import pandas as pd import finnhub import requests import numpy as np import time from datetime import datetime from sklearn.cluster import KMeans import matplotlib.pyplot as plt import pandas as pd import mplfinance as mpf from mplfinance.original_flavor import candlestick_ohlc import finnhub import matplotlib.dates as mpl_dates import numpy as np from datetime import * import seaborn as sns from datetime import datetime import requests #f = pd.DataFrame(ticker_sum) #f =pd.DataFrame(symbols) from datetime import datetime as dt import datetime # today = dt.today() # a = str(today.year) # b = str(today.month) # c = str(today.day) # f = pd.read_csv(r'C:\Users\jizha\Desktop\seabridge_datapool1\final_strategy_data_temporaly\three_strategy_buy_point_'+a+'_'+b+'_'+c+'.csv') #ticker_sum.remove('GOOG') # ticker_sum = f['0'].tolist() #ticker_sum.remove() import pandas as pd import requests import matplotlib.pyplot as plt import numpy as np import requests import matplotlib.pyplot as plt from math import floor import finnhub from termcolor import colored as cl # image_trading_signal_green .to_csv(r'C:\Users\jizha\Desktop\seabridge_datapool1\final_strategy_data_temporaly\image_trading_signal_green_'+a+'_'+b+'_'+c+'.csv') # image_trading_signal_purple.to_csv(r'C:\Users\jizha\Desktop\seabridge_datapool1\final_strategy_data_temporaly\image_trading_signal_purple_'+a+'_'+b+'_'+c+'.csv') # image_trading_signal_blue .to_csv(r'C:\Users\jizha\Desktop\seabridge_datapool1\final_strategy_data_temporaly\image_trading_signal_blue_'+a+'_'+b+'_'+c+'.csv') from datetime import datetime as dt import datetime # today = dt.today() # a=str(today.year) # b=str(today.month) # c=str(today.day) # #c1= pd.read_csv(r'C:\Users\jizha\Desktop\Strategy_auto_2_21_2022\results\startegy1.csv_'+a+'_'+b+'_'+c+'.csv') # c1= pd.read_csv(r'C:\Users\jizha\Desktop\seabridge_datapool1\final_strategy_data_temporaly\startegy1.csv_'+a+'_'+b+'_'+c+'.csv') # c1 = c1.rename(columns = {'Unnamed: 0':'symbol'}) # strategy1 = c1.set_index('symbol') # today = dt.today() # a = str(today.year) # b = str(today.month) # c = str(today.day) # d = pd.read_csv(r'C:\Users\jizha\Desktop\seabridge_datapool1\final_strategy_data_temporaly\startegy2.csv_'+a+'_'+b+'_'+c+'.csv') # d = d.rename(columns = {'Unnamed: 0':'symbol'}) # strategy2 = d.set_index('symbol') # ticker_sum.remove('L&TFH.NS') # #strategy1=pd.read_csv(r'C:\Users\jizha\Desktop\seabridge_datapool1\final_strategy_data_temporaly\startegy1.csv_'+a+'_'+b+'_'+c+'.csv') # today = dt.today() # a = str(today.year) # b = str(today.month) # c = str(today.day) # e = pd.read_csv(r'C:\Users\jizha\Desktop\seabridge_datapool1\final_strategy_data_temporaly\startegy3.csv_'+a+'_'+b+'_'+c+'.csv') # e = e.rename(columns = {'Unnamed: 0':'symbol'}) # strategy3 = e.set_index('symbol') ticker1 = strategy1.index.tolist() ticker2 = strategy2.index.tolist() ticker3 = strategy3.index.tolist() ticker_sum = ticker1+ticker2+ticker3 ticker_sum = np.unique(ticker_sum).tolist() len(ticker_sum) f = pd.DataFrame(ticker_sum) #f =pd.DataFrame(symbols) today = dt.today() a = str(today.year) b = str(today.month) c = str(today.day) #f.to_csv(r'C:\Users\jizha\Desktop\Strategy_auto_2_21_2022\results\three_strategy_buy_point_'+a+'_'+b+'_'+c+'.csv') #stock_info_data5['price'] = stock_info_data5['price'].astype(float) #stock_info_data5 =stock_info_data5[stock_info_data5['price'] >=60 ] #ticker_sum.remove('LPI') #ticker_sum = stock_info_data5.index.tolist() f.to_csv(r'C:\Users\jizha\Desktop\seabridge_datapool1\final_strategy_data_temporaly\three_strategy_buy_point_'+a+'_'+b+'_'+c+'.csv') #ticker_sum = ticker_sum2 # ticker1.remove('M&MFIN.NS') # ticker1.remove('REGI') # ticker1.remove('ADSK') # ticker_sum.remove('XYL') # strategy1 = strategy1.drop('REGI') # strategy1 = strategy1.drop('COUP') # strategy1 = strategy1.drop('AFL') # strategy1 = strategy1.drop('CBOE') # strategy1= strategy1.drop('XYL') # strategy1= strategy1.drop('L&TFH.NS') # strategy1= strategy1.drop('PPHC.L') # strategy3= strategy1.drop('CME') # strategy1= strategy1.drop('OPK.TA') # J&KBANK.NS # L&TFH.NS # L&TFH.NS pd.set_option('display.float_format', '{:.2E}'.format) from IPython.display import display # return stocks api_key = '86dd63f6b8ae774b061232685b78eb52' def get_profile(symbols): def get_companyinfo(symbols): import requests from datetime import datetime api_key = '86dd63f6b8ae774b061232685b78eb52' stocks = pd.DataFrame(columns = [ 'description','symbol', 'logourl', 'name','mktCap','lastDiv']) for symb in symbols: print(symb) comp = requests.get(f'https://financialmodelingprep.com/api/v3/profile/{symb}?apikey={api_key}').json()[0] stocks = stocks.append({'description': comp['description'], 'symbol': comp['symbol'], 'logourl': comp['image'], 'name': comp['companyName'], 'mktCap':comp['mktCap'],'lastDiv':comp['lastDiv']}, ignore_index = True) stocks['Date_access'] = datetime.now().strftime("%d/%m/%Y %H:%M:%S") # stocks['div_yiel'] = stocks['lastDiv']/stocks['price'] # stocks[['lastDiv','price']] = stocks[['lastDiv','price']].applymap("{0:,.2f}".format) # stocks['div_yiel'] =stocks['div_yiel'].apply(lambda x: "{0:.2f}%".format(x*100)) # df['var3'] = df['var3'].applymap(lambda x: "{0:.2f}%".format(x*100)) # stocks = stocks.style.format({'lastDiv': "{:.2f}".format,'price': "{:.2f}".format,'div_yiel': "{:.2%}".format}) stocks['mktCap'] = ( stocks['mktCap'].astype(float)/1000000).round(2).astype(str) + 'MM' stocks =stocks.set_index('symbol') stocks =stocks.sort_index() # stocks = stocks.style.format({'mktCap': "{:.2f}",'volAvg': "{:.2f}",'var3': "{:.2%}"}) return stocks stocks = get_companyinfo(symbols) # df[['var1','var2']] = df[['var1','var2']].applymap("{0:.2f}".format) # df['var3'] = df['var3'].applymap(lambda x: "{0:.2f}%".format(x*100)) ######################earningdate######################### def earning_date1(symbols): api_key = '86dd63f6b8ae774b061232685b78eb52' bs = requests.get(f'https://financialmodelingprep.com/api/v3/earning_calendar?apikey={api_key}').json() #bs = pd.DataFrame(bs) df =pd.DataFrame() for i in range(len(bs)): d =pd.DataFrame() d1=bs[i]['symbol'] d2 = bs[i]['date'] d['earning_date'] =[d2] d.index = [d1] df = pd.concat([d,df],axis =0) # earning_date = pd.DataFrame() earning_date = pd.DataFrame() for tick in symbols: dd= pd.DataFrame() if tick not in df.index: d1 ="Not Available" else: d1=df['earning_date'][tick] dd['symbol'] = [tick] dd['earning_date'] = [d1] dd = dd.set_index('symbol') earning_date = pd.concat([earning_date ,dd],axis =0) earning_date= earning_date.sort_index() return earning_date earning_date = earning_date1(symbols) # earning_date = earning_date1(ticker_sum) ##################peratio################## def eps_pe(symbols): api_key1 = '86dd63f6b8ae774b061232685b78eb52' da1 = pd.DataFrame(columns = [ 'pe_ratio(TTM)','symbol', 'eps(TTM)']) for symb in symbols: comp = requests.get(f'https://financialmodelingprep.com/api/v3/quote/{symb}?apikey={api_key1}').json()[0] da1 = da1.append({'pe_ratio(TTM)': comp['pe'], 'symbol': comp['symbol'], 'eps(TTM)': comp['eps']}, ignore_index = True) da1 = da1.set_index('symbol') da1[['pe_ratio(TTM)','eps(TTM)']] = da1[['pe_ratio(TTM)','eps(TTM)']] .applymap("{0:,.2f}".format) da1= da1.sort_index() return da1 # stocks['Date_access'] = datetime.now().strftime("%d/%m/%Y %H:%M:%S") da2 = eps_pe(symbols) stock_info=stocks.merge(earning_date, left_index=True, right_index=True) stock_info_data = stock_info.merge(da2, left_index=True, right_index=True) stock_info_data = stock_info_data.sort_index() return stock_info_data stock_info_data = get_profile(ticker_sum) #stock_info_data1 =stock_info_data.merge(strategy_sum3, right_index = True, left_index = True) stock_info_data =stock_info_data .sort_index() # n = 3# number of groups # groups = np.array_split(symbols, n) # da2 =pd.DataFrame() # import time # for group in groups: # da1 = eps_pe(group) # da2 =pd.concat([da2,da1], axis =0) # time.sleep(60) stock_info_data.to_csv(r'C:\Users\jizha\Desktop\seabridge_datapool1\final_strategy_data_temporaly\stock_info_data.csv') #stock_info_data = stock_info_data[['description','logourl', 'name','mktCap','volAvg','lastDiv', 'earning_date', 'pe_ratio(TTM)', 'eps(TTM)']] def get_image(symbols): image_strategy_return = pd.DataFrame() for ticker in symbols: image = pd.DataFrame() dr = "https://3arbzfbsh-cname-us.ngrok.io/Desktop/seabridge%20fintech/profit_graph_app/" + ticker + "_Return.jpg" tick = ticker image['image_return'] =[ dr] image.index =[tick] image_strategy_return = pd.concat([image_strategy_return ,image], axis =0) ################resistance and resiistance ############# image_rs = pd.DataFrame() for ticker in symbols: image = pd.DataFrame() dr = "https://3arbzfbsh-cname-us.ngrok.io/Desktop/seabridge%20fintech/image_app/" + ticker+"_resistance_support.jpg" tick = ticker image['im_res_sup']=[ dr] image.index =[tick] image_rs = pd.concat([image_rs,image], axis =0) return image_strategy_return,image_rs image_strategy_return,image_rs = get_image(ticker_sum) image_rs = image_rs.sort_index() image_strategy_return = image_strategy_return.sort_index() image_data = image_rs.merge(image_strategy_return, left_index=True, right_index=True) stock_info_data1 = stock_info_data.merge(image_data, right_index =True, left_index =True) stock_info_data1 .to_csv(r'C:\Users\jizha\Desktop\seabridge_datapool1\final_strategy_data_temporaly\company_profile.csv') #pip install python-dateutil ##GAI############################################################### from sklearn.cluster import KMeans import matplotlib.pyplot as plt import pandas as pd import mplfinance as mpf from mplfinance.original_flavor import candlestick_ohlc import matplotlib.dates as mpl_dates import numpy as np from datetime import * import seaborn as sns from datetime import datetime import requests from dateutil.relativedelta import relativedelta # ----------- 安全的 KMeans 聚类 (就地替换原 get_optimum_clusters 内部函数逻辑) ----------- def _safe_kmeans_labels(df: pd.DataFrame, saturation_point: float = 0.05): """ df: 单列 DataFrame（High 或 Low），index 为日期 返回：与 df 同长度的 labels（np.ndarray[int]） - 空数据、仅1个样本 → 返回全0或空 - k 的选择不超过 样本数-1 和 唯一值数量 - 惰性“肘部法”：当 inertia 的相邻差值小于 saturation_point 时停止 """ # 只取数值列（第一列） if df is None or df.empty: return np.array([], dtype=int) X = pd.to_numeric(df.iloc[:, 0], errors='coerce').dropna().values.reshape(-1, 1) if X.size == 0: return np.array([], dtype=int) n = X.shape[0] uniq = np.unique(X) if n == 1 or len(uniq) == 1: return np.zeros(n, dtype=int) # 可行的 k 集合：1..8，但不超过 n-1、uniq 数 max_k = max(1, min(8, n - 1, len(uniq))) ks = list(range(1, max_k + 1)) wcss, k_models = [], [] for k in ks: km = KMeans(n_clusters=k, init='random', max_iter=300, n_init=10, random_state=42) km.fit(X) wcss.append(km.inertia_) k_models.append(km) # 惰性“肘部”选择 optimum_idx = len(wcss) - 1 for i in range(0, len(wcss) - 1): if abs(wcss[i + 1] - wcss[i]) < saturation_point: optimum_idx = i break labels = k_models[optimum_idx].labels_ # 注意：labels 长度与 dropna 后的 X 相同；需要与原 df 对齐 # 用全长数组并在非 NaN 位置填入 labels，其余填充为 -1 以示“未参与聚类” full = pd.to_numeric(df.iloc[:, 0], errors='coerce') out = np.full(full.shape[0], -1, dtype=int) mask = full.notna().values out[mask] = labels return out # ----------- 你的 get_data（已加健壮性） ----------- def get_data(ticker): print(ticker) # 时间窗：过去6个月至昨天 today = datetime.today() end = (today - timedelta(days=1)).strftime('%Y-%m-%d') start = (today - relativedelta(months=6)).strftime('%Y-%m-%d') # 拉取数据（FMP） api_key = '86dd63f6b8ae774b061232685b78eb52' url = f'https://financialmodelingprep.com/api/v3/historical-price-full/{ticker}' params = {"from": start, "to": end, "apikey": api_key} try: js = requests.get(url, params=params, timeout=15).json() bs = js.get('historical', []) except Exception as e: # 返回固定结构，避免崩溃 last_day = pd.DataFrame(index=[ticker]) last_day['center_gravity'] = np.nan last_day['vol_up1'] = np.nan last_day['vol_up2'] = np.nan last_day['vol_up3'] = np.nan last_day['vol_down1'] = np.nan last_day['vol_down2'] = np.nan last_day['vol_down3'] = np.nan last_day['recent_support'] = np.nan last_day['recent_resistance'] = np.nan return last_day if not bs: # 无数据：返回 NaN 行 last_day = pd.DataFrame(index=[ticker]) for col in ['center_gravity','vol_up1','vol_up2','vol_up3','vol_down1','vol_down2','vol_down3','recent_support','recent_resistance']: last_day[col] = np.nan return last_day stock = pd.DataFrame(bs) # 列检查 need = {'date','open','high','low','close','volume'} if not need.issubset(stock.columns): last_day = pd.DataFrame(index=[ticker]) for col in ['center_gravity','vol_up1','vol_up2','vol_up3','vol_down1','vol_down2','vol_down3','recent_support','recent_resistance']: last_day[col] = np.nan return last_day # 清洗 stock = stock[['date','open','high','low','close','volume']].rename( columns={'date':'Date','open':'Open','high':'High','low':'Low','close':'Close','volume':'volume'} ) stock['Date'] = pd.to_datetime(stock['Date'], errors='coerce') stock = stock.dropna(subset=['Date']).set_index('Date').sort_index() # 体量过滤：先数值化 stock['volume'] = pd.to_numeric(stock['volume'], errors='coerce').fillna(0) stock = stock[stock['volume'] >= 1000] if stock.empty: last_day = pd.DataFrame(index=[ticker]) for col in ['center_gravity','vol_up1','vol_up2','vol_up3','vol_down1','vol_down2','vol_down3','recent_support','recent_resistance']: last_day[col] = np.nan return last_day # 删去最后4行计算聚类（保留你原逻辑） stock1 = stock.iloc[:-4, :] if stock1.empty: last_day = stock.iloc[-1:].copy() else: last_day = stock.iloc[-1:].copy() # --- 聚类：对 High/Low 做安全 KMeans --- Lows = pd.DataFrame(data=stock1, index=stock1.index, columns=['Low']).copy() Highs = pd.DataFrame(data=stock1, index=stock1.index, columns=['High']).copy() # 若全 NaN；做防护 if Lows['Low'].dropna().empty or Highs['High'].dropna().empty: # 无法聚类，后面支撑/压力统一给空 low_labels = np.full(Lows.shape[0], -1, dtype=int) high_labels = np.full(Highs.shape[0], -1, dtype=int) else: low_labels = _safe_kmeans_labels(Lows) high_labels = _safe_kmeans_labels(Highs) Lows['labels'] = pd.Series(low_labels, index=Lows.index) Highs['labels'] = pd.Series(high_labels, index=Highs.index) # 计算聚类得到的支撑/压力候选：按 label 分组取 min/max（忽略 label==-1） res = [] sup = [] if (Highs['labels'] >= 0).any(): for i in np.unique(high_labels[high_labels >= 0]): res.append(Highs.loc[Highs.labels == i, 'High'].max()) if (Lows['labels'] >= 0).any(): for i in np.unique(low_labels[low_labels >= 0]): sup.append(Lows.loc[Lows.labels == i, 'Low'].min()) # --- 枢轴点（保持你原写法） --- # last_day 是 1 行 DataFrame last_day = last_day[['Open','High','Low','Close','volume']].copy() last_day['center_gravity'] = (last_day['High'] + last_day['Low'] + last_day['Close']) / 3 last_day['vol_up1'] = 2 * last_day['center_gravity'] - last_day['Low'] last_day['vol_down1'] = 2 * last_day['center_gravity'] - last_day['High'] last_day['vol_up2'] = last_day['center_gravity'] + (last_day['High'] - last_day['Low']) last_day['vol_down2'] = last_day['center_gravity'] - (last_day['High'] - last_day['Low']) last_day['vol_up3'] = last_day['center_gravity'] + 2 * (last_day['High'] - last_day['Low']) last_day['vol_down3'] = last_day['center_gravity'] - 2 * (last_day['High'] - last_day['Low']) # --- 时间列（若为空需防守） --- if not Highs.empty: Highs = Highs.copy() Highs['Date'] = pd.to_datetime(Highs.index, errors='coerce') Highs = Highs.dropna(subset=['Date']) if not Highs.empty: Highs['Date'] = Highs['Date'].apply(mpl_dates.date2num) if not Lows.empty: Lows = Lows.copy() Lows['Date'] = pd.to_datetime(Lows.index, errors='coerce') Lows = Lows.dropna(subset=['Date']) if not Lows.empty: Lows['Date'] = Lows['Date'].apply(mpl_dates.date2num) # --- 标记是否落在支撑/压力集合（向量化替代 for 循环） --- Highs['res'] = 0 if res: Highs['res'] = Highs['High'].isin(res).astype(int) Lows['sup'] = 0 if sup: Lows['sup'] = Lows['Low'].isin(sup).astype(int) # --- 取最近（最后出现的）压力/支撑（避免 append） --- resistance = Highs.loc[Highs['res'] == 1] support = Lows.loc[Lows['sup'] == 1] # 近期压力/支撑，若为空则给 NaN rec_res = resistance['High'].iloc[-1] if not resistance.empty else np.nan rec_sup = support['Low'].iloc[-1] if not support.empty else np.nan last_day['recent_support'] = rec_sup last_day['recent_resistance'] = rec_res # 索引改为 ticker（保持你原写法） last_day.index = [ticker] return last_day # ----------- 你的 strategy_signal4（仅改成一次 concat，容错单票） ----------- def strategy_signal4(symbols): frames = [] for tick in symbols: try: data = get_data(tick) except Exception as e: # 单只出错不影响整体 data = pd.DataFrame(index=[tick]) for col in ['center_gravity','vol_up1','vol_up2','vol_up3','vol_down1','vol_down2','vol_down3','recent_support','recent_resistance']: data[col] = np.nan frames.append(data) return pd.concat(frames, axis=0) if frames else pd.DataFrame( columns=['center_gravity','vol_up1','vol_up2','vol_up3','vol_down1','vol_down2','vol_down3','recent_support','recent_resistance'] ) from sklearn.cluster import KMeans import matplotlib.pyplot as plt import pandas as pd import mplfinance as mpf from mplfinance.original_flavor import candlestick_ohlc import finnhub import matplotlib.dates as mpl_dates import numpy as np from datetime import * import seaborn as sns from datetime import datetime support_resistance_data= strategy_signal4(ticker_sum) support_resistance_data = support_resistance_data[['center_gravity','vol_up1','vol_up2','vol_up3','vol_down1', "vol_down2" , "vol_down3","recent_support","recent_resistance"]] support_resistance_data .to_csv(r'C:\Users\jizha\Desktop\seabridge_datapool1\final_strategy_data_temporaly\support_resistance_data.csv') #support_resistance_data = pd.concat([data1, data2, data3, data4, data5, data6, data7], axis = 0) # strategy_sum1 = pd.read_csv(r'C:\Users\jizha\Desktop\seabridge_datapool\support_resistop_last_peak_data.csv') # strategy_sum1 =strategy_sum1.rename({'Unnamed: 0':'symbol'}, axis =1) # strategy_sum1= strategy_sum1.rename({'pivot':'center_gravity'}, axis =1) #####################stop loss#################################### # def get_data2(ticker): # print(ticker) # from datetime import datetime as dt # import datetime # import dateutil # import requests # import pandas as pd # import numpy as np # today = datetime.datetime.today() # delta = dateutil.relativedelta.relativedelta(months=6) # end = (today - datetime.timedelta(1)).strftime('%Y-%m-%d') # start = (today - delta).strftime('%Y-%m-%d') # # 优先用外部 api_key，缺失时兜底（建议改为环境变量） # try: # _api_key = api_key # except NameError: # _api_key = '86dd63f6b8ae774b061232685b78eb52' # url = f'https://financialmodelingprep.com/api/v3/historical-price-full/{ticker}' # # 拉取数据 + 容错 # try: # resp = requests.get(url, params={"from": start, "to": end, "apikey": _api_key}, timeout=15) # resp.raise_for_status() # js = resp.json() # bs = js.get('historical', []) # except Exception: # bs = [] # def _empty_out(): # cols = ['Open','High','Low','Close','volume', # 'center_gravity','vol_up1','vol_down1','vol_up2','vol_down2','vol_up3','vol_down3', # 'stop_loss','stop_loss_pct'] # return pd.DataFrame(index=[ticker], columns=cols, dtype=float) # if not bs: # return _empty_out() # stock = pd.DataFrame(bs) # # 列检查 # need = {'date','open','high','low','close','volume'} # if not need.issubset(stock.columns): # return _empty_out() # # 清洗与索引（保持你原来的列名） # stock = stock[['date','open','high','low','close','volume']].rename( # columns={'date':'Date','open':'Open','high':'High','low':'Low','close':'Close','volume':'volume'} # ) # stock['Date'] = pd.to_datetime(stock['Date'], errors='coerce') # stock = stock.dropna(subset=['Date']).set_index('Date').sort_index() # # 数值化，避免字符串导致计算异常 # for c in ['Open','High','Low','Close','volume']: # stock[c] = pd.to_numeric(stock[c], errors='coerce') # # 可选：过滤极低成交量（如果你需要与其他函数口径一致，可解除下一行注释） # # stock = stock[stock['volume'].fillna(0) >= 1000] # if stock.empty: # return _empty_out() # def get_stop(stock): # pivotpoints（保留你的公式与列名） # last_day = stock.iloc[-1].to_frame().T # not_pivots = last_day.columns # 保留你的变量（未使用也不删除） # last_day['center_gravity'] = (last_day['High'] + last_day['Low'] + last_day['Close']) / 3 # last_day['vol_up1'] = 2 * last_day['center_gravity'] - last_day['Low'] # last_day['vol_down1'] = 2 * last_day['center_gravity'] - last_day['High'] # last_day['vol_up2'] = last_day['center_gravity'] + (last_day['High'] - last_day['Low']) # last_day['vol_down2'] = last_day['center_gravity'] - (last_day['High'] - last_day['Low']) # last_day['vol_up3'] = last_day['center_gravity'] + 2 * (last_day['High'] - last_day['Low']) # last_day['vol_down3'] = last_day['center_gravity'] - 2 * (last_day['High'] - last_day['Low']) # tick = ticker # last_day.index = [tick] # # 止损与百分比（增加除零保护与非链式赋值） # last_day['stop_loss'] = (last_day['vol_down2'] + last_day['vol_down3']) / 2 # close_val = float(last_day['Close'].iloc[0]) if pd.notna(last_day['Close'].iloc[0]) else np.nan # if close_val and close_val != 0: # sl_pct = (close_val - float(last_day['stop_loss'].iloc[0])) / close_val * 100.0 # else: # sl_pct = np.nan # last_day.loc[tick, 'stop_loss_pct'] = sl_pct # # 若止损过宽，按你原逻辑改为更紧的均值 # slp = last_day['stop_loss_pct'].iloc[0] # if pd.notna(slp) and slp > 3.5: # new_sl = (float(last_day['vol_down1'].iloc[0]) + float(last_day['vol_down2'].iloc[0])) / 2.0 # last_day.loc[tick, 'stop_loss'] = new_sl # return last_day # last_day = get_stop(stock) # return last_day # last_day = get_data2(ticker_sum) def get_data2(ticker): print(ticker) from datetime import datetime as dt import datetime import dateutil today = datetime.datetime.today() delta = dateutil.relativedelta.relativedelta(months=6) end = ( today-datetime.timedelta(1)).strftime('%Y-%m-%d') start = (today - delta).strftime('%Y-%m-%d') import requests from datetime import datetime bs = requests.get(f'https://financialmodelingprep.com/api/v3/historical-price-full/{ticker}?from={start}&to={end}&apikey={api_key}').json()['historical'] stock = pd.DataFrame(bs) stock =stock[['date','open','high','low','close','volume']] stock = stock.rename(columns = {'date':'Date', 'open':'Open', 'high':'High','low':'Low', 'close':'Close', 'volume':'volume'}) stock = stock.set_index('Date') stock= stock.sort_index() def get_stop(stock): # pivotpoints last_day = stock.iloc[-1].to_frame().T not_pivots = last_day.columns last_day['center_gravity'] = (last_day['High'] + last_day['Low'] + last_day['Close'])/3 last_day['vol_up1'] = 2*last_day['center_gravity'] - last_day['Low'] last_day['vol_down1'] = 2*last_day['center_gravity'] - last_day['High'] last_day['vol_up2'] = last_day['center_gravity'] + (last_day['High'] - last_day['Low']) last_day['vol_down2'] = last_day['center_gravity'] - (last_day['High'] - last_day['Low']) last_day['vol_up3'] = last_day['center_gravity'] + 2*(last_day['High'] - last_day['Low']) last_day['vol_down3'] = last_day['center_gravity'] - 2*(last_day['High'] - last_day['Low']) tick = ticker last_day.index = [tick] last_day['stop_loss'] = (last_day['vol_down2'] +last_day['vol_down3'] )/2 last_day['stop_loss_pct'] = (last_day['Close']- last_day['stop_loss'])/last_day['Close']*100 if last_day['stop_loss_pct'][-1] >3.5: last_day['stop_loss'][-1]= (last_day['vol_down1'][-1] +last_day['vol_down2'][-1] )/2 return last_day last_day = get_stop(stock) import pandas as pd import numpy as np import requests from datetime import datetime, timedelta from dateutil.relativedelta import relativedelta import pandas as pd import numpy as np import requests from datetime import datetime, timedelta from dateutil.relativedelta import relativedelta def get_data3(ticker): print(ticker) # ---- 时间窗口 ---- today = datetime.today() end = (today - timedelta(days=1)).strftime('%Y-%m-%d') start = (today - relativedelta(months=6)).strftime('%Y-%m-%d') # ---- 拉数据 + 容错 ---- api_key = '86dd63f6b8ae774b061232685b78eb52' url = f'https://financialmodelingprep.com/api/v3/historical-price-full/{ticker}' params = {"from": start, "to": end, "apikey": api_key} try: r = requests.get(url, params=params, timeout=15) r.raise_for_status() js = r.json() bs = js.get('historical', []) except Exception as e: out = pd.DataFrame(index=[ticker]) out['last_peak'] = [f'{ticker} data error: {type(e).__name__}'] out['last_peakday'] = [pd.NaT] return out if not bs: out = pd.DataFrame(index=[ticker]) out['last_peak'] = [f'{ticker} has not formed a green line yet'] out['last_peakday'] = [pd.NaT] return out stock = pd.DataFrame(bs) # ---- 列检查与清洗 ---- need = {'date','open','high','low','close','volume'} if not need.issubset(stock.columns): out = pd.DataFrame(index=[ticker]) miss = sorted(list(need - set(stock.columns))) out['last_peak'] = [f'{ticker} missing columns: {miss}'] out['last_peakday'] = [pd.NaT] return out stock = stock[['date','open','high','low','close','volume']].rename( columns={'date':'Date','open':'Open','high':'High','low':'Low','close':'Close','volume':'volumn'} ) stock['Date'] = pd.to_datetime(stock['Date'], errors='coerce') stock = stock.dropna(subset=['Date']).set_index('Date').sort_index() # 体量过滤：先转为数值 stock['volumn'] = pd.to_numeric(stock['volumn'], errors='coerce').fillna(0) stock = stock[stock['volumn'] >= 1000] if stock.empty: out = pd.DataFrame(index=[ticker]) out['last_peak'] = [f'{ticker} has not formed a green line yet'] out['last_peakday'] = [pd.NaT] return out # 日频 High 最大值，去 NaN dfday = stock.groupby(pd.Grouper(freq='D'))['High'].max().dropna() if dfday.empty: out = pd.DataFrame(index=[ticker]) out['last_peak'] = [f'{ticker} has not formed a green line yet'] out['last_peakday'] = [pd.NaT] return out # ---- 绿线逻辑（保持你原写法）---- now = datetime.now().date() glDate = pd.NaT lastGLV = 0.0 currentDate = pd.NaT curentGLV = 0.0 counter = 0 # 先初始化 for index, value in dfday.items(): if pd.isna(value): continue if value > curentGLV: curentGLV = float(value) currentDate = index counter = 0 elif value < curentGLV: counter += 1 if counter == 5 and index.date() != now: if curentGLV != lastGLV: pass # 可保留 print(curentGLV) glDate = currentDate lastGLV = curentGLV counter = 0 if lastGLV == 0: last_peak = f"{ticker} has not formed a green line yet" last_peak_date = pd.NaT else: last_peak = lastGLV last_peak_date = glDate out = pd.DataFrame(index=[ticker]) out['last_peak'] = [last_peak] out['last_peakday'] = [last_peak_date] return out def strategy_signal6(symbols): frames = [] for tick in symbols: print(tick) try: data = get_data3(ticker=tick) except Exception as e: data = pd.DataFrame(index=[tick]) data['last_peak'] = [f'error: {type(e).__name__}'] data['last_peakday'] = [pd.NaT] frames.append(data) return pd.concat(frames, axis=0) if frames else pd.DataFrame(columns=['last_peak','last_peakday']) #用法（保持你原来的两行） last_peak_data = strategy_signal6(ticker_sum) last_peak_data.to_csv(r'C:\Users\jizha\Desktop\seabridge_datapool1\final_strategy_data_temporaly\last_peak_data.csv') ema_s = 13 ema_l = 21 stock["EMA_S"] = stock.Close.ewm(span = ema_s, min_periods = ema_s).mean() stock["EMA_L"] = stock.Close.ewm(span = ema_l, min_periods = ema_l).mean() # display(stock.head(30)) def get_ATR(stock, n): hl = stock.High - stock.Low hc = abs(stock.High - stock.Close.shift(1)) lc = abs(stock.Low - stock.Close.shift(1)) stock['TR'] = np.maximum(np.maximum(hl, hc), lc) stock['ATR'] = stock.TR.rolling(n).mean() return stock stock = get_ATR(stock, 20) stock = stock.dropna() mul =2 stock['ATR_stop'] = (stock.Close - mul * stock.ATR).shift(1) # ATR_stop for current day rec_trailingstoploss = stock.loc[stock.index[-1],'ATR_stop'] rec_stoploss = last_day.loc[last_day.index[-1],'stop_loss'] atr = stock.loc[stock.index[-1],'ATR'] strategy_data2=pd.DataFrame() tick = ticker strategy_data2 ['recent_stoploss'] =[rec_stoploss] strategy_data2['recent_trailingstoploss'] = [rec_trailingstoploss] strategy_data2['atr'] = [atr] strategy_data2.index = [tick] return strategy_data2 # from datetime import datetime as dt # import datetime # today = dt.today() # yesterday = today-datetime.timedelta(1) # start = (2021,1,1) # end = (yesterday.year, yesterday.month, yesterday.day) ####################################优化 def strategy_signal5(symbols): strategy_data3= pd.DataFrame() for ticker in symbols: data = get_data2( ticker) strategy_data3= pd.concat([strategy_data3,data], axis=0) return strategy_data3 stop_loss_data = strategy_signal5(ticker_sum) #stop_loss = strategy_signal5(symbols) # n= 4# number of groups # groups = np.array_split(symbols, n) # import time # stop_loss_data = pd.DataFrame() # for symbol in groups: # f= strategy_signal5(symbol) # stop_loss_data = pd.concat([f, stop_loss_data], axis =0) # time.sleep(60) stop_loss_data .to_csv(r'C:\Users\jizha\Desktop\seabridge_datapool1\final_strategy_data_temporaly\stop_loss_data.csv') #####################last peak price################### def get_data3(ticker): print(ticker) from datetime import datetime as dt import datetime import dateutil today = datetime.datetime.today() delta = dateutil.relativedelta.relativedelta(months=6) end = ( today-datetime.timedelta(1)).strftime('%Y-%m-%d') start = (today - delta).strftime('%Y-%m-%d') # ticker = 'ACM' import requests api_key = '86dd63f6b8ae774b061232685b78eb52' from datetime import datetime bs = requests.get(f'https://financialmodelingprep.com/api/v3/historical-price-full/{ticker}?from={start}&to={end}&apikey={api_key}').json()['historical'] stock = pd.DataFrame(bs) stock =stock[['date','open','high','low','close','volume']] stock = stock.rename(columns = {'date':'Date', 'open':'Open', 'high':'High','low':'Low', 'close':'Close', 'volume':'volumn'}) stock = stock.set_index('Date') stock= stock.sort_index() # from datetime import datetime # finnhub_client = finnhub.Client(api_key = "bt3efpf48v6tfcs816eg") # start = np.array(start).tolist() # end =np.array(end).tolist() # start = start # end = end # start_time = int(datetime(start[0], start[1], start[2], 0, 0).replace(tzinfo = timezone.utc).timestamp()) # end_time = int(datetime(end[0], end[1], end[2], 0, 0).replace(tzinfo = timezone.utc).timestamp()) # res_d = finnhub_client.stock_candles(ticker, 'D', start_time, end_time) # stock = pd.DataFrame(res_d) # stock = stock.rename(columns = {'t':'Date', 'o':'Open', 'h':'High', 'l':'Low', 'c':'Close', 's':'status', 'v':'volumn'}) # stock['Date'] = pd.to_datetime(stock['Date'], unit = 's') # stock = stock.set_index('Date') # stock= stock[['Close', 'High', 'Low','Open','volumn']] df =stock.copy() # df['Date'] = pd.to_datetime(df.index, format='%d-%b-%y %H.%M.%S.%f %p', errors='coerce') # df =df.set_index('Date') df.drop(df[df["volumn"]<1000].index, inplace=True) df.index = pd.to_datetime(df.index) dfday=df.groupby(pd.Grouper(freq="D"))["High"].max() now = datetime.now() glDate=0 lastGLV=0 currentDate="" curentGLV=0 for index, value in dfday.items(): if value > curentGLV: curentGLV=value currentDate=index counter=0 if value < curentGLV: counter=counter+1 if counter==5 and ((index.day != now.day) or (index.month != now.month)): if curentGLV != lastGLV: print(curentGLV) glDate=currentDate lastGLV=curentGLV counter=0 if lastGLV==0: last_peak = ticker+" has not formed a green line yet" last_peak_date = 0 else: last_peak = lastGLV last_peak_date = glDate strategy_data6 =pd.DataFrame() strategy_data6['last_peak'] = [last_peak] strategy_data6['last_peakday'] = [last_peak_date] tick = ticker strategy_data6.index= [tick] return strategy_data6 # from datetime import datetime as dt # import datetime # today = dt.today() # yesterday = today-datetime.timedelta(1) # start = (2021,1,15) # end = (yesterday.year, yesterday.month, yesterday.day) def strategy_signal6(symbols): strategy_data7= pd.DataFrame() for tick in symbols: print(tick) data = get_data3( ticker =tick) strategy_data7 = pd.concat([strategy_data7,data], axis=0) return strategy_data7 last_peak_data = strategy_signal6(ticker_sum) last_peak_data.to_csv(r'C:\Users\jizha\Desktop\seabridge_datapool1\final_strategy_data_temporaly\last_peak_data.csv') # last_peak_data.to_csv(r'C:\Users\jizha\Desktop\seabridge_datapool\last_peak_data.csv') ########################################## #strategy= strategy.sort_index() support_resistance_data = support_resistance_data.sort_index() stop_loss_data = stop_loss_data.sort_index() last_peak_data = last_peak_data.sort_index() strategy_sum = support_resistance_data .merge(stop_loss_data , left_index=True, right_index=True) strategy_sum1 =strategy_sum.merge(last_peak_data,left_index=True, right_index=True) strategy_sum1 =strategy_sum1.copy() strategy_sum1 = strategy_sum1 .sort_index() strategy_sum1[['center_gravity', 'vol_up1', 'vol_up2', 'vol_up3', 'vol_down1', 'vol_down2', 'vol_down3', 'recent_support', 'recent_resistance', 'recent_stoploss', 'recent_trailingstoploss', 'atr','last_peak']]=strategy_sum1[['center_gravity', 'vol_up1', 'vol_up2', 'vol_up3', 'vol_down1', 'vol_down2', 'vol_down3', 'recent_support', 'recent_resistance', 'recent_stoploss', 'recent_trailingstoploss', 'atr','last_peak']].astype(float) strategy_sum1[['center_gravity', 'vol_up1', 'vol_up2', 'vol_up3', 'vol_down1', 'vol_down2', 'vol_down3', 'recent_support', 'recent_resistance', 'recent_stoploss', 'recent_trailingstoploss', 'atr','last_peak']]= strategy_sum1[['center_gravity', 'vol_up1', 'vol_up2', 'vol_up3', 'vol_down1', 'vol_down2', 'vol_down3', 'recent_support', 'recent_resistance', 'recent_stoploss', 'recent_trailingstoploss', 'atr','last_peak']].applymap("{0:,.2f}".format) # strategy_sum2 =strategy_sum1.merge(strategy_data4,left_index=True, right_index=True) # strategy_sum2['market_trend'] =1 stock_info_data2= stock_info_data1.merge(strategy_sum1,right_index = True, left_index = True) #stock_info_data2.to_csv(r'C:\Users\jizha\Desktop\Strategy_auto_2_21_2022\results\support_resistop_last_peak_data.csv') stock_info_data2.to_csv(r'C:\Users\jizha\Desktop\seabridge_datapool1\final_strategy_data_temporaly\support_resistop_last_peak_data.csv') #r'C:\Users\jizha\Desktop\seabridge_datapool1\final_strategy_data_temporaly