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feat: add technical indicator calculation (30 indicators via MyTT), bump to 1.4.0
Integrate MyTT library to provide 30 technical indicators (MACD, KDJ, RSI, BOLL, DMI, ATR, etc.) accessible via API and CLI with automatic EMA warm-up. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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Claude Opus 4.7
Claude Opus 4.7
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"""演示：技术指标计算。
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通过 MacClient 的 get_stock_kline_with_indicators() 获取 K 线并直接计算技术指标。
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内部自动获取 200+ 条历史数据进行 EMA 预热，仅返回最后 count 条结果。
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也可单独使用 compute_indicators() 对已有的 K 线 DataFrame 计算指标。
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支持的指标（30 个）:
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MACD KDJ RSI BOLL DMI ATR WR CCI BIAS OBV
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VR EMV MFI BRAR ASI TRIX DPO MTM ROC EXPMA
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BBI PSY DFMA CR KTN XSII MASS TAQ
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参数:
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market -- 市场代码（Market.SH=1, Market.SZ=0）
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code -- 股票代码
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indicators -- 指标名称列表（不区分大小写），如 ["MACD", "KDJ"]
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count -- 返回条数（默认 30）
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adjust -- 复权方式（默认 QFQ 前复权，技术分析推荐前复权）
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params -- 可选参数覆盖，如 {"MACD": {"SHORT": 10}}
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返回 DataFrame 列说明（以 MACD 为例）:
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datetime datetime K 线时间
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open float 开盘价
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high float 最高价
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low float 最低价
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close float 收盘价
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vol float 成交量
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amount float 成交额
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MACD_DIF float MACD 的 DIF 线
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MACD_DEA float MACD 的 DEA 线
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MACD_HIST float MACD 柱状图（(DIF-DEA)*2）
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"""
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from easy_tdx import Adjust, MacClient, Market, Period
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with MacClient.from_best_host() as c:
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# --- MACD（贵州茅台，日线，前复权）---
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print("=== MACD（贵州茅台 600519）===")
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df = c.get_stock_kline_with_indicators(
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Market.SH,
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"600519",
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indicators=["MACD"],
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count=10,
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)
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print(df[["datetime", "close", "MACD_DIF", "MACD_DEA", "MACD_HIST"]].to_string(index=False))
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# --- KDJ（平安银行）---
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print("\n=== KDJ（平安银行 000001）===")
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df = c.get_stock_kline_with_indicators(
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Market.SZ,
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"000001",
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indicators=["KDJ"],
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count=10,
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)
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print(df[["datetime", "close", "KDJ_K", "KDJ_D", "KDJ_J"]].to_string(index=False))
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# --- RSI（贵州茅台）---
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print("\n=== RSI（贵州茅台 600519，N=6 短周期）===")
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df = c.get_stock_kline_with_indicators(
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Market.SH,
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"600519",
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indicators=["RSI"],
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count=10,
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params={"RSI": {"N": 6}},
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)
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print(df[["datetime", "close", "RSI"]].to_string(index=False))
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# --- BOLL 布林带 ---
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print("\n=== BOLL 布林带（贵州茅台 600519）===")
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df = c.get_stock_kline_with_indicators(
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Market.SH,
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"600519",
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indicators=["BOLL"],
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count=10,
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)
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print(df[["datetime", "close", "BOLL_UPPER", "BOLL_MID", "BOLL_LOWER"]].to_string(index=False))
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# --- 多指标同时计算 ---
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print("\n=== MACD + KDJ + RSI + BOLL 联合计算 ===")
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df = c.get_stock_kline_with_indicators(
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Market.SH,
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"600519",
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indicators=["MACD", "KDJ", "RSI", "BOLL"],
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count=5,
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)
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cols = ["datetime", "close", "MACD_DIF", "KDJ_K", "RSI", "BOLL_UPPER", "BOLL_LOWER"]
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print(df[cols].to_string(index=False))
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# --- 仅输出指标列（不含 OHLCV）---
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print("\n=== 仅指标值（--no-ohlcv 模式）===")
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df = c.get_stock_kline_with_indicators(
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Market.SZ,
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"000001",
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indicators=["MACD", "RSI"],
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count=5,
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)
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indicator_cols = [
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c for c in df.columns if c not in ("open", "high", "low", "close", "vol", "amount")
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]
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print(df[indicator_cols].to_string(index=False))
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# --- 分钟 K 线 + 指标 ---
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print("\n=== 5 分钟线 MACD（贵州茅台 600519）===")
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df = c.get_stock_kline_with_indicators(
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Market.SH,
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"600519",
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indicators=["MACD"],
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period=Period.MIN_5,
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count=5,
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)
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print(df[["datetime", "close", "MACD_DIF", "MACD_DEA", "MACD_HIST"]].to_string(index=False))
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# --- 使用 compute_indicators 独立计算 ---
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print("\n=== 独立使用 compute_indicators ===")
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from easy_tdx.indicator import compute_indicators
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raw_df = c.get_stock_kline(Market.SH, "600519", Period.DAILY, count=200, adjust=Adjust.QFQ)
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result = compute_indicators(raw_df, ["ATR", "CCI", "WR"], tail=5)
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print(result[["datetime", "close", "ATR", "CCI", "WR1", "WR2"]].to_string(index=False))
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# 运行结果（示例）:
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# === MACD（贵州茅台 600519）===
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# datetime close MACD_DIF MACD_DEA MACD_HIST
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# 2025-05-02 00:00:00 1492.00 -4.12 -1.56 -5.12
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# 2025-05-05 00:00:00 1485.00 -5.23 -2.29 -5.88
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# 2025-05-06 00:00:00 1498.00 -4.56 -2.94 -3.24
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# 2025-05-07 00:00:00 1510.00 -3.12 -3.18 0.11
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# 2025-05-08 00:00:00 1505.00 -2.45 -3.03 1.16
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# 2025-05-09 00:00:00 1505.00 -1.89 -2.80 1.82
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# 2025-05-12 00:00:00 1498.00 -1.78 -2.60 1.64
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# 2025-05-13 00:00:00 1510.00 -0.89 -2.26 2.74
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# 2025-05-14 00:00:00 1509.00 -0.12 -1.83 3.42
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# 2025-05-15 00:00:00 1521.00 1.23 -1.22 4.90
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#
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# === KDJ（平安银行 000001）===
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# datetime close KDJ_K KDJ_D KDJ_J
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# 2025-05-02 00:00:00 12.45 65.32 58.76 78.44
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# 2025-05-05 00:00:00 12.30 42.15 53.23 19.98
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# 2025-05-06 00:00:00 12.58 71.23 59.23 95.24
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# 2025-05-07 00:00:00 12.72 82.45 65.47 116.41
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# 2025-05-08 00:00:00 12.65 74.56 67.29 89.11
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# ...
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