wukan1986 / expr_codegen Goto Github PK

def _code_block_():
    cond_1 = cs_rank(-amount, False) <= 10
    filter = and_(cond_1,
                  True)

def _code_block_():
    cond_1 = cs_rank(-amount, False)
    filter = and_(cond_1 <= 10,
                  True)

df = df.sort_values(by=["asset", "date"]).groupby(by=["asset"], group_keys=False).apply(func_0_ts__asset__date)
df = df.groupby(by=["date"], group_keys=False).apply(func_0_cs__date)
df = func_0_cl(df)
df = df.groupby(by=["date"], group_keys=False).apply(func_1_cs__date)
df = func_1_cl(df)
df = df.sort_values(by=["asset", "date"]).groupby(by=["asset"], group_keys=False).apply(func_1_ts__asset__date)
df = func_2_cl(df)
df = df.sort_values(by=["asset", "date"]).groupby(by=["asset"], group_keys=False).apply(func_2_ts__asset__date)
df = df.groupby(by=["date"], group_keys=False).apply(func_2_cs__date)
df = df.groupby(by=["date"], group_keys=False).apply(func_3_cs__date)
df = df.sort_values(by=["asset", "date"]).groupby(by=["asset"], group_keys=False).apply(func_3_ts__asset__date)

# x_0 = ts_arg_max(signed_power(if_else(RETURNS < 0, ts_std_dev(RETURNS, 20), CLOSE), 2.0), 5)
# x_1 = ts_delta(log(VOLUME), 2)
# x_2 = cs_rank(x_1)
# x_3 = CLOSE - OPEN
# x_4 = cs_rank(x_3/OPEN)
# x_5 = cs_rank(OPEN)
# x_6 = cs_rank(VOLUME)
# x_7 = cs_rank(LOW)
# x_8 = OPEN - ts_sum(VWAP, 10)/10
# x_9 = ts_sum(OPEN, 5)
# x_10 = ts_sum(RETURNS, 5)
# x_11 = x_10*x_9 - ts_delay(x_10*x_9, 10)
# x_12 = ts_delta(CLOSE, 1)
# x_13 = -x_12
# x_14 = if_else(ts_min(x_12, 4) > 0, x_12, if_else(ts_max(x_12, 4) < 0, x_12, x_13))
# x_15 = CLOSE - VWAP
# x_16 = -x_15
# x_17 = ts_max(x_16, 3)
# x_18 = ts_min(x_16, 3)
# x_19 = ts_delta(VOLUME, 3)
# x_20 = cs_rank(CLOSE)
# x_21 = ts_covariance(x_20, x_6, 5)
# x_22 = ts_delta(RETURNS, 3)
# x_23 = cs_rank(x_22)
# x_24 = cs_rank(HIGH)
# x_25 = ts_corr(x_24, x_6, 3)
# x_26 = cs_rank(x_25)
# x_27 = ts_covariance(x_24, x_6, 5)
# x_28 = ts_delta(x_12, 1)
# x_29 = ts_rank(CLOSE, 10)
# x_30 = ts_rank(VOLUME/ADV20, 5)