def rank_cumulative_change(df: pd.DataFrame, timeframe: Timeframe):
cum_sum = defaultdict(float)
# print(df)
for date in filter(lambda k: k in df.columns, timeframe.all_dates()):
for code, price_change in df[date].fillna(0.0).iteritems():
cum_sum[code] += price_change
rank = pd.Series(cum_sum).rank(method="first", ascending=False)
df[date] = rank
all_available_dates = df.columns
avgs = df.mean(axis=1) # NB: do this BEFORE adding columns...
assert len(avgs) == len(df)
df["x"] = all_available_dates[-1]
df["y"] = df[all_available_dates[-1]]
bins = ["top", "bin2", "bin3", "bin4", "bin5", "bottom"]
average_rank_binned = pd.cut(avgs, len(bins), bins)
assert len(average_rank_binned) == len(df)
df["bin"] = average_rank_binned
df["asx_code"] = df.index
stock_sector_df = (
stocks_by_sector()
) # make one DB call (cached) rather than lots of round-trips
# print(stock_sector_df)
stock_sector_df = stock_sector_df.set_index("asx_code")
# print(df.index)
df = df.merge(
stock_sector_df, left_index=True, right_on="asx_code"
) # NB: this merge will lose rows: those that dont have a sector eg. ETF's
df = pd.melt(
df,
id_vars=["asx_code", "bin", "sector_name", "x", "y"],
var_name="date",
value_name="rank",
value_vars=all_available_dates,
)
df["date"] = pd.to_datetime(df["date"], format="%Y-%m-%d")
df["x"] = pd.to_datetime(df["x"], format="%Y-%m-%d")
return df
def show_pe_trends(request):
"""
Display a plot of per-sector PE trends across stocks in each sector
ref: https://www.commsec.com.au/education/learn/choosing-investments/what-is-price-to-earnings-pe-ratio.html
"""
validate_user(request.user)
timeframe = Timeframe(past_n_days=180)
pe_df = company_prices(None,
timeframe,
fields="pe",
missing_cb=None,
transpose=True)
eps_df = company_prices(None,
timeframe,
fields="eps",
missing_cb=None,
transpose=True)
ss = stocks_by_sector()
ss_dict = {row.asx_code: row.sector_name for row in ss.itertuples()}
#print(ss_dict)
eps_stocks = set(eps_df.index)
n_stocks = len(pe_df)
positive_pe_stocks = set(pe_df[pe_df.sum(axis=1) > 0.0].index)
all_stocks = set(pe_df.index)
n_non_zero_sum = len(positive_pe_stocks)
#print(exclude_zero_sum)
records = []
trading_dates = set(pe_df.columns)
sector_counts_all_stocks = ss['sector_name'].value_counts()
all_sectors = set(ss['sector_name'].unique())
pe_pos_df = pe_df.filter(items=positive_pe_stocks,
axis=0).merge(ss,
left_index=True,
right_on='asx_code')
assert len(pe_pos_df) <= len(positive_pe_stocks) and len(pe_pos_df) > 0
market_avg_pe_df = pe_pos_df.mean(axis=0).to_frame(
name='market_pe') # avg P/E by date series
market_avg_pe_df['date'] = pd.to_datetime(market_avg_pe_df.index)
#print(market_avg_pe_df)
breakdown_by_sector_pe_pos_stocks_only = pe_pos_df[
'sector_name'].value_counts()
#print(breakdown_by_sector_pe_pos_stocks_only)
sector_counts_pe_pos_stocks_only = {
s[0]: s[1]
for s in breakdown_by_sector_pe_pos_stocks_only.items()
}
#print(sector_counts_pe_pos_stocks_only)
#print(sector_counts_all_stocks)
#print(sector_counts_pe_pos_stocks_only)
for ymd in filter(lambda d: d in trading_dates, timeframe.all_dates(
)): # needed to avoid KeyError raised during DataFrame.at[] calls below
sum_pe_per_sector = defaultdict(float)
sum_eps_per_sector = defaultdict(float)
for stock in filter(lambda code: code in ss_dict, all_stocks):
sector = ss_dict[stock]
assert isinstance(sector, str)
if stock in eps_stocks:
eps = eps_df.at[stock, ymd]
if isnan(eps):
continue
sum_eps_per_sector[sector] += eps
if stock in positive_pe_stocks:
pe = pe_df.at[stock, ymd]
if isnan(pe):
continue
assert pe >= 0.0
sum_pe_per_sector[sector] += pe
#print(sum_pe_per_sector)
assert len(sector_counts_pe_pos_stocks_only) == len(sum_pe_per_sector)
assert len(sector_counts_all_stocks) == len(sum_eps_per_sector)
for sector in all_sectors:
pe_sum = sum_pe_per_sector.get(sector, None)
n_pe = sector_counts_pe_pos_stocks_only.get(sector, None)
pe_mean = pe_sum / n_pe if pe_sum is not None else None
eps_sum = sum_eps_per_sector.get(sector, None)
records.append({
'date': ymd,
'sector': sector,
'mean_pe': pe_mean,
'sum_pe': pe_sum,
'sum_eps': eps_sum,
'n_stocks': n_stocks,
'n_sector_stocks_pe_only': n_pe
})
df = pd.DataFrame.from_records(records)
#print(df[df["sector"] == 'Utilities'])
#print(df)
context = {
"title":
"PE Trends: {}".format(timeframe.description),
"n_stocks":
n_stocks,
"timeframe":
timeframe,
"n_stocks_with_pe":
n_non_zero_sum,
"sector_pe_plot":
plot_sector_field(df, field="mean_pe"),
"sector_eps_plot":
plot_sector_field(df, field="sum_eps"),
"market_pe_plot":
plot_series(market_avg_pe_df,
x='date',
y='market_pe',
y_axis_label="Market-wide mean P/E",
color=None,
use_smooth_line=True)
}
return render(request, "pe_trends.html", context)
def show_stock(request, stock=None, n_days=2 * 365):
"""
Displays a view of a single stock via the template and associated state
"""
validate_stock(stock)
validate_user(request.user)
plot_timeframe = Timeframe(past_n_days=n_days) # for template
def dataframe(ld: LazyDictionary) -> pd.DataFrame:
momentum_timeframe = Timeframe(
past_n_days=n_days + 200
) # to warmup MA200 function
df = company_prices(
(stock,),
momentum_timeframe,
fields=all_stock_fundamental_fields,
missing_cb=None,
)
return df
# key dynamic images and text for HTML response. We only compute the required data if image(s) not cached
# print(df)
ld = LazyDictionary()
ld["stock_df"] = lambda ld: ld["stock_df_200"].filter(
items=plot_timeframe.all_dates(), axis="rows"
)
ld["cip_df"] = lambda: cached_all_stocks_cip(plot_timeframe)
ld["stock_df_200"] = lambda ld: dataframe(ld)
ld["sector_companies"] = lambda: companies_with_same_sector(stock)
ld["company_details"] = lambda: stock_info(stock, lambda msg: warning(request, msg))
ld["sector"] = lambda ld: ld["company_details"].get("sector_name", "")
# point_score_results is a tuple (point_score_df, net_points_by_rule)
ld["point_score_results"] = lambda ld: make_point_score_dataframe(
stock, default_point_score_rules(), ld
)
ld["stock_vs_sector_df"] = lambda ld: make_stock_vs_sector_dataframe(
ld["cip_df"], stock, ld["sector_companies"]
)
print(ld["stock_vs_sector_df"])
momentum_plot = cache_plot(
f"{plot_timeframe.description}-{stock}-rsi-plot",
lambda ld: plot_momentum(stock, plot_timeframe, ld),
datasets=ld,
)
monthly_maximum_plot = cache_plot(
f"{plot_timeframe.description}-{stock}-monthly-maximum-plot",
lambda ld: plot_trend("M", ld),
datasets=ld,
)
monthly_returns_plot = cache_plot(
f"{plot_timeframe.description}-{stock}-monthly returns",
lambda ld: plot_monthly_returns(plot_timeframe, stock, ld),
datasets=ld,
)
company_versus_sector_plot = cache_plot(
f"{stock}-{ld['sector']}-company-versus-sector",
lambda ld: plot_company_versus_sector(
ld["stock_vs_sector_df"], stock, ld["sector"]
),
datasets=ld,
)
point_score_plot = cache_plot(
f"{plot_timeframe.description}-{stock}-point-score-plot",
lambda ld: plot_series(ld["point_score_results"][0], x="date", y="points"),
datasets=ld,
)
net_rule_contributors_plot = cache_plot(
f"{plot_timeframe.description}-{stock}-rules-by-points",
lambda ld: plot_points_by_rule(ld["point_score_results"][1]),
datasets=ld,
)
# populate template and render HTML page with context
context = {
"asx_code": stock,
"watched": user_watchlist(request.user),
"timeframe": plot_timeframe,
"information": ld["company_details"],
"momentum": {
"rsi_plot": momentum_plot,
"monthly_highest_price": {
"title": "Highest price each month",
"plot_uri": monthly_maximum_plot,
},
},
"fundamentals": {
"plot_uri": cache_plot(
f"{stock}-{plot_timeframe.description}-fundamentals-plot",
lambda ld: plot_fundamentals(
fundamentals_dataframe(plot_timeframe, stock, ld),
stock,
),
datasets=ld,
),
"title": "Stock fundamentals: EPS, PE, DY etc.",
"timeframe": plot_timeframe,
},
"stock_vs_sector": {
"plot_uri": company_versus_sector_plot,
"title": "Company versus sector - percentage change",
"timeframe": plot_timeframe,
},
"point_score": {
"plot_uri": point_score_plot,
"title": "Points score due to price movements",
},
"net_contributors": {
"plot_uri": net_rule_contributors_plot,
"title": "Contributions to point score by rule",
},
"month_by_month_return_uri": monthly_returns_plot,
}
return render(request, "stock_page.html", context=context)
def show_purchase_performance(request):
purchase_buy_dates = []
purchases = []
stocks = []
for stock, purchases_for_stock in user_purchases(request.user).items():
stocks.append(stock)
for purchase in purchases_for_stock:
purchase_buy_dates.append(purchase.buy_date)
purchases.append(purchase)
purchase_buy_dates = sorted(purchase_buy_dates)
# print("earliest {} latest {}".format(purchase_buy_dates[0], purchase_buy_dates[-1]))
timeframe = Timeframe(from_date=str(purchase_buy_dates[0]),
to_date=all_available_dates()[-1])
df = company_prices(stocks, timeframe, transpose=True)
rows = []
stock_count = defaultdict(int)
stock_cost = defaultdict(float)
portfolio_cost = 0.0
for d in [
datetime.strptime(x, "%Y-%m-%d").date()
for x in timeframe.all_dates()
]:
d_str = str(d)
if d_str not in df.columns: # not a trading day?
continue
purchases_to_date = filter(lambda vp, d=d: vp.buy_date <= d, purchases)
for purchase in purchases_to_date:
if purchase.buy_date == d:
portfolio_cost += purchase.amount
stock_count[purchase.asx_code] += purchase.n
stock_cost[purchase.asx_code] += purchase.amount
portfolio_worth = sum_portfolio(df, d_str, stock_count.items())
#print(df)
# emit rows for each stock and aggregate portfolio
for asx_code in stocks:
cur_price = df.at[asx_code, d_str]
if np.isnan(cur_price): # price missing? ok, skip record
continue
assert cur_price is not None and cur_price >= 0.0
stock_worth = cur_price * stock_count[asx_code]
rows.append({
"portfolio_cost": portfolio_cost,
"portfolio_worth": portfolio_worth,
"portfolio_profit": portfolio_worth - portfolio_cost,
"stock_cost": stock_cost[asx_code],
"stock_worth": stock_worth,
"stock_profit": stock_worth - stock_cost[asx_code],
"date": d_str,
"stock": asx_code,
})
t = plot_portfolio(pd.DataFrame.from_records(rows))
portfolio_performance_figure, stock_performance_figure, profit_contributors_figure = t
context = {
"title": "Portfolio performance",
"portfolio_title": "Overall",
"portfolio_figure": portfolio_performance_figure,
"stock_title": "Stock",
"stock_figure": stock_performance_figure,
"profit_contributors": profit_contributors_figure,
}
return render(request, "portfolio_trends.html", context=context)