Reducing parquet files. Time window two months

app.py

@@ -24,7 +24,7 @@ from tabs.error import (
     plot_tool_error_data,
     plot_week_error_data,
 )
-from tabs.about import about_olas_predict
+from tabs.about import about_olas_predict, about_this_dashboard
 
 
 def get_logger():
@@ -151,20 +151,20 @@ with demo:
     with gr.Tabs():
         with gr.TabItem("🔥Trades Dashboard"):
             with gr.Row():
-                gr.Markdown("# Plot of number of trades by week")
+                gr.Markdown("# Number of trades per week")
             with gr.Row():
                 trades_by_week_plot = plot_trades_by_week(trades_df=trades_count_df)
             with gr.Row():
-                gr.Markdown("# Plot of winning trades by week")
+                gr.Markdown("# Percentage of winning trades per week")
             with gr.Row():
                 winning_trades_by_week_plot = plot_winning_trades_by_week(
                     trades_df=trades_winning_rate_df
                 )
             with gr.Row():
-                gr.Markdown("# Plot of trade details")
+                gr.Markdown("# Trading metrics")
             with gr.Row():
                 trade_details_selector = gr.Dropdown(
-                    label="Select a trade",
+                    label="Select a trade metric",
                     choices=[
                         "mech calls",
                         "collateral amount",
@@ -197,11 +197,11 @@ with demo:
 
         with gr.TabItem("🚀 Tool Winning Dashboard"):
             with gr.Row():
-                gr.Markdown("# Plot showing overall winning rate")
+                gr.Markdown("# All tools winning performance")
 
             with gr.Row():
                 winning_selector = gr.Dropdown(
-                    label="Select Metric",
+                    label="Select the tool metric",
                     choices=["losses", "wins", "total_request", "win_perc"],
                     value="win_perc",
                 )
@@ -228,7 +228,7 @@ with demo:
                 winning_plot
 
             with gr.Row():
-                gr.Markdown("# Plot showing winning rate by tool")
+                gr.Markdown("# Winning performance by each tool")
 
             with gr.Row():
                 sel_tool = gr.Dropdown(
@@ -256,11 +256,11 @@ with demo:
 
         with gr.TabItem("🏥 Tool Error Dashboard"):
             with gr.Row():
-                gr.Markdown("# Plot showing overall error")
+                gr.Markdown("# All tools errors")
             with gr.Row():
                 error_overall_plot = plot_error_data(error_all_df=error_overall_df)
             with gr.Row():
-                gr.Markdown("# Plot showing error by tool")
+                gr.Markdown("# Error percentage per tool")
             with gr.Row():
                 sel_tool = gr.Dropdown(
                     label="Select a tool", choices=INC_TOOLS, value=INC_TOOLS[0]
@@ -283,7 +283,7 @@ with demo:
                 tool_error_plot
 
             with gr.Row():
-                gr.Markdown("# Plot showing error by week")
+                gr.Markdown("# Tools distribution of errors per week")
 
             with gr.Row():
                 choices = error_overall_df["request_month_year_week"].unique().tolist()
@@ -321,4 +321,7 @@ with demo:
             with gr.Accordion("About Olas Predict"):
                 gr.Markdown(about_olas_predict)
 
+            with gr.Accordion("About this dashboard"):
+                gr.Markdown(about_this_dashboard)
+
 demo.queue(default_concurrency_limit=40).launch()

data/all_trades_profitability.parquet

@@ -1,3 +1,3 @@
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-oid sha256:7c5ef5d1b82a0f8c8a39488b495a2aeb4a19de7c6105e6fb398a9fee123535f4
-size 8630525
+oid sha256:76583ca11eb853af0f328a3739379afb12919cdf4751a8df4fc0d710e09ce77e
+size 2585132

data/delivers.parquet

@@ -1,3 +1,3 @@
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-oid sha256:2949c13d191d3ac55a5599f2968760d0e77128cd3dd5d7b037e204d0141df788
-size 1836081292
+oid sha256:83a8e368360e97a07a7b7e1bf4b68cd4153a9cd8f2c3a37218ca2c25fa0004a7
+size 533832215

data/fpmmTrades.parquet

@@ -1,3 +1,3 @@
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-oid sha256:6394125674b9f438857c38848b1c855e8b131d01154b5c4fbc72e390ef5d2d5b
-size 21955797
+oid sha256:c3783e00d33252efd8cec813cd9ede010d089f984b5189b3d2e03726ea4bc16c
+size 6743682

data/fpmms.parquet

@@ -1,3 +1,3 @@
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-oid sha256:500ac95f7c2456b7373f07a9dcff524ddcdfc62200bf1d19f48d6ac69cf511ec
-size 330824
+oid sha256:b080d92b95e9cb78c83a458aa9fafb1b22079c364dde7837ab89bcfd33c50481
+size 335554

data/requests.parquet

@@ -1,3 +1,3 @@
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-oid sha256:9f8273bd7fc4a129d625313af8a21205fb2f9425732bc6abe9aff401f8e0a810
-size 49704456
+oid sha256:c531cdd253c89a513fc52bf873fe8795e0a44ce6c033c89f07989d4f0932e424
+size 18043976

data/summary_profitability.parquet

@@ -1,3 +1,3 @@
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-oid sha256:9558c1992d4d3c83ed371e46c4cba0034e6801941082a78afdc5e7fe33b69d60
-size 53427
+oid sha256:4005bc0e5b1f940da6108a449f8a2a1feed75183dea917f5dcaac1f9cc64f6de
+size 40468

data/t_map.pkl

@@ -1,3 +1,3 @@
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-oid sha256:06e54df3fef5c3b9ee213497ea1c56118e8dc3adce908aaeb62d45bede1a585f
-size 8360140
+oid sha256:12faddc220fb5c6f9583b1029c19cb5a210ff3ae59f6fdef8a74667a7b36d53c
+size 8678809

data/tools.parquet

@@ -1,3 +1,3 @@
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-oid sha256:81c1f259f70d81cd64ef1c8b1bbb66484b17717c494ea4b61e464e98424b74ba
-size 1843263718
+oid sha256:166e8ec777aaa5fa8bfa2f62f22e753b5b2f32b2f8c4138f81282e99a0c03b69
+size 536907540

tabs/about.py

@@ -1,6 +1,12 @@
-
 about_olas_predict = """\
 Olas is a network of autonomous services that can run complex logic in a decentralized manner, interacting with on- and off-chain data autonomously and continuously. For other use cases check out [olas.network](https://olas.network/). 
 Since 'Olas' means 'waves' in Spanish, it is sometimes referred to as the 'ocean of services' 🌊.
 The project is co-created by [Valory](https://www.valory.xyz/). Valory aspires to enable communities, organizations and countries to co-own AI systems, beginning with decentralized autonomous agents.
-"""
+"""
+
+about_this_dashboard = """\
+This dashboard is pulling data from the omen subgraph during some specific time window. As the data is distributed by weeks, it is possible that some weeks contain incomplete data not showing the total volume of information. 
+This is in particular relevant for:
+* the first week: since we might have started collecting information not from the beginning of the week.
+* the last week: some markets have not been closed yet and the information is not published yet. 
+"""