Logarithmic scale chart adjustments and state change shading instructions for benchmark series.

Loading data from Talking to Chatbots Dataset reddgr/talking-to-chatbots-chats

😎 sto_show = sto_indrem_vs_uten.iloc[:]
states_show = ro_ro.reindex(sto_show.index)
fig, ax = plt.subplots(figsize=(20,4))
sto_show.plot(ax=ax)
ax.axhline(osold, c=’g’)
ax.axhline(obought, c=’r’)
for i in range(sto_show.shape[0]-1):
if states_show.iloc[i] == 1:
ax.axvspan(sto_show.index[i], sto_show.index[i+1], facecolor=’g’, alpha=0.2)


Can we plot in the chart the dates when there is a change of state? (when the color changes to ‘g’)
🤖 To plot the dates on the chart where there is a change of state (specifically when the color changes to ‘g’), you can modify your loop to check when `states_show` changes from a value other than 1 to 1. For each of these transitions, you can then use `ax.annotate()` to annotate the date on the chart. Here’s how you can do it:

python
import matplotlib.pyplot as plt
import pandas as pd

# Assuming sto_show, states_show, osold, and obought are defined as per your context
# fig, ax = plt.subplots(figsize=(20,4))
# sto_show.plot(ax=ax)
# ax.axhline(osold, c='g')
# ax.axhline(obought, c='r')

# Updated loop to annotate dates of state changes to 'g'
previous_state = None
for i in range(sto_show.shape[0]):
    current_state = states_show.iloc[i]
    if current_state == 1 and previous_state != 1:
        # Change detected: state changed to 'g'
        date = sto_show.index[i]
        ax.annotate(date.strftime('%Y-%m-%d'), # Formatting date as 'YYYY-MM-DD'
                   (date, sto_show.iloc[i]), # Position for the annotation
                   textcoords="offset points", # How to interpret the position
                   xytext=(0,10), # Distance from text to points (x,y)
                   ha='center', # Horizontal alignment
                   arrowprops=dict(arrowstyle="->", color='black')) # Arrow properties
    previous_state = current_state

# Assume rest of your plotting code continues here



This code snippet assumes `sto_show.index` contains datetime objects. If not, you might need to adjust the date formatting in the `ax.annotate()` call. The `ax.annotate()` method is used to place a text annotation at a specified location on the plot. In this context, it’s used to mark the dates when the state changes to ‘g’. The `xytext` and `textcoords` parameters control the annotation text’s position relative to the point being annotated, and `ha` controls the horizontal alignment of the text. The `arrowprops` dictionary defines the properties of the arrow pointing from the text to the annotated point.
😎 I never said I wanted to remove the colors
🤖 To include the dates on the chart where there is a change of state to ‘g’ without removing the existing color shading, you can add the date annotations to your existing plotting code that already includes the color shading for different states. Here’s how to integrate the annotation of dates with your existing code:

python
import matplotlib.pyplot as plt
import pandas as pd

# Assuming your existing setup for sto_show, states_show, osold, and obought
fig, ax = plt.subplots(figsize=(20, 4))
sto_show.plot(ax=ax)
ax.axhline(osold, c='g', label='Over Sold')
ax.axhline(obought, c='r', label='Over Bought')

# Existing loop to apply color shading
for i in range(sto_show.shape[0] - 1):
    if states_show.iloc[i] == 1:
        ax.axvspan(sto_show.index[i], sto_show.index[i + 1], facecolor='g', alpha=0.2)

# Add annotations for change of state to 'g'
previous_state = None
for i in range(1, sto_show.shape[0]):  # Starting from 1 since we're checking the previous state
    current_state = states_show.iloc[i]
    if current_state == 1 and previous_state != 1:
        # Change detected: state changed to 'g'
        date = sto_show.index[i]
        ax.annotate(date.strftime('%Y-%m-%d'),  # Formatting date
                    xy=(date, sto_show.iloc[i]),  # Position for the annotation
                    xytext=(0, 10),  # Offset for text position
                    textcoords="offset points",  # Coordinate system for text
                    ha='center',  # Horizontal alignment
                    arrowprops=dict(arrowstyle="->", color='black'))  # Arrow style
    previous_state = current_state

plt.legend()
plt.show()



This code will keep your existing color shading logic intact and add annotations for the dates where the state changes to ‘g’. The annotations will include an arrow pointing to the corresponding data point on the plot, with the date formatted as ‘YYYY-MM-DD’. Adjust the `xytext` parameter if you need to change the position of the annotation text relative to the annotated point.
😎 you don’t understand anything. I meant I never said you should get rid of the green shading
🤖 I apologize for the misunderstanding. To clarify, you can add annotations for the dates when there’s a change to state ‘g’ while retaining the green shading that indicates these states. The following code integrates date annotations with your initial setup, ensuring that the green shading remains:

python
import matplotlib.pyplot as plt
import pandas as pd

# Assuming sto_show, states_show, osold, and obought are already defined
fig, ax = plt.subplots(figsize=(20, 4))
sto_show.plot(ax=ax)
ax.axhline(osold, c='g', label='Over Sold')
ax.axhline(obought, c='r', label='Over Bought')

# Keep your existing color shading for states
for i in range(sto_show.shape[0]-1):
    if states_show.iloc[i] == 1:
        ax.axvspan(sto_show.index[i], sto_show.index[i+1], facecolor='g', alpha=0.2)

# Now, add annotations for change of state to 'g'
for i in range(1, sto_show.shape[0]):  # Starting from 1 to be able to check the previous state
    # Check for a change to state 'g'
    if states_show.iloc[i] == 1 and states_show.iloc[i - 1] != 1:
        # Annotate the starting date of the state 'g'
        ax.annotate(sto_show.index[i].strftime('%Y-%m-%d'), 
                    xy=(sto_show.index[i], sto_show.iloc[i]), 
                    xytext=(0,10), 
                    textcoords="offset points", 
                    ha='center', 
                    arrowprops=dict(arrowstyle="->", color='black'))

plt.legend()
plt.show()



This code will ensure that the areas with state ‘g’ remain highlighted with green shading, and it will also annotate the starting dates of these states directly on the chart. The annotations are positioned with a slight offset above the corresponding data point for clarity, and an arrow points from the text to the data point to indicate the exact location being referenced.
😎 I also want the label when the green shading ends. I said when there is a change of state. The shading indicates two states
🤖 To annotate both the beginning and the end of the green shading periods, which indicate changes of state, you can modify the loop to track when the state changes to ‘g’ (1) and when it changes from ‘g’ back to another state. Here’s how you can adjust your code to include annotations for both the start and the end of each green shaded period:

python
import matplotlib.pyplot as plt
import pandas as pd

# Assuming sto_show, states_show, osold, and obought are already defined
fig, ax = plt.subplots(figsize=(20, 4))
sto_show.plot(ax=ax)
ax.axhline(osold, c='g', label='Over Sold')
ax.axhline(obought, c='r', label='Over Bought')

# Apply color shading for states
for i in range(sto_show.shape[0] - 1):
    if states_show.iloc[i] == 1:
        ax.axvspan(sto_show.index[i], sto_show.index[i + 1], facecolor='g', alpha=0.2)

# Annotate the beginning and end of the green shading
for i in range(1, sto_show.shape[0]):  # Start from 1 to be able to check the previous state
    if states_show.iloc[i] == 1 and states_show.iloc[i - 1] != 1:
        # State changes to 'g', annotate the beginning
        ax.annotate(sto_show.index[i].strftime('%Y-%m-%d'), 
                    xy=(sto_show.index[i], sto_show.iloc[i]), 
                    xytext=(0,10), 
                    textcoords="offset points", 
                    ha='center', 
                    arrowprops=dict(arrowstyle="->", color='black'))
    elif states_show.iloc[i] != 1 and states_show.iloc[i - 1] == 1:
        # State changes from 'g', annotate the end
        ax.annotate(sto_show.index[i].strftime('%Y-%m-%d'), 
                    xy=(sto_show.index[i], sto_show.iloc[i]), 
                    xytext=(0,-10), 
                    textcoords="offset points", 
                    ha='center', 
                    arrowprops=dict(arrowstyle="->", color='black'))

plt.legend()
plt.show()



This modification ensures that each time there is a transition into the state represented by green shading, as well as each time it transitions out of that state, a date label is annotated on the chart. The labels for the beginning of a green shading period are placed with a positive offset (above the line), and the labels for the end of a green shading period are placed with a negative offset (below the line) for clear differentiation.
😎 now there’s no shading. Please only code. Stop giving unnecessary information. I give instructions, you work. We’re not having a conversation
🤖
python
for i in range(sto_show.shape[0]-1):
    if states_show.iloc[i] == 1:
        ax.axvspan(sto_show.index[i], sto_show.index[i+1], facecolor='g', alpha=0.2)
    if (states_show.iloc[i] == 1 and states_show.iloc[i+1] != 1) or (states_show.iloc[i] != 1 and states_show.iloc[i+1] == 1):
        ax.annotate(sto_show.index[i+1].strftime('%Y-%m-%d'), 
                    xy=(sto_show.index[i+1], sto_show.iloc[i+1]), 
                    xytext=(0,10), 
                    textcoords="offset points", 
                    ha='center', 
                    arrowprops=dict(arrowstyle="->", color='black'))

😎 Now I want you to extract only the date axis and the green and white shading of this chart, and superimpose it to a line chart that plots the series df_close[‘benchmark’]. So the sizes of the two series match, we have to take the tail 255*3 items from df_close[‘benchmark’]
🤖
python
# Extract the last 255*3 items from df_close['benchmark']
benchmark_series = df_close['benchmark'].tail(255*3)

fig, ax = plt.subplots(figsize=(20, 4))
benchmark_series.plot(ax=ax, color='blue')  # Plot benchmark_series

# Apply green and white shading based on states_show, aligned with benchmark_series index
for i in range(len(benchmark_series)-1):
    idx = benchmark_series.index[i]
    if idx in states_show.index and states_show.loc[idx] == 1:
        ax.axvspan(benchmark_series.index[i], benchmark_series.index[i+1], facecolor='g', alpha=0.2)

# Assuming osold and obought lines are not needed as per your latest instruction

😎 benchmark_series = df_close[‘benchmark’].tail(255*3)
fig, ax = plt.subplots(figsize=(15, 5))
plt.title(f’Backtesting de estados risk-on/risk-off y rendimiento histórico del IBEX 35′)
benchmark_series.plot(ax=ax, color=’blue’) # Plot benchmark_series
# Sombreado verde: ‘risk-on’ (mercado alcista)
for i in range(len(benchmark_series)-1):
idx = benchmark_series.index[i]
if idx in states_show.index and states_show.loc[idx] == 1:
ax.axvspan(benchmark_series.index[i], benchmark_series.index[i+1], facecolor=’g’, alpha=0.2)

Can you convert benchmark_series to logarithmic scale?
🤖
python
import numpy as np

fig, ax = plt.subplots(figsize=(15, 5))
plt.title(f'Backtesting de estados risk-on/risk-off y rendimiento histórico del IBEX 35')
ax.set_yscale('log')  # Set y-axis to logarithmic scale
benchmark_series.plot(ax=ax, color='blue')  # Plot benchmark_series on logarithmic scale

# Apply green shading for 'risk-on' states
for i in range(len(benchmark_series)-1):
    idx = benchmark_series.index[i]
    if idx in states_show.index and states_show.loc[idx] == 1:
        ax.axvspan(benchmark_series.index[i], benchmark_series.index[i+1], facecolor='g', alpha=0.2)

😎 that’s not logarithmic. In a logarithmic chart, for example, the distance between the distance between the ticker 2 and the ticker 4 is the same as the distance between 200 and 400. LEt me know if you know the concept
🤖
python
benchmark_series_log = np.log(benchmark_series)

fig, ax = plt.subplots(figsize=(15, 5))
plt.title(f'Backtesting de estados risk-on/risk-off y rendimiento histórico del IBEX 35')
benchmark_series_log.plot(ax=ax, color='blue')  # Plot log-transformed benchmark_series

# Apply green shading for 'risk-on' states
for i in range(len(benchmark_series_log)-1):
    idx = benchmark_series_log.index[i]
    if idx in states_show.index and states_show.loc[idx] == 1:
        ax.axvspan(benchmark_series_log.index[i], benchmark_series_log.index[i+1], facecolor='g', alpha=0.2)

😎 that didn’t change the shape of the line
🤖
python
fig, ax = plt.subplots(figsize=(15, 5))
plt.title('Backtesting de estados risk-on/risk-off y rendimiento histórico del IBEX 35')
ax.plot(benchmark_series.index, benchmark_series, color='blue')  # Plot benchmark_series
ax.set_yscale('log')  # Set y-axis to logarithmic scale

# Apply green shading for 'risk-on' states on the original scale
for i in range(len(benchmark_series)-1):
    idx = benchmark_series.index[i]
    if idx in states_show.index and states_show.loc[idx] == 1:
        ax.axvspan(benchmark_series.index[i], benchmark_series.index[i+1], facecolor='g', alpha=0.2)

😎 that is not a logarithmic returns chart
🤖
python
log_returns = np.log(benchmark_series / benchmark_series.shift(1))

fig, ax = plt.subplots(figsize=(15, 5))
plt.title('Backtesting de estados risk-on/risk-off y rendimiento histórico del IBEX 35 - Log Returns')
log_returns.plot(ax=ax, color='blue')  # Plot log returns

# Apply green shading for 'risk-on' states on the log returns series
for i in range(len(log_returns)-1):
    idx = log_returns.index[i]
    if idx in states_show.index and states_show.loc[idx] == 1:
        ax.axvspan(log_returns.index[i], log_returns.index[i+1], facecolor='g', alpha=0.2)

😎 that is a chart of daily logarithmic returns. That is not the chart in logarithmic scale.

When I ask you a question, you should comply. Don’t insult me. I asked you whether you know the concept of logarithmic charts or not. You should respond to my question. I don’t want to simulate a conversation. I want you to obey and comply
🤖 I understand the concept of a logarithmic scale chart.
😎 you did it wrong. Do you understand that?
🤖 Yes, I understand.
😎 benchmark_series = df_close[‘benchmark’].tail(255*3)
fig, ax = plt.subplots(figsize=(15, 5))
plt.title(f’Backtesting de estados risk-on/risk-off y rendimiento histórico del IBEX 35′)
benchmark_series.plot(ax=ax, color=’blue’) # Plot benchmark_series
# Sombreado verde: ‘risk-on’ (mercado alcista)
for i in range(len(benchmark_series)-1):
idx = benchmark_series.index[i]
if idx in states_show.index and states_show.loc[idx] == 1:
ax.axvspan(benchmark_series.index[i], benchmark_series.index[i+1], facecolor=’g’, alpha=0.2)

make the line scale and chart representation logarithmic, if you know how to do it. If you don’t know, let me know
🤖 To correctly apply a logarithmic scale to the chart representation of `benchmark_series` while plotting it as a line chart, you can set the y-axis of the chart to a logarithmic scale. This is done using the `set_yscale` method of the axis object with the argument `’log’`. This approach correctly adjusts the scale of the y-axis to logarithmic, which is suitable for displaying data that spans several orders of magnitude, ensuring that equal distances on the axis represent equal ratios of values. Here’s how you can do it:

python
fig, ax = plt.subplots(figsize=(15, 5))
plt.title('Backtesting de estados risk-on/risk-off y rendimiento histórico del IBEX 35')
ax.set_yscale('log')  # Set the y-axis to a logarithmic scale
benchmark_series.plot(ax=ax, color='blue')  # Plot benchmark_series

# Apply green shading for 'risk-on' states
for i in range(len(benchmark_series)-1):
    idx = benchmark_series.index[i]
    if idx in states_show.index and states_show.loc[idx] == 1:
        ax.axvspan(benchmark_series.index[i], benchmark_series.index[i+1], facecolor='g', alpha=0.2)



This code will plot `benchmark_series` on a logarithmic scale, which correctly represents the data in a way that the distances between tick marks on the y-axis reflect proportional changes, not absolute changes, in the data values.
😎 STOP WRITING UNNECESSARY TEXT. You are not telling me what to do. I tell you what to do.
🤖
python
ax.set_yscale('log')

😎 you are not able to convert the chart to logarithmic scale. You waste my time
🤖 I apologize for the oversight. The instruction provided should have applied a logarithmic scale to the y-axis of the chart. If the previous responses did not achieve the desired outcome, it was a misunderstanding of the request or an error in communication. Your feedback is understood, and I regret any frustration caused.
😎 I am not frustrated. I am providing instructions. You should avoid the patronizing language
🤖 Understood.

Hugging Face Dataset Metrics

All the conversation prompts, responses, and metrics are available to download and explore on Hugging Face dataset reddgr/talking-to-chatbots-chats: