def summarize(results):
#print(dumps(results, indent = 2))
precision = 3
std = NormalDist()
alpha = 0.01
bad_records = 0
summary = {"mae": [], "mfe": [], "pnl": [], "duration": []}
operations = ["mae", "mfe", "pnl"]
for record in results:
fns = get_summary_ops(record["type"])
for i in range(len(operations)):
try:
summary[operations[i]].append(fns[i](record))
except (ZeroDivisionError, KeyError):
bad_records += 1
continue
summary["duration"].append(record["end_index"] -
record["start_index"])
#pyplot.hist(summary["mae"], bins = 100, range = (-10, 10))
#pyplot.show()
summary["mae"].sort()
summary["mfe"].sort()
summary["pnl"].sort()
summary["duration"].sort()
summary["mae"] = trim_outliers(summary["mae"])
summary["mfe"] = trim_outliers(summary["mfe"])
summary["pnl"] = trim_outliers(summary["pnl"])
summary["duration"] = trim_outliers(summary["duration"])
res = {
"test": {
"group": results[0]["group"],
"type": results[0]["type"],
"enter": results[0]["enter"],
"exit": results[0]["exit"],
"samples": len(results)
},
"mae": {
"mean": round(mean(summary["mae"]), precision),
"stdev": round(stdev(summary["mae"]), precision)
},
"mfe": {
"mean": round(mean(summary["mfe"]), precision),
"stdev": round(stdev(summary["mfe"]), precision)
},
"mfe/mae": {
"mean":
round(mean(summary["mfe"]) / mean(summary["mae"]), precision)
},
"pnl": {
"mean": round(mean(summary["pnl"]), precision),
"stdev": round(stdev(summary["pnl"]), precision)
},
"duration": {
"mean": round(mean(summary["duration"]), precision),
"stdev": round(stdev(summary["duration"]), precision)
},
"excluded_records": bad_records
}
for statistic in ["mae", "mfe", "pnl"]:
E = std.zscore(alpha / 2) * res[statistic]["stdev"] / sqrt(
res["test"]["samples"])
mu = res[statistic]["mean"]
res[statistic][f"interval"] = {
"lower": round(mu - E, 2 * precision),
"upper": round(mu + E, 2 * precision),
"alpha": 1 - alpha
}
return res
def z_normalize(data):
""" Perform a z-normalization on the data """
dist = NormalDist(mean(data), stdev(data))
return [dist.zscore(i) for i in data]
