cyberosa
commited on
Commit
·
86d971d
1
Parent(s):
aa12eb6
notebooks improvements
Browse files- nbs/confidence_analysis.ipynb +0 -0
- nbs/weekly_analysis.ipynb +114 -6
- nbs/win_probability_analysis.ipynb +0 -0
nbs/confidence_analysis.ipynb
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nbs/weekly_analysis.ipynb
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@@ -50,6 +50,68 @@
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"all_trades = pd.read_parquet('../data/all_trades_profitability.parquet')"
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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@@ -128,7 +190,7 @@
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},
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{
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"cell_type": "code",
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-
"execution_count":
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"metadata": {},
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"outputs": [
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{
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@@ -175,6 +237,26 @@
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"inc_tools = [\n",
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@@ -1594,9 +1676,21 @@
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},
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{
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"cell_type": "code",
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"execution_count":
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"metadata": {},
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"outputs": [
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"source": [
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"del tools\n",
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"del tools_inc\n",
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},
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{
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"cell_type": "code",
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"execution_count":
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"metadata": {},
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"outputs": [
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-
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},
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{
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"cell_type": "code",
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"all_trades = pd.read_parquet('../data/all_trades_profitability.parquet')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"<class 'pandas.core.frame.DataFrame'>\n",
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"RangeIndex: 92662 entries, 0 to 92661\n",
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"Data columns (total 19 columns):\n",
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" # Column Non-Null Count Dtype \n",
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"--- ------ -------------- ----- \n",
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" 0 trader_address 92662 non-null object \n",
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" 1 trade_id 92662 non-null object \n",
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" 2 creation_timestamp 92662 non-null datetime64[ns, UTC]\n",
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" 3 title 92662 non-null object \n",
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" 4 market_status 92662 non-null object \n",
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" 5 collateral_amount 92662 non-null float64 \n",
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" 6 outcome_index 92662 non-null object \n",
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" 7 trade_fee_amount 92662 non-null float64 \n",
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" 8 outcomes_tokens_traded 92662 non-null float64 \n",
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" 9 current_answer 92662 non-null int64 \n",
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" 10 is_invalid 92662 non-null bool \n",
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" 11 winning_trade 92662 non-null bool \n",
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" 12 earnings 92662 non-null float64 \n",
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" 13 redeemed 92662 non-null bool \n",
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" 14 redeemed_amount 92662 non-null float64 \n",
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" 15 num_mech_calls 92662 non-null int64 \n",
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" 16 mech_fee_amount 92662 non-null float64 \n",
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" 17 net_earnings 92662 non-null float64 \n",
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" 18 roi 92662 non-null float64 \n",
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"dtypes: bool(3), datetime64[ns, UTC](1), float64(8), int64(2), object(5)\n",
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"memory usage: 11.6+ MB\n"
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]
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}
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],
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"source": [
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"all_trades.info()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"Timestamp('2023-07-12 15:17:25+0000', tz='UTC')"
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]
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},
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"all_trades.creation_timestamp.min()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'2023-07-12 11:58:40'"
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]
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},
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"execution_count": 6,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"tools.request_time.min()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"inc_tools = [\n",
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [
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{
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"ename": "NameError",
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"evalue": "name 'tools_inc' is not defined",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
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"Cell \u001b[0;32mIn[7], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m tools\n\u001b[0;32m----> 2\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m \u001b[43mtools_inc\u001b[49m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m error\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m error_total\n",
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"\u001b[0;31mNameError\u001b[0m: name 'tools_inc' is not defined"
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]
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}
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],
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"source": [
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"del tools\n",
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"del tools_inc\n",
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"31"
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]
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},
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"execution_count": 8,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"del all_trades\n",
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"gc.collect()"
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]
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},
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{
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"cell_type": "code",
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nbs/win_probability_analysis.ipynb
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