def show_sym_bs_stats(self, sym: str):
self.db.limit = None
dfp, financials, sentiment, earnings, spy = self.db.get_all_data(
self.db.time_from, sym)
dfp = FinI.add_indicators(dfp)
dfp = FinI.add_fib(dfp, last_rows=10)
# print(dfp)
# last 5 financials
financials = financials.tail(5)
days_to_earnings = FinI.days_to_earnings(earnings)
# print(earnings)
if days_to_earnings is None:
earnings = None
fig, axs = plt.subplots(2, 1, figsize=(16, 16))
PlotI.set_margins(plt)
PlotI.plot_spy(axs[0], spy.loc[spy.index.isin(dfp.index)])
axsp = PlotI.plot_stock_prices(axs[0].twinx(), dfp, sym, 0.5)
axsp = PlotI.plot_sma(axsp, dfp, ["sma9", "sma50"])
axsp.xaxis.set_major_formatter(plt.NullFormatter())
axsp = PlotI.plot_candles(dfp,
axsp,
body_w=0.5,
shadow_w=0.1,
alpha=0.5)
axsp = PlotI.plot_fib(dfp, axsp, alpha=0.4)
axsp = PlotI.plot_fib(dfp, axsp, alpha=0.4, fib_name="fb_mid")
axsp = PlotI.plot_fib(dfp, axsp, alpha=0.4, fib_name="fb_bot")
axsp = PlotI.plot_weeks(axsp, dfp)
PlotI.plot_boll(axsp, dfp, sym)
axsp2 = PlotI.plot_volume_bars(axsp.twinx(), dfp)
axsp2 = PlotI.plot_rsi(axs[1], dfp)
axsp2 = PlotI.plot_macd_boll(axs[1].twinx(), dfp)
# ax_macd = PlotI.plot_macd(ax_macd,dfp)
axsp2 = PlotI.plot_volume_bars(axs[1].twinx(), dfp)
if self.buy_sell_closed is not None and len(self.buy_sell_closed) > 0:
axsp = PlotI.plot_bs(axsp, self.buy_sell_closed)
axsp2 = PlotI.plot_bs(axsp2.twinx(), self.buy_sell_closed)
plt.show()
def get_subj_mess(self, subject, sym=None):
mess = ""
if self.stocks is None or sym != self.stocks.iloc[0].sym:
self.stocks = self.ss.get_trend_slice(table_name=None,
time_from="-120d",
time_to="0d",
symbol=sym)
curr_price = self.stocks.iloc[-1].close
# print(self.stocks)
days_to_earnings = FinI.days_to_earnings(self.earnings)
sector_mess, spy_mess, vol_mess = self.get_common_mess(self.stocks)
mess = subject + " " + str(sym) + \
self.subject_fund_info(self.financials, self.sentiment,
self.earnings, days_to_earnings = days_to_earnings)
mess += str(sector_mess)
mess += str(spy_mess)
mess += str(vol_mess)
self.stocks = FinI.add_levels(self.stocks)
# hl = FinI.get_fib_hl(self.stocks, self.stocks.iloc[-1].close)
pl = self.stocks.price_level.dropna()
low, high = FinI.get_nearest_values(pl, curr_price)
mess += " Price: " + str(curr_price)
mess += " | Loss: " + str(Utils.calc_perc(
curr_price,
low[0])) + "%, " if low is not None and len(low) > 0 else ""
mess += " " + "Prof.: " + str(Utils.calc_perc(
curr_price,
high[0])) + "% " if high is not None and len(high) > 0 else " | "
print("get_subj_mess() - done")
return mess, curr_price, days_to_earnings
def show_sym_stats(self, sym, save_img=False):
# last financials
if self.financials is not None and len(self.financials) > 0:
self.financials = self.financials.tail(5)
days_to_earnings = FinI.days_to_earnings(self.earnings)
# print(earnings)
if days_to_earnings is None:
self.earnings = None
plots_num = 4 if save_img else 5
fig, axs = plt.subplots(plots_num, 1, figsize=(16, 18))
PlotI.set_margins(plt)
if self.spy is not None:
PlotI.plot_spy(
axs[0], self.spy.loc[self.spy.index.isin(self.stocks.index)])
axsp = PlotI.plot_stock_prices(axs[0].twinx(),
self.stocks,
sym,
alpha=0.3)
axsp = PlotI.plot_sma(axsp, self.stocks, ["sma9", "sma50"])
axsp = PlotI.plot_candles(self.stocks,
axsp,
body_w=0.5,
shadow_w=0.1,
alpha=0.5)
axsp = PlotI.plot_fib(self.stocks, axsp, alpha=0.4)
axsp = PlotI.plot_fib(self.stocks, axsp, alpha=0.4, fib_name="fb_mid")
axsp = PlotI.plot_fib(self.stocks, axsp, alpha=0.4, fib_name="fb_bot")
PlotI.plot_boll(axsp, self.stocks, sym)
PlotI.plot_weeks(axsp, self.stocks)
PlotI.plot_rsi(axs[1], self.stocks)
ax_macd = PlotI.plot_macd_boll(axs[1].twinx(), self.stocks)
# ax_macd = PlotI.plot_macd(ax_macd,dfp)
PlotI.plot_volume_bars(axs[1].twinx(), self.stocks)
if self.in_day_stocks is None:
self.in_day_stocks = sdf.retype(
self.db.load_data(TableName.MIN15,
sym,
time_from="-1d",
time_to="0d"))
if len(self.in_day_stocks) < 1:
self.in_day_stocks = sdf.retype(
self.db.load_data(TableName.MIN15, sym, limit=20))
# self.plot_volume(axs[2], last_prices)
ax = PlotI.plot_candlesticks2(axs[2], self.in_day_stocks)
self.in_day_stocks = FinI.add_fib_from_day_df(self.in_day_stocks,
self.stocks)
ax = PlotI.plot_fib(self.in_day_stocks,
axs[2],
alpha=0.4,
fib_name="fb_mid")
# PlotI.plot_boll(ax, last_prices, sym)
sectors = self.ss.sectors_day_stats()
PlotI.plot_sector_stats(axs[3], sectors, self.stocks.iloc[0].sector)
if (save_img):
return plt
# self.plot_spy(axs[2], self.spy)
#self.plot_yahoo_candles(last_prices)
# self.plot_volume(axs[2], last_prices)
# set rotation of tick labels
axs[3].text(0.02,
0.9,
str(self.financials.beta.name) + ' | ' +
str(self.financials.beta.to_list()),
fontsize=8)
axs[3].text(
0.02,
0.8,
str(self.financials.priceToSalesTrailing12Months.name) + ' | ' +
str(self.financials.priceToSalesTrailing12Months.to_list()),
fontsize=8)
axs[3].text(0.02,
0.7,
str(self.financials.enterpriseToRevenue.name) + ' | ' +
str(self.financials.enterpriseToRevenue.to_list()),
fontsize=8)
axs[3].text(0.02,
0.6,
str(self.financials.profitMargins.name) + ' | ' +
str(self.financials.profitMargins.to_list()),
fontsize=8)
axs[3].text(0.02,
0.5,
str(self.financials.enterpriseToEbitda.name) + ' | ' +
str(self.financials.enterpriseToEbitda.to_list()),
fontsize=8)
axs[3].text(0.02,
0.4,
str(self.financials.trailingEps.name) + ' | ' +
str(self.financials.trailingEps.to_list()),
fontsize=8)
axs[3].text(0.02,
0.3,
str(self.financials.forwardEps.name) + ' | ' +
str(self.financials.forwardEps.to_list()),
fontsize=8)
axs[3].text(0.02,
0.2,
str(self.financials.priceToBook.name) + ' | ' +
str(self.financials.priceToBook.to_list()),
fontsize=8)
axs[3].text(0.02,
0.1,
str(self.financials.bookValue.name) + ' | ' +
str(self.financials.bookValue.to_list()),
fontsize=8)
axs[3].text(0.4,
0.9,
str(self.financials.shortRatio.name) + ' | ' +
str(self.financials.shortRatio.to_list()),
fontsize=8)
axs[3].text(0.4,
0.8,
str(self.financials.sharesShortPriorMonth.name) + ' | ' +
str(self.financials.sharesShortPriorMonth.to_list()),
fontsize=8)
axs[3].text(0.4,
0.7,
str(self.financials.pegRatio.name) + ' | ' +
str(self.financials.pegRatio.to_list()),
fontsize=8)
axs[3].text(0.4,
0.6,
str(self.financials.earningsQuarterlyGrowth.name) + ' | ' +
str(self.financials.earningsQuarterlyGrowth.to_list()),
fontsize=8)
axs[3].text(0.4,
0.5,
str(self.financials.bid.name) + ' | ' +
str(self.financials.bid.to_list()),
fontsize=8)
axs[3].text(0.4,
0.4,
str(self.financials.trailingPE.name) + ' | ' +
str(self.financials.trailingPE.to_list()),
fontsize=8)
axs[3].text(0.4,
0.3,
str(self.financials.forwardPE.name) + ' | ' +
str(self.financials.forwardPE.to_list()),
fontsize=8)
axs[3].text(0.4,
0.2,
str(self.financials.industry.to_list()) + ' | ' +
str(self.financials.sector.to_list()),
fontsize=8)
axs[3].text(0.4,
0.1,
str(self.financials.heldPercentInstitutions.name) + ' | ' +
str(self.financials.heldPercentInstitutions.to_list()) +
' ||| ' + str(self.financials.heldPercentInsiders.name) +
' | ' + str(self.financials.heldPercentInsiders.to_list()),
fontsize=8)
axs[3].text(0.6,
0.9,
str(self.financials.fiftyDayAverage.name) + ' | ' +
str(self.financials.fiftyDayAverage.to_list()),
fontsize=8)
axs[3].text(0.6,
0.7,
str("Last CLose Price: ") + ' | ' +
str(self.in_day_stocks.iloc[-1].close),
fontsize=8)
axs[3].text(
0.6,
0.5,
str("Days to earn.: ") + ' | ' + str(days_to_earnings.days) +
" D" if self.earnings is not None else str("Days to earn.: NaN "),
fontsize=8)
axs[3].text(0.6,
0.4,
str("Earn. est. | act. | surp.: ") +
str(self.earnings.iloc[-1].epsestimate) + ' | ' +
str(self.earnings.iloc[-1].epsactual) + ' | ' +
str(self.earnings.iloc[-1].epssurprisepct)
if self.earnings is not None else str("Earn est.: NaN "),
fontsize=8)
axs[3].text(0.6,
0.3,
str(" Sentiment article/title: ") +
str(self.sentiment.sentiment_summary_avg.to_list()) + '/' +
str(self.sentiment.sentiment_title_avg.to_list())
if self.sentiment is not None and len(self.sentiment) > 0
else str(" Sentiment: NaN "),
fontsize=8)
axs[3].text(0.02,
0.01,
str(self.financials.longBusinessSummary.to_list()),
fontsize=8)
# self.plot_candlesticks(last_prices)
# axs[3].plot([2], [1], 'o')
# plt.text()
# plt.show()
return plt