예제 #1
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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)
    })
예제 #2
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    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)
예제 #3
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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
예제 #4
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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
예제 #5
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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") == []
예제 #6
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파일: stock.py 프로젝트: mappin/asxtrade
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)
예제 #7
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def test_all_available_dates(crafted_quotation_fixture):
    assert all_available_dates() == ['2021-01-01']
    assert all_available_dates(reference_stock='ASX1') == []