python蓄積4

サマリ

1.時系列用辞書格納

dic ={}
for date in close.index:
    ***
    dic[date] = [...]
result = pd.DataFrame(dic_result,index=['...']).T
#     

便利な点はスライスを使わずに、書くときに変数を直接書けばいいことです
2.ブール変数表示

for i,date in enumerate(close.index):
    change = False
    index = index*(1+index_change[date])
    if (hs_change_pct + zz_change_pct) > 0.2 and (pos >= 0.15):
        pos -= 0.05
        change = True
        signal = -1
    elif (hs_change_pct + zz_change_pct) < -0.2 and (pos <= 0.75):
        pos += 0.05
        change = True
        signal = 1
    if change:
        ...

便利な点は加倉減倉式が一致している(正負号付き)2回書かないことです
3.変数
ポリシーを書くときはcashとequityが分かれます

1.加減倉時equity(t)=total_return(t-1) * pos(t)

**だから信号時にposを**num_に変える必要があるtemp(t) = equity(t) * weight(t)/close(t)

現在のnumをtempで記録しdelta_を計算するnum cash(t) = cash(t-1) +(num(t-1)-temp_num(t))*close(t)

現在のnum num=num_に付与temp

2.条件外加倉しても加倉しなくても行う操作equity=num*close return=equity+cash

4.ポリシーコード

dic_result = {}
index_change = pd.Series((df_close['  300']/df_close['  300'].shift(1)+df_close['  500']/df_close['  500'].shift(1)-2)/2)
index_change[0] = 0

for i,date in enumerate(df_close.index):
    change_rolling_time = False
    change = False
    index = index*(1+index_change[date])
    hs_change_pct = df_close.loc[date, '  300'] / hs_benchmark - 1
    zz_change_pct = df_close.loc[date, '  500'] / zz_benchmark - 1
    [hs_pct, zz_pct] = calc_pct(date)
    if date == rolling_time[i]:     #rolling-time     
        pos = calc_total_pos(hs_pct, zz_pct)    #           
        change_rolling_time = True
        signal_rolling = 1
    else:
        signal_rolling = 0
        if (hs_change_pct + zz_change_pct) > 0.2 and (pos >= 0.15):
            pos -= 0.05
            # pos = calc_total_pos(hs_pct, zz_pct)
            change = True
            signal = -1
        elif (hs_change_pct + zz_change_pct) < -0.2 and (pos <= 0.75):
            pos += 0.05
            # pos = calc_total_pos(hs_pct, zz_pct)
            change = True
            signal = 1
        else:
            signal = 0
            change = False
    if change or change_rolling_time:
        # [hs_pct, zz_pct] = calc_pct(date)
        hs_weight = calc_hs_weight(hs_pct, zz_pct)
        euqity = total_return * pos
        hs_num_temp = equity * hs_weight / df_close.loc[date, '  300']
        zz_num_temp = equity * (1 - hs_weight) / df_close.loc[date, '  500']
        cash = cash + (hs_num - hs_num_temp) * df_close.loc[date, '  300'] + (zz_num - zz_num_temp) * df_close.loc[date, '  500']
        hs_num = hs_num_temp
        zz_num = zz_num_temp
        hs_benchmark = df_close.loc[date, '  300']
        zz_benchmark = df_close.loc[date, '  500']
    euqity = hs_num * df_close.loc[date, '  300'] + zz_num * df_close.loc[date, '  500']
    total_return = euqity + cash
    dic_result[date] = [total_return,pos,signal_rolling,signal,index]