added caching
This commit is contained in:
tomgwang
parent
456bfc38d2
commit
654ef5ea32
215
indicators.py
215
indicators.py
@ -517,99 +517,7 @@ def modified_bollinger_strategy(stk):
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return order_list
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return order_list
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def intelligent_loop_plots(sym, tmp):
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# def test_get_orders():
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# sd = dt.datetime(2000,2,1)
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# # ed = dt.datetime.today()
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# ed = dt.datetime(2012,9,12)
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# symbol = ['INTC', 'XOM', 'MSFT']
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# dates = dates = pd.date_range(sd, ed)
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# prices = get_data(symbol, dates, addSPY=False)
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# stk = security(prices)
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# # order_list = simple_bollinger_strategy(stk)
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# order_list = modified_bollinger_strategy(stk)
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# # print(order_list)
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# port_val = compute_portvals(order_list,100000,9.95,0.005)
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# if isinstance(port_val, pd.DataFrame):
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# port_val = port_val[port_val.columns[0]].to_frame() # just get the first column
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# else:
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# print("warning, code did not return a DataFrame")
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# price_SPY = plot_against_sym(port_val)
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# rfr=0
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# sf=252
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# cr, adr, sddr, sr = compute_portfolio_stats(port_val, [1.0], rfr, sf)
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# crSP,adrSP,sddrSP,srSP = compute_portfolio_stats(price_SPY, [1.0], rfr, sf)
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# # Compare portfolio against $SPX
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# print("\nDate Range: {} to {}".format(sd.date(), ed.date()))
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# print()
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# print("Sharpe Ratio: {}, {}".format(sr, srSP))
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# print()
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# print("Cumulative Return: {}, {}".format(cr, crSP))
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# print()
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# print("Standard Deviation: {}, {}".format(sddr, sddrSP))
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# print()
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# print("Average Daily Return: {}, {}".format(adr, adrSP))
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# print()
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# print("Final Portfolio Value: {:.2f}".format(port_val['Portfolio'].iloc[-1]))
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def plot_basic(stk, axs):
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data = stk.price.copy()
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lower, upper = stk.bollinger(200)
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data = data.join(lower, rsuffix = '_BOL200L')
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data = data.join(upper, rsuffix = '_BOL200U')
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data = data.join(stk.sma(200), rsuffix = '_SMA200')
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data = data.join(stk.sma(50), rsuffix = '_SMA50')
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data = data.join(stk.vwma(50), rsuffix = '_WVMA50')
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plot_data(data, axs, ylabel='Price ($)')
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def plot_rsi(rsi, axs):
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poscol = 'green'
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negcol = 'red'
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axs.plot(rsi.index.values, rsi.iloc[:,0])
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axs.axhline(70, color=negcol, ls='dotted')
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axs.axhline(30, color=poscol, ls='dotted')
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axs.fill_between(rsi.index.values, rsi.iloc[:,0], 70,
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where=rsi.iloc[:,0]>=70, facecolor=negcol, edgecolor=negcol, alpha=0.5)
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axs.fill_between(rsi.index.values, rsi.iloc[:,0], 30,
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where=rsi.iloc[:,0]<=30, facecolor=poscol, edgecolor=poscol, alpha=0.5)
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axs.set_yticks([30, 50, 70])
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axs.set_ylabel('RSI')
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axs.grid()
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def plot_volume_rsi(rsi, axs):
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poscol = 'green'
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negcol = 'red'
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axs.plot(rsi.index.values, rsi.iloc[:,0])
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axs.axhline(70, color=negcol, ls='dotted')
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axs.axhline(30, color=poscol, ls='dotted')
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axs.fill_between(rsi.index.values, rsi.iloc[:,0], 70,
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where=rsi.iloc[:,0]>=70, facecolor=negcol, edgecolor=negcol, alpha=0.5)
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axs.fill_between(rsi.index.values, rsi.iloc[:,0], 30,
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where=rsi.iloc[:,0]<=30, facecolor=poscol, edgecolor=poscol, alpha=0.5)
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axs.set_yticks([30, 50, 70])
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axs.set_ylabel('VoRSI')
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axs.grid()
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def plot_macd(macd, macd_sig, macd_hist, axs):
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poscol = 'green'
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negcol = 'red'
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axs.plot(macd.index.values, macd.iloc[:, 0], label='MACD')
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axs.legend()
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axs.plot(macd.index.values, macd_sig.iloc[:, 0])
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axs.plot(macd.index.values, macd_hist.iloc[:, 0])
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axs.fill_between(macd.index.values, macd_hist.iloc[:, 0], 0,
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where=macd_hist.iloc[:, 0]>0, facecolor=poscol, edgecolor=poscol, alpha=0.5)
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axs.fill_between(macd.index.values, macd_hist.iloc[:, 0], 0,
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where=macd_hist.iloc[:, 0]<0, facecolor=negcol, edgecolor=negcol, alpha=0.5)
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axs.set_ylabel('MACD')
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axs.grid()
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def intelligent_loop_plots(sym):
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# Only plot ones that are standing out meaning:
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# Only plot ones that are standing out meaning:
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# 1. outside of bollinger bands or recently crossed over (within 9 days)
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# 1. outside of bollinger bands or recently crossed over (within 9 days)
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# 2. RSI above 70 or below 30
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# 2. RSI above 70 or below 30
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@ -625,15 +533,6 @@ def intelligent_loop_plots(sym):
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# matplotlib.rcParams.update({'figure.max_open_warning': 0})
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# matplotlib.rcParams.update({'figure.max_open_warning': 0})
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lb_year = 3 # years of stock data to retrieve
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plt_year = 2 # number of years data to plot
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lb_trigger = 5 # days to lookback for triggering events
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ed = dt.datetime.today()
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sd = ed - dt.timedelta(days = 365 * lb_year)
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plot_sd = ed - dt.timedelta(days = 365 * plt_year)
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plot_ed = ed
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tmp = si.get_data(sym, start_date=sd)[["adjclose", "volume"]]
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price = tmp["adjclose"]
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price = tmp["adjclose"]
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vol = tmp["volume"]
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vol = tmp["volume"]
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stk = security(sym, price, vol)
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stk = security(sym, price, vol)
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@ -726,18 +625,6 @@ def intelligent_loop_plots(sym):
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# plot_indicator =="[]" ):
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# plot_indicator =="[]" ):
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# continue # skipping plotting to save memory and time
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# continue # skipping plotting to save memory and time
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# Plotting
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# fig, (axs0, axs1, axs2, axs3) = plt.subplots(4, sharex=True,
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# gridspec_kw={'hspace': 0, 'height_ratios': [3, 1, 1, 1]},
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# figsize=(16, 12))
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# # fig.suptitle('{} - {} - {} - {}'.format(sym,\
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# # watchlist.loc[sym, 'Segment'], watchlist.loc[sym, 'Sub Segment']))
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# axs0.set_title('{} - {} - {} - {} - {}'.format(sym,\
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# watchlist.loc[sym, 'Segment'], watchlist.loc[sym, 'Sub Segment'],\
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# watchlist.loc[sym, 'Notes'], plot_indicator))
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# axs0.set_xlim([plot_sd, plot_ed])
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# plot basic price info
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# plot basic price info
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data = price.copy().to_frame(sym)
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data = price.copy().to_frame(sym)
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# to limit low bound when plotting in log scale
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# to limit low bound when plotting in log scale
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@ -759,6 +646,15 @@ def intelligent_loop_plots(sym):
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if __name__ == "__main__":
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if __name__ == "__main__":
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lb_year = 3 # years of stock data to retrieve
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plt_year = 2 # number of years data to plot
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lb_trigger = 5 # days to lookback for triggering events
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end_date = dt.datetime.today()
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start_date = end_date - dt.timedelta(days = 365 * lb_year)
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plot_sd = end_date - dt.timedelta(days = 365 * plt_year)
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plot_ed = end_date
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# test_sec_class()
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# test_sec_class()
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# test_smooth()
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# test_smooth()
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# test_bollinger_sell()
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# test_bollinger_sell()
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@ -767,13 +663,17 @@ if __name__ == "__main__":
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# Initialize the app
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# Initialize the app
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app = Dash()
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app = Dash()
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# CACHE_CONFIG = {'CACHE_TYPE': 'SimpleCache'}
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# cache = Cache()
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# cache.init_app(app.server, config=CACHE_CONFIG)
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watchlist = get_watchlist()
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watchlist = get_watchlist()
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symbols = watchlist.index.values.tolist()
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symbols = watchlist.index.values.tolist()
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CACHE_CONFIG = {'CACHE_TYPE': 'SimpleCache'}
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cache = Cache()
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cache.init_app(app.server, config=CACHE_CONFIG)
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@cache.memoize(timeout=14400) # cache timeout set to 4 hours
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def fetch_stk_data(sym, sd):
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return si.get_data(sym, start_date=sd)[["adjclose", "volume"]]
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# App layout
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# App layout
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app.layout = [
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app.layout = [
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# html.Button('Refresh Data', id='button', n_clicks=0),
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# html.Button('Refresh Data', id='button', n_clicks=0),
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@ -843,71 +743,6 @@ if __name__ == "__main__":
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else:
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else:
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return no_update
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return no_update
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# @cache.memoize(timeout=14400) # cache timeout set to 4 hours
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# def global_store():
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# j_obj = intelligent_loop_plots()
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# return j_obj
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# retrieve data button callbacks
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# @callback(
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# Output("button", "disabled", allow_duplicate=True),
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# Input("button", "n_clicks"),
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# prevent_initial_call=True,
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# )
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# def disable_btn(n):
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# if n:
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# return True
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# return no_update
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# @callback(
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# Output(component_id='signal', component_property='data'),
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# # Output(component_id='button', component_property='disabled'),
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# Input(component_id='button', component_property='n_clicks'),
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# prevent_initial_call=True,
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# )
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# def get_data_cb(clicks):
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# # global all_plot_sym, all_plot_ind, all_data, all_vol, all_macd, all_rsi
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# # if clicks == 0:
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# # return # no update
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# if not clicks or clicks == 0:
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# raise PreventUpdate
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# print("get data")
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# global_store()
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# print("data retrieved")
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# # unpacking
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# # json_data = json.loads(j_obj)
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# # all_plot_sym = json_data["all_plot_sym"]
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# # all_plot_ind = json_data["all_plot_ind"]
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# # all_data = pd.read_json(io.StringIO(json_data['all_data']))
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# # all_vol = pd.read_json(io.StringIO(json_data['all_vol']))
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# # all_macd = pd.read_json(io.StringIO(json_data['all_macd']))
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# # all_rsi = pd.read_json(io.StringIO(json_data['all_rsi']))
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# return clicks
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# Add controls to build the interaction
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# callback when data retrieve finishes triggered by signal
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# @callback(
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# Output(component_id='symbols_dropdown_list', component_property='options'),
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# Output(component_id='button', component_property='disabled'),
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# Output(component_id='start-button', component_property='disabled'),
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# Input(component_id='signal', component_property='data'),
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# )
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# def update_dropdown(in_data):
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# if not in_data or in_data == 0:
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# raise PreventUpdate
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# # return no_update
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# print(f"input: {in_data}")
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# j_obj = global_store()
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# # unpacking
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# json_data = json.loads(j_obj)
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# all_plot_sym = json_data["all_plot_sym"]
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# print("dropdown menu options updated")
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# # all_plot_sym = ["SPY", "AMZN"]
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# return sorted(all_plot_sym), False, False
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# dropdown callback
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# dropdown callback
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@callback(
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@callback(
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Output(component_id='controls-and-graph', component_property='figure'),
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Output(component_id='controls-and-graph', component_property='figure'),
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@ -922,19 +757,11 @@ if __name__ == "__main__":
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if not col_chosen:
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if not col_chosen:
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raise PreventUpdate
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raise PreventUpdate
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# return no_update
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# return no_update
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# col_chosen = "SPY"
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# j_obj = global_store()
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# # unpacking
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# json_data = json.loads(j_obj)
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# # all_plot_sym = json_data["all_plot_sym"]
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# all_plot_ind = json_data["all_plot_ind"]
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# all_data = pd.read_json(io.StringIO(json_data['all_data']))
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# all_vol = pd.read_json(io.StringIO(json_data['all_vol']))
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# all_macd = pd.read_json(io.StringIO(json_data['all_macd']))
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# all_rsi = pd.read_json(io.StringIO(json_data['all_rsi']))
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# get data
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# get data
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data, vol, macd, rsi, plot_ind = intelligent_loop_plots(col_chosen)
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# tmp = si.get_data(sym, start_date=sd)[["adjclose", "volume"]]
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tmp = fetch_stk_data(col_chosen, start_date)
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data, vol, macd, rsi, plot_ind = intelligent_loop_plots(col_chosen, tmp)
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fig = make_subplots(
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fig = make_subplots(
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rows=3,
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rows=3,
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