From bd1ba3ef71b6cf81aa8b04d682eb4d2a4625fab6 Mon Sep 17 00:00:00 2001
From: Nima <139926760+amirabbaskamali@users.noreply.github.com>
Date: Sat, 27 Dec 2025 17:32:13 +0330
Subject: [PATCH] feat: implement full asset analysis pipeline with synthetic
data
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit
- Added synthetic data generation for automotive and real estate
- Implemented dedicated processing logic for gold, coin, and stock
- Aligned all assets to 1360–1402 timeframe
- Included cumulative return and log-scale visualizations in analysis.ipynb
- Updated utils for monthly conversion and change calculation
---
AssetAnalysis/LICENSE | 21 +
AssetAnalysis/analysis.ipynb | 1008 ++
.../__pycache__/automotive.cpython-310.pyc | Bin 0 -> 819 bytes
AssetAnalysis/automotive/automotive.py | 29 +
AssetAnalysis/coin/Imami_coin.csv | 4337 ++++++
.../__pycache__/gold_coin.cpython-310.pyc | Bin 0 -> 812 bytes
AssetAnalysis/coin/gold_coin.py | 30 +
AssetAnalysis/common/utils.py | 42 +
AssetAnalysis/df_final.csv | 514 +
AssetAnalysis/gold/24_carat_gold.csv | 2706 ++++
.../gold/__pycache__/gold.cpython-310.pyc | Bin 0 -> 679 bytes
AssetAnalysis/gold/gold.py | 23 +
AssetAnalysis/main.py | 5 +
.../__pycache__/real_estates.cpython-310.pyc | Bin 0 -> 619 bytes
AssetAnalysis/real_estate/real_estates.py | 19 +
.../stock/__pycache__/stocks.cpython-310.pyc | Bin 0 -> 854 bytes
AssetAnalysis/stock/stock.csv | 2363 +++
AssetAnalysis/stock/stocks.py | 28 +
AssetAnalysis/usd/USD_IRR.csv | 12721 ++++++++++++++++
AssetAnalysis/usd/usd.py | 0
20 files changed, 23846 insertions(+)
create mode 100644 AssetAnalysis/LICENSE
create mode 100644 AssetAnalysis/analysis.ipynb
create mode 100644 AssetAnalysis/automotive/__pycache__/automotive.cpython-310.pyc
create mode 100644 AssetAnalysis/automotive/automotive.py
create mode 100644 AssetAnalysis/coin/Imami_coin.csv
create mode 100644 AssetAnalysis/coin/__pycache__/gold_coin.cpython-310.pyc
create mode 100644 AssetAnalysis/coin/gold_coin.py
create mode 100644 AssetAnalysis/common/utils.py
create mode 100644 AssetAnalysis/df_final.csv
create mode 100644 AssetAnalysis/gold/24_carat_gold.csv
create mode 100644 AssetAnalysis/gold/__pycache__/gold.cpython-310.pyc
create mode 100644 AssetAnalysis/gold/gold.py
create mode 100644 AssetAnalysis/main.py
create mode 100644 AssetAnalysis/real_estate/__pycache__/real_estates.cpython-310.pyc
create mode 100644 AssetAnalysis/real_estate/real_estates.py
create mode 100644 AssetAnalysis/stock/__pycache__/stocks.cpython-310.pyc
create mode 100644 AssetAnalysis/stock/stock.csv
create mode 100644 AssetAnalysis/stock/stocks.py
create mode 100644 AssetAnalysis/usd/USD_IRR.csv
create mode 100644 AssetAnalysis/usd/usd.py
diff --git a/AssetAnalysis/LICENSE b/AssetAnalysis/LICENSE
new file mode 100644
index 0000000..6088fce
--- /dev/null
+++ b/AssetAnalysis/LICENSE
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2024 Vahid Naderi
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/AssetAnalysis/analysis.ipynb b/AssetAnalysis/analysis.ipynb
new file mode 100644
index 0000000..a56a01c
--- /dev/null
+++ b/AssetAnalysis/analysis.ipynb
@@ -0,0 +1,1008 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "972102d4",
+ "metadata": {},
+ "source": [
+ "import and read Data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "d142afdd",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd \n",
+ "import numpy as np \n",
+ "from common.utils import daily_to_monthly , calculate_change\n",
+ "from automotive.automotive import generate_car_index\n",
+ "from real_estate.real_estates import generate_housing_index\n",
+ "from functools import reduce\n",
+ "import seaborn as sns\n",
+ "import matplotlib.pyplot as plt\n",
+ "from gold.gold import generate_missing_gold\n",
+ "from stock.stocks import generate_missing_stock\n",
+ "from coin.gold_coin import generate_missing_imami\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "a33312d1",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " Date | \n",
+ " Price | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 0 | \n",
+ " 1360/07 | \n",
+ " 270.0 | \n",
+ "
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+ " \n",
+ " | 1 | \n",
+ " 1360/08 | \n",
+ " 270.0 | \n",
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+ " \n",
+ " | 4 | \n",
+ " 1360/11 | \n",
+ " 270.0 | \n",
+ "
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+ " \n",
+ " | ... | \n",
+ " ... | \n",
+ " ... | \n",
+ "
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+ " \n",
+ " | 505 | \n",
+ " 1402/08 | \n",
+ " 511658.0 | \n",
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+ " \n",
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+ " 504426.0 | \n",
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+ " \n",
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+ " \n",
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+ " | 509 | \n",
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+ "
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+ " \n",
+ "
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+ "
510 rows × 2 columns
\n",
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "dfs = [\n",
+ " df_usd[[\"Date\", \"Price\"]].rename(columns={\"Price\": \"usd\"}),\n",
+ " df_gold[[\"Date\", \"Price\"]].rename(columns={\"Price\": \"gold\"}),\n",
+ " df_imami[[\"Date\", \"Price\"]].rename(columns={\"Price\": \"imami\"}),\n",
+ " df_stock[[\"Date\", \"Price\"]].rename(columns={\"Price\": \"stock\"}),\n",
+ "]\n",
+ "\n",
+ "\n",
+ "df_merged = reduce(\n",
+ " lambda left, right: pd.merge(left, right, on=\"Date\", how=\"inner\"),\n",
+ " dfs\n",
+ ")\n",
+ "\n",
+ "corr_matrix = df_merged.drop(columns=\"Date\").corr()\n",
+ "print(corr_matrix.round(3))\n",
+ "\n",
+ "plt.figure(figsize=(8, 6))\n",
+ "sns.heatmap(\n",
+ " corr_matrix,\n",
+ " annot=True,\n",
+ " cmap=\"coolwarm\",\n",
+ " center=0,\n",
+ " square=True,\n",
+ " fmt=\".3f\"\n",
+ ")\n",
+ "plt.title(\"Correlation Matrix (1393–1402)\", fontsize=14)\n",
+ "plt.tight_layout()\n",
+ "plt.show()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "4b65e398",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "iamami / usd = 383.4\n",
+ "gold / usd = 48.5\n",
+ "stock / usd = 3.1\n"
+ ]
+ }
+ ],
+ "source": [
+ "\n",
+ "merged = df_usd[[\"Date\", \"Price\"]].rename(columns={\"Price\": \"usd\"})\n",
+ "merged = merged.merge(df_imami[[\"Date\", \"Price\"]].rename(columns={\"Price\": \"imami\"}), on=\"Date\")\n",
+ "merged = merged.merge(df_gold[[\"Date\", \"Price\"]].rename(columns={\"Price\": \"gold\"}), on=\"Date\")\n",
+ "merged = merged.merge(df_stock[[\"Date\", \"Price\"]].rename(columns={\"Price\": \"stock\"}), on=\"Date\")\n",
+ "\n",
+ "\n",
+ "imami_usd = (merged[\"imami\"] / merged[\"usd\"]).mean()\n",
+ "gold_usd = (merged[\"gold\"] / merged[\"usd\"]).mean()\n",
+ "stock_usd = (merged[\"stock\"] / merged[\"usd\"]).mean()\n",
+ "\n",
+ "print(f\"iamami / usd = {imami_usd:.1f}\")\n",
+ "print(f\"gold / usd = {gold_usd:.1f}\")\n",
+ "print(f\"stock / usd = {stock_usd:.1f}\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "07a29157",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "month len 513\n",
+ "first m 1360/07\n",
+ "last m 1403/03\n"
+ ]
+ }
+ ],
+ "source": [
+ "missing_gold = generate_missing_gold(\n",
+ " usd_df=df_usd, \n",
+ " start_date=\"1393/02\", \n",
+ " ratio=48.5\n",
+ ")\n",
+ "\n",
+ "\n",
+ "df_gold_full = pd.concat(\n",
+ " [missing_gold, df_gold],\n",
+ " ignore_index=True\n",
+ ")\n",
+ "\n",
+ "\n",
+ "df_gold_full = df_gold_full.sort_values(\"Date\").reset_index(drop=True)\n",
+ "\n",
+ "\n",
+ "print(\"month len \", len(df_gold_full))\n",
+ "print(\"first m \", df_gold_full[\"Date\"].iloc[0])\n",
+ "print(\"last m \", df_gold_full[\"Date\"].iloc[-1])\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "71c49f6e",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "month len: 513\n",
+ "first m : 1360/07\n",
+ "last m : 1403/03\n"
+ ]
+ }
+ ],
+ "source": [
+ "missing_stock = generate_missing_stock(\n",
+ " usd_df=df_usd,\n",
+ " start_date=\"1393/04\", \n",
+ " ratio=3.1\n",
+ ")\n",
+ "\n",
+ "\n",
+ "df_stock_full = pd.concat(\n",
+ " [missing_stock, df_stock],\n",
+ " ignore_index=True\n",
+ ")\n",
+ "\n",
+ "\n",
+ "df_stock_full = df_stock_full.sort_values(\"Date\").reset_index(drop=True)\n",
+ "\n",
+ "\n",
+ "print(\"month len:\", len(df_stock_full))\n",
+ "print(\"first m :\", df_stock_full[\"Date\"].iloc[0])\n",
+ "print(\"last m :\", df_stock_full[\"Date\"].iloc[-1])\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "06fda698",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "month len 513\n",
+ "first m 1360/07\n",
+ "last m 1403/03\n"
+ ]
+ }
+ ],
+ "source": [
+ "missing_imami = generate_missing_imami(\n",
+ " usd_df=df_usd,\n",
+ " start_date=\"1389/01\", \n",
+ " ratio=383.4\n",
+ ")\n",
+ "\n",
+ "\n",
+ "df_imami_full = pd.concat(\n",
+ " [missing_imami, df_imami],\n",
+ " ignore_index=True\n",
+ ")\n",
+ "\n",
+ "\n",
+ "df_imami_full = df_imami_full.sort_values(\"Date\").reset_index(drop=True)\n",
+ "\n",
+ "\n",
+ "print(\"month len\", len(df_imami_full))\n",
+ "print(\"first m\", df_imami_full[\"Date\"].iloc[0])\n",
+ "print(\"last m\", df_imami_full[\"Date\"].iloc[-1])\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "bd7279c2",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " Date Price\n",
+ "0 1360/07 0.00\n",
+ "1 1360/08 0.00\n",
+ "2 1360/09 0.00\n",
+ "3 1360/10 0.00\n",
+ "4 1360/11 0.00\n",
+ ".. ... ...\n",
+ "504 1402/07 90.03\n",
+ "505 1402/08 91.69\n",
+ "506 1402/09 92.01\n",
+ "507 1402/10 91.81\n",
+ "508 1402/11 97.34\n",
+ "\n",
+ "[509 rows x 2 columns]\n",
+ " Date Price\n",
+ "0 1360/07 0.00\n",
+ "1 1360/08 0.00\n",
+ "2 1360/09 0.00\n",
+ "3 1360/10 0.00\n",
+ "4 1360/11 0.00\n",
+ ".. ... ...\n",
+ "504 1402/07 90.51\n",
+ "505 1402/08 93.17\n",
+ "506 1402/09 95.31\n",
+ "507 1402/10 96.96\n",
+ "508 1402/11 98.67\n",
+ "\n",
+ "[509 rows x 2 columns]\n"
+ ]
+ }
+ ],
+ "source": [
+ "df_car = generate_car_index(df_usd)\n",
+ "df_housing = generate_housing_index(df_usd)\n",
+ "\n",
+ "print(df_car.head(-1))\n",
+ "print(df_housing.head(-1))\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "8055eaff",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_stock_full = df_stock_full.rename(columns={\"Price\": \"stock_price\"})\n",
+ "df_gold_full = df_gold_full.rename(columns={\"Price\": \"gold_price\"})\n",
+ "df_usd = df_usd.rename(columns={\"Price\": \"usd_price\"})\n",
+ "df_housing = df_housing.rename(columns={\"Price\": \"housing_price\"})\n",
+ "df_car = df_car.rename(columns={\"Price\": \"car_price\"})\n",
+ "df_imami_full = df_imami_full.rename(columns={\"Price\": \"imami_price\"})\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "18e4537c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "base_df = df_stock_full.copy()\n",
+ "\n",
+ "df_final = base_df.copy()\n",
+ "\n",
+ "df_final = df_final.merge(\n",
+ " df_gold_full,\n",
+ " on=\"Date\",\n",
+ " how=\"left\"\n",
+ ")\n",
+ "\n",
+ "df_final = df_final.merge(\n",
+ " df_usd,\n",
+ " on=\"Date\",\n",
+ " how=\"left\"\n",
+ ")\n",
+ "\n",
+ "df_final = df_final.merge(\n",
+ " df_housing,\n",
+ " on=\"Date\",\n",
+ " how=\"left\"\n",
+ ")\n",
+ "\n",
+ "df_final = df_final.merge(\n",
+ " df_car,\n",
+ " on=\"Date\",\n",
+ " how=\"left\"\n",
+ ")\n",
+ "\n",
+ "df_final = df_final.merge(\n",
+ " df_imami_full,\n",
+ " on=\"Date\",\n",
+ " how=\"left\"\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "1668be9d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_final = df_final.sort_values(\"Date\").reset_index(drop=True)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "341b6856",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_final = df_final.sort_values(\"Date\").reset_index(drop=True)\n",
+ "\n",
+ "df_final.ffill(inplace=True)\n",
+ "df_final.bfill(inplace=True)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "ea1700ec",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
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+ " \n",
+ " | 3 | \n",
+ " 1360/10 | \n",
+ " 100.000000 | \n",
+ " 100.000000 | \n",
+ " 100.000000 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ " 102.032598 | \n",
+ "
\n",
+ " \n",
+ " | 4 | \n",
+ " 1360/11 | \n",
+ " 110.035842 | \n",
+ " 100.000000 | \n",
+ " 100.000000 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ " 98.552790 | \n",
+ "
\n",
+ " \n",
+ " | ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ "
\n",
+ " \n",
+ " | 508 | \n",
+ " 1402/11 | \n",
+ " 251486.379928 | \n",
+ " 283714.616266 | \n",
+ " 207160.000000 | \n",
+ " 9867.0 | \n",
+ " 9734.0 | \n",
+ " 312785.076426 | \n",
+ "
\n",
+ " \n",
+ " | 509 | \n",
+ " 1402/12 | \n",
+ " 253775.149343 | \n",
+ " 307483.046964 | \n",
+ " 219059.259259 | \n",
+ " 10000.0 | \n",
+ " 10000.0 | \n",
+ " 339299.720697 | \n",
+ "
\n",
+ " \n",
+ " | 510 | \n",
+ " 1403/01 | \n",
+ " 267259.617682 | \n",
+ " 360772.088583 | \n",
+ " 219059.259259 | \n",
+ " 10000.0 | \n",
+ " 10000.0 | \n",
+ " 404790.336311 | \n",
+ "
\n",
+ " \n",
+ " | 511 | \n",
+ " 1403/02 | \n",
+ " 262015.173238 | \n",
+ " 351832.172585 | \n",
+ " 219059.259259 | \n",
+ " 10000.0 | \n",
+ " 10000.0 | \n",
+ " 397012.209936 | \n",
+ "
\n",
+ " \n",
+ " | 512 | \n",
+ " 1403/03 | \n",
+ " 248193.309438 | \n",
+ " 340051.844215 | \n",
+ " 219059.259259 | \n",
+ " 10000.0 | \n",
+ " 10000.0 | \n",
+ " 386261.096551 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
513 rows × 7 columns
\n",
+ "