Detailed Instructions

Begin by performing a thorough data audit of your current portfolio tracker. This involves logging into your existing platform and navigating to all relevant sections to identify where your holdings, transactions, and metadata are stored. You must locate and verify the following key data points: current positions with tickers and quantities, cost basis information, transaction history (buys, sells, dividends), and any custom tags or notes.

• Sub-step 1: Generate Reports: Export all available standard reports, such as Holdings Summary, Transaction History, and Performance Report, typically found in the 'Reports' or 'Export' section of your tracker.
• Sub-step 2: Check for Discrepancies: Manually compare the totals in these reports against your dashboard to ensure data integrity. Look for missing dividends or corporate actions.
• Sub-step 3: Consolidate Files: Save all exported files (commonly in CSV, XLSX, or PDF format) into a single dedicated folder on your computer, naming them clearly (e.g., TrackerA_Transactions_2024.csv).

Tip: If your tracker lacks a bulk export, you may need to export data per account or timeframe. Document this process for future reference.

Detailed Instructions

Raw export files often contain inconsistent formatting, extra headers, or platform-specific codes that can cause import errors. The goal is data normalization. Open your primary transaction CSV in a spreadsheet application like Excel or Google Sheets. First, ensure all columns use standardized headers. Common required fields are Date, Action (e.g., Buy, Sell, Dividend), Symbol, Quantity, Price, and Commission.

• Sub-step 1: Standardize Date Formats: Convert all date columns to a single format, preferably YYYY-MM-DD (ISO 8601). Use a formula like =TEXT(A2, "yyyy-mm-dd") in a new column if needed.
• Sub-step 2: Validate Tickers and Actions: Check that all ticker symbols are correct and not truncated. Ensure Action fields use a consistent vocabulary (e.g., always "BUY" not "Buy" or "Bought").
• Sub-step 3: Handle Missing Data: Identify cells with #N/A or blanks. For cost basis, research and input missing prices. For commissions, input 0 if truly free, but do not leave the cell empty.

Tip: Use spreadsheet Find and Replace and Text to Columns features extensively. Save the cleaned version with a new filename to preserve the original raw data.

Detailed Instructions

Every portfolio tracker has a specific import schema. Download the official CSV template or review the import documentation from your destination platform (e.g., Sharesight, Portfolio Performance, or a custom spreadsheet). This step is critical for schema mapping. Compare your cleaned file's column headers with the required columns in the template. You will likely need to rename, reorder, or even derive new columns.

• Sub-step 1: Rename Columns: Change your header row to match exactly. For example, change Ticker to Symbol or Trade Date to Date.
• Sub-step 2: Create Missing Columns: If the destination requires a Currency column for each transaction and your data doesn't have it, add a new column and populate all rows with your base currency, e.g., USD.
• Sub-step 3: Transform Data: Some platforms require a unique identifier or a specific calculation. You might need a formula to create a Total Amount column: =(Quantity*Price)+Commission.

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// Example formula in Google Sheets for a Total Amount column (assuming Quantity in E, Price in F, Commission in G):
=ARRAYFORMULA(IF(E2:E="", "", (E2:E*F2:F)+G2:G))

Tip: Perform this mapping on a copy of your cleaned file. Keep a log of which of your columns map to which destination columns.

Detailed Instructions

Before migrating your entire portfolio history, conduct a pilot import to catch any remaining formatting or logical errors. Create a subset of your final mapped CSV file containing only 10-15 representative transactions that cover different Action types (Buy, Sell, Dividend, Split). This allows for quick iteration and troubleshooting.

• Sub-step 1: Initiate Import: In your new tracker, navigate to the import function (often under Settings > Import Data). Upload your test file.
• Sub-step 2: Analyze Error Reports: The platform will typically provide a validation report. Scrutinize any errors regarding date formats, unrecognized symbols, or invalid numeric values. Common errors include a price of 0 for a buy or a sell quantity exceeding held quantity.
• Sub-step 3: Verify Imported Data: If the import succeeds, meticulously check the test account in the new tracker. Verify that cost basis, market value, and realized gains/losses match your calculations from the source data for those test transactions.

Tip: If errors persist, correct them in your master file and create a new test file. Only proceed to the full import once the test batch is 100% accurate. This saves immense time correcting errors in a full dataset.

Detailed Instructions

Begin by exporting your complete portfolio data from your source tracker. Most platforms like Personal Capital, Mint, or broker-specific tools offer CSV or XLSX export options. The goal is to create a clean, structured dataset. Crucial data points to include are: ticker symbols, asset names, quantities, average cost basis, and acquisition dates.

• Sub-step 1: Locate the export function in your current platform's settings or portfolio overview section. Select 'Export All Transactions' or 'Export Holdings'.
• Sub-step 2: Choose the correct date range to ensure you capture your entire transaction history, not just current holdings. For a complete audit trail, export data from your account's inception.
• Sub-step 3: Save the file in a universally compatible format like portfolio_export_20231015.csv. Open the file in a spreadsheet editor to perform a preliminary visual check for obvious gaps or formatting errors.

Tip: If your platform lacks a direct export, you may need to use a third-party data aggregation API or manually compile statements. Always keep a backup of the original export.

Detailed Instructions

This step is critical to prevent garbage-in-garbage-out (GIGO) scenarios. Use a spreadsheet or a simple script to validate each column. Data validation involves checking for null values, incorrect formats, and logical inconsistencies (e.g., negative share quantities).

• Sub-step 1: Standardize ticker symbols. Ensure all symbols use a consistent format (e.g., AAPL, not Apple Inc.). Use a financial data library or manual lookup to correct any discrepancies.
• Sub-step 2: Verify numerical data. Check that cost basis and quantity columns contain only numbers. Look for outliers—a cost basis of $0.00 for a stock likely indicates missing data.
• Sub-step 3: Cross-reference totals. Manually calculate the total value of a few positions using Quantity * Current Price and compare it to your tracker's reported value to spot calculation errors.

You can use a Python script with pandas for automated checks:

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import pandas as pd
df = pd.read_csv('portfolio_export.csv')
# Check for missing tickers
missing_tickers = df[df['Ticker'].isna()]
print(f'Rows with missing tickers: {len(missing_tickers)}')
# Check for non-positive quantities
invalid_qty = df[df['Quantity'] <= 0]
print(f'Rows with invalid quantities: {len(invalid_qty)}')

Tip: Pay special attention to corporate actions like splits and dividends; ensure they are correctly represented in your transaction history.

Detailed Instructions

Every portfolio tracker has a specific import template with required and optional fields. Download this template from your destination platform (e.g., Kubera, Sharesight, or Yahoo Finance). Field mapping is the process of transforming your source column headers to match the target's expected headers.

• Sub-step 1: Acquire the target template. Log into your new tracker and navigate to the import section. Download the sample CSV file to understand the required column order and naming conventions (e.g., Symbol, Shares, PurchasePrice).
• Sub-step 2: Create a transformation script or spreadsheet mapping. For example, if your source uses Ticker and the target uses Symbol, you must rename the column. Similarly, convert date formats to YYYY-MM-DD.
• Sub-step 3: Handle discrepancies in data models. If your target requires a Security Type field (e.g., STOCK, ETF, CRYPTO) that your source lacks, you may need to enrich your data using a lookup table or manual entry.

A simple mapping command using csvkit might look like:

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csvcut -c "Ticker,Quantity,AverageCost" source.csv | csvrename -n "Symbol,Shares,PurchasePrice" > mapped_portfolio.csv

Tip: Perform this mapping on a small subset of data first. Import the test file to confirm the target tracker accepts the format before processing your entire history.

Detailed Instructions

Never import your entire portfolio in one go. Start with a test import using a small, representative sample—such as 5-10 holdings. This allows you to verify that cost basis, performance metrics, and tax lots are being interpreted correctly by the new system. After importing, conduct a line-by-line reconciliation.

• Sub-step 1: Create a test file. Extract 5-10 rows from your mapped CSV file, ensuring it includes different asset types (e.g., stocks, ETFs, cash). Save it as test_import.csv.
• Sub-step 2: Upload to the target tracker. Use the platform's import wizard. Note any errors or warnings it reports, such as unrecognized symbols or invalid dates.
• Sub-step 3: Verify imported data. In the new tracker, check that the total value, individual position sizes, and cost basis for your test holdings match your source records within an acceptable tolerance (e.g., 0.01%). Calculate the difference for each holding.

If you find a discrepancy in the cost basis for AAPL, for example, the issue might be date formatting. Correct the master file and repeat the test. Only proceed with the full-scale import once the test data is 100% accurate. After the full import, run a final audit comparing portfolio totals and generate a transaction report for the first month to ensure all activity is captured.

Tip: Keep your source data, mapped files, and import logs archived for at least one tax year for audit purposes.