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)
Example #2
0
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 _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)
Example #4
0
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)
Example #5
0
def _fitFrontier():
    short = request.form["shor"] == "true"
    df = getData()

    return getFrontier(df, short)
def _fitFrontier():
    short = request.form["shor"] == "true"
    df = getData()
        
    return getFrontier(df, short)