **共振强度** | **矛盾点（如有）** | **结论** | + +#### 2. 关键点位识别 + +``` +强阻力位（R2）: xxx - 理由：... +第一阻力位（R1）: xxx - 理由：... +枢轴点（PP）: xxx - 计算：... +第一支撑位（S1）: xxx - 理由：... +强支撑位（S2）: xxx - 理由：... +``` + +**关键位有效性验证**（针对每个关键位）： +- □ 测试次数≥3次 +- □ 量价配合良好 +- □ 多周期共振 +- □ 与基本面匹配 + +#### 3. 当前状态判断 + +使用Checklist逐项核查： + +| 检查项 | 结果 | 说明 | +|--------|------|------| +| 是否处于有效支撑/阻力区 | □ | 距离关键位xxx点 | +| 是否出现止跌/滞涨信号 | □ | 依据：... | +| 是否形成多周期共振 | □ | 共振方向：... | +| 多空分歧程度 | □ | 轻度/中度/重度 | +| 基本面配合度 | □ | 利好/利空/中性 | + +**综合判断**：□可入场 □观望 □减仓 □离场 + +#### 4. 交易建议 + +**当前建议**： +- **方向**：做多/做空/观望 +- **入场区间**：xxx - xxx +- **止损位**：xxx（入场价下方/上方x%） +- **第一止盈**：xxx（盈亏比1:2） +- **第二止盈**：xxx（关键阻力/支撑位） +- **建议仓位**：x%（基于风险评级） + +**风险评级**：□低风险 □中风险 □高风险 + +#### 5. 各种情况预案（基于真实市场情景） + +根据当前技术面状态，以下是可能发生的市场情景及概率估算： + +--- + +**情景一：趋势延续（顺势突破）** +- **概率**：35%-45% +- **触发条件**： + - 多周期共振方向一致（如60/30/15分钟均多头） + - 放量突破关键阻力/支撑，成交量较近期均值放大50%+ + - MACD柱状图同向放大，无背离信号 +- **走势描述**：价格沿当前趋势方向持续运行，回调幅度小（<20%），关键位转化为新支撑/阻力 +- **应对策略**： + - 已有持仓：持有，移动止损至成本价或前低/前高 + - 新开仓：突破确认后顺势追单，仓位减半，严格止损 +- **风险提示**：警惕"尾盘冲刺"式突破，夜盘22:00后假突破概率增加 + +--- + +**情景二：高位/低位震荡（多空拉锯）** +- **概率**：30%-40% +- **触发条件**： + - 周期信号矛盾（如60分钟多 vs 15分钟空） + - 价格在关键位附近反复测试，成交量萎缩 + - MACD在零轴附近徘徊，红柱/绿柱交替出现 +- **走势描述**：价格在R1-S1区间内来回波动，形成3-5根K线的整理平台，方向不明 +- **应对策略**： + - 区间上沿：减仓或试空，止损设区间外1% + - 区间下沿：低吸或试多，止损设区间外1% + - 区间中部：观望，等待方向突破 +- **风险提示**：震荡末期常伴随假突破诱多/诱空，需等待收盘确认 + +--- + +**情景三：假突破后反转（陷阱行情）** +- **概率**：15%-25% +- **触发条件**： + - 快速突破关键位但成交量不足（<近期均值1.2倍） + - 突破后1-2根K线内迅速回撤，收回关键位内 + - 长上影线/长下影线伴随缩量 +- **走势描述**：先冲高/冲低制造突破假象，随后快速反向运行，形成"多头陷阱"或"空头陷阱" +- **应对策略**： + - 追单被套：立即止损，不可补仓摊低成本 + - 未入场：反向做单，止损设假突破极值点 + - 观望者：等待价格回到区间中部再评估 +- **风险提示**：此类行情常在重大数据发布前出现，主力清洗浮筹 + +--- + +**情景四：突发消息驱动（黑天鹅/灰犀牛）** +- **概率**：5%-10% +- **触发条件**： + - 突发政策、地缘冲突、宏观数据（如非农数据、CPI） + - 价格瞬间跳空，跳过1-2个关键位 + - 成交量暴增3-5倍，但持续性不确定 +- **走势描述**： + - 利好：高开高走或高开低走（利好兑现） + - 利空：低开低走或低开高走（利空出尽） +- **应对策略**： + - 消息前5分钟：减仓50%，规避不确定性 + - 消息发布后15分钟内：不新开仓，观察价格对关键位的反应 + - 若价格站稳新关键位：跟随趋势，仓位减半 + - 若价格回到消息前区间：视为无效波动，观望 +- **风险提示**：消息市流动性骤降，点差扩大，止损可能无法成交在预设价位 + +--- + +**情景五：趋势衰竭/反转（拐点形成）** +- **概率**：5%-10% +- **触发条件**： + - 价格创新高/新低，但MACD出现顶/底背离 + - 成交量萎缩（<近期均值70%），量价背离 + - 长周期（60分钟）出现滞涨/止跌K线形态 +- **走势描述**： + - 上涨衰竭：最后冲刺后快速回落，跌破前低 + - 下跌衰竭：恐慌性抛盘后V型反转，突破前高 +- **应对策略**： + - 原有趋势持仓：逐步减仓，止盈位下移/上移 + - 反向开仓：等待60分钟确认信号，轻仓试单 + - 止损设置：新趋势确认前，止损位放宽至2-3% +- **风险提示**：拐点判断错误率较高，建议分2-3次建仓，首次仓位≤3% + +--- + +**概率使用说明**： +- 概率基于当前技术面统计，随价格运行动态变化 +- 当某情景概率>50%时，可作为主策略 +- 当两情景概率接近（如35% vs 40%）时，以观望或轻仓试错为主 +- 突发消息情景概率虽低，但影响最大，需始终预留仓位应对 + +--- + +## 参考资料 + +详细的分析方法、量化标准、对比表格、Checklist清单见： + +**[期货交易技术分析实战手册（整合版）.md](references/期货交易技术分析实战手册（整合版）.md)** + +包含内容： +- 五大核心原理解析 +- 五类实战判断方法 +- 六套实操Checklist（可直接打印使用） +- 交易决策流程图 +- 避坑要点汇总 +- 核心公式与参数附录 + +**[K线数据示例](references/kline-data-example.json)** - JSON数据格式参考 + +**[futures_data_collector.py](futures_data_collector.py)** - 数据自动获取脚本 +- 依赖：`pip install akshare pandas` +- 用法见脚本头部注释 + +## 输出格式（标准版） + +非JSON数据输入时的分析结果应包含： +1. **判断结论** - 明确的支撑/阻力、止跌/继续跌、洗盘/反转等结论 +2. **核查结果** - 各维度核查项达标情况 +3. **交易建议** - 具体的入场/离场/观望建议 +4. **风险提示** - 需注意的假信号、假突破等风险点 diff --git a/futures-technical-analysis/data/688385_20260309_005629.json b/futures-technical-analysis/data/688385_20260309_005629.json new file mode 100644 index 0000000..1a113dc --- /dev/null +++ b/futures-technical-analysis/data/688385_20260309_005629.json @@ -0,0 +1,2616 @@ +{ + "symbol": "688385", + "type": "stock", + "current_price": 79.87, + "timestamp": "2026-03-09T00:56:27+08:00", + "timeframes": { + "60min": [ + { + "time": "2026-02-10 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-0.3505 + }, + { + "time": "2026-03-06 13:55:00", + "open": 80.11, + "high": 80.33, + "low": 80.0, + "close": 80.15, + "volume": 183716, + "ma10": 80.6, + "ma20": 81.32, + "macd_dif": -0.4711, + "macd_dea": -0.3175, + "macd_histogram": -0.3073 + }, + { + "time": "2026-03-06 14:00:00", + "open": 80.17, + "high": 80.17, + "low": 79.84, + "close": 80.0, + "volume": 207592, + "ma10": 80.41, + "ma20": 81.21, + "macd_dif": -0.4909, + "macd_dea": -0.3522, + "macd_histogram": -0.2775 + }, + { + "time": "2026-03-06 14:05:00", + "open": 79.99, + "high": 80.03, + "low": 79.65, + "close": 79.73, + "volume": 339816, + "ma10": 80.26, + "ma20": 81.1, + "macd_dif": -0.5224, + "macd_dea": -0.3862, + "macd_histogram": -0.2723 + }, + { + "time": "2026-03-06 14:10:00", + "open": 79.73, + "high": 79.86, + "low": 79.63, + "close": 79.73, + "volume": 172827, + "ma10": 80.15, + "ma20": 80.99, + "macd_dif": -0.541, + "macd_dea": -0.4172, + "macd_histogram": -0.2477 + }, + { + "time": "2026-03-06 14:15:00", + "open": 79.66, + "high": 79.98, + "low": 79.53, + "close": 79.78, + "volume": 254756, + "ma10": 80.06, + "ma20": 80.86, + "macd_dif": -0.5455, + "macd_dea": -0.4428, + "macd_histogram": -0.2054 + }, + { + "time": "2026-03-06 14:20:00", + "open": 79.76, + "high": 79.84, + "low": 79.62, + "close": 79.75, + "volume": 125949, + "ma10": 80.01, + "ma20": 80.75, + "macd_dif": -0.5452, + "macd_dea": -0.4633, + "macd_histogram": -0.1638 + }, + { + "time": "2026-03-06 14:25:00", + "open": 79.76, + "high": 79.8, + "low": 79.64, + "close": 79.8, + "volume": 132693, + "ma10": 79.95, + "ma20": 80.62, + "macd_dif": -0.5348, + "macd_dea": -0.4776, + "macd_histogram": -0.1143 + }, + { + "time": "2026-03-06 14:30:00", + "open": 79.8, + "high": 79.96, + "low": 79.78, + "close": 79.82, + "volume": 72866, + "ma10": 79.89, + "ma20": 80.51, + "macd_dif": -0.5189, + "macd_dea": -0.4859, + "macd_histogram": -0.0661 + }, + { + "time": "2026-03-06 14:35:00", + "open": 79.83, + "high": 79.86, + "low": 79.61, + "close": 79.71, + "volume": 160909, + "ma10": 79.86, + "ma20": 80.39, + "macd_dif": -0.5093, + "macd_dea": -0.4906, + "macd_histogram": -0.0375 + }, + { + "time": "2026-03-06 14:40:00", + "open": 79.71, + "high": 79.79, + "low": 79.61, + "close": 79.73, + "volume": 187195, + "ma10": 79.82, + "ma20": 80.29, + "macd_dif": -0.4944, + "macd_dea": -0.4913, + "macd_histogram": -0.0062 + }, + { + "time": "2026-03-06 14:45:00", + "open": 79.73, + "high": 79.9, + "low": 79.68, + "close": 79.86, + "volume": 87620, + "ma10": 79.79, + "ma20": 80.19, + "macd_dif": -0.4667, + "macd_dea": -0.4864, + "macd_histogram": 0.0393 + }, + { + "time": "2026-03-06 14:50:00", + "open": 79.86, + "high": 79.97, + "low": 79.85, + "close": 79.9, + "volume": 105254, + "ma10": 79.78, + "ma20": 80.09, + "macd_dif": -0.4365, + "macd_dea": -0.4764, + "macd_histogram": 0.0798 + }, + { + "time": "2026-03-06 14:55:00", + "open": 79.89, + "high": 79.94, + "low": 79.76, + "close": 79.89, + "volume": 174248, + "ma10": 79.8, + "ma20": 80.03, + "macd_dif": -0.4087, + "macd_dea": -0.4629, + "macd_histogram": 0.1084 + }, + { + "time": "2026-03-06 15:00:00", + "open": 79.88, + "high": 80.0, + "low": 79.86, + "close": 79.87, + "volume": 110897, + "ma10": 79.81, + "ma20": 79.98, + "macd_dif": -0.3838, + "macd_dea": -0.4471, + "macd_histogram": 0.1265 + } + ] + } +} \ No newline at end of file diff --git a/futures-technical-analysis/futures-technical-analysis.tar.gz b/futures-technical-analysis/futures-technical-analysis.tar.gz new file mode 100644 index 0000000..c1531e8 Binary files /dev/null and b/futures-technical-analysis/futures-technical-analysis.tar.gz differ diff --git a/futures-technical-analysis/futures_data_collector.py b/futures-technical-analysis/futures_data_collector.py new file mode 100644 index 0000000..1d53a5e --- /dev/null +++ b/futures-technical-analysis/futures_data_collector.py @@ -0,0 +1,681 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +================================================================================ +期货/股票多周期数据获取与技术指标计算脚本 +================================================================================ + +【功能介绍】 +本脚本用于获取期货或股票的多周期K线数据，并自动计算技术指标，输出JSON格式， +可直接用于技术分析Skill的输入。 + +【核心功能】 +1. 多周期数据获取 + - 5分钟K线（至少50根） + - 15分钟K线（至少50根） + - 30分钟K线（至少50根） + - 60分钟K线（至少50根） + - 日K线（至少50根） + +2. 技术指标自动计算（每根K线都包含） + - MA10: 10日/周期移动平均线 + - MA20: 20日/周期移动平均线 + - MACD_DIF: MACD快线 + - MACD_DEA: MACD慢线（信号线） + - MACD_HISTOGRAM: MACD柱状图 + +3. 输出格式 + 每个周期为一个数组，数组中每项包含： + { + "time": "K线时间", + "open": 开盘价, + "high": 最高价, + "low": 最低价, + "close": 收盘价, + "volume": 成交量, + "ma10": MA10值, + "ma20": MA20值, + "macd_dif": DIF值, + "macd_dea": DEA值, + "macd_histogram": 柱状图值 + } + +【使用方法】 + +1. 安装依赖 + pip install akshare pandas + +2. 运行脚本 + # 获取期货数据（默认） + python futures_data_collector.py --symbol SN2504 --type futures + + # 获取股票数据 + python futures_data_collector.py --symbol 000001 --type stock + python futures_data_collector.py --symbol 600000 --type stock --output stock_data.json + +3. 参数说明 + --symbol: 代码（必填） + 期货格式：品种代码 + 年份 + 月份，如 SN2504(沪锡)、AG2506(沪银) + 股票格式：6位数字代码，如 000001(平安银行)、600000(浦发银行) + + --type: 数据类型（可选，默认 futures） + 可选值：futures(期货)、stock(股票) + + --output: 输出文件名（可选） + 默认格式：{代码}_{时间戳}.json + +【常见代码对照】 + 期货： + SN2504 - 沪锡（上海期货交易所） + AG2506 - 沪银（上海期货交易所） + LC2505 - 碳酸锂（广州期货交易所） + NI2505 - 沪镍（上海期货交易所） + + 股票： + 000001 - 平安银行 + 600000 - 浦发银行 + 000858 - 五粮液 + 600519 - 贵州茅台 + +【输出示例】 +{ + "symbol": "SN2504", + "current_price": 223500, + "timestamp": "2026-03-07T22:15:00+08:00", + "timeframes": { + "60min": [ + { + "time": "2026-03-07 14:00", + "open": 22100, + "high": 22300, + "low": 22050, + "close": 22250, + "volume": 12500, + "ma10": 22180.5, + "ma20": 22000.3, + "macd_dif": 0.0523, + "macd_dea": 0.0312, + "macd_histogram": 0.0422 + } + ], + "30min": [ ... ], + "15min": [ ... ], + "5min": [ ... ], + "daily": [ ... ] + } +} + +【注意事项】 +1. 数据源使用akshare，数据可能有延迟或频率限制 +2. 分钟数据受交易所限制，可能无法获取太多历史数据 +3. 如遇数据获取失败，请检查合约代码是否正确 +4. 脚本会自动过滤掉数据不足的周期 + +【作者】OpenClaw Assistant +【日期】2026-03-07 +================================================================================ +""" + +import akshare as ak +import pandas as pd +import json +import argparse +import os +from datetime import datetime, timedelta +from typing import Dict, List +import warnings +warnings.filterwarnings('ignore') +# 清除缓存 +ak.cache = {} + +# 数据目录配置 +DATA_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'data') +os.makedirs(DATA_DIR, exist_ok=True) + +def calculate_ma(df: pd.DataFrame, periods: List[int] = [10, 20]) -> pd.DataFrame: + """计算移动平均线""" + for period in periods: + df[f'MA{period}'] = df['close'].rolling(window=period, min_periods=1).mean() + return df + + +def calculate_macd(df: pd.DataFrame, fast: int = 12, slow: int = 26, signal: int = 9) -> pd.DataFrame: + """计算MACD指标""" + ema_fast = df['close'].ewm(span=fast, adjust=False).mean() + ema_slow = df['close'].ewm(span=slow, adjust=False).mean() + df['macd_dif'] = ema_fast - ema_slow + df['macd_dea'] = df['macd_dif'].ewm(span=signal, adjust=False).mean() + df['macd_histogram'] = (df['macd_dif'] - df['macd_dea']) * 2 + + # MACD信号判断 + df['macd_signal'] = df.apply(lambda row: + 'bullish' if row['macd_dif'] > row['macd_dea'] and row['macd_histogram'] > 0 + else 'bearish' if row['macd_dif'] < row['macd_dea'] and row['macd_histogram'] < 0 + else 'neutral', axis=1) + + return df + + +def get_current_time() -> datetime: + """获取当前北京时间（去除微秒）""" + return datetime.now().replace(microsecond=0) + + +def filter_future_data(df: pd.DataFrame, current_time: datetime = None) -> pd.DataFrame: + """ + 过滤掉未来数据 + + Args: + df: 包含datetime列的DataFrame + current_time: 当前时间，默认为系统当前时间 + + Returns: + 过滤后的DataFrame + """ + if current_time is None: + current_time = get_current_time() + + if 'datetime' not in df.columns: + return df + + # 确保datetime列是datetime类型 + df['datetime'] = pd.to_datetime(df['datetime']) + + # 过滤掉大于当前时间的数据（未来数据） + original_count = len(df) + df = df[df['datetime'] <= current_time].copy() + filtered_count = original_count - len(df) + + if filtered_count > 0: + print(f" 过滤了 {filtered_count} 条未来数据") + + return df + + +def extend_night_session_data(df: pd.DataFrame, symbol: str, period: str) -> pd.DataFrame: + """ + 尝试获取完整的夜盘数据 + 期货夜盘时间：21:00 - 02:30（次日） + """ + if df.empty or 'datetime' not in df.columns: + return df + + df['datetime'] = pd.to_datetime(df['datetime']) + df = df.sort_values('datetime').reset_index(drop=True) + + # 检查是否需要补充夜盘数据 + # 获取最后一条数据的时间 + last_time = df['datetime'].iloc[-1] + last_hour = last_time.hour + last_minute = last_time.minute + + # 如果最后数据在 21:00-23:59 或 00:00-02:30 范围内，可能需要补充 + # 夜盘结束时间是 02:30 + is_night_session = ( + (last_hour >= 21) or # 21:00 - 23:59 + (last_hour < 2) or # 00:00 - 01:59 + (last_hour == 2 and last_minute <= 30) # 02:00 - 02:30 + ) + + if not is_night_session: + return df + + # 检查是否包含 02:30 的数据（对于5分钟、15分钟等周期） + has_0230 = False + for dt in df['datetime']: + if dt.hour == 2 and dt.minute == 30: + has_0230 = True + break + + # 如果已经有 02:30 的数据，说明夜盘完整 + if has_0230: + return df + + # 尝试通过获取历史数据来补充夜盘 + # 由于akshare限制，我们记录警告信息 + print(f" 注意: 夜盘数据可能不完整（缺少02:30及之前的数据）") + + return df + + +def get_minute_data(symbol: str, period: str) -> pd.DataFrame: + """ + 获取期货分钟K线数据，过滤未来数据，确保夜盘完整 + + Args: + symbol: 合约代码，如 "SN2504" + period: 分钟周期，"5", "15", "30", "60" + + Returns: + DataFrame with OHLCV data + """ + try: + # 获取当前时间（用于过滤未来数据） + current_time = get_current_time() + + # 使用akshare获取分钟数据 + df = ak.futures_zh_minute_sina(symbol=symbol, period=period) + + # 重命名列 + df = df.rename(columns={ + 'day': 'datetime', + 'open': 'open', + 'high': 'high', + 'low': 'low', + 'close': 'close', + 'volume': 'volume' + }) + + # 转换数据类型 + for col in ['open', 'high', 'low', 'close', 'volume']: + df[col] = pd.to_numeric(df[col], errors='coerce') + + # 确保datetime列是datetime类型 + df['datetime'] = pd.to_datetime(df['datetime']) + + # 过滤未来数据 + df = filter_future_data(df, current_time) + + # 尝试补充夜盘数据 + df = extend_night_session_data(df, symbol, period) + + # 确保至少50根K线 + if len(df) < 50: + print(f" 警告: {period}分钟只获取到{len(df)}根K线，建议检查数据源") + + return df + + except Exception as e: + print(f" 获取{period}分钟数据失败: {e}") + return pd.DataFrame() + + +def get_daily_data(symbol: str, days: int = 60) -> pd.DataFrame: + """ + 获取期货日K线数据，过滤未来数据 + + Args: + symbol: 合约代码 + days: 获取天数 + + Returns: + DataFrame with OHLCV data + """ + try: + # 获取当前时间（用于过滤未来数据） + current_time = get_current_time() + + # 获取日K数据（获取较多历史数据） + df = ak.futures_zh_daily_sina(symbol=symbol) + + # 重命名列 + df = df.rename(columns={ + 'date': 'datetime', + 'open': 'open', + 'high': 'high', + 'low': 'low', + 'close': 'close', + 'volume': 'volume' + }) + + # 转换数据类型 + for col in ['open', 'high', 'low', 'close', 'volume']: + df[col] = pd.to_numeric(df[col], errors='coerce') + + # 排序 + df['datetime'] = pd.to_datetime(df['datetime']) + df = df.sort_values('datetime').reset_index(drop=True) + + # 过滤未来数据（只保留今天及之前的数据） + df = filter_future_data(df, current_time) + + # 取最近N天 + df = df.tail(days).reset_index(drop=True) + + return df + + except Exception as e: + print(f" 获取日K数据失败: {e}") + return pd.DataFrame() + + +def get_stock_minute_data(symbol: str, period: str) -> pd.DataFrame: + """ + 获取股票分钟K线数据 + + Args: + symbol: 股票代码，如 "000001" + period: 分钟周期，"5", "15", "30", "60" + + Returns: + DataFrame with OHLCV data + """ + try: + # 获取当前时间（用于过滤未来数据） + current_time = get_current_time() + + # 使用akshare获取股票分钟数据 + # stock_zh_a_minute 需要 symbol 格式为 sh600000 或 sz000001 + if symbol.startswith('6'): + full_symbol = f"sh{symbol}" + else: + full_symbol = f"sz{symbol}" + + df = ak.stock_zh_a_minute(symbol=full_symbol, period=period) + + # 重命名列 + df = df.rename(columns={ + 'day': 'datetime', + 'open': 'open', + 'high': 'high', + 'low': 'low', + 'close': 'close', + 'volume': 'volume' + }) + + # 转换数据类型 + for col in ['open', 'high', 'low', 'close', 'volume']: + df[col] = pd.to_numeric(df[col], errors='coerce') + + # 确保datetime列是datetime类型 + df['datetime'] = pd.to_datetime(df['datetime']) + + # 过滤未来数据 + df = filter_future_data(df, current_time) + + # 确保至少50根K线 + if len(df) < 50: + print(f" 警告: {period}分钟只获取到{len(df)}根K线，建议检查数据源") + + return df + + except Exception as e: + print(f" 获取{period}分钟数据失败: {e}") + return pd.DataFrame() + + +def get_stock_daily_data(symbol: str, days: int = 60) -> pd.DataFrame: + """ + 获取股票日K线数据 + + Args: + symbol: 股票代码，如 "000001" + days: 获取天数 + + Returns: + DataFrame with OHLCV data + """ + try: + # 获取当前时间（用于过滤未来数据） + current_time = get_current_time() + + # 计算开始日期（获取足够的历史数据） + end_date = current_time.strftime('%Y%m%d') + start_date = (current_time - timedelta(days=days*2)).strftime('%Y%m%d') + + # 获取日K数据 + df = ak.stock_zh_a_hist(symbol=symbol, period="daily", start_date=start_date, end_date=end_date) + + # 重命名列 + df = df.rename(columns={ + '日期': 'datetime', + '开盘': 'open', + '最高': 'high', + '最低': 'low', + '收盘': 'close', + '成交量': 'volume' + }) + + # 转换数据类型 + for col in ['open', 'high', 'low', 'close', 'volume']: + df[col] = pd.to_numeric(df[col], errors='coerce') + + # 排序 + df['datetime'] = pd.to_datetime(df['datetime']) + df = df.sort_values('datetime').reset_index(drop=True) + + # 过滤未来数据（只保留今天及之前的数据） + df = filter_future_data(df, current_time) + + # 取最近N天 + df = df.tail(days).reset_index(drop=True) + + return df + + except Exception as e: + print(f" 获取日K数据失败: {e}") + return pd.DataFrame() + + +def process_data(df: pd.DataFrame, timeframe: str) -> List[Dict]: + """ + 处理数据，计算指标并格式化输出 + 每个周期返回一个数组，每项包含交易数据+计算指标 + + Args: + df: K线DataFrame + timeframe: 周期名称 + + Returns: + 格式化后的K线数组，每项包含指标 + """ + if df.empty or len(df) < 10: + return [] + + # 计算技术指标 + df = calculate_ma(df) + df = calculate_macd(df) + + # 格式化K线数据（取最近50根或全部） + candles = [] + df_tail = df.tail(50) if len(df) > 50 else df + + for _, row in df_tail.iterrows(): + candle = { + "time": str(row['datetime']), + "open": round(float(row['open']), 2), + "high": round(float(row['high']), 2), + "low": round(float(row['low']), 2), + "close": round(float(row['close']), 2), + "volume": int(row['volume']) if not pd.isna(row['volume']) else 0, + "ma10": round(float(row['MA10']), 2) if not pd.isna(row.get('MA10')) else None, + "ma20": round(float(row['MA20']), 2) if not pd.isna(row.get('MA20')) else None, + "macd_dif": round(float(row['macd_dif']), 4) if not pd.isna(row.get('macd_dif')) else 0, + "macd_dea": round(float(row['macd_dea']), 4) if not pd.isna(row.get('macd_dea')) else 0, + "macd_histogram": round(float(row['macd_histogram']), 4) if not pd.isna(row.get('macd_histogram')) else 0 + } + candles.append(candle) + + return candles + + +def collect_futures_data(symbol: str) -> Dict: + """ + 收集期货多周期完整数据 + + Args: + symbol: 合约代码，如 "SN2504" + + Returns: + 完整的JSON格式数据 + """ + print(f"\n正在获取期货 {symbol} 的多周期数据...") + print(f"当前时间: {get_current_time().strftime('%Y-%m-%d %H:%M:%S')}") + print("-" * 50) + + result = { + "symbol": symbol, + "type": "futures", + "current_price": None, + "timestamp": datetime.now().strftime("%Y-%m-%dT%H:%M:%S+08:00"), + "timeframes": {} + } + + # 获取各周期数据 + periods = [ + ("60min", "60"), + ("30min", "30"), + ("15min", "15"), + ("5min", "5") + ] + + for tf_name, tf_period in periods: + print(f"获取 {tf_name} 数据...") + try: + df = get_minute_data(symbol, tf_period) + if not df.empty and len(df) >= 50: + candles = process_data(df, tf_name) + if candles: + result["timeframes"][tf_name] = candles + # 设置当前价格为最新收盘价 + if result["current_price"] is None: + result["current_price"] = candles[-1]["close"] + print(f" [OK] 成功获取 {len(candles)} 根K线") + else: + print(f" [FAIL] 数据不足或获取失败 (获取到{len(df)}根)") + except Exception as e: + print(f" [ERROR] 错误: {e}") + + # 获取日K数据 + print("获取 daily 数据...") + try: + df_daily = get_daily_data(symbol, days=60) + if not df_daily.empty and len(df_daily) >= 50: + candles = process_data(df_daily, "daily") + if candles: + result["timeframes"]["daily"] = candles + print(f" [OK] 成功获取 {len(candles)} 根K线") + else: + print(f" [FAIL] 数据不足或获取失败 (获取到{len(df_daily)}根)") + except Exception as e: + print(f" [ERROR] 错误: {e}") + + print("-" * 50) + return result + + +def collect_stock_data(symbol: str) -> Dict: + """ + 收集股票多周期完整数据 + + Args: + symbol: 股票代码，如 "000001" + + Returns: + 完整的JSON格式数据 + """ + print(f"\n正在获取股票 {symbol} 的多周期数据...") + print(f"当前时间: {get_current_time().strftime('%Y-%m-%d %H:%M:%S')}") + print("-" * 50) + + result = { + "symbol": symbol, + "type": "stock", + "current_price": None, + "timestamp": datetime.now().strftime("%Y-%m-%dT%H:%M:%S+08:00"), + "timeframes": {} + } + + # 获取各周期数据 + periods = [ + ("60min", "60"), + ("30min", "30"), + ("15min", "15"), + ("5min", "5") + ] + + for tf_name, tf_period in periods: + print(f"获取 {tf_name} 数据...") + try: + df = get_stock_minute_data(symbol, tf_period) + if not df.empty and len(df) >= 50: + candles = process_data(df, tf_name) + if candles: + result["timeframes"][tf_name] = candles + # 设置当前价格为最新收盘价 + if result["current_price"] is None: + result["current_price"] = candles[-1]["close"] + print(f" [OK] 成功获取 {len(candles)} 根K线") + else: + print(f" [FAIL] 数据不足或获取失败 (获取到{len(df)}根)") + except Exception as e: + print(f" [ERROR] 错误: {e}") + + # 获取日K数据 + print("获取 daily 数据...") + try: + df_daily = get_stock_daily_data(symbol, days=60) + if not df_daily.empty and len(df_daily) >= 50: + candles = process_data(df_daily, "daily") + if candles: + result["timeframes"]["daily"] = candles + print(f" [OK] 成功获取 {len(candles)} 根K线") + else: + print(f" [FAIL] 数据不足或获取失败 (获取到{len(df_daily)}根)") + except Exception as e: + print(f" [ERROR] 错误: {e}") + + print("-" * 50) + return result + + +def main(): + parser = argparse.ArgumentParser(description='期货/股票多周期数据获取与技术指标计算') + parser.add_argument('--symbol', type=str, required=True, + help='代码，期货如 SN2504(沪锡), 股票如 000001(平安银行)') + parser.add_argument('--type', type=str, default='futures', choices=['futures', 'stock'], + help='数据类型：futures(期货)、stock(股票)，默认为 futures') + parser.add_argument('--output', type=str, default=None, + help='输出JSON文件名，默认为 代码_时间戳.json') + + args = parser.parse_args() + + # 根据类型获取数据 + if args.type == 'stock': + data = collect_stock_data(args.symbol) + else: + data = collect_futures_data(args.symbol) + + # 检查是否获取到数据 + if not data["timeframes"]: + print("\n错误: 未能获取到任何数据，请检查代码是否正确") + if args.type == 'stock': + print("常见股票代码示例:") + print(" 000001 - 平安银行") + print(" 600000 - 浦发银行") + print(" 000858 - 五粮液") + print(" 600519 - 贵州茅台") + else: + print("常见期货合约代码示例:") + print(" SN2504 - 沪锡2504") + print(" AG2506 - 沪银2506") + print(" LC2505 - 碳酸锂2505") + print(" NI2505 - 沪镍2505") + return + + # 打印JSON到控制台 + print("\n" + "="*60) + print("JSON 输出:") + print("="*60) + json_output = json.dumps(data, ensure_ascii=False, indent=2) + print(json_output) + + # 保存到文件（统一放到 data 目录） + if args.output: + # 如果用户指定了文件名，也放到 data 目录下 + filename = os.path.join(DATA_DIR, args.output) + else: + timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") + filename = os.path.join(DATA_DIR, f"{data['symbol']}_{timestamp}.json") + + with open(filename, 'w', encoding='utf-8') as f: + f.write(json_output) + + print("\n" + "="*60) + print(f"[OK] 数据已保存到: {filename}") + print(f"[OK] 共获取 {len(data['timeframes'])} 个周期数据") + print("="*60) + + +if __name__ == "__main__": + main() diff --git a/futures-technical-analysis/references/kline-data-example.json b/futures-technical-analysis/references/kline-data-example.json new file mode 100644 index 0000000..8b6b61e --- /dev/null +++ b/futures-technical-analysis/references/kline-data-example.json @@ -0,0 +1,73 @@ +{ + "symbol": "沪锡2605", + "current_price": 223500, + "timestamp": "2026-01-17T14:30:00+08:00", + "timeframes": { + "60min": { + "trend": "up", + "ma10": 22350, + "ma20": 21800, + "macd_signal": "bullish", + "macd_histogram": "positive", + "volume_trend": "increasing", + "support": 21800, + "resistance": 22800, + "bollinger": { + "upper": 23000, + "middle": 22400, + "lower": 21800 + }, + "recent_high": 22850, + "recent_low": 21750, + "candles": [ + {"time": "10:00", "open": 22100, "high": 22300, "low": 22050, "close": 22250, "volume": 12500}, + {"time": "11:00", "open": 22250, "high": 22400, "low": 22150, "close": 22350, "volume": 13200}, + {"time": "13:00", "open": 22350, "high": 22500, "low": 22200, "close": 22450, "volume": 14800}, + {"time": "14:00", "open": 22450, "high": 22600, "low": 22350, "close": 22350, "volume": 11500} + ] + }, + "30min": { + "trend": "up", + "ma10": 22320, + "ma20": 22100, + "macd_signal": "bullish", + "macd_histogram": "positive", + "volume_trend": "stable", + "support": 22100, + "resistance": 22500, + "pattern": "bullish_engulfing", + "candles": [ + {"time": "13:30", "open": 22300, "high": 22380, "low": 22250, "close": 22280, "volume": 6200}, + {"time": "14:00", "open": 22280, "high": 22450, "low": 22250, "close": 22400, "volume": 7500}, + {"time": "14:30", "open": 22400, "high": 22500, "low": 22350, "close": 22350, "volume": 6800} + ] + }, + "15min": { + "trend": "sideways", + "ma5": 22360, + "ma10": 22300, + "macd_signal": "neutral", + "volume_trend": "decreasing", + "support": 22250, + "resistance": 22450, + "entry_signal": "wait", + "candles": [ + {"time": "14:15", "open": 22380, "high": 22420, "low": 22350, "close": 22380, "volume": 2800}, + {"time": "14:30", "open": 22380, "high": 22400, "low": 22320, "close": 22350, "volume": 2400}, + {"time": "14:45", "open": 22350, "high": 22380, "low": 22330, "close": 22360, "volume": 2100} + ] + } + }, + "fundamentals": { + "recent_news": "缅甸锡矿复产不及预期，供给偏紧", + "inventory_change": "库存下降5.2%", + "cost_support": 210000, + "policy_risk": "交易所上调保证金", + "demand_outlook": "新能源需求提振" + }, + "market_sentiment": { + "dominant_side": "bullish", + "divergence_level": "moderate", + "main_concern": "高位追涨风险" + } +} \ No newline at end of file diff --git a/futures-technical-analysis/references/期货交易技术分析实战手册（整合版）.md b/futures-technical-analysis/references/期货交易技术分析实战手册（整合版）.md new file mode 100644 index 0000000..5594829 --- /dev/null +++ b/futures-technical-analysis/references/期货交易技术分析实战手册（整合版）.md @@ -0,0 +1,675 @@ +# 期货交易技术分析实战手册（整合版） + +> **文档说明**：本手册整合5份期货交易技术分析文档，涵盖支撑阻力、止跌反弹、多周期共振、滞涨回调、趋势分歧五大核心主题，提供可落地的判断方法与实操Checklist。 + +--- + +## 目录 + +1. [核心原理篇](#一核心原理篇) + - 1.1 [支撑与阻力的有效性原理](#11-支撑与阻力的有效性原理) + - 1.2 [止跌与反弹的动力学原理](#12-止跌与反弹的动力学原理) + - 1.3 [多周期共振原理](#13-多周期共振原理) + - 1.4 [滞涨与回调原理](#14-滞涨与回调原理) + - 1.5 [多空分歧识别原理](#15-多空分歧识别原理) +2. [实战方法篇](#二实战方法篇) + - 2.1 [支撑/阻力位判断方法](#21-支撑阻力位判断方法) + - 2.2 [止跌判断与反弹鉴别方法](#22-止跌判断与反弹鉴别方法) + - 2.3 [多周期共振交易法](#23-多周期共振交易法) + - 2.4 [滞涨判断与回调性质区分](#24-滞涨判断与回调性质区分) + - 2.5 [多空分歧实战分析](#25-多空分歧实战分析) +3. [实操Checklist汇总](#三实操checklist汇总) + - 3.1 [支撑/阻力有效性Checklist](#31-支撑阻力有效性checklist) + - 3.2 [止跌确认Checklist](#32-止跌确认checklist) + - 3.3 [反弹性质判断Checklist](#33-反弹性质判断checklist) + - 3.4 [多周期共振入场Checklist](#34-多周期共振入场checklist) + - 3.5 [滞涨判断Checklist](#35-滞涨判断checklist) + - 3.6 [多空分歧识别Checklist](#36-多空分歧识别checklist) +4. [交易决策流程](#四交易决策流程) +5. [避坑要点汇总](#五避坑要点汇总) + +--- + +## 一、核心原理篇 + +### 1.1 支撑与阻力的有效性原理 + +**核心逻辑**：支撑位/阻力位的有效性取决于市场资金对该价位的共识程度。单一技术面点位参考意义有限，需结合**测试次数、成交量、周期共振、基本面、突破后表现**五大维度交叉验证。 + +**有效性金字塔**： +``` + ▲ + / \ + / 强 \ 5维度共振 + / 有效 \ （测试≥3次+量价同步+多周期+基本面+回踩确认） + /────────\ + / 中等 \ 3-4维度匹配 + / 有效 \ + /──────────────\ + / 弱有效 \ 1-2维度匹配 + /────────────────────\ + / 无效/临时点位 \ 孤立点位、无量、无基本面 + ────────────────────────────── +``` + +**关键原理**： +- **测试次数原理**：价位被测试次数越多，多空双方在该价位达成的平衡越稳固，突破所需能量越大 +- **量价配合原理**：无量支撑/阻力为"虚位"，量能是资金共识的具象化表现 +- **周期共振原理**：多周期点位重合形成共振效应，成为极强的关键价位 +- **基本面匹配原理**：技术点位需与成本线、政策线、供需拐点等基本面逻辑匹配才可持续 +- **转化验证原理**：突破后的回踩/反抽表现是确认有效性的最后一步 + +--- + +### 1.2 止跌与反弹的动力学原理 + +**核心逻辑**：止跌的本质是"抛压耗尽+买盘入场"，需通过**量能变化、K线形态、关键支撑位、基本面**四大维度共振确认。 + +**止跌动力学链条**： +``` +下跌末期缩量 → 抛压耗尽 → 企稳K线放量 → 买盘入场 → 关键支撑守住 → 止跌确认 + ↓ ↓ ↓ ↓ ↓ + 情绪降温 空头力竭 多头介入 资金确认 技术确认 +``` + +**反弹性质区分原理**： + +| 维度 | 下跌中继（诱多） | 反转趋势（拐点） | +|------|-----------------|-----------------| +| **量能** | 反弹缩量，遇阻量能萎缩 | 持续放量，突破阻力时量能再放大 | +| **价格** | 无法突破关键阻力位，承压回落 | 放量突破关键阻力，回踩不跌破 | +| **周期** | 仅短周期反弹，长周期仍空头排列 | 多周期共振向上，均线逐步形成多头排列 | +| **基本面** | 供需格局未变，下跌驱动因素仍存在 | 基本面发生实质性变化，利好持续发酵 | +| **持续性** | 持续时间短，冲高后快速回落创新低 | 回调幅度浅，低点逐步上移 | + +--- + +### 1.3 多周期共振原理 + +**核心逻辑**： +``` +60分钟周期（定趋势） → 30分钟周期（找拐点） → 15分钟周期（择入场） + ↓ ↓ ↓ + 过滤信号方向 确认拐点有效性 精准执行入场 +``` + +**周期层级关系**： +- **日线**：主导中期趋势，定回调大方向 +- **60分钟**：衔接趋势与短期波动，确认趋势连贯性 +- **30分钟**：捕捉回调拐点，验证短期转折 +- **15/5分钟**：执行周期，精准入场与止损设置 + +**共振标准**： +- **强共振**：3个及以上周期信号同向，判断准确率≥85% +- **弱共振**：2个周期信号同向，需结合基本面验证 +- **无共振**：周期信号矛盾，以最大周期为准或观望 + +--- + +### 1.4 滞涨与回调原理 + +**核心逻辑**：滞涨的本质是上涨动能衰竭，通过**成交量（资金动能）、K线（价格表现）、MACD（动能量化）**三大维度共振判断。 + +**滞涨识别原理**： +``` +价格创新高/微涨 + ↓ +量能显著萎缩 或 放量但价格不涨（抛压显现） + ↓ +上涨节奏骤降，高点上移乏力 + ↓ +MACD红柱缩短或顶背离 + ↓ +滞涨确认（至少2个维度达标，3个维度达标确认度≥80%） +``` + +**回调性质区分原理**： + +| 维度 | 洗盘（短期调整） | 反转（趋势结束） | +|------|-----------------|-----------------| +| **成交量** | 缩量下跌，较滞涨期萎缩≥30%，无资金出逃 | 放量下跌，越跌量越大，主力砸盘 | +| **K线** | 小阴小阳交替，无大阴线破位 | 大阴线连拉，出现阴包阳、跳空低开破位 | +| **MACD** | 红柱小幅缩短，未翻绿、无明确死叉 | 红柱快速消失并翻绿，绿柱持续放大，死叉明确 | +| **支撑位** | 不跌破短期支撑位（MA10/MA20） | 有效跌破中期支撑位（MA60） | +| **周期** | 大周期保持强势，小周期短期调整 | 大周期率先走弱，小周期同步利空 | + +--- + +### 1.5 多空分歧识别原理 + +**核心逻辑**：多空分歧本质是资金博弈加剧、多空观点对立的表现，核心特征为**"量能放大但价格无明确方向"**，是趋势延续或反转的关键预判节点。 + +**分歧识别五维度**： +1. **成交量维度**：价格创新高/新低但量能放大、涨幅/跌幅收窄（量价背离） +2. **K线形态维度**：长上影/长下影线交替，十字星密集，反转组合出现 +3. **技术指标维度**：MACD背离，KDJ钝化后反复交叉，均线纠缠 +4. **盘口与持仓维度**：主力持仓双增，大单异动，持仓量增幅大但价格波动小 +5. **基本面维度**：多空消息交织，市场对消息解读分歧 + +**分歧强度分级**： +- **轻度分歧（3-4项信号）**：大概率震荡整理，趋势放缓 +- **中度分歧（5-6项信号）**：反转概率提升，警惕关键位破位 +- **重度分歧（7项及以上）**：博弈白热化，价格即将选方向，建议观望 + +--- + +## 二、实战方法篇 + +### 2.1 支撑/阻力位判断方法 + +#### 2.1.1 五维度判断法（按优先级排序） + +| 维度 | 有效特征 | 无效特征 | +|------|---------|---------| +| **1. 测试次数** | 价位被3次及以上测试，回踩不破或反弹不过 | 仅1-2次测试，临时情绪驱动 | +| **2. 成交量** | 缩量企稳/放量突破，量价同步 | 无量支撑/阻力，或放量但无法企稳/突破 | +| **3. 周期共振** | 多周期点位重合（如5分钟+15分钟+1小时） | 仅单一短周期出现，无共振 | +| **4. 基本面** | 契合成本线、政策底线、供需拐点 | 与基本面背离（如产能过剩下的技术支撑） | +| **5. 突破后表现** | 突破后回踩/反抽确认（阻力变支撑不跌破，支撑变阻力不突破） | 突破后快速回撤，无成交量配合 | + +#### 2.1.2 关键位判断方法 + +**1. 量能密集区定位（核心）** +关键位是前期大量成交的价格区间，而非单一高低点。 +- 支撑位：前期密集成交区下沿 +- 阻力位：前期密集成交区上沿/套牢盘集中区 + +**2. 枢轴点辅助测算** +``` +枢轴点（PP）=（高+低+收）/3 +一级支撑（S1）= 2×PP - 高 +二级支撑（S2）= PP -（高-低） +一级压力（R1）= 2×PP - 低 +二级压力（R2）= PP +（高-低） +``` + +**3. 均线与前期高低点共振** +- 短期均线（MA5/MA10）：即时压力/支撑 +- 长期均线（MA20/MA60）与前期高低点形成共振，强化关键位有效性 + +#### 2.1.3 日内交易 vs 波段交易要点 + +| 场景 | 核心周期 | 关键要点 | +|------|---------|---------| +| **日内交易** | 5分钟+15分钟+1小时 | 避开1分钟孤立点位，重点关注波动高峰期（10:00-11:30、14:00-15:00）的点位测试情况 | +| **波段交易** | 日线+周线+4小时 | 绑定成本线、库存拐点等基本面逻辑，不迷信纯技术面点位 | + +--- + +### 2.2 止跌判断与反弹鉴别方法 + +#### 2.2.1 止跌判断四大维度 + +| 维度 | 核心信号 | 实操验证要点 | +|------|---------|-------------| +| **量能变化** | 下跌末期缩量，企稳时放量 | 1. 下跌末端成交量持续萎缩；2. 企稳K线成交量≥前3根K线平均量能的1.5倍；3. 日内聚焦5/15分钟周期"缩量下跌→放量企稳"信号 | +| **K线形态** | 反转类K线+底部组合结构 | 1. 单根：锤子线（下影线≥实体2倍）、看涨吞没、刺透形态；2. 组合：双底、启明之星；3. 排除十字星、小阳线等无力度形态 | +| **关键支撑位** | 支撑位回踩不破+量价配合 | 1. 回踩前期低点、成本线、均线支撑缩量企稳；2. 假跌破后快速拉回且放量收复为强信号；3. 支撑需满足"测试≥3次、多周期共振" | +| **基本面/消息面** | 利空出尽或突发实质性利好 | 1. 驱动下跌的利空已充分消化；2. 出现政策托底、需求回暖、库存大降等利好；3. 无消息面配合的纯技术止跌大概率为短期震荡 | + +#### 2.2.2 止跌确认必要条件（满足≥3项可试仓） + +- [ ] 下跌末端缩量，企稳K线放量（量能达标） +- [ ] 出现至少1种反转类K线或底部组合形态 +- [ ] 关键支撑位回踩不破，或假跌破后快速收复 +- [ ] 无新利空干扰，或有基本面利好加持 +- [ ] 5/15分钟短周期与1小时中周期同步出现企稳信号 + +#### 2.2.3 反弹强度分级 + +| 反弹强度 | 量价特征 | 幅度/持续时间 | 实战意义 | +|---------|---------|--------------|---------| +| **弱反弹** | 缩量上涨，反弹量能＜前期下跌量能 | 幅度≤前期跌幅30%；日内1-2根5分钟K线，波段1个交易日 | 大概率为下跌中继，反弹后易再创新低，坚决不抄底 | +| **中反弹** | 温和放量，反弹量能≈前期下跌量能 | 幅度达30%-50%；突破1个次要阻力位 | 需观察关键阻力位表现，警惕冲高回落，不可重仓 | +| **强反弹** | 持续放量，反弹量能＞前期下跌量能1.5倍 | 幅度＞50%；连续突破多个阻力位 | 反转概率大幅提升，需进一步验证趋势延续性 | + +--- + +### 2.3 多周期共振交易法 + +#### 2.3.1 三周期核查体系 + +**60分钟周期（主导周期：定方向，过滤信号）** + +| 看涨趋势核查 | 看跌趋势核查 | +|-------------|-------------| +| □ MA10＞MA20，价格站稳MA10上方，回调不跌破 | □ MA10＜MA20，价格跌破MA10下方，反弹不突破 | +| □ MACD在零轴上方运行，红柱持续/温和放大 | □ MACD在零轴下方运行，绿柱持续/温和放大 | +| □ 量价同步：上涨放量、回调缩量，无缩量新高 | □ 量价同步：下跌放量、反弹缩量，无缩量新低 | +| □ 布林带中轨向上倾斜，价格在中轨上方震荡 | □ 布林带中轨向下倾斜，价格在中轨下方震荡 | + +*注：若以上仅满足≤2项，判定为趋势不明，直接放弃交易* + +**30分钟周期（辅助周期：确认拐点，验证趋势）** + +| 看涨拐点核查 | 看跌拐点核查 | +|-------------|-------------| +| □ 出现阳包阴、启明星等反转K线组合 | □ 出现阴包阳、黄昏星等反转K线组合 | +| □ 突破前期小高点，且突破时放量 | □ 跌破前期小低点，且跌破时放量 | +| □ MACD零轴上金叉，或死叉后快速金叉 | □ MACD零轴下死叉，或金叉后快速死叉 | +| □ 布林带价格突破中轨，回踩不跌破 | □ 布林带价格跌破中轨，反弹不突破 | + +*注：需与60分钟趋势方向一致，若信号矛盾判定为无效拐点* + +**15分钟周期（执行周期：精准入场，设置止损）** + +| 做多入场信号核查 | 做空入场信号核查 | +|-----------------|-----------------| +| □ 突破30分钟确认的拐点压力位 | □ 跌破30分钟确认的拐点支撑位 | +| □ 突破时成交量≥前5根K线均量1.5倍 | □ 跌破时成交量≥前5根K线均量1.5倍 | +| □ MACD红柱同步放大，无顶背离 | □ MACD绿柱同步放大，无底背离 | +| □ 回踩缩量，且不跌破15分钟MA5 | □ 反弹缩量，且不突破15分钟MA5 | +| □ 止损位设置：入场K线低点下方5-10个点 | □ 止损位设置：入场K线高点上方5-10个点 | + +#### 2.3.2 风险与信号有效性补充核查 + +| 核查项目 | 核查要点 | 结果 | +|---------|---------|------| +| 信号一致性 | 三周期信号无矛盾，趋势方向统一 | □ | +| 规避假突破 | 非脉冲式放量，突破后3根K线不回踩关键位 | □ | +| 消息面风险 | 无突发政策、数据发布（发布前后15分钟暂停交易） | □ | +| 资金管理 | 单笔仓位≤10%，止损≤账户1%-2% | □ | + +#### 2.3.3 入场/离场决策规则 + +| 情况 | 判定标准 | 操作建议 | +|------|---------|---------| +| **可入场** | 60分钟≥3项√ + 30分钟≥3项√ + 15分钟≥4项√ + 补充核查全√ | 满足共振条件，按计划入场 | +| **谨慎入场** | 总√数7-8项，且无核心信号缺失（如量能、趋势项） | 可轻仓（≤5%）试错 | +| **禁止入场** | 总√数≤6项，或存在信号矛盾、假突破、消息面风险 | 直接观望 | +| **强制离场** | 触发止损位；60分钟趋势拐头（如MA10拐向）；临近收盘前30分钟 | 无论盈亏平仓 | + +--- + +### 2.4 滞涨判断与回调性质区分 + +#### 2.4.1 滞涨判断三大维度（按优先级） + +| 维度 | 核心特征（可量化） | 30分钟级 | 60分钟级 | 日线级 | +|------|-----------------|---------|---------|--------| +| **成交量（资金核心）** | 价格创新高/微涨，但量能显著萎缩；或放量但价格涨不动 | 量能较近期峰值萎缩≥30%，或放量但1-2根K线涨不足1% | 量能较峰值萎缩≥40%，或放量滞涨（成交量达近期平均1.2倍+，K线涨不动） | 量能较峰值萎缩≥50%，或放量滞涨（单日成交量达近期平均1.5倍+，收盘价未创新高） | +| **K线（价格直观）** | 上涨节奏骤降，高点上移乏力，出现顶部特征K线 | 2-3根小阳/十字星交替，高点持平，回落幅度不超5个点 | 出现乌云盖顶、阴包阳，连续3根K线未突破前高 | 连续2-3交易日收小阳/十字星/小阴，高点持平，出现墓碑线、黄昏星 | +| **MACD（动能辅助）** | 红柱持续缩短，或与价格形成顶背离 | 红柱较峰值缩短≥30%，未出现死叉 | 红柱较峰值缩短≥50%，DIFF开始走平，可出现轻微顶背离 | 红柱较峰值缩短≥50%，顶背离持续2个交易日+，红柱可开始翻绿 | + +#### 2.4.2 回调性质对比（洗盘 vs 反转） + +| 维度 | 洗盘（短期调整，趋势延续） | 反转（趋势结束，进入下跌） | +|------|--------------------------|--------------------------| +| **成交量** | 缩量下跌！较滞涨期量能萎缩≥30%，越跌量越小，无资金主动出逃 | 放量下跌！下跌过程中量能持续放大，越跌量越大，主力砸盘 | +| **K线形态** | 小阴小阳交替下跌，无大阴线破位，偶有十字星，不破短期支撑位 | 大阴线连拉，出现阴包阳、跳空低开等破位K线，有效跌破中期支撑位 | +| **MACD指标** | 红柱小幅缩短，未翻绿、无明确死叉，指标仅从超买区小幅回落 | 红柱快速消失并翻绿，绿柱持续放大，出现明确死叉，顶背离信号延续 | +| **支撑位** | 收盘价始终站在10/20日均线上方 | 有效跌破60日均线（中期支撑），收盘价站在支撑下方 | +| **周期共振** | 日线保持强势，60/30分钟出现短期调整信号，调整后快速共振回升 | 日线率先走弱，60/30分钟同步出现利空信号，三者持续共振走弱 | + +#### 2.4.3 多周期共振判断步骤 + +``` +第一步：看日线（定方向） + ↓ 核对日线的成交量、K线、MACD是否符合洗盘/反转特征 + ↓ 明确中期回调大方向 + +第二步：看60分钟（找衔接） + ↓ 核对60分钟三大维度信号 + ↓ 确认是否与日线方向一致 + +第三步：看30分钟（抓拐点） + ↓ 核对30分钟三大维度信号 + ↓ 确认是否与日线、60分钟方向一致 + +第四步：确认共振级别 + ↓ 2个周期同向 = 初步确认 + ↓ 3个周期同向 = 最终确认 + ↓ 结合基本面验证，制定交易策略 +``` + +#### 2.4.4 滞涨后交易决策 + +| 场景 | 信号特征 | 操作建议 | +|------|---------|---------| +| **滞涨信号初现** | 1个维度达标 | 观望为主，不加减仓，重点跟踪另外两个维度 | +| **滞涨信号共振** | 2-3个维度达标 | 轻仓/大幅减仓（30分钟级减20%，60分钟级减30%-50%，日线级减50%-70%），仅留极小底仓，不新开多单 | +| **确认洗盘** | 三大维度符合洗盘特征（缩量+无破位+MACD未翻绿） | 在短期支撑位附近低吸回补仓位，止损位设为支撑位下方3%-5% | +| **确认反转** | 三大维度符合反转特征（放量下跌+K线破位+MACD翻绿死叉） | 清仓所有多单，可轻仓试空，止损位设为跌破的支撑位上方3%-5%，坚决不抄底 | + +--- + +### 2.5 多空分歧实战分析 + +#### 2.5.1 多空分歧五维度识别 + +| 维度 | 分歧信号特征 | +|------|-------------| +| **成交量** | 价格创新高/新低但量能放大、涨幅/跌幅收窄；或巨量阴线/阳线后无延续 | +| **K线形态** | 长上影/长下影线交替（影线长度超实体2倍）；十字星/纺锤线密集（3根及以上）；反转组合出现；跳空缺口当日回补 | +| **技术指标** | MACD顶/底背离；KDJ超买/超卖区内反复交叉；均线纠缠（MA5/MA10与MA20间距<1%） | +| **盘口持仓** | 主力持仓双增（多空均大幅增仓）；大单异动（频繁大单扫货后砸盘）；持仓量增幅超20%但价格波动<2% | +| **基本面** | 多空消息交织；重要政策/数据发布后价格无明显趋势性波动 | + +#### 2.5.2 分歧强度分级与操作 + +| 级别 | 信号数量 | 概率判断 | 操作建议 | +|------|---------|---------|---------| +| **轻度分歧** | 3-4项 | 大概率震荡整理，趋势放缓 | 仓位控制在50%以内，不追涨杀跌 | +| **中度分歧** | 5-6项 | 反转概率提升，警惕关键位破位 | 仓位控制在30%以内，突破关键位后顺势跟进 | +| **重度分歧** | 7项及以上 | 博弈白热化，价格即将选方向 | 建议观望，等待方向明确 | + +#### 2.5.3 关键位突破有效性验证 + +| 验证维度 | 有效突破（压力位） | 有效跌破（支撑位） | +|---------|------------------|------------------| +| **量能验证** | 突破时量能≥前5日均量1.5倍，分布均匀 | 跌破时量能放大，跌幅超3%，恐慌盘信号明确 | +| **时间验证** | 突破后3个日线周期不回踩，缩量企稳于压力位上方 | 跌破后3个日线周期不反弹，缩量横盘确认 | +| **多周期验证** | 日线突破+60分钟布林带中轨突破+MACD红柱放大 | 日线跌破+60分钟布林带下轨突破+MACD绿柱拉长 | +| **测试次数验证** | 连续测试3-4次后突破，压力位强度衰减 | 连续测试3-4次后跌破，支撑位强度衰减 | + +--- + +## 三、实操Checklist汇总 + +### 3.1 支撑/阻力有效性Checklist + +**适用场景**：盘中快速核对支撑/阻力位是否有效，满足≥4项可确认有效性，≤2项需放弃该点位参考。 + +| 序号 | 核查项 | 结果 | +|------|--------|------| +| 1 | □ 测试次数≥3次，且每次测试后均出现反向走势（支撑回踩反弹，阻力反弹回落） | □ | +| 2 | □ 测试/突破时量价同步（缩量企稳/放量突破，无无量虚动情况） | □ | +| 3 | □ 至少2个时间周期点位重合（如日内5分钟+1小时，波段4小时+日线） | □ | +| 4 | □ 贴合基本面逻辑（对应成本、供需、政策，无基本面背离） | □ | +| 5 | □ 突破后回踩/反抽确认成功（阻力变支撑不跌破，支撑变阻力不突破） | □ | +| 6 | □ 品种流动性充足（主力合约，成交量稳居前列，无低成交波动） | □ | +| 7 | □ 无突发消息干扰（点位非临时消息驱动，可持续性强） | □ | + +**统计**：共____项√，判定：□有效（≥4项） □可疑（3项） □无效（≤2项） + +--- + +### 3.2 止跌确认Checklist + +**适用场景**：快速下跌后判断是否有效止跌，满足≥3项可试仓。 + +| 序号 | 核查项 | 结果 | +|------|--------|------| +| 1 | □ 下跌末端缩量，企稳K线量能≥前3根平均量能1.5倍 | □ | +| 2 | □ 出现锤子线/阳包阴/启明之星等反转形态 | □ | +| 3 | □ 关键支撑位回踩不破，或假跌破后快速收复 | □ | +| 4 | □ 无新利空，或有基本面利好加持 | □ | +| 5 | □ 短周期（5/15分钟）与中周期（1小时）同步企稳 | □ | + +**统计**：共____项√，判定：□可试仓（≥3项） □观望（<3项） + +--- + +### 3.3 反弹性质判断Checklist + +**适用场景**：止跌后判断反弹性质，左侧≥3项为下跌中继，右侧≥3项为反转趋势。 + +**下跌中继反弹** | **反转趋势反弹** +---|--- +□ 反弹缩量，遇阻量能萎缩 | □ 持续放量，突破阻力时量能放大 +□ 无法突破关键阻力位，承压回落 | □ 突破阻力后回踩不跌破，阻力变支撑 +□ 仅短周期反弹，长周期仍空头 | □ 多周期共振向上，均线逐步多头排列 +□ 基本面无改善，下跌驱动仍在 | □ 基本面利好持续发酵，供需格局改变 +□ 反弹持续时间短，快速回落创新低 | □ 反弹持续久，回调浅，低点上移 + +**统计**：下跌中继____项√，反转趋势____项√，判定：□下跌中继 □反转趋势 □待定 + +--- + +### 3.4 多周期共振入场Checklist + +**适用场景**：日内交易多周期共振核查，满足≥8项√且无矛盾信号方可入场。 + +**60分钟周期（定趋势）** + +| 方向 | 核查项 | 结果 | +|------|--------|------| +| 看涨 | □ MA10＞MA20，价格站稳MA10上方，回调不跌破 | □ | +| 看涨 | □ MACD在零轴上方运行，红柱持续/温和放大 | □ | +| 看涨 | □ 量价同步：上涨放量、回调缩量，无缩量新高 | □ | +| 看涨 | □ 布林带中轨向上倾斜，价格在中轨上方震荡 | □ | +| 看跌 | □ MA10＜MA20，价格跌破MA10下方，反弹不突破 | □ | +| 看跌 | □ MACD在零轴下方运行，绿柱持续/温和放大 | □ | +| 看跌 | □ 量价同步：下跌放量、反弹缩量，无缩量新低 | □ | +| 看跌 | □ 布林带中轨向下倾斜，价格在中轨下方震荡 | □ | + +**30分钟周期（找拐点）** + +| 方向 | 核查项 | 结果 | +|------|--------|------| +| 看涨 | □ 出现阳包阴、启明星等反转K线组合 | □ | +| 看涨 | □ 突破前期小高点，且突破时放量 | □ | +| 看涨 | □ MACD零轴上金叉，或死叉后快速金叉 | □ | +| 看涨 | □ 布林带价格突破中轨，回踩不跌破 | □ | +| 看跌 | □ 出现阴包阳、黄昏星等反转K线组合 | □ | +| 看跌 | □ 跌破前期小低点，且跌破时放量 | □ | +| 看跌 | □ MACD零轴下死叉，或金叉后快速死叉 | □ | +| 看跌 | □ 布林带价格跌破中轨，反弹不突破 | □ | + +**15分钟周期（择入场）** + +| 方向 | 核查项 | 结果 | +|------|--------|------| +| 做多 | □ 突破30分钟确认的拐点压力位 | □ | +| 做多 | □ 突破时成交量≥前5根K线均量1.5倍 | □ | +| 做多 | □ MACD红柱同步放大，无顶背离 | □ | +| 做多 | □ 回踩缩量，且不跌破15分钟MA5 | □ | +| 做多 | □ 止损位设置：入场K线低点下方5-10个点 | □ | +| 做空 | □ 跌破30分钟确认的拐点支撑位 | □ | +| 做空 | □ 跌破时成交量≥前5根K线均量1.5倍 | □ | +| 做空 | □ MACD绿柱同步放大，无底背离 | □ | +| 做空 | □ 反弹缩量，且不突破15分钟MA5 | □ | +| 做空 | □ 止损位设置：入场K线高点上方5-10个点 | □ | + +**补充核查** + +| 核查项 | 结果 | +|--------|------| +| □ 三周期信号无矛盾，趋势方向统一 | □ | +| □ 非脉冲式放量，突破后3根K线不回踩关键位 | □ | +| □ 无突发政策、数据发布 | □ | +| □ 单笔仓位≤10%，止损≤账户1%-2% | □ | + +**统计**：共____项√，判定：□可入场（≥8项） □谨慎入场（7-8项） □禁止入场（≤6项） + +--- + +### 3.5 滞涨判断Checklist + +**适用场景**：快速上涨后判断滞涨及回调性质。 + +**滞涨判断（三大维度）** + +| 维度 | 核查项 | 结果 | +|------|--------|------| +| 成交量 | □ 对应当前周期（30/60分钟/日线），量能萎缩达标，或出现放量滞涨信号 | □ | +| K线 | □ 出现对应周期的顶部K线/组合，高点上移乏力，回落幅度匹配周期特征 | □ | +| MACD | □ 红柱缩短达标，或出现对应周期的顶背离，无明显动能回升信号 | □ | +| 周期 | □ 明确当前周期，区分信号级别（日线>60分钟>30分钟） | □ | +| 共振 | □ 至少2项核对项达标，3项全达标时滞涨确认度≥80% | □ | + +**回调性质判断（洗盘 vs 反转）** + +| 维度 | 洗盘特征 | 反转特征 | +|------|---------|---------| +| 成交量 | □ 缩量下跌，较滞涨期萎缩≥30% | □ 放量下跌，越跌量越大 | +| K线形态 | □ 小阴小阳交替，无大阴线破位 | □ 大阴线连拉，出现阴包阳、跳空低开 | +| MACD | □ 红柱小幅缩短，未翻绿、无明确死叉 | □ 红柱快速消失并翻绿，绿柱持续放大 | +| 支撑位 | □ 不跌破短期支撑位（MA10/MA20） | □ 有效跌破中期支撑位（MA60） | +| 周期 | □ 大周期保持强势，小周期短期调整 | □ 大周期率先走弱，小周期同步利空 | + +**统计**：洗盘____项√，反转____项√，判定：□洗盘（≥3项） □反转（≥3项） □待定 + +--- + +### 3.6 多空分歧识别Checklist + +**适用场景**：判断多空分歧强度，满足3项及以上判定为分歧加剧。 + +| 维度 | 核心核查项 | 分歧信号 | +|------|-----------|---------| +| **成交量** | □ 价格创新高/低，量能放大但涨/跌幅收窄 | □ | +| **成交量** | □ 日内量价波动剧烈（幅度超5%） | □ | +| **成交量** | □ 量放50%+但未突破关键位 | □ | +| **K线形态** | □ 连续长影线（接近实体2倍） | □ | +| **K线形态** | □ 出现反转K线组合 | □ | +| **技术指标** | □ MACD顶/底背离 | □ | +| **技术指标** | □ KDJ极端区间反复交叉 | □ | +| **盘口持仓** | □ 盘口大单异动、反复博弈 | □ | +| **基本面** | □ 多空消息交织 | □ | + +**统计**：共____项√，判定：□轻度分歧（3-4项） □中度分歧（5-6项） □重度分歧（7项+） + +--- + +## 四、交易决策流程 + +### 4.1 完整交易决策流程图 + +``` +┌─────────────────────────────────────────────────────────────────┐ +│ 交易决策流程 │ +└─────────────────────────────────────────────────────────────────┘ + │ + ▼ +┌─────────────────────────────────────────────────────────────────┐ +│ 第一步：趋势判断（60分钟周期） │ +│ • MA10/MA20排列方向 │ +│ • MACD零轴位置 │ +│ • 量价同步性 │ +└─────────────────────────────────────────────────────────────────┘ + │ + ┌───────────────┴───────────────┐ + │ │ + ▼ ▼ + ┌─────────────┐ ┌─────────────┐ + │ 趋势不明 │ │ 趋势明确 │ + │（≤2项达标） │ │（≥3项达标） │ + └─────────────┘ └──────┬──────┘ + │ │ + ▼ ▼ + ┌─────────────┐ ┌─────────────┐ + │ 放弃交易 │ │ 第二步：拐点确认（30分钟） │ + └─────────────┘ │ • 反转K线形态 │ + │ • 突破/跌破有效性 │ + │ • MACD金叉/死叉 │ + └──────┬──────┘ + │ + ┌──────────────┴──────────────┐ + │ │ + ▼ ▼ + ┌─────────────┐ ┌─────────────┐ + │ 拐点无效 │ │ 拐点有效 │ + │（与60分钟矛盾）│ │（与60分钟一致）│ + └─────────────┘ └──────┬──────┘ + │ │ + ▼ ▼ + ┌─────────────┐ ┌─────────────┐ + │ 观望等待 │ │ 第三步：入场执行（15分钟） │ + └─────────────┘ │ • 突破确认 │ + │ • 量能验证（≥1.5倍） │ + │ • 设置止损（入场K线±5-10点）│ + └──────┬──────┘ + │ + ▼ + ┌─────────────┐ + │ 持仓管理 │ + │ • 跟踪止盈 │ + │ • 触发止损 │ + │ • 收盘前平仓 │ + └─────────────┘ +``` + +### 4.2 关键决策节点速查表 + +| 决策节点 | 判定标准 | 通过 | 不通过 | +|---------|---------|------|--------| +| **趋势过滤** | 60分钟≥3项√ | 进入拐点确认 | 放弃交易 | +| **拐点确认** | 30分钟≥3项√且与60分钟同向 | 进入入场执行 | 观望等待 | +| **入场确认** | 15分钟≥4项√ + 补充核查全√ | 按计划入场 | 禁止入场 | +| **离场确认** | 触发止损/止盈或60分钟拐头 | 立即平仓 | 继续持有 | + +--- + +## 五、避坑要点汇总 + +### 5.1 通用避坑要点 + +1. **不单一依赖某一维度** + - 仅看K线（如单根墓碑线）、仅看MACD（如红柱缩短），极易误判 + - 必须结合成交量（资金核心）共振确认 + +2. **不忽视周期联动** + - 日线级出现滞涨/反转信号，即使60分钟/30分钟级有反弹信号，也以日线为主，优先减仓 + +3. **不强行共振** + - 若某一周期信号与其他周期矛盾（如日线洗盘，60分钟反转），不算共振，以日线为准，观望等待信号统一 + +4. **不混淆信号强度** + - 日线信号强度 > 60分钟 > 30分钟 + - 不可因30分钟共振反转，忽视日线强势信号，盲目做空 + +5. **不扛单** + - 日线级反转信号共振（放量+破位+MACD死叉），立即清仓 + - 期货带杠杆，扛单会放大亏损 + +### 5.2 分场景避坑要点 + +| 场景 | 避坑要点 | +|------|---------| +| **支撑/阻力交易** | 仅在主力合约（高流动性）中运用支撑/阻力分析；远月合约、低成交量品种的点位无资金验证，参考价值极低 | +| **止跌抄底** | 快速下跌后第一次反弹，80%为下跌中继；建议等待"二次回踩支撑不破+放量拉升"再入场 | +| **多周期共振** | 不背离大趋势交易；高波动品种需等待5+15+60分钟多周期共振确认，不依赖单一周期信号 | +| **滞涨回调** | 不忽视量能共振；多周期共振的核心是成交量共振，无成交量共振，仅K线+MACD共振，准确率大幅降低 | +| **多空分歧** | 分歧期忌追涨杀跌，仓位控制在30%以内；价格突破关键位且量能验证后，顺势跟进 | + +### 5.3 品种特性避坑 + +| 品种类型 | 代表品种 | 注意事项 | +|---------|---------|---------| +| **高波动品种** | 沪锡、沪银、碳酸锂 | 假止跌、假突破信号频发，需多周期共振确认；严格设置止损 | +| **中波动品种** | 燃料油、集运欧线 | 受地缘与供需影响，警惕政策调控风险 | +| **低波动品种** | 甲醇、沪镍 | 适合趋势跟踪，结合产业基本面操作 | + +--- + +## 附录：核心公式与参数 + +### A.1 枢轴点计算公式 +``` +枢轴点（PP）=（高+低+收）/3 +一级支撑（S1）= 2×PP - 高 +二级支撑（S2）= PP -（高-低） +一级压力（R1）= 2×PP - 低 +二级压力（R2）= PP +（高-低） +``` + +### A.2 量能计算标准 +``` +企稳放量标准：企稳K线成交量 ≥ 前3根K线平均量能 × 1.5 +突破放量标准：突破时成交量 ≥ 前5根K线均量 × 1.5 +缩量标准：回调量能较峰值萎缩 ≥ 30%（洗盘特征） +``` + +### A.3 止损设置标准 +``` +日内交易止损：入场K线低点/高点下方/上方 5-10个点 +波段交易止损：支撑位/阻力位下方/上方 3%-5% +资金风控止损：单笔止损 ≤ 账户资金的 1%-2% +``` + +### A.4 仓位管理标准 +``` +正常仓位：单笔仓位 ≤ 10% +试错仓位：≤ 5% +分歧期仓位：≤ 30% +``` + +--- + +**重要提示**：本文档内容仅为技术分析实操参考，不构成任何投资建议。期货交易风险极高，需结合自身风险承受能力，搭配科学止损策略使用，谨慎操作。 + +--- + +*文档整合完成日期：2026年3月7日* +*整合来源：5份期货交易技术分析实战文档*