Extracting results for quantstats

Hello, after creating a model and running results = model.learn(int(1000)) how do I use the results to compare with a benchmark in quantstats? Currently the results doesn’t hold the data that quantstats expect to be able to use in conjunction with qs.reports.html(results, "SPY", output="D:\ReinforcementLearning\BaseLines\Trading\Myreport.html")

[AminHP]

Hi again. Can you try this code and check if it works?

# Imports
import numpy as np
import pandas as pd

import gym
import gym_anytrading
import quantstats as qs

from stable_baselines import A2C
from stable_baselines.common.vec_env import DummyVecEnv

import matplotlib.pyplot as plt


# Create Env
df = gym_anytrading.datasets.STOCKS_GOOGL.copy()
df.index = pd.to_datetime(df.index)
env_maker = lambda: gym.make('stocks-v0', df=df, window_size=10, frame_bound=(100, 5000))
env = DummyVecEnv([env_maker])


# Train Env
policy_kwargs = dict(net_arch=[64, 'lstm',dict(vf=[128, 128, 128], pi=[64, 64])])
model = A2C('MlpLstmPolicy', env, verbose=1, policy_kwargs=policy_kwargs)
model.learn(total_timesteps=1)


# Test Env 
env = env_maker()
observation = env.reset()

profits = []

while True:
    observation = observation[np.newaxis, ...]
    # action = env.action_space.sample()
    action, _states = model.predict(observation)

    observation, reward, done, info = env.step(action)
    profits.append(info['total_profit'])

    # env.render()
    if done:
        print("info:", info)
        break


# Plot Results
plt.cla()
env.render_all()
plt.show()


# Analysis Using quantstats
qs.extend_pandas()

net_worth = pd.Series(profits, index=df.index[-len(profits):])
returns = net_worth.pct_change().iloc[1:]

qs.reports.full(returns)
qs.reports.html(returns, output="report.html")

[sword134]

@AminHP just ran the test code you posted. Damn you are an absolute champ. Going to implement this with my own my RL now and I just want to thank you a lot! Perhaps for even easier implementation it would be possible to get the returns variable through a profit function that could easily be built into gym-anytrading.

Thanks a lot!