def slope(q, entry, real_life):
tree_object = n.gen_tree(q, entry, real_life)
tree = [
tree_object.n0, tree_object.n1, tree_object.n2, tree_object.n3,
tree_object.n4, tree_object.n5, tree_object.n6, tree_object.n7,
tree_object.n8, tree_object.n9, tree_object.n10, tree_object.n11,
tree_object.n12, tree_object.n13, tree_object.n14
]
# Expected value plot
real_life_data_day = [0, 1, 2, 3, 4]
day_0_ev = tree[0].price
day_1_ev = (sum(tree[i].price * tree[i].probability for i in range(1, 3)))
day_2_ev = (sum(tree[i].price * tree[i].probability for i in range(3, 6)))
day_3_ev = (sum(tree[i].price * tree[i].probability for i in range(6, 10)))
day_4_ev = (sum(tree[i].price * tree[i].probability
for i in range(10, 15)))
ev = [day_0_ev, day_1_ev, day_2_ev, day_3_ev, day_4_ev]
x = np.array(real_life_data_day)
y = np.array(ev)
m, b = np.polyfit(x, y, 1)
scaled_m = m / day_0_ev
return scaled_m, day_4_ev
def error_fun(q, stock, real_life):
# Predicted nodes
tree_object = n.gen_tree(q, stock, real_life)
tree = [tree_object.n0,
tree_object.n1,
tree_object.n2,
tree_object.n3,
tree_object.n4,
tree_object.n5,
tree_object.n6,
tree_object.n7,
tree_object.n8,
tree_object.n9,
tree_object.n10,
tree_object.n11,
tree_object.n12,
tree_object.n13,
tree_object.n14]
# Real-life data
real_life_data_day = [0,1,2,3,4]
real_life_data_price = [real_life[i] for i in range(5,10)]
# Day 1 error
day_1 = tree[1:3]
error_day_1 = min([abs(real_life_data_price[1]-day_1[i].price) for i in range(2)])/real_life_data_price[1]
#print(error_day_1)
# Day 2 error
day_2 = tree[3:6]
error_day_2 = min([abs(real_life_data_price[2]-day_2[i].price) for i in range(3)])/real_life_data_price[2]
#print(error_day_2)
# Day 3 error
day_3 = tree[6:10]
error_day_3 = min([abs(real_life_data_price[3]-day_3[i].price) for i in range(4)])/real_life_data_price[3]
#print(error_day_3)
# Day 4 error
day_4 = tree[10:15]
error_day_4 = min([abs(real_life_data_price[4]-day_4[i].price) for i in range(5)])/real_life_data_price[4]
#print(error_day_4)
# Error set
errors = [error_day_1, error_day_2, error_day_3, error_day_4]
total_error = sum(errors)
#print(total_error)
return total_error
def plot(q, entry, real_life):
u = u_and_d.u(q, entry, real_life)
d = u_and_d.d(q, entry, real_life)
tree_object = n.gen_tree(q, entry, real_life)
tree = [
tree_object.n0, tree_object.n1, tree_object.n2, tree_object.n3,
tree_object.n4, tree_object.n5, tree_object.n6, tree_object.n7,
tree_object.n8, tree_object.n9, tree_object.n10, tree_object.n11,
tree_object.n12, tree_object.n13, tree_object.n14
]
fig, ax = plt.subplots()
day = [i.day for i in tree]
price = [i.price for i in tree]
#scale = [1000*abs(i.probability) for i in tree]
scale = 150
#colors = [i.probability for i in tree]
colors = 'royalblue'
colors_2 = 'grey'
colors_3 = 'white'
colors_4 = 'black'
colors_5 = 'red'
marker = '.'
font = 'Tahoma'
# Add arrows
for i in tree[0:10]:
x_tail = i.day
y_tail = i.price
x_head = i.day + 1
y_head_u = i.price * u
y_head_d = i.price * d
dx = x_head - x_tail
#dy_u = y_head_u - y_tail
#dy_d = y_head_d - y_tail
arrow_u = mpatches.FancyArrowPatch((x_tail, y_tail),
(x_head, y_head_u),
mutation_scale=15,
zorder=1,
color=colors_2)
arrow_d = mpatches.FancyArrowPatch((x_tail, y_tail),
(x_head, y_head_d),
mutation_scale=15,
zorder=1,
color=colors_2)
ax.add_patch(arrow_u)
ax.add_patch(arrow_d)
# Plot the chart
ax.scatter(day, price, s=scale, c=colors, marker=marker, zorder=2)
# Add rectangles
width = 0.5
height = (tree_object.n10.price - tree_object.n14.price) / 15
for x, y in zip(day, price):
ax.add_patch(
mpatches.Rectangle(
xy=(x - width / 2, y - height / 2 +
(tree_object.n1.price - tree_object.n0.price) / 2),
width=width,
height=height,
linewidth=1,
color=colors))
# Zip joins x and y coordinates in pairs
for x, y in zip(day, price):
label = "{:.2f}".format(y)
# Label each point with the price
ax.annotate(
label, # this is the text
(x, y + (tree_object.n1.price - tree_object.n0.price) /
2), # this is the point to label
va='center',
ha='center',
c=colors_3,
fontname=font) # horizontal alignment can be left, right or center
# Expected value plot
real_life_data_day = [0, 1, 2, 3, 4]
day_0_ev = tree[0].price
day_1_ev = (sum(tree[i].price * tree[i].probability for i in range(1, 3)))
day_2_ev = (sum(tree[i].price * tree[i].probability for i in range(3, 6)))
day_3_ev = (sum(tree[i].price * tree[i].probability for i in range(6, 10)))
day_4_ev = (sum(tree[i].price * tree[i].probability
for i in range(10, 15)))
ev = [day_0_ev, day_1_ev, day_2_ev, day_3_ev, day_4_ev]
print(ev)
x = np.array(real_life_data_day)
y = np.array(ev)
m, b = np.polyfit(x, y, 1)
print(m)
ax.scatter(real_life_data_day,
ev,
s=scale,
c=colors_5,
marker="",
zorder=2)
ax.plot(x, m * x + b, c=colors_5)
label2 = str(round(m, 3)) + "(x)+ " + str(round(b, 2))
print(label2)
ax.annotate(
label2, # this is the text
(0.5, (tree_object.n14.price)), # this is the point to label
va='center',
ha='center',
c=colors_5,
fontname=font) # horizontal alignment can be left, right or center
label3 = "q = " + str(q)
ax.annotate(
label3, # this is the text
(2, tree_object.n14.price), # this is the point to label
va='center',
ha='center',
c=colors,
fontname=font) # horizontal alignment can be left, right or center
# Real-life data
real_life_data_price = [real_life[i] for i in range(5, 10)]
# Plot real-life data as a line plot
ax.plot(real_life_data_day, real_life_data_price, c=colors_4, zorder=4)
plt.xticks(np.arange(0, 5, 1))
stock = entry.upper()
start_day = datetime.today() - timedelta(days=4)
d1 = start_day.strftime("%B %d, %Y")
ax.set_xlabel('Days', fontname=font, weight='bold')
ax.set_ylabel('Price ($)', fontname=font, weight='bold')
ax.set_title(stock + ' Predicted Close Price Over Time',
fontname=font,
weight='bold')
fig.tight_layout()
plt.show()