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Use context.portfolio to inspect strategy performance and positions during a run. Use context.account for account-level leverage and margin fields. Use context.record(...) to write custom metrics into the final performance output.

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Portfolio fields

context.portfolio is a Portfolio object updated as the simulation runs. Common fields:
FieldMeaning
starting_cashStarting cash balance
cashCurrent cash
portfolio_valueCash plus position value
pnlProfit and loss
returnsCurrent return value
cash_flowCash changes
positions_valueTotal market value of positions
positions_exposureTotal exposure of positions
positionsNested position storage by exchange/account/asset in current runtime
Example: 
async def handle_data(context, data):
    cash = context.portfolio.cash
    value = context.portfolio.portfolio_value

    if cash > 10_000:
        ...

    context.record(cash=cash, portfolio_value=value)

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Position helpers

Prefer helper methods when you need amounts or values for one asset.

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await context.portfolio.get_asset_positions(asset, exchange_name=None, trading_account_id=None)

Return positions that match the underlying asset id. This can aggregate across exchange listings of the same underlying asset. 
positions = await context.portfolio.get_asset_positions(context.asset)

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await context.portfolio.get_exchange_asset_positions(asset, exchange_name=None, trading_account_id=None)

Return positions that match the exact exchange asset SID. 
positions = await context.portfolio.get_exchange_asset_positions(
    context.asset,
    exchange_name="LIME",
)

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Amount and value helpers

amount = await context.portfolio.get_asset_positions_amount(context.asset)
value = await context.portfolio.get_asset_positions_value(context.asset)

exchange_amount = await context.portfolio.get_exchange_asset_positions_amount(
    context.asset,
    exchange_name="LIME",
)
exchange_value = await context.portfolio.get_exchange_asset_positions_value(
    context.asset,
    exchange_name="LIME",
)
Use the exchange_asset versions when the exact listing matters. Use the asset versions when you want to treat listings of the same underlying asset together.

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Position fields

Each Position has:
FieldMeaning
assetExchange asset
amountCurrent amount
cost_basisAverage cost per share or contract
last_sale_priceLast synced price
last_sale_dateLast sale datetime, when available
positions = await context.portfolio.get_asset_positions(context.asset)
for position in positions:
    print(position.amount, position.cost_basis, position.last_sale_price)

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Account fields

context.account contains account and leverage metrics. Common fields:
FieldMeaning
settled_cashSettled cash
buying_powerBuying power
equity_with_loanEquity with loan value
total_positions_valueTotal position value
total_positions_exposureTotal position exposure
available_fundsAvailable funds
excess_liquidityExcess liquidity
leverageGross leverage
net_leverageNet leverage
net_liquidationNet liquidation value
Example: 
from ziplime.finance.execution import MarketOrder

if context.account.leverage > 1.5:
    await context.order_target_percent(context.asset, 0.0, style=MarketOrder())

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Recording custom metrics

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context.record(*args, **kwargs)

Recorded values are added to the daily performance output and are available in analyze. 
context.record(
    signal=signal,
    short_ma=short_ma,
    long_ma=long_ma,
    cash=context.portfolio.cash,
)
You can also pass alternating positional name/value pairs: 
context.record("signal", signal, "cash", context.portfolio.cash)
Prefer keyword arguments for readability.

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Analyze recorded values

def analyze(context, perf):
    print(perf[["portfolio_value", "cash", "signal"]].tail())
perf is produced after the run. Its exact columns depend on the metrics set and on the names you recorded.