samchaaa

# Fade if price closes outside of running 1h % change (w/2h ema)... closes after 15m.
close_time = 1
pct_change = 4
smoothing = 8

results = []
for ticker in tqdm(sp):
    
    data = pd.read_json(json.load(open('./{}/{}.json'.format(data_path, ticker), 'r')))

samchaaa

# Concat all trades
trades_all = pd.DataFrame()
for x in results:
    trades = pd.DataFrame(x['trades'])
    trades['sym'] = x['ticker']
    trades_all = pd.concat([
        trades_all,
        trades
    ], axis=0)
trades_all = trades_all[trades_all['change'] != 0].dropna()

samchaaa

def get_max_min(prices, smoothing, window_range):
    # Get prices
    smooth_prices = prices['close'].rolling(window=smoothing).mean().dropna()
    # Get max and min for window
    local_max = argrelextrema(smooth_prices.values, np.greater)[0]
    local_min = argrelextrema(smooth_prices.values, np.less)[0]
    print('local_max, local_min', local_max, local_min)
    price_local_max_dt = []
    # iterate thru points
    for i in local_max:

samchaaa

(
    agg_flow
    .reset_index()
    .style
    # after this we are not working a a dataframe but a "styler" object
    .format({'cost': '${:,.2f}', 'datetime': '{:%Y/%m}/01',
            'percent_quarterly flow': '{:.1%}',
            'off_goal': '{:+.1%}',
            **{col: '{:.1f}' for col in ['cfs', 'total_flow', 'quarterly_flow']}},
            na_rep='Missing')