# # IMPORTING PACKAGES # import pandas as pd # import requests # import numpy as np # from math import floor # from termcolor import colored as cl # import requests # from math import floor # import finnhub # import requests # import matplotlib.pyplot as plt # from sklearn.cluster import KMeans # import mplfinance as mpf # from mplfinance.original_flavor import candlestick_ohlc # import matplotlib.dates as mpl_dates # #from datetime import * 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 # ticker1 = strategy1.index.tolist() # ticker2 = strategy2.index.tolist() # ticker3 = strategy3.index.tolist() # #ticker3.remove('ZEUS') # ticker_sum = ticker1+ticker2+ticker3 # ticker_sum = np.unique(ticker_sum).tolist() # # plt.style.use('fivethirtyeight') # # plt.rcParams['figure.figsize'] = (20, 10) # from datetime import datetime as dt # import datetime # today = dt.today() # a = str(today.year) # b = str(today.month) # c = str(today.day) # f1 = pd.read_csv(r'C:\Users\jizha\Desktop\seabridge_datapool1\final_strategy_data_temporaly\three_strategy_buy_point_'+a+'_'+b+'_'+c+'.csv') # ticker_sum = f1['0'].tolist() # IMPORTING PACKAGES import pandas as pd import requests import numpy as np from math import floor from termcolor import colored as cl import requests from math import floor import finnhub import requests import matplotlib.pyplot as plt from sklearn.cluster import KMeans import mplfinance as mpf from mplfinance.original_flavor import candlestick_ohlc import matplotlib.dates as mpl_dates 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 import seaborn as sns from datetime import datetime def get_data(ticker): from datetime import datetime as dt import datetime import dateutil today = datetime.datetime.today() delta = dateutil.relativedelta.relativedelta(months=6) end = today.strftime('%Y-%m-%d') start = (today - delta).strftime('%Y-%m-%d') 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() 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 def get_signal(ticker): print(ticker) from datetime import datetime as dt import datetime today = datetime.datetime.today() end =today.strftime('%Y-%m-%d') start = (today-datetime.timedelta(200)).strftime('%Y-%m-%d') 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':'volume'}) stock = stock.set_index('Date') stock= stock.sort_index() data1 = stock.copy() # strategy CALCULATION def get_tsi(Close, long, short, signal): diff = Close - Close.shift(1) abs_diff = abs(diff) diff_smoothed = diff.ewm(span = long, adjust = False).mean() diff_double_smoothed = diff_smoothed.ewm(span = short, adjust = False).mean() abs_diff_smoothed = abs_diff.ewm(span = long, adjust = False).mean() abs_diff_double_smoothed = abs_diff_smoothed.ewm(span = short, adjust = False).mean() tsi = (diff_double_smoothed / abs_diff_double_smoothed) * 100 signal = tsi.ewm(span = signal, adjust = False).mean() # tsi = tsi[tsi.index >= '2020-01-01'].dropna() # signal = signal[signal.index >= '2020-01-01'].dropna() return tsi, signal data1['tsi'], data1['signal_line'] = get_tsi(data1['Close'], 25, 13, 12) def implement_tsi_strategy(prices, tsi, signal_line): buy_price = [] sell_price = [] tsi_signal = [] signal = 0 for i in range(len(prices)): if tsi[i-1] < signal_line[i-1] and tsi[i] > signal_line[i]: if signal != 1: buy_price.append(prices[i]) sell_price.append(np.nan) signal = 1 tsi_signal.append(signal) else: buy_price.append(np.nan) sell_price.append(np.nan) tsi_signal.append(0) elif tsi[i-1] > signal_line[i-1] and tsi[i] < signal_line[i]: if signal != -1: buy_price.append(np.nan) sell_price.append(prices[i]) signal = -1 tsi_signal.append(signal) else: buy_price.append(np.nan) sell_price.append(np.nan) tsi_signal.append(0) else: buy_price.append(np.nan) sell_price.append(np.nan) tsi_signal.append(0) return buy_price, sell_price, tsi_signal buy_price, sell_price, tsi_signal = implement_tsi_strategy(data1['Close'], data1['tsi'], data1['signal_line']) d =stock_info_data5.loc[ticker,'recent_stoploss'] stock = stock.iloc[-15:,:] data = stock.copy() def ema(n, close): return close.ewm(span = n).mean() data['ema8'] = ema(8, data.Close) data['ema21'] = ema(21, data.Close) data['ema50'] = ema(50, data.Close) data['vol10'] = data['volume'].rolling(window = 10).mean() data3 = stock.copy() data3 =data3.reset_index() vup = [] vup.append(data3.loc[0, 'Date']) vdown = [] for i in range(1, len(data3)): if data3.loc[i, 'Close'] >= data3.loc[i-1, 'Close']: vup.append(data3.loc[i, 'Date']) if data3.loc[i, 'Close'] < data3.loc[i-1, 'Close']: vdown.append(data3.loc[i, 'Date']) plt.rcParams['axes.xmargin'] = 0 plt.rcParams['figure.figsize'] = (15, 10) fig, axs = plt.subplots(2, gridspec_kw={'height_ratios': [7, 2]}, sharex=True) fig.patch.set_facecolor('white') data['Date'] = pd.to_datetime(data.index) data['Date'] = data['Date'].apply(mpl_dates.date2num) data_plot = data.loc[:,['Date', 'Open', 'High', 'Low', 'Close']] candlestick_ohlc(axs[0], data_plot.values, width = 0.4,alpha = 1, colorup = 'green', colordown = 'red') data =data.set_index('Date') # data['ema8'].plot(ax = axs[0],linewidth = 0.5,color = 'blue') # data['ema21'].plot(ax = axs[0], linewidth = 0.5,color = 'red') # data['ema50'].plot(ax = axs[0], linewidth = 0.5, color = 'grey') vup = mpl_dates.date2num(vup) vdown = mpl_dates.date2num(vdown) axs[1].bar(x = vup, height = data.loc[vup, 'volume'], color = 'green', width = 0.4) axs[1].bar(x = vdown, height = data.loc[vdown, 'volume'], color = 'red', width = 0.4) data['vol10'].plot(ax = axs[1], label = 'Avg_10days', color = 'steelblue', linewidth = 1) axs[1].ticklabel_format(useOffset=False, style='plain', axis='y') c = buy_price[-2] # c_an = (max(data.High)-0.01*max(data.High)+ c) / 2 c_an = c+c*0.01 g = data.index[-1]+3 g_adjusted = data.index[-1] + 1 # 将水平位置调整得更接近 c_an_adjusted = c + c * 0.005 # 将垂直位置稍微调整，使箭头不那么长 f_adjusted = data.index[-1] + 1 # 同样调整卖出箭头的水平位置 # 对于不同的价格区间，调整l_an的值以缩短箭头长度 if buy_price[-1] < 120: axs[0].set_ylim(min(data.Low)-5, max(data.High) + 5) else: axs[0].set_ylim(min(data.Low)-15, max(data.High) + 8) if d < 50: l_an_adjusted = d - 1 elif 50 < d < 100: l_an_adjusted = d - 2 elif 100 < d <= 200: l_an_adjusted = d - 3 elif 200 < d <= 400: l_an_adjusted = d - 4 else: l_an_adjusted = d - 4 # 使用调整后的值来绘制箭头 axs[0].annotate('Buy: $'+ str(round(c,2)), size=24, xy = (data.index[-1], c), xycoords = 'data', xytext = (g_adjusted, c_an_adjusted), bbox = dict(boxstyle = 'round', fc = 'none', edgecolor='blue',pad = 0.5), textcoords = 'data', arrowprops = dict(arrowstyle = '->', connectionstyle = 'arc3', lw =3, color = 'black')) axs[0].annotate("Stop: $" + str(round(d,2)), size = 24, xy = (data.index[-1], d), xycoords = 'data', xytext = (f_adjusted, l_an_adjusted), bbox = dict(boxstyle = 'round', fc = 'none', edgecolor='blue', pad = 0.5), textcoords = 'data', arrowprops = dict(arrowstyle = '->', connectionstyle = 'arc3', lw =3, color = 'black')) # c = buy_price[-2] # # c_an = (max(data.High)-0.01*max(data.High)+ c) / 2 # c_an = c+c*0.01 # g = data.index[-1]+3 # if buy_price[-1] < 120: # axs[0].set_ylim(min(data.Low)-5, max(data.High) + 5) # else: # axs[0].set_ylim(min(data.Low)-15, max(data.High) + 8) # axs[0].annotate('Buy: $'+ str(round(c,2)),size=24, xy = (data.index[-1], c),\ # xycoords = 'data', xytext = (g, c_an),\ # bbox = dict(boxstyle = 'round', # fc = 'none', edgecolor='green',pad = 0.5),\ # textcoords = 'data', arrowprops = dict(arrowstyle = '->',\ # connectionstyle = 'arc3',lw =3, color = 'black')) # f = data.index[-1] +2 # # l_an = d-d*0.01 # if d<50: # l_an = d-2 # elif 50 400: # l_an = d-8 # axs[0].annotate("Stop: $" + str(round(d,2)), size = 24,xy = (data.index[-1], d),\ # xycoords = 'data', xytext = (f, l_an),\ # bbox = dict(boxstyle = 'round', fc = 'none',edgecolor='green', pad = 0.5),\ # textcoords = 'data', arrowprops = dict(arrowstyle = '->',\ # connectionstyle = 'arc3', lw =3,color = 'black')) axs[0].set_facecolor('white') axs[1].set_facecolor('white') axs[0].grid(which='major', color='grey', linestyle='--', alpha=0.8) axs[1].grid(which='major', color='grey', linestyle='--',alpha=0.8) #axs[1].grid(b=True, which='major', color='grey', linestyle='--',alpha=0.8) axs[0].set_xlim(min(data.index), max(data.index) + 8) axs[1].set_xlim(min(data.index) - 1, max(data.index) + 8) date_format = mpl_dates.DateFormatter('%d %b %Y') axs[0].xaxis.set_major_formatter(date_format) axs[1].xaxis.set_major_formatter(date_format) axs[0].yaxis.tick_right() axs[1].yaxis.tick_right() axs[0].set(xlabel = '', ylabel = 'Price') axs[1].set(ylabel = 'Volume') axs[0].spines["bottom"].set_visible(True) axs[1].spines["top"].set_visible(True) axs[1].legend(loc = 'upper left') axs[0].set_axisbelow(False) plt.subplots_adjust(hspace=0.1) # g =data.index.tolist()[-1] plt.xticks(list(np.arange(min(data.index), max(data.index) + 1, 20))) axs[0].set_title(ticker+ " Trading Signal-GreenLine ", size = 14) path ="C:\\users\jizha\Desktop\seabridge fintech\image_trading_signal_green\\" plt.savefig(path +'{}.jpg'.format(ticker+"_Signal"), dpi = 25,pad_inches = 0,transparent=True) plt.show() for ticker in ticker1: print(ticker) strategy1 = get_signal(ticker)