def _rebalancing(): df = getData() pos = { row["symbol"]: row["p"] for _, row in pd.read_json(request.form["pos"]).iterrows() } pos = np.array([pos[s] for s in df.columns]) freq = str(request.form["rbfreq"]) + "m" return getRebalance(df, freq, pos)
def _fitModel(): risk = float(request.form["risk"]) short = request.form["shor"] == "true" unused = filter(lambda s: len(s) > 0, request.form["unused"].split(",")) l2 = float(request.form["l2"]) data = getData() return getPortfolio(data, unused, risk, short, l2)
def _fitFrontier(): short = request.form["shor"] == "true" df = getData() return getFrontier(df, short)