def find_linear_metrics(ld: LazyDictionary) -> Iterable[str]:
linear_metrics = calculate_trends(ld["data_df"])
good_linear_metrics = []
for k, t in linear_metrics.items():
if t[1] < 0.1:
good_linear_metrics.append(k)
return good_linear_metrics
def show_trends(request):
user = request.user
validate_user(user)
stocks = user_watchlist(user)
timeframe = Timeframe(past_n_days=300)
ld = LazyDictionary()
ld["cip_df"] = lambda ld: selected_cached_stocks_cip(stocks, timeframe)
ld["trends"] = lambda ld: calculate_trends(ld["cip_df"])
ld["rank"] = lambda ld: rank_cumulative_change(
ld["cip_df"].filter(ld["trends"].keys(), axis="index"), timeframe
)
trending_companies_plot = cache_plot(
f"{user.username}-watchlist-trends",
lambda ld: plot_company_rank(ld),
datasets=ld,
)
context = {
"watchlist_trends": ld["trends"],
"timeframe": timeframe,
"trending_companies_uri": trending_companies_plot,
"trending_companies_plot_title": "Trending watchlist stocks (ranked): {}".format(
timeframe.description
),
}
return render(request, "watchlist-rank.html", context=context)
def test_calculate_trends():
df = pd.DataFrame.from_records(
[
{
"asx_code": "ANZ",
"2021-01-02": 1.0,
"2021-01-03": 2.0,
"2021-01-04": 3.0,
"2021-01-05": 4.0,
},
{
"asx_code": "BHP",
"2021-01-02": 1.0,
"2021-01-03": 1.0,
"2021-01-04": 1.0,
"2021-01-05": 1.0,
},
],
index="asx_code",
)
result = calculate_trends(df)
assert result is not None
assert isinstance(result, OrderedDict)
assert len(result) == 1
for stock, val in result.items():
assert stock == "ANZ"
assert isinstance(val, tuple)
assert val[0] - 1.0 < 1e-6
assert val[1] < 1e-6
assert val[
2] == 0.0 # since 30 days data is not available, these must be zero
assert val[3] == 0.0
assert val[4] == "none"
def test_calculate_trends():
df = pd.DataFrame.from_records([
{
'asx_code': 'ANZ',
'2021-01-02': 1.0,
'2021-01-03': 2.0,
'2021-01-04': 3.0,
'2021-01-05': 4.0
},
{
'asx_code': 'BHP',
'2021-01-02': 1.0,
'2021-01-03': 1.0,
'2021-01-04': 1.0,
'2021-01-05': 1.0
},
],
index='asx_code')
result = calculate_trends(df, set(['ANZ', 'BHP']))
assert result is not None
assert isinstance(result, OrderedDict)
assert len(result) == 1
for stock, val in result.items():
assert stock == 'ANZ'
assert isinstance(val, tuple)
assert val[0] - 1.0 < 1e-6
assert val[1] < 1e-6
assert val[2] == '1.00'
assert val[3] == 'none'
def find_exp_metrics(ld: LazyDictionary) -> Iterable[str]:
exp_metrics = calculate_trends(
ld["data_df"], polynomial_degree=2, nrmse_cutoff=0.05
)
good_linear_metrics = set(ld["linear_metrics"])
good_exp_metrics = []
for k, t in exp_metrics.items():
if t[1] < 0.1 and k not in good_linear_metrics:
good_exp_metrics.append(k)
return good_exp_metrics
def show_trends(request):
validate_user(request.user)
watchlist_stocks = user_watchlist(request.user)
all_dates = desired_dates(start_date=300) # last 300 days
cip = company_prices(watchlist_stocks,
all_dates=all_dates,
fields='change_in_percent',
fail_missing_months=False)
trends = calculate_trends(cip, watchlist_stocks, all_dates)
# for now we only plot trending companies... too slow and unreadable to load the page otherwise!
cip = rank_cumulative_change(cip.filter(trends.keys(), axis='index'),
all_dates=all_dates)
trending_companies_plot = plot_company_rank(cip)
context = {
'watchlist_trends':
trends,
'trending_companies_plot':
trending_companies_plot,
'trending_companies_plot_title':
'Trending watchlist companies by rank (past 300 days)'
}
return render(request, 'trends.html', context=context)
def show_trends(request):
validate_user(request.user)
watchlist_stocks = user_watchlist(request.user)
timeframe = Timeframe(past_n_days=300)
cip = selected_cached_stocks_cip(watchlist_stocks, timeframe)
trends = calculate_trends(cip, watchlist_stocks)
#print(trends)
# for now we only plot trending companies... too slow and unreadable to load the page otherwise!
cip = rank_cumulative_change(cip.filter(trends.keys(), axis="index"),
timeframe)
#print(cip)
trending_companies_plot = plot_company_rank(cip)
context = {
"watchlist_trends":
trends,
"trending_companies_plot":
trending_companies_plot,
"trending_companies_plot_title":
"Trending watchlist companies by rank: {}".format(
timeframe.description),
}
return render(request, "trends.html", context=context)