1 Minute Chart Bitcoin Trading Stratgy Data Analysis

Let's import all necessary libraries and read the data!

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
df = pd.read_csv('btc_strategy_demo.csv')
df

	Day	Result	Benefit	Commission	Total
0	1	0.0000	0.00	0.00	10000.00
1	1	0.0047	47.00	0.04	10046.96
2	1	0.0031	31.15	0.02	10078.09
3	1	-0.0011	-11.09	0.01	10066.99
4	2	0.0022	22.15	0.02	10089.12
...	...	...	...	...	...
119	27	0.0088	97.38	0.07	11163.73
120	27	0.0088	98.24	0.07	11261.90
121	28	0.0088	99.10	0.07	11360.93
122	29	0.0041	46.58	0.03	11407.47
123	30	-0.0022	-25.10	0.02	11382.36

124 rows × 5 columns

Is the data format adequate? I used Pandas .dtypes to know what type of data is in the csv file.

df.dtypes

Day             int64
Result        float64
Benefit       float64
Commission    float64
Total         float64
dtype: object

There is no need to have the first row on the data set, since it doesn'r represent a trade itself and would distort the averages and further calculations. I use Panda's .iloc in order to drop rows.

df = df.iloc[1:]
df.head()

	Day	Result	Benefit	Commission	Total
1	1	0.0047	47.00	0.04	10046.96
2	1	0.0031	31.15	0.02	10078.09
3	1	-0.0011	-11.09	0.01	10066.99
4	2	0.0022	22.15	0.02	10089.12
5	2	0.0026	26.23	0.02	10115.34

Plot 1

Portfolio Value Over 30 Days in Euros

sns.set_theme(style="darkgrid")
sns.lineplot(x="Day", y="Total", data=df, ci=None).set(title='Portfolio Value Over 30 Days in €')
plt.savefig("value_evolution.png",format='png',dpi=300)

Plot 2

Percentaje of Wins and Loses in Positions taken First I need to calculate how many positions generated profit (.gt(0) --> greater than Zero) and how many generted loses (.lt(0) --> lower than Zero). A data frame is created in order to plot it.

positive = df['Result'].gt(0).sum()
negative = df['Result'].lt(0).sum()
data = [positive,negative]

labels = ["Wins","Loses"]
colors = sns.color_palette('pastel')[0:2]
plt.pie(data, labels = labels, colors = colors, autopct='%.0f%%')
plt.savefig("wins_loses.png",format='png',dpi=300)
plt.show()

In order to create the next plot, a new data frame is created where we can observe in one column the average benefit each day and another colum where the total average benefit is showed.

final = []
for day in range (1,31):
    df_new =df[df['Day'] == day]
    df_new['Avg_Benefit_Day'] = df_new['Benefit'].mean()
    final.append(df_new)
    
final_df = pd.concat(final, ignore_index=True)

avg_benf_df = final_df[['Day','Avg_Benefit_Day']].drop_duplicates()
avg_benf_df['Avg_Total'] = avg_benf_df['Avg_Benefit_Day'].mean()
avg_benf_df['Zero_Line'] = 0
avg_benf_df.head()

/var/folders/xc/p1vn9pj537x8bjlw0zlhgh0r0000gn/T/ipykernel_89019/1994231351.py:4: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_new['Avg_Benefit_Day'] = df_new['Benefit'].mean()

	Day	Avg_Benefit_Day	Avg_Total	Zero_Line
0	1	22.353333	13.368048	0
3	2	13.930000	13.368048	0
9	3	6.614000	13.368048	0
14	4	2.208000	13.368048	0
24	5	-8.081250	13.368048	0

Plot 3

Average Benefits Obtained each Day & Total Average in €

fig = plt.figure
ax1 = plt.subplot(111)
plt.title("Average Benefits Obtained each Day & Total Average in €")
ax1.plot(avg_benf_df.Day, avg_benf_df.Avg_Benefit_Day, linewidth=2, label='Daily Average')
ax1.plot(avg_benf_df.Day, avg_benf_df.Avg_Total, '--', label='Total Average')
plt.xlabel("Days")
plt.ylabel("Euros")
plt.legend(loc='best')
ax1.plot(avg_benf_df.Day, avg_benf_df.Zero_Line, color='k', linewidth=0.7)
plt.savefig("average_graph.png",format='png',dpi=300)
plt.show()

Final calculations for the Return Of Investment over the 30 days, a posible Annual Percentage Yield and the average number of positions taken every day.

final = df.at[123,'Total']
roi = (((final)-10000)*100)/10000
roi

13.823600000000006

apy = (roi*365)/30
apy

168.1871333333334

day_count = df.pivot_table(columns=['Day'], aggfunc='size')
day_count = day_count.reset_index()
day_count[0].mean()

4.1