Over the past six months, Interactive Brokers had {abbreviateNumber(totalAvailableShares)} shares available for borrowing, with an average fee of {avgFee}%.
Over the next quarter the model forecasts a
{#if !deactivateContent}
{flowSentiment} price movement.
diff --git a/src/lib/components/ImpliedVolatility.svelte b/src/lib/components/ImpliedVolatility.svelte
index b443f0c2..4fc70cf6 100644
--- a/src/lib/components/ImpliedVolatility.svelte
+++ b/src/lib/components/ImpliedVolatility.svelte
@@ -247,19 +247,8 @@ function findLowestAndhighestIV(data, lastDateStr) {
}
}
- let charNumber = 20;
-
- $: {
- if($screenWidth < 640)
- {
- charNumber = 20;
- }
- else {
- charNumber =40;
- }
- }
-
+ $: charNumber = $screenWidth < 640 ? 20 : 40;
@@ -285,7 +274,7 @@ function findLowestAndhighestIV(data, lastDateStr) {
{#if rawData?.length !== 0}
-
+
Based on the past 12 months of historical data, {$displayCompanyName} has an IV Rank of {ivRank}%, with the current implied volatility standing at {rawData?.slice(-1)?.at(0)?.iv60}%.
Over the past year, {$displayCompanyName} has seen a weekly average of
{abbreviateNumber(avgTradeCount)} trades, involving an average of
{abbreviateNumber(avgShareQuantity)} shares bought and sold.
@@ -338,19 +338,19 @@ else {
{#each (showFullStats ? topMarketMakers?.slice(0,10) : topMarketMakers?.slice(0,3)) as item,index}
Analysis of the 20-day moving average of the options net flow demonstrates a {sentiment} trend, characterized by the {sentiment === 'bullish' ? 'Net Call Flow exceeding the Net Put Flow' : 'Net Put Flow exceeding the Net Call Flow'} .
Our model predicts future prices by analyzing trends, seasonal variations, and holiday impacts. Here are the stats of the model for {$displayCompanyName} to ensure transparency and reliability.
@@ -291,7 +291,7 @@ $: {
-
+
Over the next 12 months, the model predicts a
{priceSentiment}
trend, suggesting that the future price is expected to {priceSentiment === 'Bullish' ? 'surpass' : 'to be less than'} the previous price of
diff --git a/src/lib/components/RetailVolume.svelte b/src/lib/components/RetailVolume.svelte
index e931513b..5178a9f8 100644
--- a/src/lib/components/RetailVolume.svelte
+++ b/src/lib/components/RetailVolume.svelte
@@ -259,7 +259,7 @@ $: {
{#if Object?.keys(rawData)?.length !== 0}
-
+
In the past six months, the {$displayCompanyName} had an average retail investor volume of {avgVolume > 100e3 ? abbreviateNumber(avgVolume,true) : '< $100K'}, with a prevailing
{#if avgSentiment === 'Bullish' }
diff --git a/src/lib/components/ShareStatistics.svelte b/src/lib/components/ShareStatistics.svelte
index cbb6cf7a..4a78608f 100644
--- a/src/lib/components/ShareStatistics.svelte
+++ b/src/lib/components/ShareStatistics.svelte
@@ -174,7 +174,7 @@ $: {
-
+
{$displayCompanyName}'s' has {abbreviateNumber(rawData?.latestOutstandingShares)} shares outstanding with {abbreviateNumber(rawData?.latestFloatShares)} of those shares currently floating.
The swap data from the past 100 days shows an average notional amount of {abbreviateNumber(avgNotionalAmount,true)} and an average notional quantity of {abbreviateNumber(avgNotionalQuantity)}.
Our model uses technical indicators to predict the next trend. Here are the stats of the model for {$displayCompanyName} to ensure transparency and reliability.
@@ -198,7 +198,7 @@ $: {
-
+
Over the next {displayData === 'threeMonth' ? '3 months' : displayData === 'oneMonth' ? '1 month' : '1 week'}, the model forecasts a
{flowSentiment}
trend, indicating that the future price is expected to {flowSentiment === 'Bullish' ? 'exceed' : 'to be less than'} the previous price of
diff --git a/src/lib/components/WIIM.svelte b/src/lib/components/WIIM.svelte
index cc90f2d4..7bbb6360 100644
--- a/src/lib/components/WIIM.svelte
+++ b/src/lib/components/WIIM.svelte
@@ -81,16 +81,16 @@ $: {