diff --git a/czsc/connectors/jq_connector.py b/czsc/connectors/jq_connector.py index a2b1ef2bf..000d900b3 100644 --- a/czsc/connectors/jq_connector.py +++ b/czsc/connectors/jq_connector.py @@ -22,11 +22,27 @@ date_fmt = "%Y-%m-%d" # 1m, 5m, 15m, 30m, 60m, 120m, 1d, 1w, 1M -freq_convert = {"1min": "1m", "5min": '5m', '15min': '15m', - "30min": "30m", "60min": '60m', "D": "1d", "W": '1w', "M": "1M"} - -freq_map = {'1min': Freq.F1, '5min': Freq.F5, '15min': Freq.F15, '30min': Freq.F30, - '60min': Freq.F60, 'D': Freq.D, 'W': Freq.W, 'M': Freq.M} +freq_convert = { + "1min": "1m", + "5min": "5m", + "15min": "15m", + "30min": "30m", + "60min": "60m", + "D": "1d", + "W": "1w", + "M": "1M", +} + +freq_map = { + "1min": Freq.F1, + "5min": Freq.F5, + "15min": Freq.F15, + "30min": Freq.F30, + "60min": Freq.F60, + "D": Freq.D, + "W": Freq.W, + "M": Freq.M, +} def set_token(jq_mob, jq_pwd): @@ -38,7 +54,7 @@ def set_token(jq_mob, jq_pwd): Password为聚宽官网登录密码，新申请用户默认为手机号后6位 :return: None """ - with open(file_token, 'wb') as f: + with open(file_token, "wb") as f: pickle.dump([jq_mob, jq_pwd], f) @@ -47,7 +63,7 @@ def get_token(): if not os.path.exists(file_token): raise ValueError(f"{file_token} 文件不存在，请先调用 set_token 进行设置") - with open(file_token, 'rb') as f: + with open(file_token, "rb") as f: jq_mob, jq_pwd = pickle.load(f) body = { @@ -61,7 +77,7 @@ def get_token(): def text2df(text): - rows = [x.split(",") for x in text.strip().split('\n')] + rows = [x.split(",") for x in text.strip().split("\n")] df = pd.DataFrame(rows[1:], columns=rows[0]) return df @@ -120,14 +136,9 @@ def get_concept_stocks(symbol, date=None): if isinstance(date, datetime): date = str(date.date()) - data = { - "method": "get_concept_stocks", - "token": get_token(), - "code": symbol, - "date": date - } + data = {"method": "get_concept_stocks", "token": get_token(), "code": symbol, "date": date} r = requests.post(url, data=json.dumps(data)) - return r.text.split('\n') + return r.text.split("\n") def get_index_stocks(symbol, date=None): @@ -152,14 +163,9 @@ def get_index_stocks(symbol, date=None): if isinstance(date, datetime): date = str(date.date()) - data = { - "method": "get_index_stocks", - "token": get_token(), - "code": symbol, - "date": date - } + data = {"method": "get_index_stocks", "token": get_token(), "code": symbol, "date": date} r = requests.post(url, data=json.dumps(data)) - return r.text.split('\n') + return r.text.split("\n") def get_industry(symbol): @@ -168,28 +174,23 @@ def get_industry(symbol): :param symbol: :return: """ - data = { - "method": "get_industry", - "token": get_token(), - "code": symbol, - "date": str(datetime.now().date()) - } + data = {"method": "get_industry", "token": get_token(), "code": symbol, "date": str(datetime.now().date())} r = requests.post(url, data=json.dumps(data)) df = text2df(r.text) res = { "股票代码": symbol, - "证监会行业代码": df[df['industry'] == 'zjw']['industry_code'].iloc[0], - "证监会行业名称": df[df['industry'] == 'zjw']['industry_name'].iloc[0], - "聚宽一级行业代码": df[df['industry'] == 'jq_l1']['industry_code'].iloc[0], - "聚宽一级行业名称": df[df['industry'] == 'jq_l1']['industry_name'].iloc[0], - "聚宽二级行业代码": df[df['industry'] == 'jq_l2']['industry_code'].iloc[0], - "聚宽二级行业名称": df[df['industry'] == 'jq_l2']['industry_name'].iloc[0], - "申万一级行业代码": df[df['industry'] == 'sw_l1']['industry_code'].iloc[0], - "申万一级行业名称": df[df['industry'] == 'sw_l1']['industry_name'].iloc[0], - "申万二级行业代码": df[df['industry'] == 'sw_l2']['industry_code'].iloc[0], - "申万二级行业名称": df[df['industry'] == 'sw_l2']['industry_name'].iloc[0], - "申万三级行业代码": df[df['industry'] == 'sw_l3']['industry_code'].iloc[0], - "申万三级行业名称": df[df['industry'] == 'sw_l3']['industry_name'].iloc[0], + "证监会行业代码": df[df["industry"] == "zjw"]["industry_code"].iloc[0], + "证监会行业名称": df[df["industry"] == "zjw"]["industry_name"].iloc[0], + "聚宽一级行业代码": df[df["industry"] == "jq_l1"]["industry_code"].iloc[0], + "聚宽一级行业名称": df[df["industry"] == "jq_l1"]["industry_name"].iloc[0], + "聚宽二级行业代码": df[df["industry"] == "jq_l2"]["industry_code"].iloc[0], + "聚宽二级行业名称": df[df["industry"] == "jq_l2"]["industry_name"].iloc[0], + "申万一级行业代码": df[df["industry"] == "sw_l1"]["industry_code"].iloc[0], + "申万一级行业名称": df[df["industry"] == "sw_l1"]["industry_name"].iloc[0], + "申万二级行业代码": df[df["industry"] == "sw_l2"]["industry_code"].iloc[0], + "申万二级行业名称": df[df["industry"] == "sw_l2"]["industry_name"].iloc[0], + "申万三级行业代码": df[df["industry"] == "sw_l3"]["industry_code"].iloc[0], + "申万三级行业名称": df[df["industry"] == "sw_l3"]["industry_name"].iloc[0], } return res @@ -213,18 +214,14 @@ def get_all_securities(code, date=None) -> pd.DataFrame: if isinstance(date, datetime): date = str(date.date()) - data = { - "method": "get_all_securities", - "token": get_token(), - "code": code, - "date": date - } + data = {"method": "get_all_securities", "token": get_token(), "code": code, "date": date} r = requests.post(url, data=json.dumps(data)) return text2df(r.text) -def get_kline(symbol: str, end_date: [datetime, str], freq: str, - start_date: [datetime, str] = None, count=None, fq: bool = True) -> List[RawBar]: +def get_kline( + symbol: str, end_date: [datetime, str], freq: str, start_date: [datetime, str] = None, count=None, fq: bool = True +) -> List[RawBar]: """获取K线数据 https://www.joinquant.com/help/api/help#JQDataHttp:get_priceget_bars-%E8%8E%B7%E5%8F%96%E6%8C%87%E5%AE%9A%E6%97%B6%E9%97%B4%E5%91%A8%E6%9C%9F%E7%9A%84%E8%A1%8C%E6%83%85%E6%95%B0%E6%8D%AE @@ -274,7 +271,7 @@ def get_kline(symbol: str, end_date: [datetime, str], freq: str, data.update({"fq_ref_date": end_date.strftime("%Y-%m-%d")}) r = requests.post(url, data=json.dumps(data)) - rows = [x.split(",") for x in r.text.strip().split('\n')][1:] + rows = [x.split(",") for x in r.text.strip().split("\n")][1:] bars = [] i = -1 @@ -286,12 +283,20 @@ def get_kline(symbol: str, end_date: [datetime, str], freq: str, if int(row[5]) > 0: i += 1 - bars.append(RawBar(symbol=symbol, dt=dt, id=i, freq=freq_map[freq], - open=round(float(row[1]), 2), - close=round(float(row[2]), 2), - high=round(float(row[3]), 2), - low=round(float(row[4]), 2), - vol=int(row[5]), amount=int(float(row[6])))) + bars.append( + RawBar( + symbol=symbol, + dt=dt, + id=i, + freq=freq_map[freq], + open=round(float(row[1]), 4), + close=round(float(row[2]), 4), + high=round(float(row[3]), 4), + low=round(float(row[4]), 4), + vol=int(row[5]), + amount=int(float(row[6])), + ) + ) # amount 单位：元 if start_date: bars = [x for x in bars if x.dt >= start_date] @@ -301,8 +306,9 @@ def get_kline(symbol: str, end_date: [datetime, str], freq: str, return bars -def get_kline_period(symbol: str, start_date: [datetime, str], - end_date: [datetime, str], freq: str, fq=True) -> List[RawBar]: +def get_kline_period( + symbol: str, start_date: [datetime, str], end_date: [datetime, str], freq: str, fq=True +) -> List[RawBar]: """获取指定时间段的行情数据 https://www.joinquant.com/help/api/help#JQDataHttp:get_price_periodget_bars_period-%E8%8E%B7%E5%8F%96%E6%8C%87%E5%AE%9A%E6%97%B6%E9%97%B4%E6%AE%B5%E7%9A%84%E8%A1%8C%E6%83%85%E6%95%B0%E6%8D%AE @@ -331,7 +337,7 @@ def get_kline_period(symbol: str, start_date: [datetime, str], data.update({"fq_ref_date": end_date.strftime("%Y-%m-%d")}) r = requests.post(url, data=json.dumps(data)) - rows = [x.split(",") for x in r.text.strip().split('\n')][1:] + rows = [x.split(",") for x in r.text.strip().split("\n")][1:] bars = [] i = -1 for row in rows: @@ -342,12 +348,20 @@ def get_kline_period(symbol: str, start_date: [datetime, str], if int(row[5]) > 0: i += 1 - bars.append(RawBar(symbol=symbol, dt=dt, id=i, freq=freq_map[freq], - open=round(float(row[1]), 2), - close=round(float(row[2]), 2), - high=round(float(row[3]), 2), - low=round(float(row[4]), 2), - vol=int(row[5]), amount=int(float(row[6])))) + bars.append( + RawBar( + symbol=symbol, + dt=dt, + id=i, + freq=freq_map[freq], + open=round(float(row[1]), 4), + close=round(float(row[2]), 4), + high=round(float(row[3]), 4), + low=round(float(row[4]), 4), + vol=int(row[5]), + amount=int(float(row[6])), + ) + ) # amount 单位：元 if start_date: bars = [x for x in bars if x.dt >= start_date] @@ -357,12 +371,7 @@ def get_kline_period(symbol: str, start_date: [datetime, str], return bars -def get_init_bg(symbol: str, - end_dt: [str, datetime], - base_freq: str, - freqs: List[str], - max_count=1000, - fq=True): +def get_init_bg(symbol: str, end_dt: [str, datetime], base_freq: str, freqs: List[str], max_count=1000, fq=True): """获取 symbol 的初始化 bar generator""" if isinstance(end_dt, str): end_dt = pd.to_datetime(end_dt, utc=False) @@ -380,7 +389,8 @@ def get_init_bg(symbol: str, data = [x for x in bars2 if x.dt > last_day] assert len(data) > 0 print( - f"{symbol}: bar generator 最新时间 {bg.bars[base_freq][-1].dt.strftime(dt_fmt)}，还有{len(data)}行数据需要update") + f"{symbol}: bar generator 最新时间 {bg.bars[base_freq][-1].dt.strftime(dt_fmt)}，还有{len(data)}行数据需要update" + ) return bg, data @@ -411,7 +421,7 @@ def get_fundamental(table: str, symbol: str, date: str, columns: str = "") -> di "columns": columns, "code": symbol, "date": date, - "count": 1 + "count": 1, } r = requests.post(url, data=json.dumps(data)) df = text2df(r.text) @@ -435,15 +445,9 @@ def run_query(table: str, conditions: str, columns=None, count=1): :param count: 数量 :return: """ - data = { - "method": "run_query", - "token": get_token(), - "table": table, - "conditions": conditions, - "count": count - } + data = {"method": "run_query", "token": get_token(), "table": table, "conditions": conditions, "count": count} if columns: - data['columns'] = columns + data["columns"] = columns r = requests.post(url, data=json.dumps(data)) df = text2df(r.text) return df @@ -459,64 +463,80 @@ def get_share_basic(symbol): basic_info = basic_info.iloc[0].to_dict() f10 = OrderedDict() - f10['股票代码'] = basic_info['code'] - f10['股票名称'] = basic_info['short_name'] - f10['行业'] = "{}-{}".format(basic_info['industry_1'], basic_info['industry_2']) - f10['地域'] = "{}{}".format(basic_info['province'], basic_info['city']) - f10['主营'] = basic_info['main_business'] - f10['同花顺F10'] = "http://basic.10jqka.com.cn/{}".format(basic_info['code'][:6]) + f10["股票代码"] = basic_info["code"] + f10["股票名称"] = basic_info["short_name"] + f10["行业"] = "{}-{}".format(basic_info["industry_1"], basic_info["industry_2"]) + f10["地域"] = "{}{}".format(basic_info["province"], basic_info["city"]) + f10["主营"] = basic_info["main_business"] + f10["同花顺F10"] = "http://basic.10jqka.com.cn/{}".format(basic_info["code"][:6]) # 市盈率，总市值，流通市值，流通比 # ------------------------------------------------------------------------------------------------------------------ last_date = datetime.now() - timedelta(days=1) res = get_fundamental(table="valuation", symbol=symbol, date=last_date.strftime("%Y-%m-%d")) - f10['总市值（亿）'] = float(res['market_cap']) - f10['流通市值（亿）'] = float(res['circulating_market_cap']) - f10['流通比（%）'] = round(float(res['circulating_market_cap']) / float(res['market_cap']) * 100, 2) - f10['PE_TTM'] = float(res['pe_ratio']) - f10['PE'] = float(res['pe_ratio_lyr']) - f10['PB'] = float(res['pb_ratio']) + f10["总市值（亿）"] = float(res["market_cap"]) + f10["流通市值（亿）"] = float(res["circulating_market_cap"]) + f10["流通比（%）"] = round(float(res["circulating_market_cap"]) / float(res["market_cap"]) * 100, 2) + f10["PE_TTM"] = float(res["pe_ratio"]) + f10["PE"] = float(res["pe_ratio_lyr"]) + f10["PB"] = float(res["pb_ratio"]) # 净资产收益率 # ------------------------------------------------------------------------------------------------------------------ - for year in ['2017', '2018', '2019', '2020']: + for year in ["2017", "2018", "2019", "2020"]: indicator = get_fundamental(table="indicator", symbol=symbol, date=year) - f10['{}EPS'.format(year)] = float(indicator.get('eps', 0)) if indicator.get('eps', 0) else 0 - f10['{}ROA'.format(year)] = float(indicator.get('roa', 0)) if indicator.get('roa', 0) else 0 - f10['{}ROE'.format(year)] = float(indicator.get('roe', 0)) if indicator.get('roe', 0) else 0 - f10['{}销售净利率(%)'.format(year)] = float(indicator.get('net_profit_margin', 0)) if indicator.get( - 'net_profit_margin', 0) else 0 - f10['{}销售毛利率(%)'.format(year)] = float(indicator.get('gross_profit_margin', 0)) if indicator.get( - 'gross_profit_margin', 0) else 0 - f10['{}营业收入同比增长率(%)'.format(year)] = float( - indicator.get('inc_revenue_year_on_year', 0)) if indicator.get('inc_revenue_year_on_year', 0) else 0 - f10['{}营业收入环比增长率(%)'.format(year)] = float(indicator.get('inc_revenue_annual', 0)) if indicator.get( - 'inc_revenue_annual', 0) else 0 - f10['{}营业利润同比增长率(%)'.format(year)] = float( - indicator.get('inc_operation_profit_year_on_year', 0)) if indicator.get('inc_operation_profit_year_on_year', - 0) else 0 - f10['{}经营活动产生的现金流量净额/营业收入(%)'.format(year)] = float( - indicator.get('ocf_to_revenue', 0)) if indicator.get('ocf_to_revenue', 0) else 0 + f10["{}EPS".format(year)] = float(indicator.get("eps", 0)) if indicator.get("eps", 0) else 0 + f10["{}ROA".format(year)] = float(indicator.get("roa", 0)) if indicator.get("roa", 0) else 0 + f10["{}ROE".format(year)] = float(indicator.get("roe", 0)) if indicator.get("roe", 0) else 0 + f10["{}销售净利率(%)".format(year)] = ( + float(indicator.get("net_profit_margin", 0)) if indicator.get("net_profit_margin", 0) else 0 + ) + f10["{}销售毛利率(%)".format(year)] = ( + float(indicator.get("gross_profit_margin", 0)) if indicator.get("gross_profit_margin", 0) else 0 + ) + f10["{}营业收入同比增长率(%)".format(year)] = ( + float(indicator.get("inc_revenue_year_on_year", 0)) if indicator.get("inc_revenue_year_on_year", 0) else 0 + ) + f10["{}营业收入环比增长率(%)".format(year)] = ( + float(indicator.get("inc_revenue_annual", 0)) if indicator.get("inc_revenue_annual", 0) else 0 + ) + f10["{}营业利润同比增长率(%)".format(year)] = ( + float(indicator.get("inc_operation_profit_year_on_year", 0)) + if indicator.get("inc_operation_profit_year_on_year", 0) + else 0 + ) + f10["{}经营活动产生的现金流量净额/营业收入(%)".format(year)] = ( + float(indicator.get("ocf_to_revenue", 0)) if indicator.get("ocf_to_revenue", 0) else 0 + ) # 组合成可以用来推送的文本 - msg = "{}（{}）@{}\n".format(f10['股票代码'], f10['股票名称'], f10['地域']) + msg = "{}（{}）@{}\n".format(f10["股票代码"], f10["股票名称"], f10["地域"]) msg += "\n{}\n".format("*" * 30) - for k in ['行业', '主营', 'PE_TTM', 'PE', 'PB', '总市值（亿）', '流通市值（亿）', '流通比（%）', '同花顺F10']: + for k in ["行业", "主营", "PE_TTM", "PE", "PB", "总市值（亿）", "流通市值（亿）", "流通比（%）", "同花顺F10"]: msg += "{}：{}\n".format(k, f10[k]) msg += "\n{}\n".format("*" * 30) - cols = ['EPS', 'ROA', 'ROE', '销售净利率(%)', '销售毛利率(%)', '营业收入同比增长率(%)', '营业利润同比增长率(%)', - '经营活动产生的现金流量净额/营业收入(%)'] + cols = [ + "EPS", + "ROA", + "ROE", + "销售净利率(%)", + "销售毛利率(%)", + "营业收入同比增长率(%)", + "营业利润同比增长率(%)", + "经营活动产生的现金流量净额/营业收入(%)", + ] msg += "2017~2020 财务变化\n\n" for k in cols: msg += k + "：{} | {} | {} | {}\n".format( - *[f10['{}{}'.format(year, k)] for year in ['2017', '2018', '2019', '2020']]) + *[f10["{}{}".format(year, k)] for year in ["2017", "2018", "2019", "2020"]] + ) - f10['msg'] = msg + f10["msg"] = msg return f10 -def get_symbols(name='ALL', **kwargs): +def get_symbols(name="ALL", **kwargs): """获取指定分组下的所有标的代码 :param name: 分组名称，可选值： @@ -526,17 +546,19 @@ def get_symbols(name='ALL', **kwargs): :param kwargs: 其他参数 :return: """ - if name.upper() == 'ALL': - codes = get_all_securities('stock', date=None)['code'].unique().tolist() + \ - get_all_securities('index', date=None)['code'].unique().tolist() + \ - get_all_securities('futures', date=None)['code'].unique().tolist() + \ - get_all_securities('etf', date=None)['code'].unique().tolist() + if name.upper() == "ALL": + codes = ( + get_all_securities("stock", date=None)["code"].unique().tolist() + + get_all_securities("index", date=None)["code"].unique().tolist() + + get_all_securities("futures", date=None)["code"].unique().tolist() + + get_all_securities("etf", date=None)["code"].unique().tolist() + ) else: - codes = get_all_securities(name, date=None)['code'].unique().tolist() + codes = get_all_securities(name, date=None)["code"].unique().tolist() return codes -def get_raw_bars(symbol, freq, sdt, edt, fq='前复权', **kwargs): +def get_raw_bars(symbol, freq, sdt, edt, fq="前复权", **kwargs): """获取 CZSC 库定义的标准 RawBar 对象列表 :param symbol: 标的代码 @@ -548,9 +570,17 @@ def get_raw_bars(symbol, freq, sdt, edt, fq='前复权', **kwargs): :param kwargs: :return: """ - kwargs['fq'] = fq + kwargs["fq"] = fq freq = str(freq) fq = True if fq == "前复权" else False - _map = {"1分钟": "1min", "5分钟": "5min", "15分钟": "15min", "30分钟": "30min", - "60分钟": "60min", "日线": "D", "周线": "W", "月线": "M"} + _map = { + "1分钟": "1min", + "5分钟": "5min", + "15分钟": "15min", + "30分钟": "30min", + "60分钟": "60min", + "日线": "D", + "周线": "W", + "月线": "M", + } return get_kline(symbol, freq=_map[freq], start_date=sdt, end_date=edt, fq=fq)