"""R624：同机合成三股票计时，计时无速度保证，优化结果必须一致。"""
import json
from statistics import median
from time import perf_counter_ns


def indicator(series,optimized):
    prefix=[0]
    if optimized:
        for value in series:prefix.append(prefix[-1]+value)
    out=[]
    for t in range(len(series)):
        if t<19:out.append(None);continue
        fast=(prefix[t+1]-prefix[t-2])/3 if optimized else sum(series[t-2:t+1])/3
        slow=(prefix[t+1]-prefix[t-19])/20 if optimized else sum(series[t-19:t+1])/20
        out.append((fast,slow))
    return out


def pipeline(n,optimized):
    source=json.dumps({name:[100+((i*7+offset)%17) for i in range(n)]
                       for offset,name in enumerate(['A','B','C'])})
    times={};start=perf_counter_ns();prices=json.loads(source);times['load']=perf_counter_ns()-start
    start=perf_counter_ns();ind={k:indicator(v,optimized) for k,v in prices.items()};times['indicators']=perf_counter_ns()-start
    start=perf_counter_ns();signals={k:[int(v[0]>v[1]) if v else 0 for v in rows] for k,rows in ind.items()};times['signals']=perf_counter_ns()-start
    start=perf_counter_ns();targets={k:list(rows) for k,rows in signals.items()};times['portfolio_targets']=perf_counter_ns()-start
    start=perf_counter_ns();fills={};equity={}
    for key,p in prices.items():
        cash,shares=1000,0;rows=[];values=[1000]
        for t in range(1,n):
            target=targets[key][t-1];qty=target-shares
            if qty:rows.append((t,qty,p[t]))
            cash-=qty*p[t];shares=target;values.append(cash+shares*p[t])
        fills[key]=rows;equity[key]=values
    times['account_and_fills']=perf_counter_ns()-start
    start=perf_counter_ns();metrics={'ending':sum(v[-1] for v in equity.values()),'fills':sum(map(len,fills.values())),'stocks':len(prices),'observations':n};times['metrics']=perf_counter_ns()-start
    return {'indicators':ind,'signals':signals,'fills':fills,'equity':equity,'metrics':metrics},times


for n in [2000,8000]:
    records={False:[],True:[]};reference=None
    for optimized in [False,True]:
        for _ in range(3):
            result,times=pipeline(n,optimized)
            assert result['metrics']['stocks']==3 and result['metrics']['observations']==n
            expected={2000:{'ending':4732,'fills':2794,'stocks':3,'observations':2000},
                      8000:{'ending':10047,'fills':11266,'stocks':3,'observations':8000}}
            assert result['metrics']==expected[n]
            if reference is None:reference=result
            assert result==reference
            assert all(t>=0 for t in times.values())
            records[optimized].append(times)
    for optimized in [False,True]:
        med={k:round(median(row[k] for row in records[optimized])/1e6,4) for k in records[optimized][0]}
        print('n='+str(n)+' optimized='+str(optimized)+' median_ms='+str(med))
        print('n='+str(n)+' optimized='+str(optimized)+' raw_ns='+str(records[optimized]))
    print('n='+str(n)+' equivalent=True metrics='+str(reference['metrics']))
print('cache_policy=in_memory_JSON_reparsed_each_run no_external_cache no_speed_assertion')