diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..763513e910f7036a1a3cdb21dce8e57da2451891
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1 @@
+.ipynb_checkpoints
diff --git a/.streamlit/config.toml b/.streamlit/config.toml
new file mode 100644
index 0000000000000000000000000000000000000000..37b9ad344ad428d449f8911d61d9fe5737202311
--- /dev/null
+++ b/.streamlit/config.toml
@@ -0,0 +1,2 @@
+[theme]
+base="dark"
diff --git a/README.md b/README.md
index 45230d639126c3d55874bd1636b28e4212c562b7..97dbe9cedc8394c5d7d66d5c3b65777438050857 100644
--- a/README.md
+++ b/README.md
@@ -1,12 +1 @@
----
-title: CryptoTester
-emoji: 🐢
-colorFrom: purple
-colorTo: pink
-sdk: streamlit
-sdk_version: 1.9.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
+This is the README file for the crypto port project.
diff --git a/__pycache__/data_creator.cpython-39.pyc b/__pycache__/data_creator.cpython-39.pyc
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diff --git a/__pycache__/risk_metrics.cpython-310.pyc b/__pycache__/risk_metrics.cpython-310.pyc
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diff --git a/aggrid_viewer.py b/aggrid_viewer.py
new file mode 100644
index 0000000000000000000000000000000000000000..cc4f1b5023a670db4fcb1351b8245eb192acfa03
--- /dev/null
+++ b/aggrid_viewer.py
@@ -0,0 +1,238 @@
+import streamlit as st
+import plotly.express as px
+from datetime import date, timedelta
+from data_creator import create_market_cap_dict, gen_rebased_df, ids2names_dict, names2ids_dict, create_assets, gen_symbols, create_histories_df, create_unix_dates, create_returns_df, create_rebased_df, date_range
+from plot_creator import get_pre_selected_idx, write_coins, write_coins_custom, write_bespoke_coins, create_comparison_df, load_images, gen_performance_ag_df, add_drawdown
+from port_creator import gen_all_returns, markowitz_weights_dict, uniform_weights_dict, ids_with_histories, uniform, create_port_rtns, markowitz_weights, create_weights_df
+from risk_metrics import max_drawdown
+from st_aggrid import AgGrid, GridOptionsBuilder
+from persist import persist, load_widget_state
+
+load_widget_state()
+
+st.markdown(
+ """
+
+ """,
+ unsafe_allow_html = True
+)
+
+# load start and end dates for investment analysis
+lookback_years = 5 # max date range for backtest will be: lookback_years - 1
+start_date = date.today() - timedelta(365)
+end_date = date.today()
+
+if 'start_date' not in st.session_state:
+ st.session_state.start_date = start_date
+ st.session_state.end_date = end_date
+
+if 'max_coins' not in st.session_state:
+ st.session_state.max_coins = 10
+
+if 'start_id' not in st.session_state:
+ st.session_state.start_id = 1
+
+# Pull down histories from coincap, and create dataframes for historic prices,
+# returns and rebased cumulative price; histories_df, returns_df, and
+# rebased_df, respectively.
+assets_json = create_assets(total_coins=50)
+symbols, names, coin_ids = gen_symbols(assets_json)
+ids2symbols = ids2names_dict(coin_ids, symbols)
+ids2names_dict=ids2names_dict(coin_ids, names)
+names2ids_dict = names2ids_dict(names, coin_ids)
+market_cap_dict = create_market_cap_dict(assets_json)
+start_unix, end_unix = create_unix_dates(today=date.today(), lookback_years=lookback_years)
+histories_df = create_histories_df(coin_ids, start_unix, end_unix)
+
+# Create list of coin ids with full hisoties over the backtest period
+ids_with_histories = ids_with_histories(histories_df,
+ st.session_state.start_date, st.session_state.end_date)
+names_with_histories = list(map(ids2names_dict.get, ids_with_histories))
+
+
+def change_date_range():
+ st.session_state.start_date = st.session_state.myslider[0]
+ st.session_state.end_date = st.session_state.myslider[1]
+
+# calculate weghts for the uniform and markowitz pfs
+uniform_weights_dict = uniform_weights_dict(ids_with_histories[:st.session_state.max_coins])
+#markowitz_weights_dict = markowitz_weights_dict(histories_df,
+# st.session_state.start_date ,ids_with_histories[:max_coins], analysis_days=365)
+strategy_dict = {'Uniform': uniform_weights_dict}#, 'Markowitz':markowitz_weights_dict}
+
+if "strategy_dict" not in st.session_state:
+ st.session_state.strategy_dict=strategy_dict
+
+if 'selected_assets' not in st.session_state:
+ st.session_state.selected_assets = ["Uniform"]
+
+with st.sidebar:
+ st.subheader("Portfolio weights viewer")
+ portfolio_type = st.selectbox(
+ 'Select portfolio strategy',
+ ['Create your own'] + (list(st.session_state.strategy_dict.keys())),
+ index = st.session_state.start_id
+ )
+
+
+if st.checkbox("Explain this"):
+ st.subheader("What's this all about then, eh?")
+ st.write('''
+ The app allows you to construct your own portfolios of crypto currencies and view their
+ historic performance alongside the performance of individual crypto
+ currencies over an investment period of your choosing.
+
+ To view the assets and weights comprising a particular portfolio select the
+ portfolio of interest in the 'Select portfolio strategy' dropdown (a uniform
+ portfolio for the top ten largest coins has been automatically created for you
+ to start with).
+
+ To create your own portfolio:
+
+ 1. Select 'Create your own' in the 'select portfolio strategy' dropdown;
+ 2. Select the maximum number of coins in your portfolio;
+ 3. Select the relative weights for each of these assets;
+ 4. Choose a name for your portfolio and click add portfolio;
+ 5. Click update viewer;
+
+ You can sort and filter the performance metrics table on each of the columns.
+
+ To add an asset to the performance chart, select the corresponding select box.
+ ''')
+
+# Add select slider to allow
+date_list = date_range(end_date,lookback_years-1)
+start_port_date, end_port_date = st.select_slider(
+ 'Select backtest date range',
+ key="myslider",
+ options=date_list,
+ #value=(date.today() - timedelta(365), date.today()),
+ value = (st.session_state.start_date, st.session_state.end_date),
+ on_change=change_date_range
+ )
+
+
+# Move the definition of strategy_dict to about the potfolio_type selectbox
+# This will require that you define max_coins in session state,a dn the
+# have the max_coins number_input update the max coins session state.
+# = 10 and let it be
+
+
+# calculate returns for the portfolios and add to it the rebased df for assets
+# with hisories. This is the new returns_df
+rebased_df = gen_rebased_df(histories_df, ids_with_histories,
+ st.session_state.start_date, st.session_state.end_date)
+
+all_returns_df = gen_all_returns(rebased_df, ids_with_histories,st.session_state.strategy_dict)
+
+def write_something():
+ st.write("")
+
+def rerun_aggrid():
+ st.session_state.selected_indexes = selected_indexes
+ st.session_state.performance_ag_df = gen_performance_ag_df(all_returns_df, market_cap_dict,
+ st.session_state.strategy_dict)
+ st.header('ran')
+
+if portfolio_type == 'Create your own':
+ with st.sidebar:
+ st.session_state.max_coins = st.number_input(
+ "Maximum number of coins in portfolio",
+ min_value=1,
+ max_value=20,
+ value=10,
+ help='''
+ Coins will be added to your "investment set" in order of largest market cap.
+
+ The "investment set" is the group of assets from which your portfolio is
+ constructed. Depending on the portfolio strategy you choose, not all of the
+ assets in your investment set will be included in your portfolio.
+
+ '''
+ )
+ st.markdown("Bespoke portfolio weights (relative):" , unsafe_allow_html=False)
+ bespoke_weights = write_coins_custom(names_with_histories[:st.session_state.max_coins])
+ #bespoke_weights = write_bespoke_coins(names_with_histories[:st.session_state.max_coins])
+ bespoke_cols = st.columns(2)
+ bespoke_cols[0].write(" ")
+ bespoke_cols[0].write(" ")
+ add_bespoke = bespoke_cols[0].button("Add portfolio", key='bespoke_button')
+ bespoke_name = bespoke_cols[1].text_input("Choose portfolio name")
+ if add_bespoke:
+ if bespoke_name=="" or bespoke_name in all_returns_df.columns:
+ st.warning("Please give your portfolio a unique name")
+ else:
+ beskpoke_weights_dict={}
+ for i, wt in enumerate(bespoke_weights):
+ beskpoke_weights_dict[coin_ids[i]] = wt
+ st.session_state.strategy_dict[bespoke_name] = beskpoke_weights_dict
+ st.session_state.start_id = len(st.session_state.strategy_dict)
+ #st.session_state.selected_assets.append(bespoke_name)
+ st.success("Porfolio added, update viewer to see results")
+ #st.button('Update viewer', on_click = change_date_range)
+ st.button('Update viewer', on_click = rerun_aggrid)
+ #st.button('Update viewer', on_click = st.experimental_rerun())
+ #st.write(st.session_state.strategy_dict)
+else:
+ non_zero_coins = [key for key in st.session_state.strategy_dict[portfolio_type].keys() if st.session_state.strategy_dict[portfolio_type][key]>0]
+ with st.sidebar:
+ st.markdown(portfolio_type + " portfolio weights (%):" , unsafe_allow_html=False)
+ write_coins(non_zero_coins, st.session_state.strategy_dict[portfolio_type], ids2names_dict)
+
+st.session_state.performance_ag_df = gen_performance_ag_df(all_returns_df, market_cap_dict,
+ st.session_state.strategy_dict)
+
+if 'selected_assets' not in st.session_state:
+ st.session_state.selected_assets = ["Uniform"]
+
+selected_indexes = []
+for asset in st.session_state.selected_assets:
+ try:
+ selected_indexes.append(list(st.session_state.performance_ag_df['Asset']).index(asset))
+ except:
+ pass
+
+if 'selected_indexes' not in st.session_state:
+ st.session_state.selected_indexes = selected_indexes
+
+gb = GridOptionsBuilder.from_dataframe(st.session_state.performance_ag_df)
+gb.configure_selection('multiple', use_checkbox=True,
+ pre_selected_rows = st.session_state.selected_indexes)
+gridOptions = gb.build()
+
+st.subheader("Performance metrics")
+grid_response = AgGrid(st.session_state.performance_ag_df, gridOptions=gridOptions,
+ data_return_mode = 'FILTERED', allow_unsafe_jscode=True, height = 200,
+ update_mode='MODEL_CHANGED', key = persist("aggrid")) # MANUAL SELECTION_CHANGED MODEL_CHANGED, VALUE_CHANGED
+
+selected_assets = []
+for row in grid_response['selected_rows']:
+ selected_assets.append(row['Asset'])
+st.session_state.selected_assets = selected_assets
+
+
+
+#selected_indexes = []
+#for asset in st.session_state.selected_assets:
+# selected_indexes.append(list(performance_ag_df['Asset']).index(asset))
+
+chart_df = create_comparison_df(all_returns_df, st.session_state.selected_assets)
+
+fig = px.line(chart_df, x=chart_df.index, y='Value (USD)', color='Asset')
+
+st.subheader("Performance chart")
+st.write(fig)
diff --git a/app.py b/app.py
new file mode 100644
index 0000000000000000000000000000000000000000..97a49cb59047088cceddf2b96830976a17660ea1
--- /dev/null
+++ b/app.py
@@ -0,0 +1,14 @@
+import streamlit as st
+from streamlit_custom_slider import st_custom_slider
+from streamlit_custom_slider import st_range_slider
+
+v_custom = st_custom_slider('Hello world', 0, 100, 50, key="slider1")
+st.write(v_custom)
+
+# Add a range slider
+v_custom_range = st_range_slider('Hello world', 0, 100, (20, 60), key="slider2")
+st.write(v_custom_range)
+
+cols = st.columns(2)
+with cols[0]:
+ st_range_slider('Hello world', 0, 100, (20, 60), key="slider3")
diff --git a/cbpro.ipynb b/cbpro.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..b2cc14690167de01f01b82768fcd7f63e4b2c602
--- /dev/null
+++ b/cbpro.ipynb
@@ -0,0 +1,573 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "167330a4-9cfe-4e11-a3e5-9b788e710b07",
+ "metadata": {},
+ "source": [
+ "!conda activate cbpro"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "d5b44fe7-a66c-4708-9d70-9df144e000bb",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import requests"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "d665ee3b-259c-4733-af10-d7dc4e91bbcd",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import cbpro"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "b0d24761-fcca-4526-ad2c-4fadaaab3320",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Collecting pandas\n",
+ " Downloading pandas-1.4.2-cp39-cp39-macosx_10_9_x86_64.whl (11.1 MB)\n",
+ "\u001b[K |████████████████████████████████| 11.1 MB 3.0 MB/s eta 0:00:01\n",
+ "\u001b[?25hRequirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.9/site-packages (from pandas) (2022.1)\n",
+ "Collecting numpy>=1.18.5\n",
+ " Downloading numpy-1.22.3-cp39-cp39-macosx_10_14_x86_64.whl (17.6 MB)\n",
+ "\u001b[K |████████████████████████████████| 17.6 MB 25.2 MB/s eta 0:00:01\n",
+ "\u001b[?25hRequirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.9/site-packages (from pandas) (2.8.2)\n",
+ "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.9/site-packages (from python-dateutil>=2.8.1->pandas) (1.10.0)\n",
+ "Installing collected packages: numpy, pandas\n",
+ "Successfully installed numpy-1.22.3 pandas-1.4.2\n",
+ "\u001b[33mWARNING: You are using pip version 21.1.1; however, version 22.0.4 is available.\n",
+ "You should consider upgrading via the '/usr/local/opt/python@3.9/bin/python3.9 -m pip install --upgrade pip' command.\u001b[0m\n"
+ ]
+ }
+ ],
+ "source": [
+ "#!pip3 install pandas"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "6d1cec56-82d3-4915-b271-765bbe767bc2",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
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+ " 2022-04-30 \n",
+ " 2768.64 \n",
+ " 2842.08 \n",
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+ " 2791.04 \n",
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+ " 0.089077 \n",
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+ " \n",
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+ " 2022-05-17 00:00:00 \n",
+ " 0.089524 \n",
+ " \n",
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+ " 0.943743 \n",
+ " \n",
+ " \n",
+ " 14 \n",
+ " bitcoin \n",
+ " 2021-05-29 00:00:00 \n",
+ " 0.962769 \n",
+ " \n",
+ " \n",
+ " 15 \n",
+ " bitcoin \n",
+ " 2021-05-30 00:00:00 \n",
+ " 1.006038 \n",
+ " \n",
+ " \n",
+ " 16 \n",
+ " bitcoin \n",
+ " 2021-05-31 00:00:00 \n",
+ " 1.01863 \n",
+ " \n",
+ " \n",
+ " 17 \n",
+ " bitcoin \n",
+ " 2021-06-01 00:00:00 \n",
+ " 1.012513 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mrebased_prices_df\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+ "\u001b[0;31mNameError\u001b[0m: name 'rebased_prices_df' is not defined"
+ ]
+ }
+ ],
+ "source": [
+ "rebased_prices_df"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "id": "74351170-0603-46c9-be5c-5946d788e274",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.plotly.v1+json": {
+ "config": {
+ "plotlyServerURL": "https://plot.ly"
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+ "\u001b[0;31mNameError\u001b[0m: name 'date_conv' is not defined"
+ ]
+ }
+ ],
+ "source": [
+ "histories_df= pd.read_csv('histories.csv')\n",
+ "histories_df['date'] = list(map(date_conv,histories_df['date']))\n",
+ "histories_df = histories_df.set_index('date')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "id": "870fdbdd-af77-4063-87e4-72726e6bab50",
+ "metadata": {},
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\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mport_returns\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0muniform_returns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_frame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmarkowitz_returns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+ "\u001b[0;32m/usr/local/lib/python3.9/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mjoin\u001b[0;34m(self, other, on, how, lsuffix, rsuffix, sort)\u001b[0m\n\u001b[1;32m 9252\u001b[0m \u001b[0;36m5\u001b[0m \u001b[0mK1\u001b[0m \u001b[0mA5\u001b[0m \u001b[0mB1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9253\u001b[0m \"\"\"\n\u001b[0;32m-> 9254\u001b[0;31m return self._join_compat(\n\u001b[0m\u001b[1;32m 9255\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mon\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mon\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhow\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mhow\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlsuffix\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlsuffix\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrsuffix\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrsuffix\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msort\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msort\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9256\u001b[0m )\n",
+ "\u001b[0;32m/usr/local/lib/python3.9/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m_join_compat\u001b[0;34m(self, other, on, how, lsuffix, rsuffix, sort)\u001b[0m\n\u001b[1;32m 9270\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSeries\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9271\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 9272\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Other Series must have a name\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 9273\u001b[0m \u001b[0mother\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9274\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;31mValueError\u001b[0m: Other Series must have a name"
+ ]
+ }
+ ],
+ "source": [
+ "port_returns = uniform_returns.to_frame().join(markowitz_returns)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 71,
+ "id": "ab5808b5-da69-496d-9cf0-3486b753db2a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "port_returns = gen_portfolio_returns(rebased_df, ids_with_histories,uniform_weights_dict,\n",
+ " markowitz_weights_dict)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 75,
+ "id": "5eb080ed-3ca4-48f9-9b94-e2846e03df0b",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
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359 rows × 2 columns
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+ "\n",
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\n",
+ " \n",
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+ " 1.136181 \n",
+ " 0.439168 \n",
+ " 0.345518 \n",
+ " 0.356643 \n",
+ " \n",
+ " \n",
+ "
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+ "
359 rows × 85 columns
\n",
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " Uniform \n",
+ " Markowitz \n",
+ " bitcoin \n",
+ " ethereum \n",
+ " tether \n",
+ " usd-coin \n",
+ " binance-coin \n",
+ " xrp \n",
+ " binance-usd \n",
+ " cardano \n",
+ " ... \n",
+ " arweave \n",
+ " compound \n",
+ " kava \n",
+ " holo \n",
+ " gatetoken \n",
+ " fei-protocol \n",
+ " kyber-network \n",
+ " qtum \n",
+ " bancor \n",
+ " 1inch \n",
+ " \n",
+ " \n",
+ " date \n",
+ " \n",
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+ " \n",
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+ " 1.104084 \n",
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+ " 1.117736 \n",
+ " 1.141806 \n",
+ " 1.004241 \n",
+ " 1.067167 \n",
+ " ... \n",
+ " 1.015432 \n",
+ " 1.051228 \n",
+ " 1.084118 \n",
+ " 1.231873 \n",
+ " 1.106947 \n",
+ " 1.004524 \n",
+ " 1.037904 \n",
+ " 1.264676 \n",
+ " 1.078122 \n",
+ " 1.064602 \n",
+ " \n",
+ " \n",
+ " 2021-05-26 \n",
+ " 1.103699 \n",
+ " 1.018414 \n",
+ " 1.059262 \n",
+ " 1.182178 \n",
+ " 0.998764 \n",
+ " 0.998087 \n",
+ " 1.211152 \n",
+ " 1.172456 \n",
+ " 0.998892 \n",
+ " 1.180429 \n",
+ " ... \n",
+ " 1.157293 \n",
+ " 1.142980 \n",
+ " 1.216742 \n",
+ " 1.508202 \n",
+ " 1.167414 \n",
+ " 1.001874 \n",
+ " 1.153683 \n",
+ " 1.288506 \n",
+ " 1.173015 \n",
+ " 1.220068 \n",
+ " \n",
+ " \n",
+ " 2021-05-27 \n",
+ " 1.094057 \n",
+ " 1.016329 \n",
+ " 1.047055 \n",
+ " 1.171337 \n",
+ " 0.998721 \n",
+ " 0.997785 \n",
+ " 1.209819 \n",
+ " 1.151604 \n",
+ " 0.998899 \n",
+ " 1.166563 \n",
+ " ... \n",
+ " 1.159935 \n",
+ " 1.114319 \n",
+ " 1.253248 \n",
+ " 1.432746 \n",
+ " 1.155625 \n",
+ " 0.999451 \n",
+ " 1.178437 \n",
+ " 1.424998 \n",
+ " 1.192224 \n",
+ " 1.226486 \n",
+ " \n",
+ " \n",
+ " 2021-05-28 \n",
+ " 1.035221 \n",
+ " 1.005436 \n",
+ " 0.988150 \n",
+ " 1.081310 \n",
+ " 0.998400 \n",
+ " 0.997946 \n",
+ " 1.118547 \n",
+ " 1.055065 \n",
+ " 0.998932 \n",
+ " 1.070248 \n",
+ " ... \n",
+ " 1.011761 \n",
+ " 1.007871 \n",
+ " 1.218521 \n",
+ " 1.269149 \n",
+ " 1.053198 \n",
+ " 0.994776 \n",
+ " 1.072810 \n",
+ " 1.336266 \n",
+ " 1.095105 \n",
+ " 1.125980 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
5 rows × 87 columns
\n",
+ "
\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0massets\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mall_returns_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mperformance_df\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0massets\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mperformance_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Type'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\"Portfolio\"\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'Uniform'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'Markowitz'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m\"Coin\"\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0massets\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;31mNameError\u001b[0m: name 'all_returns_df' is not defined"
+ ]
+ }
+ ],
+ "source": [
+ "assets = all_returns_df.columns\n",
+ "performance_df = pd.DataFrame(index = assets)\n",
+ "performance_df['Type'] = [\"Portfolio\" if x in ['Uniform','Markowitz'] else \"Coin\" for x in assets]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "600f2068-ac8c-4006-a5bd-73b75c0b86b1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def absolute_return(prices):\n",
+ " 'a function to calculate the absolute return given a daily price series'\n",
+ " abs_rtn = ((prices.iloc[-1]/prices[0])-1)\n",
+ " return abs_rtn\n",
+ "\n",
+ "def annual_return(prices):\n",
+ " 'a function to calculate the annualised return given a daily price series'\n",
+ " abs_rtn = absolute_return(prices)\n",
+ " annual_rnt = (pow((abs_rtn/100)+1, 365/len(prices))-1)*100\n",
+ " return annual_rnt\n",
+ "\n",
+ "def max_drawdown(prices):\n",
+ " '''\n",
+ " A function to calculate the max drawdown for a given price series \"prices\"\n",
+ " as well as the index of the start of the max drawdown period, \"start_idx\"\n",
+ " and the index of end of the max drawdwon period, \"end index\"\n",
+ " '''\n",
+ " if type(prices)==type(pd.Series(dtype='object')):\n",
+ " prices = prices.values\n",
+ " end_idx = np.argmax(np.maximum.accumulate(prices) - prices) # end of the period\n",
+ " start_idx = np.argmax(prices[:end_idx]) # start of period\n",
+ " max_dd = (prices[start_idx]-prices[end_idx])/prices[start_idx]\n",
+ " return max_dd, start_idx, end_idx\n",
+ "\n",
+ "def annual_vol(prices):\n",
+ " '''\n",
+ " A function to calculate the annuaised volatility of a price series assuming\n",
+ " cryptos trade 365 days a year\n",
+ " '''\n",
+ " return prices.pct_change().std()*(365**0.5)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 95,
+ "id": "a59742d5-3fdc-4d22-a227-5f9751167c8f",
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "NameError",
+ "evalue": "name 'absolute_return' is not defined",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_23455/393568676.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mabsolute_return\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall_returns_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Uniform'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+ "\u001b[0;31mNameError\u001b[0m: name 'absolute_return' is not defined"
+ ]
+ }
+ ],
+ "source": [
+ "absolute_return(all_returns_df['Uniform'])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d578fca9-0ed3-4c09-8a36-bd11b33574fb",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.9.5"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/cryptoTester.py b/cryptoTester.py
new file mode 100644
index 0000000000000000000000000000000000000000..2e56bbba50318e1283be9960dd548e46d665ff1a
--- /dev/null
+++ b/cryptoTester.py
@@ -0,0 +1,281 @@
+import streamlit as st
+import plotly.express as px
+from datetime import date, timedelta
+from data_creator import create_market_cap_dict, gen_rebased_df, ids2names_dict, names2ids_dict, create_assets, gen_symbols, create_histories_df, create_unix_dates, create_returns_df, create_rebased_df, date_range
+from plot_creator import write_coins, write_coins_custom, create_chart_df, load_images, gen_performance_df, add_drawdown
+from port_creator import gen_all_returns, markowitz_weights_dict, uniform_weights_dict, ids_with_histories, uniform, create_port_rtns, markowitz_weights, create_weights_df
+from risk_metrics import max_drawdown
+
+
+st.markdown(
+ """
+
+ """,
+ unsafe_allow_html = True
+)
+
+# load start and end dates for investment analysis
+lookback_years = 5 # max date range for backtest will be: lookback_years - 1
+start_date = date.today() - timedelta(365)
+end_date = date.today()
+
+if 'start_date' not in st.session_state:
+ st.session_state.start_date = start_date
+ st.session_state.end_date = end_date
+
+if 'max_coins' not in st.session_state:
+ st.session_state.max_coins = 8
+
+if 'start_id' not in st.session_state:
+ st.session_state.start_id = 1 # this is the id of the selected portfolio.
+
+# Pull down histories from coincap, and create dataframes for historic prices,
+# returns and rebased cumulative price; histories_df, returns_df, and
+# rebased_df, respectively.
+assets_json = create_assets(total_coins=50)
+symbols, names, coin_ids = gen_symbols(assets_json)
+ids2symbols = ids2names_dict(coin_ids, symbols)
+ids2names_dict=ids2names_dict(coin_ids, names)
+names2ids_dict = names2ids_dict(names, coin_ids)
+market_cap_dict = create_market_cap_dict(assets_json)
+start_unix, end_unix = create_unix_dates(today=date.today(), lookback_years=lookback_years)
+histories_df = create_histories_df(coin_ids, start_unix, end_unix)
+
+# Create list of coin ids with full hisoties over the backtest period
+ids_with_histories = ids_with_histories(histories_df,
+ st.session_state.start_date, st.session_state.end_date)
+names_with_histories = list(map(ids2names_dict.get, ids_with_histories))
+
+def change_date_range():
+ st.session_state.start_date = st.session_state.myslider[0]
+ st.session_state.end_date = st.session_state.myslider[1]
+
+st.write('''
+N.B. This app is in Beta, it will be buggy and some of the calculations may be
+ erroneous. **It is deeeeeefinitely not investment advice**.
+''', unsafe_allow_html = True)
+with st.expander("Explain this app 🤔"):
+ st.subheader("What's this all about then, eh?")
+ st.write('''
+
+ This app allows you to graph the historic performance of a portfolio of
+ your choosing against an individual coin of your choosing. You can
+ compare the historic performance of all available assets (portfolios and coins)
+ in the overview table at the bottom of the page.
+
+ The 'Portfolio vs coin' chart displays the historic performance of the selected
+ portfolio and coin for the selected date range, rebased to $1 at the start
+ date.
+
+ To switch focus between the selected portfolio and coin, use the radio buttons
+ above the graph. For the asset under focus the "maximum drawdown" is drawn
+ on the graph (this is the maximum loss the asset suffered over the selected
+ period). A high level view of the performance of the asset under focus is also
+ given inside the 'Asset performance' expander.
+
+ The 'Overview of performance section' sets out performance metrics for all of
+ the portfolios and coins over the selected investment period. The table can
+ be sorted based on the metric of interest.
+
+ There are two pre-defined portfolio construction strategies, but you can also
+ define your own allocation using the 'Create your own' option in the 'Select
+ portfolio strategy' dropdown box in the sidebar.
+
+ Pre-defined portfolios:
+ - Uniform - An equal propotion of your initial investment is allocated to each coin in the 'investment set' (i.e. the total number of coins available for investment).
+ - Markowitz - Your initial investment is allocated to each coin to achieve the portfolio with the highest sharpe ratio in the 365 day period prior to the investment start date. For some date ranges and investment sets, the optimiser used in the background of this app cannot find an optimal solution in which case the Markowitz weights will not be updated and you will see a warning at the top of the screen. I have used the default settings in the PyPortfolioOpt python package to construct this portfolio.
+
+ To select how many coins you would like in your investment set use the
+ 'Maximum number of coins in portfolio' inside the 'Portfolio construction
+ settings' expander.
+
+ To adjust the date range for the portfolio backtest using the slider
+ widget.
+
+ To create your own portfolio:
+
+ 1. Select 'Create your own' in the 'Select portfolio strategy' dropdown;
+ 2. Select the maximum number of coins in your portfolio;
+ 3. Select the relative weights for each of these assets;
+ 4. Choose a name for your portfolio and click add portfolio;
+ 5. Click the 'Update viewer' button.
+
+ The data for this app has been generated using the coincap api and is updated
+ every day to include the previous day's closing prices.
+
+ ''')
+
+# Add select slider to allow
+date_list = date_range(end_date,lookback_years-1)
+start_port_date, end_port_date = st.select_slider(
+ 'Select date range (max 4 years)',
+ key="myslider",
+ options=date_list,
+ #value=(date.today() - timedelta(365), date.today()),
+ value = (st.session_state.start_date, st.session_state.end_date),
+ on_change=change_date_range
+ )
+
+with st.sidebar:
+ st.subheader("Select a coin and a portfolio to compare on the graph")
+ with st.expander("Portfolio construction settings", expanded = False):
+ st.session_state.max_coins = st.number_input(
+ "Maximum number of coins in portfolio",
+ min_value=1,
+ max_value=50,
+ value=8,
+ key='max_coin_selector',
+ help='''
+ Coins will be added to your "investment set" in order of largest market cap.
+
+ The "investment set" is the group of assets from which your portfolio is
+ constructed. Depending on the portfolio strategy you choose, not all of the
+ assets in your investment set will be included in your portfolio.
+
+ '''
+ )
+
+# calculate weghts for the uniform and markowitz pfs
+uniform_weights = uniform_weights_dict(ids_with_histories[:int(st.session_state.max_coins)])
+update_Markowitz = True
+try:
+ markowitz_weights = markowitz_weights_dict(histories_df,
+ st.session_state.start_date ,ids_with_histories[:int(st.session_state.max_coins)], analysis_days=365)
+except:
+ update_Markowitz = False
+ st.warning('Markowitz weights could not be updated for this date range')
+
+if 'strategy_dict' not in st.session_state:
+ st.session_state.strategy_dict = {'Uniform': uniform_weights, 'Markowitz':markowitz_weights}
+else:
+ st.session_state.strategy_dict['Uniform'] = uniform_weights
+ if update_Markowitz == True:
+ st.session_state.strategy_dict['Markowitz'] = markowitz_weights
+
+#if "strategy_dict" not in st.session_state:
+# st.session_state.strategy_dict=strategy_dict
+
+with st.sidebar:
+ selected_coin = st.selectbox(
+ 'Select coin',
+ names_with_histories
+ )
+ portfolio_type = st.selectbox(
+ 'Select portfolio strategy',
+ ['Create your own'] + (list(st.session_state.strategy_dict.keys())),
+ index = st.session_state.start_id
+ )
+
+
+
+
+
+
+# calculate returns for the portfolios and add to it the rebased df for assets
+# with hisories. This is the new returns_df
+rebased_df = gen_rebased_df(histories_df, ids_with_histories,
+ st.session_state.start_date, st.session_state.end_date)
+
+all_returns_df = gen_all_returns(rebased_df, ids_with_histories, st.session_state.strategy_dict)
+
+if portfolio_type != 'Create your own':
+ st.session_state.portfolio_type = portfolio_type
+
+st.subheader("Portfolio vs coin")
+focus=st.radio("Focus on",("Portfolio 📉","Coin 📈"))
+
+chart_df = create_chart_df(all_returns_df, st.session_state.portfolio_type, names2ids_dict[selected_coin])
+
+fig = px.line(chart_df, x=chart_df.index, y='Value (USD)', color='Asset')
+if focus == "Portfolio 📉":
+ fig, port_dd, port_dd_start, port_dd_end = add_drawdown(fig, all_returns_df, st.session_state.portfolio_type)
+else:
+ fig, coin_dd, coin_dd_start, coin_dd_end = add_drawdown(fig, all_returns_df, names2ids_dict[selected_coin])
+
+st.write(fig)
+
+with st.expander("Asset performance"):
+ if focus == "Portfolio 📉":
+ st.subheader("{} portfolio performance".format(st.session_state.portfolio_type))
+
+ cols = st.columns([1,3])
+ outlay = cols[0].number_input('Initial $ amount', min_value=0, value=1000,
+ step=1)
+ final_amount = outlay*all_returns_df[st.session_state.portfolio_type][-1]
+ max_loss=outlay*port_dd
+
+ with cols[1]:
+ st.markdown('''For an initial investment of **${:,}**'''.format(int(outlay)), unsafe_allow_html = True)
+ st.markdown('''You would have ended up with **${:,}**'''.format(int(final_amount)), unsafe_allow_html = True)
+ st.markdown('''You would have suffered a maximum loss of **{:.0f}%** of your portfolio value
+ between **{}** and **{}**'''.format(port_dd*100, port_dd_start, port_dd_end), unsafe_allow_html = True)
+ else:
+ st.subheader("{} coin performance".format(selected_coin))
+
+ cols = st.columns([1,3])
+ outlay = cols[0].number_input('Initial $ amount', min_value=0, value=1000,
+ step=1)
+ final_amount = outlay*all_returns_df[names2ids_dict[selected_coin]][-1]
+ max_loss=outlay*coin_dd
+
+ with cols[1]:
+ st.markdown('''For an initial investment of **${:,}**'''.format(int(outlay)), unsafe_allow_html = True)
+ st.markdown('''You would have ended up with **${:,}**'''.format(int(final_amount)), unsafe_allow_html = True)
+ st.markdown('''You would have suffered a maximum loss of **{:.0f}%** of your investment value
+ between **{}** and **{}**'''.format(coin_dd*100, coin_dd_start, coin_dd_end), unsafe_allow_html = True)
+
+non_zero_coins = [key for key in st.session_state.strategy_dict[st.session_state.portfolio_type].keys() if st.session_state.strategy_dict[st.session_state.portfolio_type][key]>0]
+
+with st.sidebar:
+ if portfolio_type == 'Create your own':
+ st.markdown("Bespoke portfolio weights (relative):" , unsafe_allow_html=False)
+ bespoke_weights = write_coins_custom(names_with_histories[:int(st.session_state.max_coins)])
+ #bespoke_weights = write_bespoke_coins(names_with_histories[:st.session_state.max_coins])
+ bespoke_cols = st.columns(2)
+ bespoke_cols[0].write(" ")
+ bespoke_cols[0].write(" ")
+ add_bespoke = bespoke_cols[0].button("Add portfolio", key='bespoke_button')
+ bespoke_name = bespoke_cols[1].text_input("Choose portfolio name")
+ if add_bespoke:
+ if bespoke_name=="" or bespoke_name in all_returns_df.columns:
+ st.warning("Please give your portfolio a unique name")
+ else:
+ beskpoke_weights_dict={}
+ for i, wt in enumerate(bespoke_weights):
+ beskpoke_weights_dict[coin_ids[i]] = wt
+ st.session_state.strategy_dict[bespoke_name] = beskpoke_weights_dict
+ st.session_state.start_id = len(st.session_state.strategy_dict)
+ #st.session_state.selected_assets.append(bespoke_name)
+ st.success("Porfolio added, update viewer to see results")
+ st.button('Update viewer', on_click = change_date_range)
+ #st.button('Update viewer', on_click = change_date_range)
+ else:
+ st.markdown(st.session_state.portfolio_type + " portfolio weights (%):" , unsafe_allow_html=False)
+ write_coins(non_zero_coins, st.session_state.strategy_dict[st.session_state.portfolio_type], ids2names_dict)
+
+performance_df = gen_performance_df(all_returns_df, market_cap_dict, st.session_state.strategy_dict)
+
+
+st.subheader("Overview of performance of all available assets")
+st.dataframe(performance_df.style.background_gradient(cmap='Greens',
+ subset=['Risk / return', 'Total return %'])
+ .background_gradient(cmap='Reds',
+ subset=['Annual vol', 'Max loss %']).format("{:,.2f}",
+ subset=['Risk / return', 'Total return %', 'Annual vol',
+ 'Max loss %']).format("{:,.0f}", subset=['Market cap $M']))
+
+
diff --git a/crypto_viewer.py b/crypto_viewer.py
new file mode 100644
index 0000000000000000000000000000000000000000..36f3abffed228b97d9132d5f98ec89e9c0aa89b8
--- /dev/null
+++ b/crypto_viewer.py
@@ -0,0 +1,314 @@
+import streamlit as st
+import requests
+import json
+import plotly.express as px
+import pandas as pd
+import datetime as dt
+from risk_metrics import annual_return, absolute_return, annual_vol, max_drawdown
+try:
+ from PIL import Image
+except ImportError:
+ import Image
+import numpy as np
+
+st.markdown(
+ """
+
+ """,
+ unsafe_allow_html = True
+)
+
+def load_data(limit='10'):
+ '''
+ Returns a dictionary with data for each of the top 'limit' cypto currencies
+ ranked by market cap. The data is generated by querying the coincap API
+ /assets endpoint. See coincap documentation for more info:
+ https://docs.coincap.io/
+
+ Parameters:
+ limit (str): The number of crypto coins that you want to return data for.
+ Ranked in order of market cap.
+
+ Returns:
+ (dict): A dictionary object of data.
+
+ '''
+ url = "https://api.coincap.io/v2/assets"
+ # N.B. here adampt the params dict to only request what you need
+ payload={'limit': limit}
+ headers = {}
+ return requests.request("GET", url, params=payload, headers=headers).json()
+
+def load_histories(ids_list):
+ url = "http://api.coincap.io/v2/assets/{}/history?interval=d1"
+
+ payload={}
+ headers = {}
+
+ histories_dict = {}
+ for id in ids_list:
+ response_histories = requests.request("GET", url.format(id), headers=headers, data=payload)
+ histories_json = response_histories.json()
+ histories_dict[id] = histories_json['data']
+ return histories_dict
+
+
+def gen_symbols(assets_json):
+ symbols_list = []
+ names_list = []
+ ids_list =[]
+ for dict in assets_json['data']:
+ symbols_list.append(dict['symbol'])
+ names_list.append(dict['name'])
+ ids_list.append(dict['id'])
+ return symbols_list, names_list, ids_list
+
+def write_symbols(symbols_list):
+ cols = st.columns(len(symbols_list))
+ for i, symbol in enumerate(symbols_list):
+ col = cols[i]
+ col.image(f'logos/{symbol}.png',width=40)
+ globals()[st.session_state.names[i]] = col.checkbox(symbol, value = 0)
+ #col.checkbox(symbol, st.image(f'logos/{symbol}.png',width=40))
+
+if "assets_json" not in st.session_state:
+ st.session_state.assets_json = load_data()
+ symbols, names, ids = gen_symbols(st.session_state.assets_json)
+ st.session_state.symbols = symbols
+ st.session_state.names = names
+ st.session_state.ids = ids
+ st.session_state.histories = load_histories(ids)
+ id_symbol_map = {}
+ for i, id in enumerate(ids):
+ id_symbol_map[id]=symbols[i]
+ st.session_state.id_symbol_map = id_symbol_map
+
+
+
+#write_symbols(st.session_state.symbols)
+symbols_list = st.session_state.symbols
+names_list = st.session_state.names
+ids_list = st.session_state.ids
+asset_json = st.session_state.assets_json
+histories_dict = st.session_state.histories
+id_symbol_map = st.session_state.id_symbol_map
+
+def date_conv(date):
+ return dt.datetime.strptime(date, '%Y-%m-%d')
+price_histories_df = pd.DataFrame(columns=['coin','date','price'])
+return_histories_df = pd.DataFrame(columns=['coin','date','price'])
+for id in ids_list:
+ price=[]
+ date=[]
+ for observation in histories_dict[id]:
+ date.append(date_conv(observation['date'][0:10]))
+ #date.append(observation['time'])
+ price.append(float(observation['priceUsd']))
+ price_df = pd.DataFrame({"coin": id, "date":date, "price": price})
+ price_histories_df = pd.concat([price_histories_df, price_df])
+ returns = [float(b) / float(a) for b,a in zip(price[1:], price[:-1])]
+ returns_df = pd.DataFrame({"coin": id, "date":date[1:], "price": returns})
+ return_histories_df = pd.concat([return_histories_df, returns_df])
+
+
+
+start_date = dt.date.today()-dt.timedelta(360)
+rebased_prices_df = pd.DataFrame(columns=['coin','date','price','rebased_price'])
+for id in ids_list:
+ temp_rebase_df = return_histories_df[(return_histories_df['date']>=pd.Timestamp(start_date))
+ & (return_histories_df['coin']==id)]
+ rebased_price=[1]
+ for i in range(1,len(temp_rebase_df)):
+ rebased_price.append(temp_rebase_df['price'].iloc[i]*rebased_price[i-1])
+ temp_rebase_df['rebased_price']=rebased_price
+ rebased_prices_df = pd.concat([rebased_prices_df, temp_rebase_df])
+
+fig2 = px.line(rebased_prices_df, x="date", y="rebased_price", color="coin")
+st.write(fig2)
+cols = st.columns(len(symbols_list))
+checkboxes=[]
+
+def write_coins(id_symbol_map, n_cols=5):
+ n_coins = len(id_symbol_map)
+ n_rows = 1 + n_coins // int(n_cols)
+
+ rows = [st.container() for _ in range(n_rows)]
+ cols_per_row = [r.columns(n_cols) for r in rows]
+ cols = [column for row in cols_per_row for column in row]
+
+ #cols = st.columns(n_coins)
+ #checkboxes=[]
+ for i, id in enumerate(id_symbol_map):
+ cols[i].image('logos/{}.png'.format(id_symbol_map[id]),width=40)
+ globals()[st.session_state.names[i]] = cols[i].checkbox("include", value = 1, key=id)
+ globals()["slider_"+ids_list[i]] = cols[i].slider(id, min_value=0, max_value=100, value=50, key=id)
+ checkboxes.append(globals()[st.session_state.names[i]])
+
+write_coins(id_symbol_map)
+
+
+
+#for i, symbol in enumerate(symbols_list):
+# col = cols[i]
+# col.image(f'logos/{symbol}.png',width=40)
+# globals()[st.session_state.names[i]] = col.checkbox(symbol, value = 1)
+# checkboxes.append(globals()[st.session_state.names[i]])
+
+
+
+
+
+
+#if any(checkboxes):
+# checked_ids=[]
+# cols2 = st.columns(sum(checkboxes))
+# j=0
+# for i, value in enumerate(checkboxes):
+# if value==1:
+# checked_ids.append(ids_list[i])
+# col2=cols2[j]
+# col2.image(f'logos/{symbols_list[i]}.png',width=20)
+# j+=1
+
+def create_grid(top_left, bottom_right):
+ num_rows=3
+ num_cols=7
+ col_positions = np.linspace(top_left[0], bottom_right[0], num=num_cols)
+ row_positions = np.linspace(top_left[1], bottom_right[1], num=num_rows)
+ return [(int(col_positions[i]),int(row_positions[j])) for j in range(num_rows) for i in range(num_cols)]
+
+# These are the coordinates of the top left and bottom right of the cart image
+# given it's curent size. You need to change these if you change the size of the
+# cart
+top_left=[300,300]
+bottom_right=[650, 450]
+
+grid = create_grid(top_left, bottom_right)
+
+def add_logo(background, symbol, position, size=(70,70)):
+ bg = Image.open(background)
+ fg = Image.open("logos/{}.png".format(symbol))
+
+ bg = bg.convert("RGBA")
+ fg = fg.convert("RGBA")
+
+ # Resize logo
+ fg_resized = fg.resize(size)
+
+ # Overlay logo onto background at position
+ bg.paste(fg_resized,box=position,mask=fg_resized)
+
+ # Save result
+ bg.save(background)
+
+
+
+cart_cols = st.columns([3,2])
+
+
+
+if any(checkboxes):
+ checked_ids=[]
+ for i, value in enumerate(checkboxes):
+ if value==1:
+ checked_ids.append(ids_list[i])
+ #cart_cols[1].image(f'logos/{symbols_list[i]}.png',width=20)
+ #cart_cols[2].slider(ids_list[i],min_value=0, max_value=100, value=50)
+
+
+# change the below to make it run only if checked_ids ecists - i.e. wrap it up oin a function
+original = Image.open("images/cart.png")
+original.save('images/background.png')
+position_ids = [round(x) for x in np.linspace(0, len(grid)-1, num=len(checked_ids))]
+for i, id in enumerate(checked_ids):
+ size = tuple([int(num * globals()["slider_"+id]/50) for num in (70,70)])
+
+ add_logo('images/background.png', id_symbol_map[id], grid[position_ids[i]], size=size)
+
+weights=[]
+for id in checked_ids:
+ weights.append(globals()["slider_"+id])
+sum_weights = sum(weights)
+weights = [weight/sum_weights for weight in weights]
+
+weights_df = pd.DataFrame({'ids':checked_ids, 'weights': weights, 'portfolio': 'port_1'})
+pie_fig = px.pie(weights_df, values='weights', names='ids')
+pie_fig.update_layout(showlegend=False)
+
+bar_fig = px.bar(weights_df, x="portfolio", y="weights", color="ids", width=200)
+bar_fig.update_layout(showlegend=False)
+
+cart_cols[0].image('images/background.png', width=400)
+cart_cols[1].write(bar_fig)
+gen_port = st.button('Generate portfolio return')
+
+metrics_dict= {'annual_return' : "Return (annualised)", 'absolute_return': "Return over period",
+ 'annual_vol': 'Annual volatility', 'max_drawdown': 'Max loss'}
+
+def write_metrics(prices, *metrics):
+ for metric in metrics:
+ cols = st.columns(2)
+ if metric.__name__ == 'max_drawdown':
+ cols[0].write(metrics_dict[metric.__name__] +': ')
+ cols[1].write('{:.2%}'.format(metric(prices)[0]))
+ else:
+ cols[0].write(metrics_dict[metric.__name__] +': ')
+ cols[1].write('{:.2%}'.format(metric(prices)))
+
+if gen_port:
+ # adjust weight calculation to read in from globals()["slider_"+ids_list[i]]
+ #weights = [1/len(checked_ids)]*len(checked_ids)
+ portfolio_dict={checked_ids[i]:weights[i] for i in range(len(checked_ids))}
+ start_date = dt.date.today()-dt.timedelta(360)
+ weighted_prices_df = pd.DataFrame(columns=['coin','date','price','weighted_price'])
+ for id in checked_ids:
+ temp_weight_df = return_histories_df[(return_histories_df['date']>=pd.Timestamp(start_date))
+ & (return_histories_df['coin']==id)]
+ weighted_price=[portfolio_dict[id]]
+ for i in range(1,len(temp_weight_df)):
+ weighted_price.append(temp_weight_df['price'].iloc[i]*weighted_price[i-1])
+ temp_weight_df['weighted_price']=weighted_price
+ weighted_prices_df = pd.concat([weighted_prices_df, temp_weight_df])
+ date_list = [start_date + dt.timedelta(days=x) for x in range(360)]
+ port_returns=[]
+ for date in date_list:
+ port_returns.append(weighted_prices_df['weighted_price'][weighted_prices_df['date']==pd.Timestamp(date)].sum())
+ port_returns_df = pd.DataFrame({'date':date_list, 'price': port_returns})
+ prices = port_returns_df['price']
+ max_dd, start_idx, end_idx = max_drawdown(prices)
+ start_dt = port_returns_df['date'].iloc[start_idx]
+ end_dt = port_returns_df['date'].iloc[end_idx]
+ fig3 = px.line(port_returns_df, x="date", y="price")
+ fig3.add_vline(x=start_dt, line_width=1, line_color="red")
+ fig3.add_vline(x=end_dt, line_width=1, line_color="red")
+ fig3.add_vrect(x0=start_dt, x1=end_dt, line_width=0, fillcolor="red", opacity=0.05, annotation_text="max loss ")
+ st.write(fig3)
+
+ st.title("Risk metrics")
+ write_metrics(prices, absolute_return, annual_return, annual_vol, max_drawdown)
+
+
+
+
+
+ #for i, symbol in enumerate(symbols_list):
+ # col2 = cols2[i]
+ # col.image(f'logos/{symbol}.png',width=40)
+ #price_subset_df = price_histories_df[price_histories_df['coin'].isin(checked_ids)]
+ #rebased_subset_df = rebased_prices_df[rebased_prices_df['coin'].isin(checked_ids)]
+ #fig1 = px.line(price_subset_df, x="date", y="price", color="coin")
+ #st.write(fig1)
+ #fig2 = px.line(rebased_subset_df, x="date", y="rebased_price", color="coin")
+ #st.write(fig2)
+
+
+
+
+
+
+
diff --git a/data_creator.py b/data_creator.py
new file mode 100644
index 0000000000000000000000000000000000000000..4649118a294fdf49bdf52a209f3be68e58df2ab4
--- /dev/null
+++ b/data_creator.py
@@ -0,0 +1,150 @@
+import requests
+from datetime import date, timedelta, datetime
+import time
+import pandas as pd
+from os.path import exists
+import streamlit as st
+
+@st.cache(persist=True)
+def create_assets(total_coins=50):
+ '''
+ A function to retrieve info about the largest total_coins number of
+ cryptocurrencies, ranked by market cap, generated by a call to coincap assets
+ api.
+ '''
+ url = "https://api.coincap.io/v2/assets"
+
+ # N.B. here adampt the params dict to only request what you need
+ payload={'limit': total_coins}
+ headers = {}
+
+ assets_json = requests.request("GET", url, params=payload, headers=headers).json()
+ return assets_json
+
+@st.cache(persist=True)
+def gen_symbols(assets_json):
+ '''
+ Function to generate three lists: symbols, names and ids, from the result of
+ a call to the coincap assets api, assets_json.
+ '''
+ symbols_list = []
+ names_list = []
+ ids_list =[]
+ for dict in assets_json['data']:
+ symbols_list.append(dict['symbol'])
+ names_list.append(dict['name'])
+ ids_list.append(dict['id'])
+ return symbols_list, names_list, ids_list
+
+@st.cache(persist=True, show_spinner=False)
+def create_market_cap_dict(assets_json):
+ market_cap_dict = {}
+ for asset_dict in assets_json['data']:
+ market_cap_dict[asset_dict['id']] = int(float(asset_dict['marketCapUsd']))
+ return market_cap_dict
+
+def load_histories(coin_ids, start, end):
+ '''
+ Function to load daily historic prices for all crypto currencies in the
+ coin_ids list within the time period defined by the interval [start, end].
+ '''
+ url = "http://api.coincap.io/v2/assets/{}/history"
+
+ payload={'interval':'d1', 'start':start, 'end':end}
+ headers = {}
+
+ histories_dict = {}
+ for id in coin_ids:
+ response_histories = requests.request("GET", url.format(id), headers=headers, params=payload)
+ histories_json = response_histories.json()
+ histories_dict[id] = histories_json['data']
+ return histories_dict
+
+@st.cache(persist=True, show_spinner=False)
+def date_conv(date):
+ '''
+ Function to convert string to datetime.date.
+ '''
+ return datetime.strptime(date, '%Y-%m-%d').date()
+
+@st.cache(persist=True)
+def create_unix_dates(today=date.today(), lookback_years = 5):
+ '''
+ A function to create start_unix and end_unix times in UNIX time in milliseconds
+ '''
+ start_datetime = today-timedelta(365*lookback_years)
+ start_unix = int(time.mktime(start_datetime.timetuple()) * 1000)
+ end_unix = int(time.mktime(date.today().timetuple()) * 1000)
+ return start_unix, end_unix
+
+@st.cache(persist=True, show_spinner=False)
+def create_histories_df(coin_ids, start_unix, end_unix):
+ '''
+ A function to create a dataframe of historical prices for all of the
+ crypto currencies in the coin_ids=ids list, over a period defined by the
+ interval [start_unix, end_unix].
+ N.B. This code uses the data for bitcoin as the first dataframe on which
+ other temp_df are outer joined from the right. This is because bitcoin
+ has the longest history.
+ '''
+ print('Downloading data from coincap.io, may take several minutes...')
+
+ # download histories from coincap.io
+ with st.spinner("You're the first user today so asset histories are being updated. May take several minutes."):
+ histories_dict = load_histories(coin_ids, start_unix, end_unix)
+
+ # convert all dates in histories_dict to python datetime.date objects and remove 'time' key
+ for id in coin_ids:
+ for dict in histories_dict[id]:
+ dict.pop('time')
+ dict['priceUsd']=float(dict['priceUsd'])
+ dict['date'] = date_conv(dict['date'][0:10])
+
+ # convert histories_dict to pd.DataFrame
+ histories_df = pd.json_normalize(histories_dict['bitcoin'])
+ histories_df = histories_df.set_index('date', drop=True)
+ for id in [x for x in coin_ids if x != "bitcoin"]:
+ temp_df = pd.json_normalize(histories_dict[id])
+ temp_df = temp_df.set_index('date', drop=True)
+ histories_df = histories_df.merge(temp_df, how='outer', left_index=True, right_index=True)
+
+ histories_df.columns = coin_ids
+ return histories_df
+
+# N.B. allow_output_mutation set to True because in create_rebased_df I am
+# deliberately chaning the value returns_df[start_date:start_date] to 0
+# however I want the cahced value to remain unchanged so that if I rebase to a
+# different start_date we go back to the orrignal returns_df.
+@st.cache(persist=True, show_spinner=False, allow_output_mutation=True)
+def create_returns_df(histories_df):
+ return histories_df.pct_change(1)
+
+@st.cache(persist=True, show_spinner=False)
+def create_rebased_df(returns_df, start_date, end_date):
+ returns_df[start_date:start_date]=0
+ return (1 + returns_df[start_date:end_date]).cumprod()
+
+@st.cache(persist=True, show_spinner=False)
+def date_range(end_date, lookback_years):
+ return [end_date - timedelta(x) for x in range(365 * lookback_years)][::-1]
+
+@st.cache(persist=True, show_spinner=False)
+def ids2names_dict(coin_ids, names):
+ ids2names_dict={}
+ for i, id in enumerate(coin_ids):
+ ids2names_dict[id] = names[i]
+ return ids2names_dict
+
+@st.cache(persist=True, show_spinner=False)
+def names2ids_dict(names, coin_ids):
+ names2ids_dict={}
+ for i, name in enumerate(names):
+ names2ids_dict[name] = coin_ids[i]
+ return names2ids_dict
+
+@st.cache(persist=True, show_spinner=False)
+def gen_rebased_df(histories_df, ids_with_histories, start_date, end_date):
+ returns_df = histories_df[ids_with_histories].pct_change(1)
+ returns_df[start_date:start_date]=0
+ return (1 + returns_df[start_date:end_date]).cumprod()
+
diff --git a/frontpage.py b/frontpage.py
new file mode 100644
index 0000000000000000000000000000000000000000..73fdbbcc0621e0306b981ef51dabff54098d05fe
--- /dev/null
+++ b/frontpage.py
@@ -0,0 +1,211 @@
+
+
+#-------------------
+# Imports
+#-------------------
+import streamlit as st
+import yfinance as yf
+import pandas as pd
+import numpy as np
+import plotly.graph_objs as go
+import plotly.io as pio
+from bs4 import BeautifulSoup
+import requests
+from datetime import datetime
+
+# today's date
+today = datetime.today().strftime('%d %B %Y')
+
+st.set_page_config(layout="wide")
+
+
+#-------------------
+# Web scraping Yahoo Finance
+#-------------------
+dic = {}
+url = 'https://finance.yahoo.com/cryptocurrencies?offset=0&count=100'
+soup = BeautifulSoup(requests.get(url).text)
+
+# store values in separate lists and then in a dictionary
+for listing in soup.find_all('div', attrs={'id':'fin-scr-res-table'}):
+ symbol_list = []
+ name_list = []
+ price_list = []
+ change_list = []
+ mcap_list = []
+for symbol in listing.find_all('td', attrs={'aria-label':'Symbol'}):
+ symbol_list.append(symbol.text)
+ dic['Symbol'] = symbol_list
+for name in listing.find_all('td', attrs={'aria-label':'Name'}):
+ name_list.append(name.text)
+ dic['Name'] = name_list
+for price in listing.find_all('td', attrs={'aria-label':'Price (Intraday)'}):
+ price_list.append(price.text)
+ dic['Price'] = price_list
+for change in listing.find_all('td', attrs={'aria-label':'% Change'}):
+ change_list.append(change.text)
+ dic['% Change'] = change_list
+for mcap in listing.find_all('td', attrs={'aria-label':'Market Cap'}):
+ mcap_list.append(mcap.text)
+ dic['Market Cap'] = mcap_list
+
+# create a dataframe from dictionary
+df_scrape = pd.DataFrame(dic)
+df_scrape.Symbol = df_scrape.Symbol.str.replace('-USD','')
+df_scrape.Name = df_scrape.Name.str.replace(' USD','')
+dic1 = dict(zip(df_scrape.Symbol,df_scrape.Name))
+
+
+#-------------------
+# Streamlit Sidebar
+#-------------------
+fiat = ['USD','EUR','GBP']
+tokens = df_scrape.Symbol.values
+
+# filters selectbox
+st.sidebar.title("Filters")
+select_token = st.sidebar.selectbox('Tokens', tokens)
+select_fiat = st.sidebar.selectbox('Fiat', fiat)
+
+# special expander objects
+st.sidebar.markdown('***')
+with st.sidebar.expander('Help'):
+ st.markdown('''
+ - Select token and fiat of your choice.
+ - Interactive plots can be zoomed or hovered to retrieve more info.
+ - Plots can be downloaded using Plotly tools.''')
+
+with st.sidebar.expander('Sources'):
+ st.markdown('''
+ - Python Libraries: yfinance, BeautifulSoup, Plotly, Pandas, Streamlit
+ - Prices: https://finance.yahoo.com
+ - Logos: https://cryptologos.cc/
+ ''')
+
+
+st.write("Dashboard adapted from https://github.com/rohithteja/the-crypto-dashboard")
+#-------------------
+# Add crypto logo and name
+#-------------------
+col1, col2 = st.columns([1,10])
+with col1:
+ try:
+ st.image(f'logos/{select_token}.png',width=70)
+ except:
+ pass
+with col2:
+ st.markdown(f'''## {dic1[select_token]}''')
+
+
+#-------------------
+# Candlestick chart with moving averages
+#-------------------
+st.markdown('''
+- The following is an interactive Candlestick chart for the price fluctuations over the past 5 years.
+- Simple moving averages were computed for 20, 50 and 100 day frequencies.
+- Aids in trading strategy and to better interpret the price fluctuations.''')
+
+# download 5 year crypto prices from Yahoo Finance
+df = yf.download(tickers=f'{select_token}-{select_fiat}', period = '5y', interval = '1d')
+
+# compute moving averages
+df['MA100'] = df.Close.rolling(100).mean()
+df['MA50'] = df.Close.rolling(50).mean()
+df['MA20'] = df.Close.rolling(20).mean()
+
+# Plotly candlestick chart
+fig = go.Figure(data=
+ [go.Candlestick(x=df.index,
+ open=df.Open,
+ high=df.High,
+ low=df.Low,
+ close=df.Close,
+ name=f'{select_token}'),
+ go.Scatter(x=df.index, y=df.MA20,
+ line=dict(color='yellow',width=1),name='MA20'),
+ go.Scatter(x=df.index, y=df.MA50,
+ line=dict(color='green',width=1),name='MA50'),
+ go.Scatter(x=df.index, y=df.MA100,
+ line=dict(color='red',width=1),name='MA100')])
+
+fig.update_layout(go.Layout(xaxis = {'showgrid': False},
+ yaxis = {'showgrid': False}),
+ title=f'{dic1[select_token]} Price Fluctuation with Moving Averages',
+ yaxis_title=f'Price ({select_fiat})',
+ xaxis_rangeslider_visible=False)
+
+st.plotly_chart(fig, use_container_width=True)
+
+#-------------------
+# Line Chart with daily trends
+#-------------------
+st.markdown('## Daily Trends')
+st.markdown(f'''
+- Line graph below shows the price fluctuation of {dic1[select_token]} every minute for today's date ({today}).
+- The data is automatically updated for the current day.
+- The horizontal line shows the current day's open price.
+- Green portion indicates the price greater than open price and red for lower.
+''')
+
+# download daily crypto prices from Yahoo Finance
+df = yf.download(tickers=f'{select_token}-{select_fiat}', period = '1d', interval = '1m')
+
+# Plotly line chart
+fig = go.Figure()
+fig.add_scattergl(x=df.index, y=df.Close,
+ line={'color': 'green'},name='Up trend')
+fig.add_scattergl(x=df.index, y=df.Close.where(df.Close <= df.Open[0]),
+ line={'color': 'red'},name='Down trend')
+fig.add_hline(y=df.Open[0])
+fig.update_layout(go.Layout(xaxis = {'showgrid': False},
+ yaxis = {'showgrid': False}),
+ title=f'{dic1[select_token]} Daily Trends in Comparison to Open Price',
+ yaxis_title=f'Price ({select_fiat})',template='plotly_dark',
+ xaxis_rangeslider_visible=False)
+st.plotly_chart(fig, use_container_width=True)
+
+#-------------------
+# Table showing top 25 cryptos
+#-------------------
+st.markdown('## Top 25 Cryptocurrency Prices and Stats')
+st.markdown('''
+- Realtime price changes (in USD).
+- Values updated every few minutes.
+- Colour coded column indicates the increase or decrease in price.
+''')
+
+# create table from webscraped data
+df_scrape = df_scrape.rename(columns={'Symbol':'Token'})
+df_scrape['% Change'] = df_scrape['% Change'].str.replace('%','').str.replace(',','').astype(float)
+
+df_scrape["color"] = df_scrape["% Change"].map(lambda x:'red' if x<0 else 'green')
+cols_to_show = ['Name','Token', 'Price', '% Change', 'Market Cap']
+
+# to change color of "% change" column
+fill_color = []
+n = len(df_scrape)
+for col in cols_to_show:
+ if col!='% Change':
+ fill_color.append(['black']*n)
+ else:
+ fill_color.append(df_scrape["color"].to_list())
+
+# Plotly Table
+data=[go.Table(columnwidth = [20,15,15,15,15],
+ header=dict(values=[f"{col} " for col in cols_to_show],
+ font=dict(color='white', size=20),
+ height=30,
+ line_color='black',
+ fill_color='dimgrey',
+ align=['left','left', 'right','right','right']),
+ cells=dict(values=df_scrape[cols_to_show].values.T,
+ fill_color=fill_color,
+ font=dict(color='white', size=20),
+ height=30,
+ line_color='black',
+ align=['left','left', 'right','right','right']))]
+
+fig = go.Figure(data=data)
+fig.update_layout(go.Layout(xaxis = {'showgrid': False},
+ yaxis = {'showgrid': False}))
+st.plotly_chart(fig, use_container_width=True)
diff --git a/goofing.ipynb b/goofing.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..87814ccaaddbbe7cd6d8b4c1218fb8f4a24a7ac9
--- /dev/null
+++ b/goofing.ipynb
@@ -0,0 +1,37140 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "id": "4937ef6f-0dc7-4dee-8a5b-0501f5066daa",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import requests\n",
+ "from datetime import date, timedelta, datetime\n",
+ "import time\n",
+ "import pandas as pd\n",
+ "from os.path import exists"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "id": "59d6bfcd-0654-4888-b9ab-83f5f09f3d43",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def create_assets(total_coins=100):\n",
+ " url = \"https://api.coincap.io/v2/assets\"\n",
+ "\n",
+ " # N.B. here adampt the params dict to only request what you need\n",
+ " payload={'limit': total_coins}\n",
+ " headers = {}\n",
+ "\n",
+ " assets_json = requests.request(\"GET\", url, params=payload, headers=headers).json()\n",
+ " return assets_json"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "id": "33610a50-83ad-4ce5-b271-6646b88844b0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def gen_symbols(assets_json):\n",
+ " symbols_list = []\n",
+ " names_list = []\n",
+ " ids_list =[]\n",
+ " for dict in assets_json['data']:\n",
+ " symbols_list.append(dict['symbol'])\n",
+ " names_list.append(dict['name'])\n",
+ " ids_list.append(dict['id'])\n",
+ " return symbols_list, names_list, ids_list"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "id": "c99999c1-28ee-40ad-a6e9-6c96924a7129",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def load_histories(coin_ids, start, end):\n",
+ " url = \"http://api.coincap.io/v2/assets/{}/history\"\n",
+ "\n",
+ " payload={'interval':'d1', 'start':start, 'end':end}\n",
+ " headers = {}\n",
+ "\n",
+ " histories_dict = {}\n",
+ " for id in coin_ids:\n",
+ " response_histories = requests.request(\"GET\", url.format(id), headers=headers, params=payload)\n",
+ " histories_json = response_histories.json()\n",
+ " histories_dict[id] = histories_json['data']\n",
+ " return histories_dict"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 37,
+ "id": "8e925370-1cad-4778-a493-618fbfc7f2e0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# convert string to datetime.date\n",
+ "def date_conv(date):\n",
+ " return datetime.strptime(date, '%Y-%m-%d').date()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 38,
+ "id": "707b6ab6-d83d-4ec9-8af8-572df067a4e1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def create_unix_dates(today=date.today(), lookback_years = 5):\n",
+ " '''\n",
+ " A function to create start_unix and end_unix times in UNIX time in milliseconds\n",
+ " '''\n",
+ " start_datetime = today-timedelta(365*lookback_years)\n",
+ " start_unix = int(time.mktime(start_datetime.timetuple()) * 1000)\n",
+ " end_unix = int(time.mktime(date.today().timetuple()) * 1000)\n",
+ " return start_unix, end_unix"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 52,
+ "id": "cee6d439-fc64-45c6-bea2-3902e9f1ce55",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def create_histories_df(coin_ids, start_unix, end_unix):\n",
+ " '''\n",
+ " A function to create a dataframe of historical prices for all of the \n",
+ " crypto currencies in the coin_ids=ids list, over a period of\n",
+ " lookback_years = 5.\n",
+ " N.B. This code uses the data for bitcoin as the first dataframe on which\n",
+ " other temp_df are outer joined from the right. This is because bitcoin \n",
+ " has the longest history.\n",
+ " '''\n",
+ " print('Downloading data from coincap.io, may take several minutes...')\n",
+ " \n",
+ " # download histories from coincap.io\n",
+ " histories_dict = load_histories(coin_ids, start_unix, end_unix)\n",
+ " \n",
+ " # convert all dates in histories_dict to python datetime.date objects and remove 'time' key\n",
+ " for id in coin_ids:\n",
+ " for dict in histories_dict[id]:\n",
+ " dict.pop('time')\n",
+ " dict['priceUsd']=float(dict['priceUsd'])\n",
+ " dict['date'] = date_conv(dict['date'][0:10])\n",
+ " \n",
+ " # convert histories_dict to pd.DataFrame\n",
+ " histories_df = pd.json_normalize(histories_dict['bitcoin'])\n",
+ " histories_df = histories_df.set_index('date', drop=True)\n",
+ " for id in [x for x in coin_ids if x != \"bitcoin\"]:\n",
+ " temp_df = pd.json_normalize(histories_dict[id])\n",
+ " temp_df = temp_df.set_index('date', drop=True)\n",
+ " histories_df = histories_df.merge(temp_df, how='outer', left_index=True, right_index=True)\n",
+ " \n",
+ " histories_df.columns = coin_ids\n",
+ " return histories_df\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 79,
+ "id": "b4129ae7-034b-4210-98e8-d3bdec5219b9",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# put cache decorator on this finction\n",
+ "assets_json = create_assets(total_coins=100)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 80,
+ "id": "7f199fa9-1803-40b9-a7f0-c39dbb2c3735",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# put cache decorator on this finction\n",
+ "symbols, names, coin_ids = gen_symbols(assets_json)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 81,
+ "id": "acdf6c39-b3ff-4978-b3f0-e66290ef759d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "start_unix, end_unix = create_unix_dates(today=date.today(),lookback_years = 5)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 82,
+ "id": "8266876b-79bf-42d4-9bab-bfa1b4b64d9b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "histories_dict = load_histories(coin_ids, start_unix, end_unix)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 50,
+ "id": "b02e0c4e-16eb-451d-919a-a697623449eb",
+ "metadata": {
+ "collapsed": true,
+ "jupyter": {
+ "outputs_hidden": true
+ },
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'bitcoin': [{'priceUsd': '39147.8638098693520315',\n",
+ " 'time': 1621382400000,\n",
+ " 'date': '2021-05-19T00:00:00.000Z'},\n",
+ " {'priceUsd': '39865.2257138631182931',\n",
+ " 'time': 1621468800000,\n",
+ " 'date': '2021-05-20T00:00:00.000Z'},\n",
+ " {'priceUsd': '39161.3031174275061390',\n",
+ " 'time': 1621555200000,\n",
+ " 'date': '2021-05-21T00:00:00.000Z'},\n",
+ " {'priceUsd': '37543.6829244262777489',\n",
+ " 'time': 1621641600000,\n",
+ " 'date': '2021-05-22T00:00:00.000Z'},\n",
+ " {'priceUsd': '35095.1658253822589551',\n",
+ " 'time': 1621728000000,\n",
+ " 'date': '2021-05-23T00:00:00.000Z'},\n",
+ " {'priceUsd': '37105.8657082232936165',\n",
+ " 'time': 1621814400000,\n",
+ " 'date': '2021-05-24T00:00:00.000Z'},\n",
+ " {'priceUsd': '38419.2573581555758190',\n",
+ " 'time': 1621900800000,\n",
+ " 'date': '2021-05-25T00:00:00.000Z'},\n",
+ " {'priceUsd': '39304.8247191829242468',\n",
+ " 'time': 1621987200000,\n",
+ " 'date': '2021-05-26T00:00:00.000Z'},\n",
+ " {'priceUsd': '38851.8752200497785424',\n",
+ " 'time': 1622073600000,\n",
+ " 'date': '2021-05-27T00:00:00.000Z'},\n",
+ " {'priceUsd': '36666.1779571691927065',\n",
+ " 'time': 1622160000000,\n",
+ " 'date': '2021-05-28T00:00:00.000Z'},\n",
+ " {'priceUsd': '35301.0491995096393796',\n",
+ " 'time': 1622246400000,\n",
+ " 'date': '2021-05-29T00:00:00.000Z'},\n",
+ " {'priceUsd': '35514.2038402494780354',\n",
+ " 'time': 1622332800000,\n",
+ " 'date': '2021-05-30T00:00:00.000Z'},\n",
+ " {'priceUsd': '36175.8345718996082390',\n",
+ " 'time': 1622419200000,\n",
+ " 'date': '2021-05-31T00:00:00.000Z'},\n",
+ " {'priceUsd': '36628.5155184308958238',\n",
+ " 'time': 1622505600000,\n",
+ " 'date': '2021-06-01T00:00:00.000Z'},\n",
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+ " 'date': '2022-04-29T00:00:00.000Z'},\n",
+ " {'priceUsd': '394.6780502294345093',\n",
+ " 'time': 1651276800000,\n",
+ " 'date': '2022-04-30T00:00:00.000Z'},\n",
+ " {'priceUsd': '386.0425625948540438',\n",
+ " 'time': 1651363200000,\n",
+ " 'date': '2022-05-01T00:00:00.000Z'},\n",
+ " {'priceUsd': '389.2069802247551525',\n",
+ " 'time': 1651449600000,\n",
+ " 'date': '2022-05-02T00:00:00.000Z'},\n",
+ " {'priceUsd': '387.3378538798995978',\n",
+ " 'time': 1651536000000,\n",
+ " 'date': '2022-05-03T00:00:00.000Z'},\n",
+ " {'priceUsd': '391.7769950309583687',\n",
+ " 'time': 1651622400000,\n",
+ " 'date': '2022-05-04T00:00:00.000Z'},\n",
+ " {'priceUsd': '394.0009321169327402',\n",
+ " 'time': 1651708800000,\n",
+ " 'date': '2022-05-05T00:00:00.000Z'},\n",
+ " {'priceUsd': '379.1577736002105705',\n",
+ " 'time': 1651795200000,\n",
+ " 'date': '2022-05-06T00:00:00.000Z'},\n",
+ " {'priceUsd': '376.5399814151459469',\n",
+ " 'time': 1651881600000,\n",
+ " 'date': '2022-05-07T00:00:00.000Z'},\n",
+ " {'priceUsd': '360.0959800555931531',\n",
+ " 'time': 1651968000000,\n",
+ " 'date': '2022-05-08T00:00:00.000Z'},\n",
+ " {'priceUsd': '332.8024852423291161',\n",
+ " 'time': 1652054400000,\n",
+ " 'date': '2022-05-09T00:00:00.000Z'},\n",
+ " {'priceUsd': '319.4201516525066091',\n",
+ " 'time': 1652140800000,\n",
+ " 'date': '2022-05-10T00:00:00.000Z'},\n",
+ " {'priceUsd': '297.3898655090924890',\n",
+ " 'time': 1652227200000,\n",
+ " 'date': '2022-05-11T00:00:00.000Z'},\n",
+ " {'priceUsd': '261.9241061111955087',\n",
+ " 'time': 1652313600000,\n",
+ " 'date': '2022-05-12T00:00:00.000Z'},\n",
+ " {'priceUsd': '298.7930293608914094',\n",
+ " 'time': 1652400000000,\n",
+ " 'date': '2022-05-13T00:00:00.000Z'},\n",
+ " {'priceUsd': '287.3927157418903443',\n",
+ " 'time': 1652486400000,\n",
+ " 'date': '2022-05-14T00:00:00.000Z'},\n",
+ " {'priceUsd': '298.2107301245594047',\n",
+ " 'time': 1652572800000,\n",
+ " 'date': '2022-05-15T00:00:00.000Z'},\n",
+ " {'priceUsd': '298.8575522579206131',\n",
+ " 'time': 1652659200000,\n",
+ " 'date': '2022-05-16T00:00:00.000Z'},\n",
+ " {'priceUsd': '304.2808838400823271',\n",
+ " 'time': 1652745600000,\n",
+ " 'date': '2022-05-17T00:00:00.000Z'}]}"
+ ]
+ },
+ "execution_count": 50,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "histories_dict"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "326a30bc-a76c-4c39-a265-08e809934106",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Downloading data from coincap.io, may take several minutes...\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_21400/1004101742.py:32: FutureWarning: Passing 'suffixes' which cause duplicate columns {'priceUsd_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
+ " histories_df = histories_df.merge(temp_df, how='outer', left_index=True, right_index=True)\n"
+ ]
+ }
+ ],
+ "source": [
+ "# if histories.csv exists load it into histories_df dataframe\n",
+ "if exists('histories.csv'):\n",
+ " histories_df = pd.read_csv('histories.csv', index_col='date')\n",
+ "\n",
+ "# if histories_df exists set the last_date of the history and yesterday's date, \n",
+ "# otherwise set last_date to False\n",
+ "try:\n",
+ " last_date = date_conv(histories_df.index[-1])\n",
+ "except NameError:\n",
+ " last_date=False\n",
+ "\n",
+ "# if last_date is not yesterday's date then redownload the data and save it to csv\n",
+ "yesterday = date.today() - timedelta(1)\n",
+ "if not(last_date == yesterday):\n",
+ " # redownload and save the datafram, print (this maay take up to 2 minutes)\n",
+ " histories_df = create_histories_df(coin_ids, start_unix, end_unix)\n",
+ " histories_df.to_csv('histories.csv')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 83,
+ "id": "f9438317-f856-41f8-91f6-a7d149bd2ddf",
+ "metadata": {
+ "collapsed": true,
+ "jupyter": {
+ "outputs_hidden": true
+ },
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Downloading data from coincap.io, may take several minutes...\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_23487/3528659140.py:28: FutureWarning:\n",
+ "\n",
+ "Passing 'suffixes' which cause duplicate columns {'priceUsd_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
+ "\n",
+ "/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_23487/3528659140.py:28: FutureWarning:\n",
+ "\n",
+ "Passing 'suffixes' which cause duplicate columns {'priceUsd_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
+ "\n",
+ "/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_23487/3528659140.py:28: FutureWarning:\n",
+ "\n",
+ "Passing 'suffixes' which cause duplicate columns {'priceUsd_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
+ "\n",
+ "/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_23487/3528659140.py:28: FutureWarning:\n",
+ "\n",
+ "Passing 'suffixes' which cause duplicate columns {'priceUsd_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
+ "\n",
+ "/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_23487/3528659140.py:28: FutureWarning:\n",
+ "\n",
+ "Passing 'suffixes' which cause duplicate columns {'priceUsd_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
+ "\n",
+ "/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_23487/3528659140.py:28: FutureWarning:\n",
+ "\n",
+ "Passing 'suffixes' which cause duplicate columns {'priceUsd_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
+ "\n",
+ "/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_23487/3528659140.py:28: FutureWarning:\n",
+ "\n",
+ "Passing 'suffixes' which cause duplicate columns {'priceUsd_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
+ "\n",
+ "/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_23487/3528659140.py:28: FutureWarning:\n",
+ "\n",
+ "Passing 'suffixes' which cause duplicate columns {'priceUsd_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
+ "\n",
+ "/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_23487/3528659140.py:28: FutureWarning:\n",
+ "\n",
+ "Passing 'suffixes' which cause duplicate columns {'priceUsd_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
+ "\n",
+ "/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_23487/3528659140.py:28: FutureWarning:\n",
+ "\n",
+ "Passing 'suffixes' which cause duplicate columns {'priceUsd_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
+ "\n",
+ "/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_23487/3528659140.py:28: FutureWarning:\n",
+ "\n",
+ "Passing 'suffixes' which cause duplicate columns {'priceUsd_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
+ "\n",
+ "/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_23487/3528659140.py:28: FutureWarning:\n",
+ "\n",
+ "Passing 'suffixes' which cause duplicate columns {'priceUsd_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
+ "\n",
+ "/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_23487/3528659140.py:28: FutureWarning:\n",
+ "\n",
+ "Passing 'suffixes' which cause duplicate columns {'priceUsd_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
+ "\n",
+ "/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_23487/3528659140.py:28: FutureWarning:\n",
+ "\n",
+ "Passing 'suffixes' which cause duplicate columns {'priceUsd_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
+ "\n",
+ "/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_23487/3528659140.py:28: FutureWarning:\n",
+ "\n",
+ "Passing 'suffixes' which cause duplicate columns {'priceUsd_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
+ "\n",
+ "/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_23487/3528659140.py:28: FutureWarning:\n",
+ "\n",
+ "Passing 'suffixes' which cause duplicate columns {'priceUsd_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
+ "\n",
+ "/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_23487/3528659140.py:28: FutureWarning:\n",
+ "\n",
+ "Passing 'suffixes' which cause duplicate columns {'priceUsd_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
+ "\n",
+ "/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_23487/3528659140.py:28: FutureWarning:\n",
+ "\n",
+ "Passing 'suffixes' which cause duplicate columns {'priceUsd_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
+ "\n",
+ "/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_23487/3528659140.py:28: FutureWarning:\n",
+ "\n",
+ "Passing 'suffixes' which cause duplicate columns {'priceUsd_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
+ "\n",
+ "/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_23487/3528659140.py:28: FutureWarning:\n",
+ "\n",
+ "Passing 'suffixes' which cause duplicate columns {'priceUsd_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
+ "\n",
+ "/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_23487/3528659140.py:28: FutureWarning:\n",
+ "\n",
+ "Passing 'suffixes' which cause duplicate columns {'priceUsd_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
+ "\n",
+ "/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_23487/3528659140.py:28: FutureWarning:\n",
+ "\n",
+ "Passing 'suffixes' which cause duplicate columns {'priceUsd_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
+ "\n",
+ "/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_23487/3528659140.py:28: FutureWarning:\n",
+ "\n",
+ "Passing 'suffixes' which cause duplicate columns {'priceUsd_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
+ "\n",
+ "/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_23487/3528659140.py:28: FutureWarning:\n",
+ "\n",
+ "Passing 'suffixes' which cause duplicate columns {'priceUsd_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
+ "\n",
+ "/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_23487/3528659140.py:28: FutureWarning:\n",
+ "\n",
+ "Passing 'suffixes' which cause duplicate columns {'priceUsd_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
+ "\n",
+ "/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_23487/3528659140.py:28: FutureWarning:\n",
+ "\n",
+ "Passing 'suffixes' which cause duplicate columns {'priceUsd_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
+ "\n",
+ "/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_23487/3528659140.py:28: FutureWarning:\n",
+ "\n",
+ "Passing 'suffixes' which cause duplicate columns {'priceUsd_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
+ "\n",
+ "/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_23487/3528659140.py:28: FutureWarning:\n",
+ "\n",
+ "Passing 'suffixes' which cause duplicate columns {'priceUsd_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
+ "\n",
+ "/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_23487/3528659140.py:28: FutureWarning:\n",
+ "\n",
+ "Passing 'suffixes' which cause duplicate columns {'priceUsd_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
+ "\n",
+ "/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_23487/3528659140.py:28: FutureWarning:\n",
+ "\n",
+ "Passing 'suffixes' which cause duplicate columns {'priceUsd_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
+ "\n",
+ "/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_23487/3528659140.py:28: FutureWarning:\n",
+ "\n",
+ "Passing 'suffixes' which cause duplicate columns {'priceUsd_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
+ "\n",
+ "/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_23487/3528659140.py:28: FutureWarning:\n",
+ "\n",
+ "Passing 'suffixes' which cause duplicate columns {'priceUsd_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
+ "\n",
+ "/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_23487/3528659140.py:28: FutureWarning:\n",
+ "\n",
+ "Passing 'suffixes' which cause duplicate columns {'priceUsd_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
+ "\n",
+ "/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_23487/3528659140.py:28: FutureWarning:\n",
+ "\n",
+ "Passing 'suffixes' which cause duplicate columns {'priceUsd_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
+ "\n",
+ "/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_23487/3528659140.py:28: FutureWarning:\n",
+ "\n",
+ "Passing 'suffixes' which cause duplicate columns {'priceUsd_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
+ "\n",
+ "/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_23487/3528659140.py:28: FutureWarning:\n",
+ "\n",
+ "Passing 'suffixes' which cause duplicate columns {'priceUsd_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
+ "\n",
+ "/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_23487/3528659140.py:28: FutureWarning:\n",
+ "\n",
+ "Passing 'suffixes' which cause duplicate columns {'priceUsd_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
+ "\n",
+ "/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_23487/3528659140.py:28: FutureWarning:\n",
+ "\n",
+ "Passing 'suffixes' which cause duplicate columns {'priceUsd_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
+ "\n",
+ "/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_23487/3528659140.py:28: FutureWarning:\n",
+ "\n",
+ "Passing 'suffixes' which cause duplicate columns {'priceUsd_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
+ "\n",
+ "/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_23487/3528659140.py:28: FutureWarning:\n",
+ "\n",
+ "Passing 'suffixes' which cause duplicate columns {'priceUsd_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
+ "\n",
+ "/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_23487/3528659140.py:28: FutureWarning:\n",
+ "\n",
+ "Passing 'suffixes' which cause duplicate columns {'priceUsd_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
+ "\n",
+ "/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_23487/3528659140.py:28: FutureWarning:\n",
+ "\n",
+ "Passing 'suffixes' which cause duplicate columns {'priceUsd_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
+ "\n",
+ "/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_23487/3528659140.py:28: FutureWarning:\n",
+ "\n",
+ "Passing 'suffixes' which cause duplicate columns {'priceUsd_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
+ "\n",
+ "/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_23487/3528659140.py:28: FutureWarning:\n",
+ "\n",
+ "Passing 'suffixes' which cause duplicate columns {'priceUsd_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
+ "\n",
+ "/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_23487/3528659140.py:28: FutureWarning:\n",
+ "\n",
+ "Passing 'suffixes' which cause duplicate columns {'priceUsd_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
+ "\n",
+ "/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_23487/3528659140.py:28: FutureWarning:\n",
+ "\n",
+ "Passing 'suffixes' which cause duplicate columns {'priceUsd_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
+ "\n",
+ "/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_23487/3528659140.py:28: FutureWarning:\n",
+ "\n",
+ "Passing 'suffixes' which cause duplicate columns {'priceUsd_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
+ "\n",
+ "/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_23487/3528659140.py:28: FutureWarning:\n",
+ "\n",
+ "Passing 'suffixes' which cause duplicate columns {'priceUsd_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
+ "\n",
+ "/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_23487/3528659140.py:28: FutureWarning:\n",
+ "\n",
+ "Passing 'suffixes' which cause duplicate columns {'priceUsd_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "#histories_df = create_histories_df(coin_ids, start_unix, end_unix)\n",
+ "#histories_df.to_csv('histories.csv')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 84,
+ "id": "c33e1cae-80b3-4086-a93e-3dce681f4692",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(1823, 100)"
+ ]
+ },
+ "execution_count": 84,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "histories_df.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 55,
+ "id": "adb3a3cb-f782-4b94-a83c-922cfadc51fa",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
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+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 2017-05-20 \n",
+ " NaN \n",
+ " NaN \n",
+ " NaN \n",
+ " NaN \n",
+ " NaN \n",
+ " NaN \n",
+ " NaN \n",
+ " NaN \n",
+ " NaN \n",
+ " NaN \n",
+ " ... \n",
+ " NaN \n",
+ " NaN \n",
+ " NaN \n",
+ " NaN \n",
+ " NaN \n",
+ " NaN \n",
+ " NaN \n",
+ " NaN \n",
+ " NaN \n",
+ " NaN \n",
+ " \n",
+ " \n",
+ " 2017-05-21 \n",
+ " 0.026185 \n",
+ " 0.067805 \n",
+ " 0.004166 \n",
+ " NaN \n",
+ " 0.000000 \n",
+ " 0.014179 \n",
+ " NaN \n",
+ " NaN \n",
+ " NaN \n",
+ " 0.492958 \n",
+ " ... \n",
+ " NaN \n",
+ " NaN \n",
+ " NaN \n",
+ " NaN \n",
+ " NaN \n",
+ " NaN \n",
+ " NaN \n",
+ " NaN \n",
+ " NaN \n",
+ " NaN \n",
+ " \n",
+ " \n",
+ " 2017-05-22 \n",
+ " 0.044866 \n",
+ " 0.219867 \n",
+ " 0.000592 \n",
+ " NaN \n",
+ " 0.098008 \n",
+ " -0.100012 \n",
+ " NaN \n",
+ " NaN \n",
+ " NaN \n",
+ " 0.272052 \n",
+ " ... \n",
+ " NaN \n",
+ " NaN \n",
+ " NaN \n",
+ " NaN \n",
+ " NaN \n",
+ " NaN \n",
+ " NaN \n",
+ " NaN \n",
+ " NaN \n",
+ " NaN \n",
+ " \n",
+ " \n",
+ " 2017-05-23 \n",
+ " 0.027846 \n",
+ " 0.002811 \n",
+ " 0.000201 \n",
+ " NaN \n",
+ " 0.000000 \n",
+ " 0.023940 \n",
+ " NaN \n",
+ " NaN \n",
+ " NaN \n",
+ " 0.116055 \n",
+ " ... \n",
+ " NaN \n",
+ " NaN \n",
+ " NaN \n",
+ " NaN \n",
+ " NaN \n",
+ " NaN \n",
+ " NaN \n",
+ " NaN \n",
+ " NaN \n",
+ " NaN \n",
+ " \n",
+ " \n",
+ " 2017-05-24 \n",
+ " 0.075455 \n",
+ " 0.139865 \n",
+ " 0.005555 \n",
+ " NaN \n",
+ " 0.115009 \n",
+ " 0.003623 \n",
+ " NaN \n",
+ " NaN \n",
+ " NaN \n",
+ " 0.057439 \n",
+ " ... \n",
+ " NaN \n",
+ " NaN \n",
+ " NaN \n",
+ " NaN \n",
+ " NaN \n",
+ " NaN \n",
+ " NaN \n",
+ " 0.28851 \n",
+ " NaN \n",
+ " NaN \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
5 rows × 100 columns
\n",
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " bitcoin \n",
+ " ethereum \n",
+ " tether \n",
+ " usd-coin \n",
+ " binance-coin \n",
+ " xrp \n",
+ " binance-usd \n",
+ " cardano \n",
+ " solana \n",
+ " dogecoin \n",
+ " ... \n",
+ " kava \n",
+ " kadena \n",
+ " lido-dao \n",
+ " holo \n",
+ " gatetoken \n",
+ " fei-protocol \n",
+ " kyber-network \n",
+ " qtum \n",
+ " bancor \n",
+ " 1inch \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 2021-05-19 \n",
+ " -0.115180 \n",
+ " -0.175795 \n",
+ " 0.000344 \n",
+ " 0.001714 \n",
+ " -0.210172 \n",
+ " -0.160558 \n",
+ " -0.000084 \n",
+ " -0.182779 \n",
+ " -0.126117 \n",
+ " -0.194173 \n",
+ " ... \n",
+ " -0.182321 \n",
+ " NaN \n",
+ " NaN \n",
+ " -0.208927 \n",
+ " -0.215737 \n",
+ " -0.012234 \n",
+ " -0.182182 \n",
+ " -0.216530 \n",
+ " -0.151146 \n",
+ " -0.191624 \n",
+ " \n",
+ " \n",
+ " 2021-05-20 \n",
+ " 0.018324 \n",
+ " -0.046458 \n",
+ " 0.000268 \n",
+ " -0.002304 \n",
+ " -0.091096 \n",
+ " -0.118872 \n",
+ " -0.000105 \n",
+ " 0.018307 \n",
+ " -0.077929 \n",
+ " -0.033091 \n",
+ " ... \n",
+ " -0.070520 \n",
+ " NaN \n",
+ " NaN \n",
+ " -0.061187 \n",
+ " -0.019975 \n",
+ " 0.021214 \n",
+ " -0.070491 \n",
+ " -0.122613 \n",
+ " -0.072125 \n",
+ " -0.145646 \n",
+ " \n",
+ " \n",
+ " 2021-05-21 \n",
+ " -0.017658 \n",
+ " -0.025315 \n",
+ " 0.000102 \n",
+ " 0.000747 \n",
+ " -0.024327 \n",
+ " -0.060753 \n",
+ " 0.000238 \n",
+ " -0.032925 \n",
+ " -0.012728 \n",
+ " -0.006047 \n",
+ " ... \n",
+ " 0.022428 \n",
+ " NaN \n",
+ " NaN \n",
+ " -0.013591 \n",
+ " 0.019588 \n",
+ " -0.006183 \n",
+ " -0.029013 \n",
+ " -0.008434 \n",
+ " -0.038107 \n",
+ " -0.039256 \n",
+ " \n",
+ " \n",
+ " 2021-05-22 \n",
+ " -0.041307 \n",
+ " -0.104646 \n",
+ " -0.000258 \n",
+ " 0.000169 \n",
+ " -0.137914 \n",
+ " -0.155483 \n",
+ " 0.000257 \n",
+ " -0.093762 \n",
+ " -0.141491 \n",
+ " -0.090236 \n",
+ " ... \n",
+ " -0.124879 \n",
+ " NaN \n",
+ " NaN \n",
+ " -0.112529 \n",
+ " -0.187009 \n",
+ " 0.002130 \n",
+ " -0.117576 \n",
+ " -0.168363 \n",
+ " -0.121015 \n",
+ " -0.145770 \n",
+ " \n",
+ " \n",
+ " 2021-05-23 \n",
+ " -0.065218 \n",
+ " -0.104612 \n",
+ " 0.000092 \n",
+ " 0.000878 \n",
+ " -0.155485 \n",
+ " -0.123637 \n",
+ " 0.000165 \n",
+ " -0.124935 \n",
+ " -0.279095 \n",
+ " -0.080092 \n",
+ " ... \n",
+ " -0.108002 \n",
+ " NaN \n",
+ " NaN \n",
+ " -0.130236 \n",
+ " -0.141261 \n",
+ " -0.000125 \n",
+ " -0.156447 \n",
+ " -0.152963 \n",
+ " -0.140479 \n",
+ " -0.167531 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
5 rows × 100 columns
\n",
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " bitcoin \n",
+ " ethereum \n",
+ " tether \n",
+ " usd-coin \n",
+ " binance-coin \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 2021-05-19 \n",
+ " 1.000000 \n",
+ " 1.000000 \n",
+ " 1.000000 \n",
+ " 1.000000 \n",
+ " 1.000000 \n",
+ " \n",
+ " \n",
+ " 2021-05-20 \n",
+ " 1.018324 \n",
+ " 0.953542 \n",
+ " 1.000268 \n",
+ " 0.997696 \n",
+ " 0.908904 \n",
+ " \n",
+ " \n",
+ " 2021-05-21 \n",
+ " 1.000343 \n",
+ " 0.929403 \n",
+ " 1.000370 \n",
+ " 0.998441 \n",
+ " 0.886794 \n",
+ " \n",
+ " \n",
+ " 2021-05-22 \n",
+ " 0.959023 \n",
+ " 0.832145 \n",
+ " 1.000112 \n",
+ " 0.998610 \n",
+ " 0.764492 \n",
+ " \n",
+ " \n",
+ " 2021-05-23 \n",
+ " 0.896477 \n",
+ " 0.745093 \n",
+ " 1.000204 \n",
+ " 0.999487 \n",
+ " 0.645625 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mhistories_dict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mload_histories\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcoin_ids\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstart_unix\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend_unix\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+ "\u001b[0;32m/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_63357/437834857.py\u001b[0m in \u001b[0;36mload_histories\u001b[0;34m(coin_ids, start, end)\u001b[0m\n\u001b[1;32m 41\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mid\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcoin_ids\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 42\u001b[0m \u001b[0mresponse_histories\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrequests\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"GET\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mid\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mheaders\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpayload\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 43\u001b[0;31m \u001b[0mhistories_json\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresponse_histories\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjson\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 44\u001b[0m \u001b[0mhistories_dict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mid\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhistories_json\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'data'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 45\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mhistories_dict\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/usr/local/lib/python3.9/site-packages/requests/models.py\u001b[0m in \u001b[0;36mjson\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m 864\u001b[0m \u001b[0;31m# used.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 865\u001b[0m \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 866\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mcomplexjson\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloads\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 867\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 868\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/usr/local/Cellar/python@3.9/3.9.5/Frameworks/Python.framework/Versions/3.9/lib/python3.9/json/__init__.py\u001b[0m in \u001b[0;36mloads\u001b[0;34m(s, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw)\u001b[0m\n\u001b[1;32m 344\u001b[0m \u001b[0mparse_int\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mparse_float\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 345\u001b[0m parse_constant is None and object_pairs_hook is None and not kw):\n\u001b[0;32m--> 346\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0m_default_decoder\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 347\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcls\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 348\u001b[0m \u001b[0mcls\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mJSONDecoder\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/usr/local/Cellar/python@3.9/3.9.5/Frameworks/Python.framework/Versions/3.9/lib/python3.9/json/decoder.py\u001b[0m in \u001b[0;36mdecode\u001b[0;34m(self, s, _w)\u001b[0m\n\u001b[1;32m 335\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 336\u001b[0m \"\"\"\n\u001b[0;32m--> 337\u001b[0;31m \u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraw_decode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0midx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_w\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 338\u001b[0m \u001b[0mend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_w\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 339\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mend\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/usr/local/Cellar/python@3.9/3.9.5/Frameworks/Python.framework/Versions/3.9/lib/python3.9/json/decoder.py\u001b[0m in \u001b[0;36mraw_decode\u001b[0;34m(self, s, idx)\u001b[0m\n\u001b[1;32m 353\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscan_once\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0midx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 354\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mStopIteration\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 355\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mJSONDecodeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Expecting value\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 356\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;31mJSONDecodeError\u001b[0m: Expecting value: line 1 column 1 (char 0)"
+ ]
+ }
+ ],
+ "source": [
+ "histories_dict = load_histories(coin_ids, start_unix, end_unix)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "c687db6d-bfd8-4e43-9e7a-b03923eed314",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def date_conv(date):\n",
+ " return datetime.strptime(date, '%Y-%m-%d')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "8a6a3771-f0f0-41dc-b34b-e9f5cd403dc4",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "histories_df= pd.read_csv('histories.csv')\n",
+ "histories_df['date'] = list(map(date_conv,histories_df['date']))\n",
+ "histories_df = histories_df.set_index('date')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "93ccd49c-55d4-4984-a768-2d1d961adf7c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def ids_with_histories(histories_df, start_date, end_date):\n",
+ " investment_df = histories_df[start_date:end_date]\n",
+ " investment_df.dropna(axis=1, inplace=True) # drop cols with any NaN values\n",
+ " return investment_df.columns\n",
+ "\n",
+ "def uniform_weights_dict(ids_with_histories):\n",
+ " weight = 1/len(ids_with_histories)\n",
+ " uniform_weights_dict = {}\n",
+ " for id in ids_with_histories:\n",
+ " uniform_weights_dict[id] = weight\n",
+ " return uniform_weights_dict\n",
+ "\n",
+ "\n",
+ "def markowitz_weights_dict(histories_df,start_port_date,ids_with_histories, analysis_days=365):\n",
+ " start_analysis_date = start_port_date - timedelta(analysis_days)\n",
+ " analysis_df = histories_df[start_analysis_date:start_port_date][ids_with_histories]\n",
+ "\n",
+ " # Calculate expected returns and sample covariance\n",
+ " mu = expected_returns.mean_historical_return(analysis_df)\n",
+ " S = risk_models.sample_cov(analysis_df)\n",
+ " # Optimize for maximal Sharpe ratio\n",
+ " attempts=0\n",
+ " while attempts < 10:\n",
+ " try:\n",
+ " ef = EfficientFrontier(mu, S, weight_bounds=(0, 1))\n",
+ " ef.max_sharpe()\n",
+ " break\n",
+ " except Exception as e:\n",
+ " attempts += 1\n",
+ " try:\n",
+ " cleaned_weights = ef.clean_weights()\n",
+ " except Exception as e:\n",
+ " print(\"Could not find optimal solution, try changing optimisation constraints or investment set\")\n",
+ " return cleaned_weights\n",
+ "\n",
+ "\n",
+ "def gen_port_rtns(rebased_df, weights_dict):\n",
+ " return rebased_df[list(weights_dict.keys())].dot(list(weights_dict.values()))\n",
+ "\n",
+ "def gen_rebased_df(histories_df, ids_with_histories, start_date, end_date):\n",
+ " returns_df = histories_df[ids_with_histories].pct_change(1)\n",
+ " returns_df[start_date:start_date]=0\n",
+ " return (1 + returns_df[start_date:end_date]).cumprod()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "6092ed85-0472-447d-90e5-ab0e357d1a5f",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_63357/2151836904.py:3: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " investment_df.dropna(axis=1, inplace=True) # drop cols with any NaN values\n"
+ ]
+ }
+ ],
+ "source": [
+ "lookback_years = 5\n",
+ "start_date = date.today() - timedelta(365)\n",
+ "end_date = date.today()\n",
+ "ids_with_histories = ids_with_histories(histories_df,\n",
+ " start_date, end_date)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "6044aecd-752f-4097-8598-2ca9b1693c2f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "uniform_weights_dict = uniform_weights_dict(ids_with_histories[:10])\n",
+ "markowitz_weights_dict = markowitz_weights_dict(histories_df,\n",
+ " start_date ,ids_with_histories[:10], analysis_days=365)\n",
+ "rebased_df = gen_rebased_df(histories_df, ids_with_histories,\n",
+ " start_date, end_date)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "0741b8f0-32f5-411a-a412-f40320297ef2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def gen_all_returns(rebased_df, ids_with_histories,uniform_weights_dict,\n",
+ " markowitz_weights_dict):\n",
+ " '''\n",
+ " A function to generate returns for all portfolios and all coins with full\n",
+ " histories over the backtest period, rebased to the start of the backtest\n",
+ " period.\n",
+ " '''\n",
+ " uniform_returns = gen_port_rtns(rebased_df, uniform_weights_dict)\n",
+ " uniform_returns.name = \"Uniform\"\n",
+ " markowitz_returns = gen_port_rtns(rebased_df, markowitz_weights_dict)\n",
+ " markowitz_returns.name = \"Markowitz\"\n",
+ " port_returns = uniform_returns.to_frame().join(markowitz_returns)\n",
+ " return port_returns.join(rebased_df[ids_with_histories])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "0b67f3d3-ae6f-4d8e-b52c-1c4c7c18d662",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "all_returns_df = gen_all_returns(rebased_df, ids_with_histories,uniform_weights_dict,\n",
+ " markowitz_weights_dict)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "7dd39fac-3015-4024-a045-04f8a273ffec",
+ "metadata": {},
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+ {
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5 rows × 87 columns
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\n",
+ "
\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall_returns_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Uniform'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'bitcoin'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+ "\u001b[0;31mValueError\u001b[0m: ['Uniform', 'bitcoin'] is not in list"
+ ]
+ }
+ ],
+ "source": [
+ "list(all_returns_df.columns).index(['Uniform','bitcoin'])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 82,
+ "id": "4c8bdb96-620f-418d-8d76-3cb7d633168c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "N = [i for i in range(len(all_returns_df.columns)) if all_returns_df.columns[i] in ['Uniform','bitcoin']]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 83,
+ "id": "f21e4471-a78d-42d9-8d44-782a36b80931",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[0, 2]"
+ ]
+ },
+ "execution_count": 83,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "N"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 95,
+ "id": "6cce98be-4c73-4b2b-98d2-4f995e3be60f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "dic={'a':1, 'b':2}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 98,
+ "id": "37c4c19f-21ec-4817-a142-cde2b767a179",
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "SyntaxError",
+ "evalue": "invalid syntax (1228105859.py, line 1)",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;36m File \u001b[0;32m\"/var/folders/ff/pmf9d5156jz_pr_s8ybs3x780000gn/T/ipykernel_63357/1228105859.py\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m for temp in del dic['a']:\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
+ ]
+ }
+ ],
+ "source": [
+ "for temp in del dic['a']:\n",
+ " print('yes')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 101,
+ "id": "72199468-c241-4550-9686-c4349c7c0734",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "del dic['b']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 104,
+ "id": "09e95fe9-5000-419e-be53-686ca0e88a12",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "False"
+ ]
+ },
+ "execution_count": 104,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "len(dic)!=0"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 108,
+ "id": "46b8e046-810c-4284-be8b-fb24fcf46588",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "strategy_dict = {'Uniform': {'a':2}, 'Markowitz':{'b':3}}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 113,
+ "id": "770e265a-d31d-43bd-a7f6-053a1f9cdcf3",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Uniform\n",
+ "Markowitz\n"
+ ]
+ }
+ ],
+ "source": [
+ "for name, weights in strategy_dict.items():\n",
+ " print(name)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 114,
+ "id": "ed0aea65-24c0-48a2-a957-0b1a1a4b518d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "port_returns = gen_port_rtns(rebased_df, uniform_weights_dict)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 115,
+ "id": "72ec929c-629e-4e3c-841a-9fcae6610d2c",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "date\n",
+ "2021-05-24 1.000000\n",
+ "2021-05-25 1.057165\n",
+ "2021-05-26 1.103699\n",
+ "2021-05-27 1.094057\n",
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+ " ... \n",
+ "2022-05-13 0.858603\n",
+ "2022-05-14 0.842769\n",
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+ "2022-05-16 0.868936\n",
+ "2022-05-17 0.878021\n",
+ "Length: 359, dtype: float64"
+ ]
+ },
+ "execution_count": 115,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "port_returns"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 116,
+ "id": "b0694bbc-9941-41a4-bf49-96d581f82907",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "port_returns = pd.DataFrame({'Uniform': port_returns})"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 117,
+ "id": "02b1c5fd-0b51-4b4d-9bf1-f5dcf46477a1",
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+ " -2.77% \n",
+ " 27.499B \n",
+ " \n",
+ " \n",
+ " 9 \n",
+ " ADA \n",
+ " Cardano \n",
+ " 0.773429 \n",
+ " -3.24% \n",
+ " 26.105B \n",
+ " \n",
+ " \n",
+ " 10 \n",
+ " UST \n",
+ " TerraUSD \n",
+ " 1.0009 \n",
+ " -0.09% \n",
+ " 18.528B \n",
+ " \n",
+ " \n",
+ " 11 \n",
+ " BUSD \n",
+ " Binance \n",
+ " 1.0010 \n",
+ " +0.03% \n",
+ " 17.706B \n",
+ " \n",
+ " \n",
+ " 12 \n",
+ " DOGE \n",
+ " Dogecoin \n",
+ " 0.131755 \n",
+ " -1.16% \n",
+ " 17.48B \n",
+ " \n",
+ " \n",
+ " 13 \n",
+ " AVAX \n",
+ " Avalanche \n",
+ " 58.20 \n",
+ " -4.84% \n",
+ " 15.634B \n",
+ " \n",
+ " \n",
+ " 14 \n",
+ " DOT \n",
+ " Polkadot \n",
+ " 14.96 \n",
+ " -5.06% \n",
+ " 14.779B \n",
+ " \n",
+ " \n",
+ " 15 \n",
+ " SHIB \n",
+ " SHIBA INU \n",
+ " 0.000021 \n",
+ " -4.53% \n",
+ " 11.509B \n",
+ " \n",
+ " \n",
+ " 16 \n",
+ " WBTC \n",
+ " Wrapped Bitcoin \n",
+ " 37,916.12 \n",
+ " -1.18% \n",
+ " 10.689B \n",
+ " \n",
+ " \n",
+ " 17 \n",
+ " STETH \n",
+ " Lido stETH \n",
+ " 2,748.98 \n",
+ " -1.45% \n",
+ " 9.588B \n",
+ " \n",
+ " \n",
+ " 18 \n",
+ " DAI \n",
+ " Dai \n",
+ " 0.999584 \n",
+ " +0.04% \n",
+ " 8.788B \n",
+ " \n",
+ " \n",
+ " 19 \n",
+ " MATIC \n",
+ " Polygon \n",
+ " 1.0845 \n",
+ " -3.60% \n",
+ " 8.512B \n",
+ " \n",
+ " \n",
+ " 20 \n",
+ " CRO \n",
+ " Crypto.com Coin \n",
+ " 0.332459 \n",
+ " -9.53% \n",
+ " 8.399B \n",
+ " \n",
+ " \n",
+ " 21 \n",
+ " NEAR \n",
+ " NEAR Protocol \n",
+ " 11.00 \n",
+ " -3.09% \n",
+ " 7.42B \n",
+ " \n",
+ " \n",
+ " 22 \n",
+ " WTRX \n",
+ " Wrapped TRON \n",
+ " 0.068104 \n",
+ " +7.78% \n",
+ " 6.925B \n",
+ " \n",
+ " \n",
+ " 23 \n",
+ " TRX \n",
+ " TRON \n",
+ " 0.068075 \n",
+ " +7.93% \n",
+ " 6.917B \n",
+ " \n",
+ " \n",
+ " 24 \n",
+ " LTC \n",
+ " Litecoin \n",
+ " 96.66 \n",
+ " -4.15% \n",
+ " 6.785B \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " Open \n",
+ " High \n",
+ " Low \n",
+ " Close \n",
+ " Adj Close \n",
+ " Volume \n",
+ " \n",
+ " \n",
+ " Date \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 2017-05-01 \n",
+ " 1348.300049 \n",
+ " 1434.319946 \n",
+ " 1348.300049 \n",
+ " 1421.599976 \n",
+ " 1421.599976 \n",
+ " 713624000 \n",
+ " \n",
+ " \n",
+ " 2017-05-02 \n",
+ " 1421.030029 \n",
+ " 1473.900024 \n",
+ " 1415.689941 \n",
+ " 1452.819946 \n",
+ " 1452.819946 \n",
+ " 477337984 \n",
+ " \n",
+ " \n",
+ " 2017-05-03 \n",
+ " 1453.780029 \n",
+ " 1492.770020 \n",
+ " 1447.489990 \n",
+ " 1490.089966 \n",
+ " 1490.089966 \n",
+ " 583795968 \n",
+ " \n",
+ " \n",
+ " 2017-05-04 \n",
+ " 1490.719971 \n",
+ " 1608.910034 \n",
+ " 1490.719971 \n",
+ " 1537.670044 \n",
+ " 1537.670044 \n",
+ " 933548992 \n",
+ " \n",
+ " \n",
+ " 2017-05-05 \n",
+ " 1540.869995 \n",
+ " 1618.030029 \n",
+ " 1530.310059 \n",
+ " 1555.449951 \n",
+ " 1555.449951 \n",
+ " 946035968 \n",
+ " \n",
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+ " 38120.300781 \n",
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+ " 39241.121094 \n",
+ " 39241.121094 \n",
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+ " \n",
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+ " 40269.464844 \n",
+ " 38941.421875 \n",
+ " 39773.828125 \n",
+ " 39773.828125 \n",
+ " 33903704907 \n",
+ " \n",
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+ " 2022-04-29 \n",
+ " 39768.617188 \n",
+ " 39887.269531 \n",
+ " 38235.535156 \n",
+ " 38609.824219 \n",
+ " 38609.824219 \n",
+ " 30882994649 \n",
+ " \n",
+ " \n",
+ " 2022-04-30 \n",
+ " 38605.859375 \n",
+ " 38771.210938 \n",
+ " 37697.941406 \n",
+ " 37714.875000 \n",
+ " 37714.875000 \n",
+ " 23895713731 \n",
+ " \n",
+ " \n",
+ " 2022-05-01 \n",
+ " 37715.718750 \n",
+ " 38116.277344 \n",
+ " 37610.785156 \n",
+ " 38004.464844 \n",
+ " 38004.464844 \n",
+ " 26912634880 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
1827 rows × 6 columns
\n",
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " ADA \n",
+ " AVAX \n",
+ " BNB \n",
+ " BTC \n",
+ " BUSD \n",
+ " CRO \n",
+ " DAI \n",
+ " DOGE \n",
+ " DOT \n",
+ " ETH \n",
+ " ... \n",
+ " SHIB \n",
+ " SOL \n",
+ " STETH \n",
+ " TRX \n",
+ " USDC \n",
+ " USDT \n",
+ " UST \n",
+ " WBTC \n",
+ " WTRX \n",
+ " XRP \n",
+ " \n",
+ " \n",
+ " Date \n",
+ " \n",
+ " \n",
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+ " 2020-10-01 \n",
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+ " NaN \n",
+ " NaN \n",
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+ " NaN \n",
+ " NaN \n",
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+ " 76.290001 \n",
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+ " NaN \n",
+ " NaN \n",
+ " \n",
+ " \n",
+ " 2020-09-30 \n",
+ " NaN \n",
+ " NaN \n",
+ " NaN \n",
+ " NaN \n",
+ " NaN \n",
+ " NaN \n",
+ " NaN \n",
+ " NaN \n",
+ " NaN \n",
+ " NaN \n",
+ " ... \n",
+ " NaN \n",
+ " 2.07 \n",
+ " NaN \n",
+ " 0.70 \n",
+ " 0.0001 \n",
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+ " 75.930000 \n",
+ " NaN \n",
+ " NaN \n",
+ " NaN \n",
+ " \n",
+ " \n",
+ " 2022-04-29 \n",
+ " NaN \n",
+ " NaN \n",
+ " NaN \n",
+ " NaN \n",
+ " NaN \n",
+ " NaN \n",
+ " NaN \n",
+ " NaN \n",
+ " NaN \n",
+ " NaN \n",
+ " ... \n",
+ " NaN \n",
+ " 4.82 \n",
+ " NaN \n",
+ " 0.30 \n",
+ " NaN \n",
+ " NaN \n",
+ " 54.340000 \n",
+ " NaN \n",
+ " NaN \n",
+ " NaN \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
1260 rows × 25 columns
\n",
+ "
Streamlit Component
+ You need to enable JavaScript to run this app.
+
+
+
+
diff --git a/streamlit_custom_slider/frontend/src/CustomSlider.tsx b/streamlit_custom_slider/frontend/src/CustomSlider.tsx
new file mode 100644
index 0000000000000000000000000000000000000000..492d4fe7a9ee0f75d5a510418e30014fa931f5ba
--- /dev/null
+++ b/streamlit_custom_slider/frontend/src/CustomSlider.tsx
@@ -0,0 +1,43 @@
+import React, { useEffect, useState } from "react"
+import { ComponentProps, Streamlit, withStreamlitConnection } from "streamlit-component-lib"
+import { Slider } from "baseui/slider"
+
+/**
+ * We can use a Typescript interface to destructure the arguments from Python
+ * and validate the types of the input
+ */
+interface PythonArgs {
+ label: string
+ minValue?: number
+ maxValue?: number
+ initialValue: number[]
+}
+
+/**
+ * No more props manipulation in the code.
+ * We store props in state and pass value directly to underlying Slider
+ * and then back to Streamlit.
+ */
+const CustomSlider = (props: ComponentProps) => {
+ // Destructure using Typescript interface
+ // This ensures typing validation for received props from Python
+ const { label, minValue, maxValue, initialValue }: PythonArgs = props.args
+ const [value, setValue] = useState(initialValue)
+
+ useEffect(() => Streamlit.setFrameHeight())
+
+ return (
+ <>
+ {label}
+ value && setValue(value)}
+ onFinalChange={({ value }) => Streamlit.setComponentValue(value)}
+ min={minValue}
+ max={maxValue}
+ />
+
+ )
+}
+
+export default withStreamlitConnection(CustomSlider)
diff --git a/streamlit_custom_slider/frontend/src/index.tsx b/streamlit_custom_slider/frontend/src/index.tsx
new file mode 100644
index 0000000000000000000000000000000000000000..f8b8863ce2082ca728467c87615a213183fe4ab1
--- /dev/null
+++ b/streamlit_custom_slider/frontend/src/index.tsx
@@ -0,0 +1,22 @@
+import React from "react"
+import ReactDOM from "react-dom"
+import CustomSlider from "./CustomSlider"
+
+// Lots of import to define a Styletron engine and load the light theme of baseui
+import { Client as Styletron } from "styletron-engine-atomic"
+import { Provider as StyletronProvider } from "styletron-react"
+import { ThemeProvider, LightTheme } from "baseui"
+
+const engine = new Styletron()
+
+// Wrap your CustomSlider with the baseui them
+ReactDOM.render(
+
+
+
+
+
+ ,
+ document.getElementById("root")
+)
diff --git a/streamlit_custom_slider/frontend/src/react-app-env.d.ts b/streamlit_custom_slider/frontend/src/react-app-env.d.ts
new file mode 100644
index 0000000000000000000000000000000000000000..6431bc5fc6b2c932dfe5d0418fc667b86c18b9fc
--- /dev/null
+++ b/streamlit_custom_slider/frontend/src/react-app-env.d.ts
@@ -0,0 +1 @@
+///