def show_all_stocks(request):
all_dates = all_available_dates()
if len(all_dates) < 1:
raise Http404("No ASX price data available!")
ymd = all_dates[-1]
validate_date(ymd)
qs = valid_quotes_only(ymd)
timeframe = Timeframe()
return show_companies(qs, request, timeframe, extra_context={
"title": "All stocks",
"sentiment_heatmap_title": "All stock sentiment: {}".format(timeframe.description)
})
def data_factory(
ld: LazyDictionary,
): # dont create the dataframe unless we have to - avoid exxpensive call!
purchase_buy_dates = []
purchases = []
stocks = []
for stock, purchases_for_stock in user_purchases(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])
return make_portfolio_performance_dataframe(stocks, timeframe,
purchases)
def test_find_movers(
quotation_fixture, monkeypatch
): # pylint: disable=unused-argument,redefined-outer-name
def mock_all_stocks(): # to correspond to fixture data
return set(["ABC", "OTHER"])
def mock_superdf(*args, **kwargs): # to correspond to fixture data
assert len(args) == 2
assert args[0] == ("uber-01-2021-asx",)
assert args[1] is None
assert kwargs == {}
rows = [
{
"asx_code": q.asx_code,
"change_in_percent": q.change_in_percent,
"fetch_date": q.fetch_date,
}
for q in Quotation.objects.all()
]
raw_df = pd.DataFrame.from_records(rows)
# print(raw_df)
df = raw_df.pivot(
index="fetch_date", columns="asx_code", values="change_in_percent"
)
return df
all_dates = all_available_dates(reference_stock="ABC")
assert len(all_dates) > 5
monkeypatch.setattr(mdl, "all_stocks", mock_all_stocks)
monkeypatch.setattr(mdl, "make_superdf", mock_superdf)
results = find_movers(
10.0, Timeframe(from_date=all_dates[0], to_date=all_dates[-1])
)
assert results is not None
# print(results)
assert len(results) == 1
assert results.loc["ABC"] == 60.0
def test_find_movers(quotation_fixture, monkeypatch):
def mock_all_stocks(): # to correspond to fixture data
return set(['ABC', 'OTHER'])
def mock_superdf(*args, **kwargs): # to correspond to fixture data
assert args[0] == {'uber-01-2021-asx'}
assert args[1] == {'ABC', 'OTHER'}
assert kwargs == {}
rows = [{'asx_code': q.asx_code, 'change_in_percent': q.change_in_percent, 'fetch_date': q.fetch_date} for q in Quotation.objects.all()]
raw_df = pd.DataFrame.from_records(rows)
#print(raw_df)
df = raw_df.pivot(index='fetch_date', columns='asx_code', values='change_in_percent')
return df
all_dates = all_available_dates(reference_stock='ABC')
assert len(all_dates) > 5
monkeypatch.setattr(mdl, 'all_stocks', mock_all_stocks)
monkeypatch.setattr(mdl, 'make_superdf', mock_superdf)
results = find_movers(10.0, Timeframe(from_date=all_dates[0], to_date=all_dates[-1]))
assert results is not None
#print(results)
assert len(results) == 1
assert results.loc['ABC'] == 60.0
def test_all_available_dates(
crafted_quotation_fixture,
): # pylint: disable=unused-argument,redefined-outer-name
assert all_available_dates() == ["2021-01-01"]
assert all_available_dates(reference_stock="ASX1") == []
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)
def test_all_available_dates(crafted_quotation_fixture):
assert all_available_dates() == ['2021-01-01']
assert all_available_dates(reference_stock='ASX1') == []