gucasbrg
gucasbrg
1. taking A shares as an example, calculate features for all stocks . Is there a relatively simple configuration plan? 2. As you know, cross-sectional features are very important. are...
how to generate training data for this link? could you provide this script ? thanks https://drive.google.com/drive/folders/1UECiIOGjIua9ORIDfcZft8XGTQ-iTzuD?usp=sharing
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``` import talib as ta from talib import stream as ta_stream close = np.random.random(100) output = ta.RSI(close, timeperiod=period) latest = ta_stream.RSI(close, timeperiod=period) print(output) print(latest) print(output[-1] - latest) ```
``` import bottleneck as bn a = [0.008196721311475436, -0.01626016260162607, 0.012396694214876205, -0.016326530612245076, 0.008298755186722151, 0.004115226337448442, 0.0, -0.008196721311475436, -0.008264462809917252, -0.00416666666666668, -0.012552301255230165, -0.012711864406779568, 0.017167381974248982, 0.008438818565400736, 0.004184100418410055, -0.00833333333333336, 0.008403361344537843, -0.01666666666666672, 0.0, 0.016949152542372937, -0.00416666666666668, 0.0, 0.004184100418410055,...
hi, 用训练集生成的model，想用 model 把测试集每一项结果输出，看参数代码，有一个 output 参数； 运行./hector-run --method ftrl --action test --test test.text --model model.ftrl --output result.ftrl 但是没有结果输出，是我参数配置不对还是别的原因？
``` import numpy as np import talib def _ta_natr_14(x1, x2, x3): t = 14 x1 = x1.flatten() x2 = x2.flatten() x3 = x3.flatten() print(np.nan_to_num(talib.NATR(x1, x2, x3, timeperiod=t))[:100]) def _ta_natr_14_qs(x1, x2,...
``` x = pd.read_csv('x.csv').to_numpy().flatten() n1 = pd.DataFrame(x).rolling(window=4000, min_periods=1).std().to_numpy().flatten() n2 = rolling_std(x, 4000, 1) for x, (i, j) in enumerate(zip(n1, n2)): if x