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Implement progressive position sizing with max_size parameter

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- Add max_size support to trading logic, defaulting to trade_size
- Continue quoting trade_size amounts until max_size is reached
- Implement progressive exit strategy: sell trade_size increments when at max_size
- Track both token positions for better exposure management
- Update buy conditions to use max_size instead of 0.9 * trade_size

🤖 Generated with [Claude Code](https://claude.ai/code)

Co-Authored-By: Claude <noreply@anthropic.com>
This commit is contained in:
warproxxx
2025-06-22 09:38:27 -07:00
co-authored by Claude
parent b8de7e7bb1
commit 8456697577
2 changed files with 50 additions and 11 deletions
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trading.py
+18 -6
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@@ -161,6 +161,10 @@ async def perform_trade(market):
# Get market depth and price information
deets = get_best_bid_ask_deets(market, detail['name'], 100, 0.1)
#if deet has None for one these values below, call it with min size of 20
if deets['best_bid'] is None or deets['best_ask'] is None or deets['best_bid_size'] is None or deets['best_ask_size'] is None:
deets = get_best_bid_ask_deets(market, detail['name'], 20, 0.1)
# Extract all order book details
best_bid = deets['best_bid']
@@ -217,8 +221,12 @@ async def perform_trade(market):
f"avgPrice: {avgPrice}, Best Bid: {best_bid}, Best Ask: {best_ask}, "
f"Bid Price: {bid_price}, Ask Price: {ask_price}, Mid Price: {mid_price}")
# Get position for the opposite token to calculate total exposure
other_token = global_state.REVERSE_TOKENS[str(token)]
other_position = get_position(other_token)['size']
# Calculate how much to buy or sell based on our position
buy_amount, sell_amount = get_buy_sell_amount(position, bid_price, row)
buy_amount, sell_amount = get_buy_sell_amount(position, bid_price, row, other_position)
# Prepare order object with all necessary information
order = {
@@ -231,7 +239,8 @@ async def perform_trade(market):
'row': row
}
print(f"Position: {position}, Trade Size: {row['trade_size']}, "
print(f"Position: {position}, Other Position: {other_position}, "
f"Trade Size: {row['trade_size']}, Max Size: {max_size}, "
f"buy_amount: {buy_amount}, sell_amount: {sell_amount}")
# File to store risk management information for this market
@@ -298,11 +307,14 @@ async def perform_trade(market):
continue
# ------- BUY ORDER LOGIC -------
# Get max_size, defaulting to trade_size if not specified
max_size = row.get('max_size', row['trade_size'])
# Only buy if:
# 1. Position is less than 90% of target size
# 1. Position is less than max_size (new logic)
# 2. Position is less than absolute cap (250)
# 3. Buy amount is above minimum size
if position < 0.9 * row['trade_size'] and position < 250 and buy_amount > 0 and buy_amount >= row['min_size']:
if position < max_size and position < 250 and buy_amount > 0 and buy_amount >= row['min_size']:
# Get reference price from market data
sheet_value = row['best_bid']
@@ -366,8 +378,8 @@ async def perform_trade(market):
print(f"Sending Buy Order for {token} because better price. "
f"Orders look like this: {orders['buy']}. Best Bid: {best_bid}")
send_buy_order(order)
# 2. Current position + orders is not enough to reach target
elif position + orders['buy']['size'] < 0.95 * row['trade_size']:
# 2. Current position + orders is not enough to reach max_size
elif position + orders['buy']['size'] < 0.95 * max_size:
print(f"Sending Buy Order for {token} because not enough position + size")
send_buy_order(order)
# 3. Our current order is too large and needs to be resized