def main():
fname = "../data/USDJPY30.csv"
init_t = 6000
T = 1000
M = 200
mu = 10000
sigma = 0.04
rho = 1.0
n_epoch = 10000
# RRL agent with initial weight.
ini_rrl = TradingRRL(T, M, init_t, mu, sigma, rho, n_epoch)
ini_rrl.load_csv(fname)
ini_rrl.set_t_p_r()
ini_rrl.calc_dSdw()
# RRL agent for training
rrl = TradingRRL(T, M, init_t, mu, sigma, rho, n_epoch)
rrl.all_t = ini_rrl.all_t
rrl.all_p = ini_rrl.all_p
rrl.set_t_p_r()
rrl.fit()
# Plot results.
# Training for initial term T.
plt.plot(range(len(rrl.epoch_S)), rrl.epoch_S)
plt.title("Sharp's ratio optimization")
plt.xlabel("Epoch times")
plt.ylabel("Sharp's ratio")
plt.grid(True)
plt.savefig("sharp's ratio optimization.png", dpi=300)
plt.close
fig, ax = plt.subplots(nrows=3, figsize=(15, 10))
t = np.linspace(1, rrl.T, rrl.T)[::-1]
ax[0].plot(t, rrl.p[:rrl.T])
ax[0].set_xlabel("time")
ax[0].set_ylabel("USDJPY")
ax[0].grid(True)
ax[1].plot(t,
ini_rrl.F[:rrl.T],
color="blue",
label="With initial weights")
ax[1].plot(t, rrl.F[:rrl.T], color="red", label="With optimized weights")
ax[1].set_xlabel("time")
ax[1].set_ylabel("F")
ax[1].legend(loc="upper left")
ax[1].grid(True)
ax[2].plot(t, ini_rrl.sumR, color="blue", label="With initial weights")
ax[2].plot(t, rrl.sumR, color="red", label="With optimized weights")
ax[2].set_xlabel("time")
ax[2].set_ylabel("Sum of reward[yen]")
ax[2].legend(loc="upper left")
ax[2].grid(True)
plt.savefig("rrl_train.png", dpi=300)
fig.clear()
# Prediction for next term T with optimized weight.
# RRL agent with initial weight.
ini_rrl_f = TradingRRL(T, M, init_t - T, mu, sigma, rho, n_epoch)
ini_rrl_f.all_t = ini_rrl.all_t
ini_rrl_f.all_p = ini_rrl.all_p
ini_rrl_f.set_t_p_r()
ini_rrl_f.calc_dSdw()
# RRL agent with optimized weight.
rrl_f = TradingRRL(T, M, init_t - T, mu, sigma, rho, n_epoch)
rrl_f.all_t = ini_rrl.all_t
rrl_f.all_p = ini_rrl.all_p
rrl_f.set_t_p_r()
rrl_f.w = rrl.w
rrl_f.calc_dSdw()
fig, ax = plt.subplots(nrows=3, figsize=(15, 10))
t_f = np.linspace(rrl.T + 1, rrl.T + rrl.T, rrl.T)[::-1]
ax[0].plot(t_f, rrl_f.p[:rrl_f.T])
ax[0].set_xlabel("time")
ax[0].set_ylabel("USDJPY")
ax[0].grid(True)
ax[1].plot(t_f,
ini_rrl_f.F[:rrl_f.T],
color="blue",
label="With initial weights")
ax[1].plot(t_f,
rrl_f.F[:rrl_f.T],
color="red",
label="With optimized weights")
ax[1].set_xlabel("time")
ax[1].set_ylabel("F")
ax[1].legend(loc="lower right")
ax[1].grid(True)
ax[2].plot(t_f, ini_rrl_f.sumR, color="blue", label="With initial weights")
ax[2].plot(t_f, rrl_f.sumR, color="red", label="With optimized weights")
ax[2].set_xlabel("time")
ax[2].set_ylabel("Sum of reward[yen]")
ax[2].legend(loc="lower right")
ax[2].grid(True)
plt.savefig("rrl_prediction.png", dpi=300)
fig.clear()
def main():
fname = "../data/USDJPY30.csv"
init_t = 6000
T = 1000
M = 200
mu = 10000
sigma = 0.04
rho = 1.0
n_epoch = 10000
# RRL agent with initial weight.
ini_rrl = TradingRRL(T, M, init_t, mu, sigma, rho, n_epoch)
ini_rrl.load_csv(fname)
ini_rrl.set_t_p_r()
ini_rrl.calc_dSdw()
# RRL agent for training
rrl = TradingRRL(T, M, init_t, mu, sigma, rho, n_epoch)
rrl.all_t = ini_rrl.all_t
rrl.all_p = ini_rrl.all_p
rrl.set_t_p_r()
#--- GA fit
#--- Set w ranges.
min_w = np.ones(M+2+9) * -3.0
max_w = np.ones(M+2+9) * 3.0
#min_w[0] = -5.0
#max_w[0] = 5.0
min_w[M+2-1:M+2-1+9] = -5.0
max_w[M+2-1:M+2-1+9] = 5.0
nind = 200
ngen = 20000
random_state = 64
best_ind, logbook = ga_fit(rrl, min_w, max_w, random_state, nind, ngen)
rrl.w = best_ind
rrl.calc_dSdw()
# Plot results.
# Training for initial term T.
df = pd.DataFrame(logbook)
plt.plot(np.cumsum(df["nevals"]),df["max"])
plt.title("Sharp's ratio optimization")
plt.xlabel("Epoch times")
plt.ylabel("Sharp's ratio")
plt.grid(True)
plt.savefig("sharp's ratio optimization.png", dpi=300)
plt.close
fig, ax = plt.subplots(nrows=3, figsize=(15, 10))
t = np.linspace(1, rrl.T, rrl.T)[::-1]
ax[0].plot(t, rrl.p[:rrl.T])
ax[0].set_xlabel("time")
ax[0].set_ylabel("USDJPY")
ax[0].grid(True)
ax[1].plot(t, ini_rrl.F[:rrl.T], color="blue", label="With initial weights")
ax[1].plot(t, rrl.F[:rrl.T], color="red", label="With optimized weights")
ax[1].set_xlabel("time")
ax[1].set_ylabel("F")
ax[1].legend(loc="upper left")
ax[1].grid(True)
ax[2].plot(t, ini_rrl.sumR, color="blue", label="With initial weights")
ax[2].plot(t, rrl.sumR, color="red", label="With optimized weights")
ax[2].set_xlabel("time")
ax[2].set_ylabel("Sum of reward[yen]")
ax[2].legend(loc="upper left")
ax[2].grid(True)
plt.savefig("rrl_train.png", dpi=300)
fig.clear()
# Prediction for next term T with optimized weight.
# RRL agent with initial weight.
ini_rrl_f = TradingRRL(T, M, init_t-T, mu, sigma, rho, n_epoch)
ini_rrl_f.all_t = ini_rrl.all_t
ini_rrl_f.all_p = ini_rrl.all_p
ini_rrl_f.set_t_p_r()
ini_rrl_f.calc_dSdw()
# RRL agent with optimized weight.
rrl_f = TradingRRL(T, M, init_t-T, mu, sigma, rho, n_epoch)
rrl_f.all_t = ini_rrl.all_t
rrl_f.all_p = ini_rrl.all_p
rrl_f.set_t_p_r()
rrl_f.w = rrl.w
rrl_f.calc_dSdw()
fig, ax = plt.subplots(nrows=3, figsize=(15, 10))
t_f = np.linspace(rrl.T+1, rrl.T+rrl.T, rrl.T)[::-1]
ax[0].plot(t_f, rrl_f.p[:rrl_f.T])
ax[0].set_xlabel("time")
ax[0].set_ylabel("USDJPY")
ax[0].grid(True)
ax[1].plot(t_f, ini_rrl_f.F[:rrl_f.T], color="blue", label="With initial weights")
ax[1].plot(t_f, rrl_f.F[:rrl_f.T], color="red", label="With optimized weights")
ax[1].set_xlabel("time")
ax[1].set_ylabel("F")
ax[1].legend(loc="lower right")
ax[1].grid(True)
ax[2].plot(t_f, ini_rrl_f.sumR, color="blue", label="With initial weights")
ax[2].plot(t_f, rrl_f.sumR, color="red", label="With optimized weights")
ax[2].set_xlabel("time")
ax[2].set_ylabel("Sum of reward[yen]")
ax[2].legend(loc="lower right")
ax[2].grid(True)
plt.savefig("rrl_prediction.png", dpi=300)
fig.clear()